diff --git a/.env.example b/.env.example index a63a9b4b1..2725e3b67 100644 --- a/.env.example +++ b/.env.example @@ -4,6 +4,19 @@ # Do not commit .env file to git # Do not change .env.example file +# Config directory +# Directory where files, logs and database will be stored +# Example: LANGFLOW_CONFIG_DIR=~/.langflow +LANGFLOW_CONFIG_DIR= + +# Save database in the config directory +# Values: true, false +# If false, the database will be saved in Langflow's root directory +# This means that the database will be deleted when Langflow is uninstalled +# and that the database will not be shared between different virtual environments +# Example: LANGFLOW_SAVE_DB_IN_CONFIG_DIR=true +LANGFLOW_SAVE_DB_IN_CONFIG_DIR= + # Database URL # Postgres example: LANGFLOW_DATABASE_URL=postgresql://postgres:postgres@localhost:5432/langflow # SQLite example: @@ -56,7 +69,6 @@ LANGFLOW_REMOVE_API_KEYS= # LANGFLOW_REDIS_CACHE_EXPIRE (default: 3600) LANGFLOW_CACHE_TYPE= - # Set AUTO_LOGIN to false if you want to disable auto login # and use the login form to login. LANGFLOW_SUPERUSER and LANGFLOW_SUPERUSER_PASSWORD # must be set if AUTO_LOGIN is set to false diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index e1b427322..3c5898369 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -22,8 +22,9 @@ jobs: strategy: matrix: python-version: - - "3.10" + - "3.12" - "3.11" + - "3.10" steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} diff --git a/.github/workflows/python_test.yml b/.github/workflows/python_test.yml index b0cfee04e..dac1d4f6a 100644 --- a/.github/workflows/python_test.yml +++ b/.github/workflows/python_test.yml @@ -23,8 +23,9 @@ jobs: strategy: matrix: python-version: - - "3.10" + - "3.12" - "3.11" + - "3.10" env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} steps: diff --git a/.github/workflows/typescript_test.yml b/.github/workflows/typescript_test.yml index 71ea535da..dc5bc061e 100644 --- a/.github/workflows/typescript_test.yml +++ b/.github/workflows/typescript_test.yml @@ -8,7 +8,7 @@ on: env: POETRY_VERSION: "1.8.2" NODE_VERSION: "21" - PYTHON_VERSION: "3.10" + PYTHON_VERSION: "3.12" # Define the directory where Playwright browsers will be installed. # Adjust if your project uses a different path. PLAYWRIGHT_BROWSERS_PATH: "ms-playwright" diff --git a/Makefile b/Makefile index 7053ef8cb..d056654f9 100644 --- a/Makefile +++ b/Makefile @@ -141,7 +141,7 @@ backend: @echo 'Setting up the environment' @make setup_env make install_backend - @-kill -9 `lsof -t -i:7860` + @-kill -9 $(lsof -t -i:7860) ifdef login @echo "Running backend autologin is $(login)"; LANGFLOW_AUTO_LOGIN=$(login) poetry run uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --env-file .env --loop asyncio diff --git a/README.md b/README.md index d7c7014aa..a753ea830 100644 --- a/README.md +++ b/README.md @@ -100,6 +100,12 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google ## Deploy on Railway +Use this template to deploy Langflow 1.0 Preview on Railway: + +[![Deploy 1.0 Preview on Railway](https://railway.app/button.svg)](https://railway.app/template/UsJ1uB?referralCode=MnPSdg) + +Or this one to deploy Langflow 0.6.x: + [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/JMXEWp?referralCode=MnPSdg) ## Deploy on Render diff --git a/build_and_push.Dockerfile b/build_and_push.Dockerfile index f552dd695..e27599454 100644 --- a/build_and_push.Dockerfile +++ b/build_and_push.Dockerfile @@ -10,7 +10,7 @@ # PYTHON-BASE # Sets up all our shared environment variables ################################ -FROM python:3.10-slim as python-base +FROM python:3.12-slim as python-base # python ENV PYTHONUNBUFFERED=1 \ @@ -47,7 +47,7 @@ ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH" # Used to build deps + create our virtual environment ################################ FROM python-base as builder-base -RUN + RUN apt-get update \ && apt-get install --no-install-recommends -y \ # deps for installing poetry @@ -55,7 +55,12 @@ RUN apt-get update \ # deps for building python deps build-essential \ # npm - npm + npm \ + # gcc + gcc \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + # Now we need to copy the entire project into the image @@ -70,15 +75,12 @@ RUN --mount=type=cache,target=/root/.cache \ RUN python -m pip install requests && cd ./scripts && python update_dependencies.py RUN $POETRY_HOME/bin/poetry lock RUN $POETRY_HOME/bin/poetry build -# Final stage for the application -FROM python-base as final # Copy virtual environment and built .tar.gz from builder base RUN useradd -m -u 1000 user -COPY --from=builder-base /app/dist/*.tar.gz ./ # Install the package from the .tar.gz -RUN python -m pip install *.tar.gz --user +RUN python -m pip install /app/dist/*.tar.gz --user + -WORKDIR /app ENTRYPOINT ["python", "-m", "langflow", "run"] CMD ["--host", "0.0.0.0", "--port", "7860"] \ No newline at end of file diff --git a/build_and_push_base.Dockerfile b/build_and_push_base.Dockerfile index db6d0cf58..cbcdbceee 100644 --- a/build_and_push_base.Dockerfile +++ b/build_and_push_base.Dockerfile @@ -10,7 +10,7 @@ # PYTHON-BASE # Sets up all our shared environment variables ################################ -FROM python:3.10-slim as python-base +FROM python:3.12-slim as python-base # python ENV PYTHONUNBUFFERED=1 \ @@ -55,6 +55,8 @@ RUN apt-get update \ build-essential \ # npm npm \ + # gcc + gcc \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* @@ -78,15 +80,11 @@ RUN cp -r src/frontend/build src/backend/base/langflow/frontend RUN rm -rf src/backend/base/dist RUN cd src/backend/base && $POETRY_HOME/bin/poetry build --format sdist -# Final stage for the application -FROM python-base as final - # Copy virtual environment and built .tar.gz from builder base RUN useradd -m -u 1000 user -COPY --from=builder-base /app/src/backend/base/dist/*.tar.gz ./ # Install the package from the .tar.gz -RUN pip install *.tar.gz --user +RUN python -m pip install /app/dist/*.tar.gz --user + -WORKDIR /app ENTRYPOINT ["python", "-m", "langflow", "run"] CMD ["--host", "0.0.0.0", "--port", "7860"] \ No newline at end of file diff --git a/docs/docs/administration/global-env.mdx b/docs/docs/administration/global-env.mdx new file mode 100644 index 000000000..fca7ec16a --- /dev/null +++ b/docs/docs/administration/global-env.mdx @@ -0,0 +1,45 @@ +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import Admonition from "@theme/Admonition"; + +# Global environment variables + +Langflow 1.0 alpha includes the option to add **Global Environment Variables** for your application. + +## Add a global variable to a project + +In this example, you'll add the `openai_api_key` credential as a global environment variable to the **Basic Prompting** starter project. + +For more information on the starter flow, see [Basic prompting](../starter-projects/basic-prompting.mdx). + +1. From the Langflow dashboard, click **New Project**. +2. Select **Basic Prompting**. + +The **Basic Prompting** flow is created. + +3. To create an environment variable for the **OpenAI** component: + 1. In the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. + 2. In the **Variable Name** field, enter `openai_api_key`. + 3. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 4. For the variable **Type**, select **Credential**. + 5. In the **Apply to Fields** field, select **OpenAI API Key** to apply this variable to all fields named **OpenAI API Key**. + 6. Click **Save Variable**. + +You now have a `openai_api_key` global environment variable for your Langflow project. + + + You can also create global variables in **Settings** > **Variables and + Secrets**. + + + + +4. To view and manage your project's global environment variables, visit **Settings** > **Variables and Secrets**. + +For more on variables in HuggingFace Spaces, see [Managing Secrets](https://huggingface.co/docs/hub/spaces-overview#managing-secrets). diff --git a/docs/docs/administration/playground.mdx b/docs/docs/administration/playground.mdx new file mode 100644 index 000000000..57f3f2de0 --- /dev/null +++ b/docs/docs/administration/playground.mdx @@ -0,0 +1,29 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import ReactPlayer from "react-player"; +import Admonition from "@theme/Admonition"; + +# Playground + +In Langflow 1.0 alpha, the **Playground** replaces the **Interaction Panel**. + +The **Playground** provides an interface for interacting with flows without opening them in the flow editor. + +It even works for flows hosted on the Langflow store! + +As long as you have a flow's environment variables set, you can run it by clicking the **Playground** button. + +1. From your **Collections** page, click **Playground** in one of your flows. + The **Playground** window opens. + + + +2. Chat with your bot as you normally would, all without having to open the editor. diff --git a/docs/docs/contributing/contribute-component.md b/docs/docs/contributing/contribute-component.md new file mode 100644 index 000000000..b178a5882 --- /dev/null +++ b/docs/docs/contributing/contribute-component.md @@ -0,0 +1,45 @@ +import ZoomableImage from "/src/theme/ZoomableImage.js"; + +# How to contribute components? + +As of Langflow 1.0 alpha, new components are added as objects of the [CustomComponent](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/interface/custom/custom_component/custom_component.py) class and any dependencies are added to the [pyproject.toml](https://github.com/langflow-ai/langflow/blob/dev/pyproject.toml#L27) file. + +## Add an example component + +You have a new document loader called **MyCustomDocumentLoader** and it would look awesome in Langflow. + +1. Write your loader as an object of the [CustomComponent](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/interface/custom/custom_component/custom_component.py) class. You'll create a new class, `MyCustomDocumentLoader`, that will inherit from `CustomComponent` and override the base class's methods. +2. Define optional attributes like `display_name`, `description`, and `documentation` to provide information about your custom component. +3. Implement the `build_config` method to define the configuration options for your custom component. +4. Implement the `build` method to define the logic for taking input parameters specified in the `build_config` method and returning the desired output. +5. Add the code to the [/components/documentloaders](https://github.com/langflow-ai/langflow/tree/dev/src/backend/base/langflow/components) folder. +6. Add the dependency to [/documentloaders/\_\_init\_\_.py](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/components/documentloaders/__init__.py) as `from .MyCustomDocumentLoader import MyCustomDocumentLoader`. +7. Add any new dependencies to the outer [pyproject.toml](https://github.com/langflow-ai/langflow/blob/dev/pyproject.toml#L27) file. +8. Submit documentation for your component. For this example, you'd submit documentation to the [loaders page](https://github.com/langflow-ai/langflow/blob/dev/docs/docs/components/loaders.mdx). +9. Submit your changes as a pull request. The Langflow team will have a look, suggest changes, and add your component to Langflow. + +## User Sharing + +You might want to share and test your custom component with others, but don't need it merged into the main source code. + +If so, you can share your component on the Langflow store. + +1. [Register at the Langflow store](https://www.langflow.store/login/). +2. Undergo pre-validation before receiving an API key. +3. To deploy your amazing component directly to the Langflow store, without it being merged into the main source code, navigate to your flow, and then click **Share**. + The share window appears: + + + +5. Choose whether you want to flow to be public or private. + You can also **Export** your flow as a JSON file from this window. + When you're ready to share the flow, click **Share Flow**. + You should see a **Flow shared successfully** popup. +6. To confirm, navigate to the **Langflow Store** and filter results by **Created By Me**. You should see your new flow on the **Langflow Store**. diff --git a/docs/sidebars.js b/docs/sidebars.js index ef163e282..81b255a66 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -38,6 +38,8 @@ module.exports = { "administration/login", "administration/api", "administration/cli", + "administration/playground", + "administration/global-env", "administration/components", "administration/collection", "administration/prompt-customization", @@ -126,6 +128,7 @@ module.exports = { "contributing/how-contribute", "contributing/github-issues", "contributing/community", + "contributing/contribute-component", ], }, ], diff --git a/docs/static/img/add-component-to-store.png b/docs/static/img/add-component-to-store.png new file mode 100644 index 000000000..2ccb0a046 Binary files /dev/null and b/docs/static/img/add-component-to-store.png differ diff --git a/docs/static/img/global-env.png b/docs/static/img/global-env.png new file mode 100644 index 000000000..763444dc4 Binary files /dev/null and b/docs/static/img/global-env.png differ diff --git a/docs/static/img/playground-chat.png b/docs/static/img/playground-chat.png new file mode 100644 index 000000000..d2625c28f Binary files /dev/null and b/docs/static/img/playground-chat.png differ diff --git a/example.har b/example.har index 5021c7da5..a4c59bf31 100644 --- a/example.har +++ b/example.har @@ -28,8 +28,14 @@ { "name": "Sec-Fetch-Dest", "value": "empty" }, { "name": "Sec-Fetch-Mode", "value": "cors" }, { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" + }, { "name": "sec-ch-ua-mobile", "value": "?0" }, { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } ], @@ -80,8 +86,14 @@ { "name": "Sec-Fetch-Dest", "value": "empty" }, { "name": "Sec-Fetch-Mode", "value": "cors" }, { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" + }, { "name": "sec-ch-ua-mobile", "value": "?0" }, { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } ], @@ -132,8 +144,14 @@ { "name": "Sec-Fetch-Dest", "value": "empty" }, { "name": "Sec-Fetch-Mode", "value": "cors" }, { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" + }, { "name": "sec-ch-ua-mobile", "value": "?0" }, { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } ], @@ -184,8 +202,14 @@ { "name": "Sec-Fetch-Dest", "value": "empty" }, { "name": "Sec-Fetch-Mode", "value": "cors" }, { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" + }, { "name": "sec-ch-ua-mobile", "value": "?0" }, { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } ], @@ -236,8 +260,14 @@ { "name": "Sec-Fetch-Dest", "value": "empty" }, { "name": "Sec-Fetch-Mode", "value": "cors" }, { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" + }, { "name": "sec-ch-ua-mobile", "value": "?0" }, { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } ], @@ -282,16 +312,28 @@ { "name": "Accept", "value": "application/json, text/plain, */*" }, { "name": "Accept-Encoding", "value": "gzip, deflate, br" }, { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJkMjUzYmZiYS02MzY4LTQ0ZGMtODVmNy0wZDZkYTllNDU5NjgiLCJleHAiOjE3MzM4NTY4OTh9.5MFFb0JCck3ITSKXbxhwO9yAscnXcwXNTV70ZYBRB20" }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJkMjUzYmZiYS02MzY4LTQ0ZGMtODVmNy0wZDZkYTllNDU5NjgiLCJleHAiOjE3MzM4NTY4OTh9.5MFFb0JCck3ITSKXbxhwO9yAscnXcwXNTV70ZYBRB20" + }, { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_tkn_lflw=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJkMjUzYmZiYS02MzY4LTQ0ZGMtODVmNy0wZDZkYTllNDU5NjgiLCJleHAiOjE3MzM4NTY4OTh9.5MFFb0JCck3ITSKXbxhwO9yAscnXcwXNTV70ZYBRB20; refresh_tkn_lflw=auto" }, + { + "name": "Cookie", + "value": "access_tkn_lflw=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJkMjUzYmZiYS02MzY4LTQ0ZGMtODVmNy0wZDZkYTllNDU5NjgiLCJleHAiOjE3MzM4NTY4OTh9.5MFFb0JCck3ITSKXbxhwO9yAscnXcwXNTV70ZYBRB20; refresh_tkn_lflw=auto" + }, { "name": "Host", "value": "localhost:3000" }, { "name": "Referer", "value": "http://localhost:3000/flows" }, { "name": "Sec-Fetch-Dest", "value": "empty" }, { "name": "Sec-Fetch-Mode", "value": "cors" }, { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"" + }, { "name": "sec-ch-ua-mobile", "value": "?0" }, { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } ], @@ -315,7 +357,7 @@ "content": { "size": -1, "mimeType": "application/json", - "text": "[{\"name\":\"Awesome Euclid\",\"description\":\"Language Models, Mapped and Mastered.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-m2yFu\",\"type\":\"genericNode\",\"position\":{\"x\":462.6456058272081,\"y\":1033.9314297130313},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"transcription\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"create an image prompt based on the song's lyrics to be used as the album cover of this song:\\n\\nlyrics:\\n{transcription}\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"transcription\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"transcription\",\"display_name\":\"transcription\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"PromptTemplate\",\"StringPromptTemplate\",\"BasePromptTemplate\"],\"name\":\"\",\"display_name\":\"PromptTemplate\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"custom_fields\":{\"\":[\"transcription\"],\"template\":[\"transcription\",\"question\"]},\"output_types\":[],\"full_path\":null,\"field_formatters\":{},\"beta\":false,\"error\":null},\"id\":\"PromptTemplate-m2yFu\"},\"selected\":false,\"positionAbsolute\":{\"x\":462.6456058272081,\"y\":1033.9314297130313},\"dragging\":false},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-urapv\",\"type\":\"genericNode\",\"position\":{\"x\":189.94856095084924,\"y\":-85.06556385338186},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI 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Chat large language models API.\",\"base_classes\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-urapv\"},\"selected\":false,\"positionAbsolute\":{\"x\":189.94856095084924,\"y\":-85.06556385338186},\"dragging\":false},{\"width\":384,\"height\":806,\"id\":\"CustomComponent-IEIUl\",\"type\":\"genericNode\",\"position\":{\"x\":2364.865919667005,\"y\":88.43094097025096},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nfrom platformdirs import user_cache_dir\\nimport base64\\nfrom PIL import Image\\nfrom io import BytesIO\\nfrom openai import OpenAI\\nfrom pathlib import Path\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Image generator\\\"\\n description: str = \\\"generate images using Dall-E\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\",\\\"input_types\\\":[\\\"str\\\"]},\\n \\\"api_key\\\":{\\\"display_name\\\":\\\"OpenAI API key\\\",\\\"password\\\":True},\\n \\\"model\\\":{\\\"display_name\\\":\\\"Model name\\\",\\\"advanced\\\":True,\\\"options\\\":[\\\"dall-e-2\\\",\\\"dall-e-3\\\"], \\\"value\\\":\\\"dall-e-3\\\"},\\n \\\"file_name\\\":{\\\"display_name\\\":\\\"File Name\\\"},\\n \\\"output_format\\\":{\\\"display_name\\\":\\\"Output format\\\",\\\"options\\\":[\\\"jpeg\\\",\\\"png\\\"],\\\"value\\\":\\\"jpeg\\\"},\\n \\\"width\\\":{\\\"display_name\\\":\\\"Width\\\" ,\\\"value\\\":1024},\\n \\\"height\\\":{\\\"display_name\\\":\\\"Height\\\", \\\"value\\\":1024}\\n }\\n\\n def build(self, prompt:str,api_key:str,model:str,file_name:str,output_format:str,width:int,height:int):\\n client = OpenAI(api_key=api_key)\\n cache_dir = Path(user_cache_dir(\\\"langflow\\\"))\\n images_dir = cache_dir / \\\"images\\\"\\n images_dir.mkdir(parents=True, exist_ok=True)\\n image_path = images_dir / f\\\"{file_name}.{output_format}\\\"\\n response = client.images.generate(\\n model=model,\\n prompt=prompt,\\n size=f\\\"{height}x{width}\\\",\\n response_format=\\\"b64_json\\\",\\n n=1,\\n )\\n # Decode base64-encoded image string\\n binary_data = base64.b64decode(response.data[0].b64_json)\\n # Create PIL Image object from binary image data\\n image_pil = Image.open(BytesIO(binary_data))\\n image_pil.save(image_path, format=output_format.upper())\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"api_key\",\"display_name\":\"OpenAI API key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"file_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"file_name\",\"display_name\":\"File Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"album\"},\"height\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1024,\"password\":false,\"name\":\"height\",\"display_name\":\"Height\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"model\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"dall-e-3\",\"password\":false,\"options\":[\"dall-e-2\",\"dall-e-3\"],\"name\":\"model\",\"display_name\":\"Model name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"output_format\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"jpeg\",\"password\":false,\"options\":[\"jpeg\",\"png\"],\"name\":\"output_format\",\"display_name\":\"Output format\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"input_types\":[\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"width\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1024,\"password\":false,\"name\":\"width\",\"display_name\":\"Width\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false}},\"description\":\"generate images using Dall-E\",\"base_classes\":[\"Data\"],\"display_name\":\"Image generator\",\"custom_fields\":{\"api_key\":null,\"file_name\":null,\"height\":null,\"model\":null,\"output_format\":null,\"prompt\":null,\"width\":null},\"output_types\":[\"Data\"],\"documentation\":\"http://docs.langflow.org/components/custom\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-IEIUl\"},\"selected\":false,\"positionAbsolute\":{\"x\":2364.865919667005,\"y\":88.43094097025096}},{\"width\":384,\"height\":338,\"id\":\"LLMChain-KlJb3\",\"type\":\"genericNode\",\"position\":{\"x\":1242.9851164540805,\"y\":-139.74634696823108},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-KlJb3\"},\"selected\":false,\"positionAbsolute\":{\"x\":1242.9851164540805,\"y\":-139.74634696823108},\"dragging\":false},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-bCuc0\",\"type\":\"genericNode\",\"position\":{\"x\":1747.8107777342625,\"y\":319.13729017446667},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Custom Component\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n def build(self, param: Chain) -> str:\\n result = param.run({})\\n self.status = result\\n return result\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"param\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"param\",\"display_name\":\"Parameter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Chain\",\"list\":false}},\"description\":\"Create any custom component you want!\",\"base_classes\":[\"str\"],\"display_name\":\"Custom Component\",\"custom_fields\":{\"param\":null},\"output_types\":[\"str\"],\"documentation\":\"http://docs.langflow.org/components/custom\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-bCuc0\"},\"selected\":false,\"positionAbsolute\":{\"x\":1747.8107777342625,\"y\":319.13729017446667}}],\"edges\":[{\"source\":\"LLMChain-KlJb3\",\"target\":\"CustomComponent-bCuc0\",\"sourceHandle\":\"{œbaseClassesœ:[œChainœ,œCallableœ],œdataTypeœ:œLLMChainœ,œidœ:œLLMChain-KlJb3œ}\",\"targetHandle\":\"{œfieldNameœ:œparamœ,œidœ:œCustomComponent-bCuc0œ,œinputTypesœ:null,œtypeœ:œChainœ}\",\"id\":\"reactflow__edge-LLMChain-KlJb3{œbaseClassesœ:[œChainœ,œCallableœ],œdataTypeœ:œLLMChainœ,œidœ:œLLMChain-KlJb3œ}-CustomComponent-bCuc0{œfieldNameœ:œparamœ,œidœ:œCustomComponent-bCuc0œ,œinputTypesœ:null,œtypeœ:œChainœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"param\",\"id\":\"CustomComponent-bCuc0\",\"inputTypes\":null,\"type\":\"Chain\"},\"sourceHandle\":{\"baseClasses\":[\"Chain\",\"Callable\"],\"dataType\":\"LLMChain\",\"id\":\"LLMChain-KlJb3\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 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LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-NgFyo\"},\"selected\":false,\"positionAbsolute\":{\"x\":225.3113389084088,\"y\":-193.3520019494289},\"dragging\":false},{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-oAFjh\",\"type\":\"genericNode\",\"position\":{\"x\":-342.62522294052764,\"y\":391.20629510686103},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"links\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Answer everything with links\\n{links}\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"links\",\"display_name\":\"links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"BasePromptTemplate\",\"StringPromptTemplate\",\"PromptTemplate\"],\"name\":\"\",\"display_name\":\"PromptTemplate\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"custom_fields\":{\"\":[\"links\"]},\"output_types\":[],\"full_path\":null,\"field_formatters\":{},\"beta\":false,\"error\":null},\"id\":\"PromptTemplate-oAFjh\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-342.62522294052764,\"y\":391.20629510686103}}],\"edges\":[{\"source\":\"ChatOpenAI-VPh47\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseChatModelœ,œChatOpenAIœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-VPh47œ}\",\"target\":\"LLMChain-NgFyo\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œLLMChain-NgFyoœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"LLMChain-NgFyo\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"},\"sourceHandle\":{\"baseClasses\":[\"BaseChatModel\",\"ChatOpenAI\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-VPh47\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-foreground stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-VPh47{œbaseClassesœ:[œBaseChatModelœ,œChatOpenAIœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-VPh47œ}-LLMChain-NgFyo{œfieldNameœ:œllmœ,œidœ:œLLMChain-NgFyoœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\"},{\"source\":\"PromptTemplate-oAFjh\",\"sourceHandle\":\"{œbaseClassesœ:[œBasePromptTemplateœ,œStringPromptTemplateœ,œPromptTemplateœ],œdataTypeœ:œPromptTemplateœ,œidœ:œPromptTemplate-oAFjhœ}\",\"target\":\"LLMChain-NgFyo\",\"targetHandle\":\"{œfieldNameœ:œpromptœ,œidœ:œLLMChain-NgFyoœ,œinputTypesœ:null,œtypeœ:œBasePromptTemplateœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"prompt\",\"id\":\"LLMChain-NgFyo\",\"inputTypes\":null,\"type\":\"BasePromptTemplate\"},\"sourceHandle\":{\"baseClasses\":[\"BasePromptTemplate\",\"StringPromptTemplate\",\"PromptTemplate\"],\"dataType\":\"PromptTemplate\",\"id\":\"PromptTemplate-oAFjh\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-foreground 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API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false,\"value\":60},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseChatModel\",\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\"},\"id\":\"ChatOpenAI-fBcfh\",\"value\":null},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":228.87326389541306,\"y\":465.8628482073749}},{\"width\":384,\"height\":310,\"id\":\"ConversationChain-bVNex\",\"type\":\"genericNode\",\"position\":{\"x\":806,\"y\":554},\"data\":{\"type\":\"ConversationChain\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"_type\":\"default\"},\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLMOutputParser\",\"list\":false},\"prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"history\",\"input\"],\"output_parser\":null,\"partial_variables\":{},\"template\":\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\\n\\nCurrent conversation:\\n{history}\\nHuman: 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to have a conversation and load context from memory.\",\"base_classes\":[\"ConversationChain\",\"Chain\",\"LLMChain\",\"function\"],\"display_name\":\"ConversationChain\",\"documentation\":\"\"},\"id\":\"ConversationChain-bVNex\",\"value\":null},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":806,\"y\":554}}],\"edges\":[{\"source\":\"ChatOpenAI-fBcfh\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseChatModelœ,œBaseLanguageModelœ,œChatOpenAIœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-fBcfhœ}\",\"target\":\"ConversationChain-bVNex\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œConversationChain-bVNexœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"className\":\"stroke-gray-900 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File Loader\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import os\\n\\nfrom langchain.schema import Document\\nfrom langflow import CustomComponent\\nimport glob\\n\\nclass DirectoryLoaderComponent(CustomComponent):\\n display_name: str = \\\"Directory Loader\\\"\\n description: str = \\\"Generic File Loader\\\"\\n beta = True\\n loaders_info = [\\n {\\n \\\"loader\\\": \\\"AirbyteJSONLoader\\\",\\n \\\"name\\\": \\\"Airbyte JSON (.jsonl)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.AirbyteJSONLoader\\\",\\n \\\"defaultFor\\\": [\\\"jsonl\\\"],\\n \\\"allowdTypes\\\": [\\\"jsonl\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"JSONLoader\\\",\\n \\\"name\\\": \\\"JSON (.json)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.JSONLoader\\\",\\n \\\"defaultFor\\\": [\\\"json\\\"],\\n \\\"allowdTypes\\\": [\\\"json\\\"],\\n \\\"kwargs\\\": {\\\"jq_schema\\\": \\\".\\\", \\\"text_content\\\": False}\\n },\\n {\\n \\\"loader\\\": \\\"BSHTMLLoader\\\",\\n \\\"name\\\": \\\"BeautifulSoup4 HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.BSHTMLLoader\\\",\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"CSVLoader\\\",\\n \\\"name\\\": \\\"CSV (.csv)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.CSVLoader\\\",\\n \\\"defaultFor\\\": [\\\"csv\\\"],\\n \\\"allowdTypes\\\": [\\\"csv\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"CoNLLULoader\\\",\\n \\\"name\\\": \\\"CoNLL-U (.conllu)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.CoNLLULoader\\\",\\n \\\"defaultFor\\\": [\\\"conllu\\\"],\\n \\\"allowdTypes\\\": [\\\"conllu\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"EverNoteLoader\\\",\\n \\\"name\\\": \\\"EverNote (.enex)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.EverNoteLoader\\\",\\n \\\"defaultFor\\\": [\\\"enex\\\"],\\n \\\"allowdTypes\\\": [\\\"enex\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"FacebookChatLoader\\\",\\n \\\"name\\\": \\\"Facebook Chat (.json)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.FacebookChatLoader\\\",\\n \\\"allowdTypes\\\": [\\\"json\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"OutlookMessageLoader\\\",\\n \\\"name\\\": \\\"Outlook Message (.msg)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.OutlookMessageLoader\\\",\\n \\\"defaultFor\\\": [\\\"msg\\\"],\\n \\\"allowdTypes\\\": [\\\"msg\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"PyPDFLoader\\\",\\n \\\"name\\\": \\\"PyPDF (.pdf)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.PyPDFLoader\\\",\\n \\\"defaultFor\\\": [\\\"pdf\\\"],\\n \\\"allowdTypes\\\": [\\\"pdf\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"STRLoader\\\",\\n \\\"name\\\": \\\"Subtitle (.str)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.STRLoader\\\",\\n \\\"defaultFor\\\": [\\\"str\\\"],\\n \\\"allowdTypes\\\": [\\\"str\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"TextLoader\\\",\\n \\\"name\\\": \\\"Text (.txt)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.TextLoader\\\",\\n \\\"defaultFor\\\": [\\\"txt\\\"],\\n \\\"allowdTypes\\\": [\\\"txt\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredEmailLoader\\\",\\n \\\"name\\\": \\\"Unstructured Email (.eml)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredEmailLoader\\\",\\n \\\"defaultFor\\\": [\\\"eml\\\"],\\n \\\"allowdTypes\\\": [\\\"eml\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredHTMLLoader\\\",\\n \\\"name\\\": \\\"Unstructured HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredHTMLLoader\\\",\\n \\\"defaultFor\\\": [\\\"html\\\", \\\"htm\\\"],\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredMarkdownLoader\\\",\\n \\\"name\\\": \\\"Unstructured Markdown (.md)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredMarkdownLoader\\\",\\n \\\"defaultFor\\\": [\\\"md\\\"],\\n \\\"allowdTypes\\\": [\\\"md\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredPowerPointLoader\\\",\\n \\\"name\\\": \\\"Unstructured PowerPoint (.pptx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredPowerPointLoader\\\",\\n \\\"defaultFor\\\": [\\\"pptx\\\"],\\n \\\"allowdTypes\\\": [\\\"pptx\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredWordLoader\\\",\\n \\\"name\\\": \\\"Unstructured Word (.docx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredWordLoader\\\",\\n \\\"defaultFor\\\": [\\\"docx\\\"],\\n \\\"allowdTypes\\\": [\\\"docx\\\"],\\n },\\n]\\n\\n\\n def build_config(self):\\n loader_options = [\\\"Automatic\\\"] + [\\n loader_info[\\\"name\\\"] for loader_info in self.loaders_info\\n ]\\n\\n file_types = []\\n suffixes = []\\n\\n for loader_info in self.loaders_info:\\n if \\\"allowedTypes\\\" in loader_info:\\n file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n return {\\n \\\"directory_path\\\": {\\n \\\"display_name\\\": \\\"Directory Path\\\",\\n \\\"required\\\": True,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": loader_options,\\n \\\"value\\\": \\\"Automatic\\\",\\n },\\n }\\n\\n def build(self, directory_path: str, loader: str) -> Document:\\n # Verifique se o diretório existe\\n if not os.path.exists(directory_path):\\n raise ValueError(f\\\"Directory not found: {directory_path}\\\")\\n\\n files = glob.glob(directory_path + \\\"/*.*\\\")\\n\\n\\n loader_info = None\\n if loader == \\\"Automatic\\\":\\n for file in files:\\n file_type = file.split(\\\".\\\")[-1]\\n\\n\\n for info in self.loaders_info:\\n if \\\"defaultFor\\\" in info:\\n if file_type in info[\\\"defaultFor\\\"]:\\n loader_info = info\\n break\\n if loader_info:\\n break\\n\\n if not loader_info:\\n raise ValueError(\\n \\\"No default loader found for any file in the directory\\\"\\n )\\n\\n else:\\n for info in self.loaders_info:\\n if info[\\\"name\\\"] == loader:\\n loader_info = info\\n break\\n\\n if not loader_info:\\n raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n loader_import = loader_info[\\\"import\\\"]\\n module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n try:\\n # Importe o loader dinamicamente\\n loader_module = __import__(module_name, fromlist=[class_name])\\n loader_instance = getattr(loader_module, class_name)\\n except ImportError as e:\\n raise ValueError(\\n f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{loader_info}\\\"\\n ) from e\\n\\n results = []\\n for file in files:\\n file_path = os.path.join(directory_path, file)\\n kwargs = loader_info.get(\\\"kwargs\\\", {})\\n result = loader_instance(file_path=file_path, **kwargs).load()\\n results.append(result)\\n self.status = results\\n return results\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"directory_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"directory_path\",\"display_name\":\"Directory Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"/Users/ogabrielluiz/Projects/langflow2/docs\"},\"loader\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true}},\"description\":\"Generic File Loader\",\"base_classes\":[\"Document\"],\"display_name\":\"Directory Loader\",\"custom_fields\":{\"directory_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Ip6tG\"},\"id\":\"CustomComponent-Ip6tG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:38.570303\",\"folder\":null,\"id\":\"5fe4debc-b6a7-45d4-a694-c02d8aa93b08\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Whisper Transcriber\",\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, List, Dict, Union\\nfrom langflow.field_typing import (\\n AgentExecutor,\\n BaseChatMemory,\\n BaseLanguageModel,\\n BaseLLM,\\n BaseLoader,\\n BaseMemory,\\n BaseOutputParser,\\n BasePromptTemplate,\\n BaseRetriever,\\n Callable,\\n Chain,\\n ChatPromptTemplate,\\n Data,\\n Document,\\n Embeddings,\\n NestedDict,\\n Object,\\n PromptTemplate,\\n TextSplitter,\\n Tool,\\n VectorStore,\\n)\\n\\nfrom openai import OpenAI\\nclass Component(CustomComponent):\\n display_name: str = \\\"Whisper Transcriber\\\"\\n description: str = \\\"Converts audio to text using OpenAI's Whisper.\\\"\\n\\n def build_config(self):\\n return {\\\"audio\\\":{\\\"field_type\\\":\\\"file\\\",\\\"suffixes\\\":[\\\".mp3\\\", \\\".mp4\\\", \\\".m4a\\\"]},\\\"OpenAIKey\\\":{\\\"field_type\\\":\\\"str\\\",\\\"password\\\":True}}\\n\\n def build(self, audio:str, OpenAIKey:str) -> str:\\n \\n # TODO: if output path, persist & load it instead\\n \\n client = OpenAI(api_key=OpenAIKey)\\n \\n audio_file= open(audio, \\\"rb\\\")\\n transcript = client.audio.transcriptions.create(\\n model=\\\"whisper-1\\\", \\n file=audio_file,\\n response_format=\\\"text\\\"\\n )\\n \\n \\n self.status = transcript\\n return transcript\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"OpenAIKey\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"OpenAIKey\",\"display_name\":\"OpenAIKey\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"audio\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"suffixes\":[\".mp3\",\".mp4\",\".m4a\"],\"password\":false,\"name\":\"audio\",\"display_name\":\"audio\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"file_path\":\"/Users/rodrigonader/Library/Caches/langflow/19b3e1c9-90bf-405f-898a-e982f47adf76/a3308ce7e9b10088fcd985aabb6d17b012c6b6e81ff8806356574474c9d10229.m4a\",\"value\":\"Audio Recording 2023-11-29 at 12.12.04.m4a\"}},\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"base_classes\":[\"str\"],\"display_name\":\"Whisper Transcriber\",\"custom_fields\":{\"OpenAIKey\":null,\"audio\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-GJRrs\"},\"id\":\"CustomComponent-GJRrs\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:39.710502\",\"folder\":null,\"id\":\"bd9e911d-46bd-429f-aa75-dd740430695e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Youtube Conversation\",\"description\":\"Craft Meaningful Interactions, Generate Value.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":589,\"id\":\"CustomComponent-h0NSI\",\"type\":\"genericNode\",\"position\":{\"x\":714.9531293402755,\"y\":0.4994374160582993},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import requests\\nfrom langflow import CustomComponent\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.schema import Document\\nfrom langchain.document_loaders import YoutubeLoader\\n\\n# Example usage:\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"YouTube Transcript\\\"\\n description: str = \\\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\\\"\\n\\n def build_config(self):\\n return {\\\"url\\\": {\\\"multiline\\\": True, \\\"required\\\": True}}\\n\\n def build(self, url: str, llm: BaseLLM, dependencies: Document, language: str = \\\"en\\\") -> Document:\\n dependencies\\n response = requests.get(url)\\n loader = YoutubeLoader.from_youtube_url(url, add_video_info=True, language=[language])\\n result = loader.load()\\n self.repr_value = str(result)\\n return result\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"dependencies\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"dependencies\",\"display_name\":\"dependencies\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"language\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"language\",\"display_name\":\"language\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\",\"base_classes\":[\"Document\"],\"display_name\":\"YouTube Transcript\",\"custom_fields\":{\"dependencies\":null,\"language\":null,\"llm\":null,\"url\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-h0NSI\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":714.9531293402755,\"y\":0.4994374160582993}},{\"width\":384,\"height\":629,\"id\":\"ChatOpenAI-q61Y2\",\"type\":\"genericNode\",\"position\":{\"x\":18,\"y\":625.5521045590924},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-16k\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k-0613\",\"gpt-3.5-turbo-16k\",\"gpt-4-0613\",\"gpt-4-32k-0613\",\"gpt-4\",\"gpt-4-32k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-q61Y2\"},\"selected\":false,\"positionAbsolute\":{\"x\":18,\"y\":625.5521045590924},\"dragging\":false},{\"width\":384,\"height\":539,\"id\":\"Chroma-gVhy7\",\"type\":\"genericNode\",\"position\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"data\":{\"type\":\"Chroma\",\"node\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.Client\",\"list\":false},\"client_settings\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client_settings\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.config.Setting\",\"list\":true},\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"chroma_server_cors_allow_origins\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Chroma Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"chroma_server_grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Chroma Server GRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Chroma Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_http_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_http_port\",\"display_name\":\"Chroma Server HTTP Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_ssl_enabled\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Chroma Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"collection_metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"collection_metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"collection_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"persist\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"persist\",\"display_name\":\"Persist\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"persist_directory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"persist_directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"_type\":\"Chroma\"},\"description\":\"Create a Chroma vectorstore from a raw documents.\",\"base_classes\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Chroma\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma\",\"beta\":false,\"error\":null},\"id\":\"Chroma-gVhy7\"},\"selected\":false,\"positionAbsolute\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"dragging\":false},{\"width\":384,\"height\":501,\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"type\":\"genericNode\",\"position\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"data\":{\"type\":\"RecursiveCharacterTextSplitter\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.utils.util import build_loader_repr_from_documents\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chunk_overlap\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"50\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"400\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"type\":\"int\",\"list\":false},\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"type\":\"Document\",\"list\":true},\"separators\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"type\":\"str\",\"list\":true,\"value\":[\" \"]}},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separators\":null},\"output_types\":[\"RecursiveCharacterTextSplitter\"],\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"beta\":true,\"error\":null},\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"selected\":false,\"positionAbsolute\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"dragging\":false},{\"width\":384,\"height\":367,\"id\":\"OpenAIEmbeddings-cduOO\",\"type\":\"genericNode\",\"position\":{\"x\":1405.1176670984544,\"y\":1029.459061269321},\"data\":{\"type\":\"OpenAIEmbeddings\",\"node\":{\"template\":{\"allowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[],\"password\":false,\"name\":\"allowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"disallowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"all\",\"password\":false,\"name\":\"disallowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"deployment\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"embedding_ctx_length\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":8191,\"password\":false,\"name\":\"embedding_ctx_length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"{'Authorization': 'Bearer '}\",\"password\":false,\"name\":\"headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"model\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_api_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_version\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"show_progress_bar\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"show_progress_bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"skip_empty\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"skip_empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"_type\":\"OpenAIEmbeddings\"},\"description\":\"OpenAI embedding models.\",\"base_classes\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"display_name\":\"OpenAIEmbeddings\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"OpenAIEmbeddings-cduOO\"},\"selected\":false,\"positionAbsolute\":{\"x\":1405.1176670984544,\"y\":1029.459061269321}},{\"width\":384,\"height\":339,\"id\":\"RetrievalQA-4PmTB\",\"type\":\"genericNode\",\"position\":{\"x\":2711.7786966415715,\"y\":709.4450828605463},\"data\":{\"type\":\"RetrievalQA\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"combine_documents_chain\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"combine_documents_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseCombineDocumentsChain\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"list\":false},\"input_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"query\",\"password\":false,\"name\":\"input_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"output_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"result\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"return_source_documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_source_documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"RetrievalQA\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"RetrievalQA\",\"Chain\",\"BaseRetrievalQA\",\"function\"],\"display_name\":\"RetrievalQA\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/vector_db_qa\",\"beta\":false,\"error\":null},\"id\":\"RetrievalQA-4PmTB\"},\"selected\":false,\"positionAbsolute\":{\"x\":2711.7786966415715,\"y\":709.4450828605463}},{\"width\":384,\"height\":333,\"id\":\"CombineDocsChain-yk0JF\",\"type\":\"genericNode\",\"position\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"data\":{\"type\":\"CombineDocsChain\",\"node\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"chain_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"BaseCombineDocumentsChain\",\"function\"],\"display_name\":\"CombineDocsChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"id\":\"CombineDocsChain-yk0JF\"},\"selected\":false,\"positionAbsolute\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"dragging\":false},{\"width\":384,\"height\":417,\"id\":\"CustomComponent-y6Wg0\",\"type\":\"genericNode\",\"position\":{\"x\":39.400034102832365,\"y\":-499.03551482195707},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nimport subprocess\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\nfrom typing import List\\n\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"PIP Install\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n\\n def build(self, libs: List[str]) -> Document:\\n def install_package(package_name):\\n subprocess.check_call([\\\"pip\\\", \\\"install\\\", package_name])\\n for lib in libs:\\n install_package(lib)\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"libs\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"libs\",\"display_name\":\"libs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"requests\",\"pytube\"]}},\"description\":\"Create any custom component you want!\",\"base_classes\":[\"Document\"],\"display_name\":\"PIP Install\",\"custom_fields\":{\"libs\":null},\"output_types\":[],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-y6Wg0\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":39.400034102832365,\"y\":-499.03551482195707}}],\"edges\":[{\"source\":\"ChatOpenAI-q61Y2\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CustomComponent-h0NSI{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"BaseLLM\"}}},{\"source\":\"CustomComponent-y6Wg0\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CustomComponent-y6Wg0{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}-CustomComponent-h0NSI{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-y6Wg0\"},\"targetHandle\":{\"fieldName\":\"dependencies\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"Document\"}}},{\"source\":\"CustomComponent-h0NSI\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-h0NSIœ}\",\"target\":\"RecursiveCharacterTextSplitter-1eaOX\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CustomComponent-h0NSI{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-h0NSIœ}-RecursiveCharacterTextSplitter-1eaOX{œfieldNameœ:œdocumentsœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-h0NSI\"},\"targetHandle\":{\"fieldName\":\"documents\",\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"inputTypes\":null,\"type\":\"Document\"}},\"selected\":false},{\"source\":\"OpenAIEmbeddings-cduOO\",\"sourceHandle\":\"{œbaseClassesœ:[œEmbeddingsœ,œOpenAIEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-cduOOœ}\",\"target\":\"Chroma-gVhy7\",\"targetHandle\":\"{œfieldNameœ:œembeddingœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-OpenAIEmbeddings-cduOO{œbaseClassesœ:[œEmbeddingsœ,œOpenAIEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-cduOOœ}-Chroma-gVhy7{œfieldNameœ:œembeddingœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"dataType\":\"OpenAIEmbeddings\",\"id\":\"OpenAIEmbeddings-cduOO\"},\"targetHandle\":{\"fieldName\":\"embedding\",\"id\":\"Chroma-gVhy7\",\"inputTypes\":null,\"type\":\"Embeddings\"}}},{\"source\":\"RecursiveCharacterTextSplitter-1eaOX\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ}\",\"target\":\"Chroma-gVhy7\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-RecursiveCharacterTextSplitter-1eaOX{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ}-Chroma-gVhy7{œfieldNameœ:œdocumentsœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"RecursiveCharacterTextSplitter\",\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"targetHandle\":{\"fieldName\":\"documents\",\"id\":\"Chroma-gVhy7\",\"inputTypes\":null,\"type\":\"Document\"}}},{\"source\":\"ChatOpenAI-q61Y2\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}\",\"target\":\"CombineDocsChain-yk0JF\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œCombineDocsChain-yk0JFœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CombineDocsChain-yk0JF{œfieldNameœ:œllmœ,œidœ:œCombineDocsChain-yk0JFœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CombineDocsChain-yk0JF\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"}}},{\"source\":\"CombineDocsChain-yk0JF\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CombineDocsChain-yk0JF{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}-RetrievalQA-4PmTB{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"BaseCombineDocumentsChain\",\"function\"],\"dataType\":\"CombineDocsChain\",\"id\":\"CombineDocsChain-yk0JF\"},\"targetHandle\":{\"fieldName\":\"combine_documents_chain\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseCombineDocumentsChain\"}}},{\"source\":\"Chroma-gVhy7\",\"sourceHandle\":\"{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-Chroma-gVhy7{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}-RetrievalQA-4PmTB{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"dataType\":\"Chroma\",\"id\":\"Chroma-gVhy7\"},\"targetHandle\":{\"fieldName\":\"retriever\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseRetriever\"}}}],\"viewport\":{\"x\":189.54413265004274,\"y\":259.89949657174975,\"zoom\":0.291027508374689}},\"is_component\":false,\"updated_at\":\"2023-12-04T22:59:08.830269\",\"folder\":null,\"id\":\"bc7eb94b-6fc6-49cb-9b19-35347ba51afa\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Web Scraper - Content & Links\",\"description\":\"Fetch and parse text and links from a given URL.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":338,\"id\":\"LLMChain-H7PBy\",\"type\":\"genericNode\",\"position\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-H7PBy\"},\"selected\":false,\"positionAbsolute\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"dragging\":false},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-VSAdc\",\"type\":\"genericNode\",\"position\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-VSAdc\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859}},{\"width\":384,\"height\":374,\"data\":{\"id\":\"GroupNode-WXPMk\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"give me links\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-f6nOg\"},\"selected\":true,\"positionAbsolute\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"dragging\":false},{\"width\":384,\"height\":375,\"id\":\"CustomComponent-FGzJJ\",\"type\":\"genericNode\",\"position\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse HTML\",\"custom_fields\":{\"html_content\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-0XtUN\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":214.00059169497604,\"y\":177.27071061129823}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-ODIcp\",\"type\":\"genericNode\",\"position\":{\"x\":845.0502195222412,\"y\":366.54344452019404},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract 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is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser 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Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-NTTcv\"},\"selected\":false,\"positionAbsolute\":{\"x\":-221.33853765781578,\"y\":-35.48749923706055}},{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-VELMV\",\"type\":\"genericNode\",\"position\":{\"x\":-222,\"y\":682.560723386973},\"data\":{\"id\":\"PromptTemplate-VELMV\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input 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requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown 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{\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse 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links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract 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is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_markdown\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_markdown\",\"display_name\":\"html_markdown\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_links\",\"display_name\":\"html_links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"query\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A 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Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":null,\"base_classes\":[\"str\"],\"display_name\":\"URL 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PromptRunner(CustomComponent):\\n display_name: str = \\\"Prompt Runner\\\"\\n description: str = \\\"Run a Chain with the given PromptTemplate\\\"\\n beta: bool = True\\n field_config = {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt Template\\\",\\n \\\"info\\\": \\\"Make sure the prompt has all variables filled.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}) -> Document:\\n chain = prompt | llm\\n # The input is an empty dict because the prompt is already filled\\n result = chain.invoke(input=inputs)\\n if hasattr(result, \\\"content\\\"):\\n result = result.content\\n self.repr_value = result\\n return 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(1)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per 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string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-ipifC\"},\"id\":\"CustomComponent-ipifC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:49.799612\",\"folder\":null,\"id\":\"ca83ee08-2f93-427d-9897-80fef6dd5447\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per 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Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Y4qL7\"},\"id\":\"CustomComponent-Y4qL7\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:53.719960\",\"folder\":null,\"id\":\"5847602b-769a-4a82-82e8-a70f54a59929\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Lively Williams\",\"description\":\"Conversational Cartography Unlocked.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T13:27:45.691254\",\"folder\":null,\"id\":\"0e3bdba9-127a-4399-81a4-2865b70a4a47\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Shared Component\",\"description\":\"Conversational Cartography Unlocked.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":328,\"id\":\"CSVAgent-TK9Ea\",\"type\":\"genericNode\",\"position\":{\"x\":251.25514772667083,\"y\":160.7424529887874},\"data\":{\"type\":\"CSVAgent\",\"node\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"_type\":\"csv_agent\"},\"description\":\"Construct a CSV agent from a CSV and 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files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-Z0uol\"},\"id\":\"AirbyteJSONLoader-Z0uol\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:33.764117\",\"folder\":null,\"id\":\"2a37c76c-65c8-4c01-a72d-eb46f3d41a56\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-28ASv\"},\"id\":\"AmazonBedrockEmbeddings-28ASv\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:41.066618\",\"folder\":null,\"id\":\"850ba4e6-96ca-49a7-9b0c-4b7048fd52c8\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"ConversationBufferMemory\",\"description\":\"Buffer for storing conversation memory.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"ConversationBufferMemory\",\"node\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"memory_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\"],\"display_name\":\"ConversationBufferMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"ConversationBufferMemory-WaoLx\"},\"id\":\"ConversationBufferMemory-WaoLx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:04:46.058568\",\"folder\":null,\"id\":\"be650940-0449-4eb0-a708-056310f924fd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (1)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\"},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:05:50.570800\",\"folder\":null,\"id\":\"53ad1fe2-f3af-4354-86e0-eb141ce12859\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (2)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\"},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:06:11.415088\",\"folder\":null,\"id\":\"e9ed1c90-146e-452d-8ccd-ebf2e0da4331\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (3)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\"},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:07:06.411108\",\"folder\":null,\"id\":\"83783c57-64e3-41a6-aa7e-ed82f80f1e53\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (6)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"z2b\":\"z9\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-2pn2o\"},\"id\":\"CSVLoader-2pn2o\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:38:30.706258\",\"folder\":null,\"id\":\"c4ae9de6-9df3-4d3c-afc9-1752af4fecd7\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-MJrAC\"},\"id\":\"AgentInitializer-MJrAC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:42:54.715163\",\"folder\":null,\"id\":\"ff84b5f9-5680-4084-a9f1-7d94fe675486\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer (1)\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-3lcZ4\"},\"id\":\"AgentInitializer-3lcZ4\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:43:20.819934\",\"folder\":null,\"id\":\"97f5db9f-d6cc-4555-88fb-3be102c67814\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Exuberant Banach\",\"description\":\"Unfolding Linguistic Possibilities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T14:52:49.951416\",\"folder\":null,\"id\":\"a600acd1-213a-4ce7-8648-ab2ee59f5918\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (3)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-JhQtx\"},\"id\":\"AirbyteJSONLoader-JhQtx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:44:49.587337\",\"folder\":null,\"id\":\"07c52ad7-ca52-4cdd-ab40-74a91dbad0dd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (4)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-bIvDc\"},\"id\":\"AirbyteJSONLoader-bIvDc\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:45:13.458500\",\"folder\":null,\"id\":\"53e4d256-3653-444b-b8e0-fb97b3ae5c7e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (5)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-2BLS8\"},\"id\":\"AirbyteJSONLoader-2BLS8\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:45:44.461699\",\"folder\":null,\"id\":\"b5158cc1-c6b8-4e25-907a-bc7e7637aa38\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (4)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-NsBzN\"},\"id\":\"AmazonBedrockEmbeddings-NsBzN\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:50:29.791575\",\"folder\":null,\"id\":\"3fb77f72-1be7-4ce0-ae89-a82b0eee9acf\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Evil Golick\",\"description\":\"Where Language Meets Logic.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.707854\",\"folder\":null,\"id\":\"9c5d21c2-ea82-4cf4-a591-c3d3fd3f13e6\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Gleeful Davinci (1)\",\"description\":\"Unleashing Linguistic Creativity.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.761150\",\"folder\":null,\"id\":\"f8e2e16e-129c-4116-9626-2c6b524d97b7\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Effervescent Degrasse\",\"description\":\"Language Models, Mapped and Mastered.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.764440\",\"folder\":null,\"id\":\"d7f4f7b5-effe-4834-8cab-4f2fc4f6e9de\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Prickly Archimedes\",\"description\":\"Language Chainlink Master.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.767546\",\"folder\":null,\"id\":\"2265d813-4a87-410f-b06a-65e35db8219e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Boring Heyrovsky\",\"description\":\"Building Intelligent Interactions.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.765105\",\"folder\":null,\"id\":\"10bfd881-e67e-4c33-965d-ee041695edce\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Nostalgic Carroll\",\"description\":\"Unfolding Linguistic Possibilities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.811794\",\"folder\":null,\"id\":\"63fa5653-4513-47f2-8dfa-baeb1d981f93\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Pensive Perlman\",\"description\":\"Empowering Communication, Enabling Opportunities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.814993\",\"folder\":null,\"id\":\"f4bc5945-20d9-4703-a5bb-6a4a3ef7fc8e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Hilarious Stallman\",\"description\":\"Connect the Dots, Craft Language.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.813144\",\"folder\":null,\"id\":\"8d8b80f9-4b74-4cd5-bc5c-08a32c4d2b95\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Awesome Einstein\",\"description\":\"The Power of Language at Your Fingertips.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:19.518262\",\"folder\":null,\"id\":\"21808fd7-a492-4e29-8863-301c2785f542\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Funky Swanson\",\"description\":\"Sculpting Language with Precision.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.185637\",\"folder\":null,\"id\":\"7752299a-4aa9-44db-8d9c-5c4a2ac1b071\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Romantic Pascal\",\"description\":\"Unlock the Power of AI in Your Business Conversations.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.223064\",\"folder\":null,\"id\":\"78f48a13-6a9f-42ce-9d58-ec9b63b381d2\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Twinkly Bartik\",\"description\":\"Sculpting Language with Precision.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.239758\",\"folder\":null,\"id\":\"38074091-3d7c-4d42-b61b-ae195c1b8a4e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Condescending Joliot\",\"description\":\"Language Models, Unleashed.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.271996\",\"folder\":null,\"id\":\"773020aa-5c2d-4632-ad9e-21276861ab93\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Gleeful Kalam\",\"description\":\"The Power of Language at Your Fingertips.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.283929\",\"folder\":null,\"id\":\"0092d077-6d31-401f-a739-b164476b90ed\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Grinning Bhabha\",\"description\":\"Your Passport to Linguistic Landscapes.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.467739\",\"folder\":null,\"id\":\"4a395211-712d-4dd2-b3bb-e6b19200f15d\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Mirthful Babbage\",\"description\":\"Design Dialogues with Langflow.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.742990\",\"folder\":null,\"id\":\"3f086a82-3e29-4328-9da8-e02dc9201744\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Tiny Volta\",\"description\":\"Nurture NLP Nodes Here.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:24:18.594247\",\"folder\":null,\"id\":\"659b8289-c8e8-413d-9b2b-51e68411f170\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Radiant Jennings\",\"description\":\"Design, Develop, Dialogize.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:24:42.640309\",\"folder\":null,\"id\":\"f55f0640-d47e-4471-b1ba-3411d38ecac5\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Distracted 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Bose\",\"description\":\"Unravel the Art of Articulation.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:32:23.972035\",\"folder\":null,\"id\":\"65f49582-c9fa-4c67-a2bc-4e8fa73ca731\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Tender Perlman\",\"description\":\"Maximize Impact with Intelligent Conversations.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:33:26.984083\",\"folder\":null,\"id\":\"9ae57fe2-c677-47fa-a059-7d3d915a1178\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Sharp Cori\",\"description\":\"Conversation Catalyst 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Mastered.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-m2yFu\",\"type\":\"genericNode\",\"position\":{\"x\":462.6456058272081,\"y\":1033.9314297130313},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"transcription\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"create an image prompt based on the song's lyrics to be used as the album cover of this song:\\n\\nlyrics:\\n{transcription}\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"transcription\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"transcription\",\"display_name\":\"transcription\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt 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Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI 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Chat large language models API.\",\"base_classes\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-urapv\"},\"selected\":false,\"positionAbsolute\":{\"x\":189.94856095084924,\"y\":-85.06556385338186},\"dragging\":false},{\"width\":384,\"height\":806,\"id\":\"CustomComponent-IEIUl\",\"type\":\"genericNode\",\"position\":{\"x\":2364.865919667005,\"y\":88.43094097025096},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nfrom platformdirs import user_cache_dir\\nimport base64\\nfrom PIL import Image\\nfrom io import BytesIO\\nfrom openai import OpenAI\\nfrom pathlib import Path\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Image generator\\\"\\n description: str = \\\"generate images using Dall-E\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\",\\\"input_types\\\":[\\\"str\\\"]},\\n \\\"api_key\\\":{\\\"display_name\\\":\\\"OpenAI API key\\\",\\\"password\\\":True},\\n \\\"model\\\":{\\\"display_name\\\":\\\"Model name\\\",\\\"advanced\\\":True,\\\"options\\\":[\\\"dall-e-2\\\",\\\"dall-e-3\\\"], \\\"value\\\":\\\"dall-e-3\\\"},\\n \\\"file_name\\\":{\\\"display_name\\\":\\\"File Name\\\"},\\n \\\"output_format\\\":{\\\"display_name\\\":\\\"Output format\\\",\\\"options\\\":[\\\"jpeg\\\",\\\"png\\\"],\\\"value\\\":\\\"jpeg\\\"},\\n \\\"width\\\":{\\\"display_name\\\":\\\"Width\\\" ,\\\"value\\\":1024},\\n \\\"height\\\":{\\\"display_name\\\":\\\"Height\\\", \\\"value\\\":1024}\\n }\\n\\n def build(self, prompt:str,api_key:str,model:str,file_name:str,output_format:str,width:int,height:int):\\n client = OpenAI(api_key=api_key)\\n cache_dir = Path(user_cache_dir(\\\"langflow\\\"))\\n images_dir = cache_dir / \\\"images\\\"\\n images_dir.mkdir(parents=True, exist_ok=True)\\n image_path = images_dir / f\\\"{file_name}.{output_format}\\\"\\n response = client.images.generate(\\n model=model,\\n prompt=prompt,\\n size=f\\\"{height}x{width}\\\",\\n response_format=\\\"b64_json\\\",\\n n=1,\\n )\\n # Decode base64-encoded image string\\n binary_data = base64.b64decode(response.data[0].b64_json)\\n # Create PIL Image object from binary image data\\n image_pil = Image.open(BytesIO(binary_data))\\n image_pil.save(image_path, format=output_format.upper())\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"api_key\",\"display_name\":\"OpenAI API key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"file_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"file_name\",\"display_name\":\"File Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"album\"},\"height\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1024,\"password\":false,\"name\":\"height\",\"display_name\":\"Height\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"model\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"dall-e-3\",\"password\":false,\"options\":[\"dall-e-2\",\"dall-e-3\"],\"name\":\"model\",\"display_name\":\"Model name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"output_format\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"jpeg\",\"password\":false,\"options\":[\"jpeg\",\"png\"],\"name\":\"output_format\",\"display_name\":\"Output format\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"input_types\":[\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"width\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1024,\"password\":false,\"name\":\"width\",\"display_name\":\"Width\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false}},\"description\":\"generate images using Dall-E\",\"base_classes\":[\"Data\"],\"display_name\":\"Image generator\",\"custom_fields\":{\"api_key\":null,\"file_name\":null,\"height\":null,\"model\":null,\"output_format\":null,\"prompt\":null,\"width\":null},\"output_types\":[\"Data\"],\"documentation\":\"http://docs.langflow.org/components/custom\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-IEIUl\"},\"selected\":false,\"positionAbsolute\":{\"x\":2364.865919667005,\"y\":88.43094097025096}},{\"width\":384,\"height\":338,\"id\":\"LLMChain-KlJb3\",\"type\":\"genericNode\",\"position\":{\"x\":1242.9851164540805,\"y\":-139.74634696823108},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-KlJb3\"},\"selected\":false,\"positionAbsolute\":{\"x\":1242.9851164540805,\"y\":-139.74634696823108},\"dragging\":false},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-bCuc0\",\"type\":\"genericNode\",\"position\":{\"x\":1747.8107777342625,\"y\":319.13729017446667},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Custom Component\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n documentation: str = 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to have a conversation and load context from memory.\",\"base_classes\":[\"ConversationChain\",\"Chain\",\"LLMChain\",\"function\"],\"display_name\":\"ConversationChain\",\"documentation\":\"\"},\"id\":\"ConversationChain-bVNex\",\"value\":null},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":806,\"y\":554}}],\"edges\":[{\"source\":\"ChatOpenAI-fBcfh\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseChatModelœ,œBaseLanguageModelœ,œChatOpenAIœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-fBcfhœ}\",\"target\":\"ConversationChain-bVNex\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œConversationChain-bVNexœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"className\":\"stroke-gray-900 stroke-connection\",\"id\":\"reactflow__edge-ChatOpenAI-fBcfh{œbaseClassesœ:[œBaseChatModelœ,œBaseLanguageModelœ,œChatOpenAIœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-fBcfhœ}-ConversationChain-bVNex{œfieldNameœ:œllmœ,œidœ:œConversationChain-bVNexœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"BaseChatModel\",\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-fBcfh\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"ConversationChain-bVNex\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"}},\"style\":{\"stroke\":\"#555\"},\"animated\":false}],\"viewport\":{\"x\":-118.21416568593895,\"y\":-240.64815770363373,\"zoom\":0.7642485855675408}},\"is_component\":false,\"updated_at\":\"2023-12-04T22:57:55.879806\",\"folder\":null,\"id\":\"bddebeea-b80a-4b28-8895-c4425687dcce\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Directory Loader\",\"description\":\"Generic File Loader\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import os\\n\\nfrom langchain.schema import Document\\nfrom langflow import CustomComponent\\nimport glob\\n\\nclass DirectoryLoaderComponent(CustomComponent):\\n display_name: str = \\\"Directory Loader\\\"\\n description: str = \\\"Generic File Loader\\\"\\n beta = True\\n loaders_info = [\\n {\\n \\\"loader\\\": \\\"AirbyteJSONLoader\\\",\\n \\\"name\\\": \\\"Airbyte JSON (.jsonl)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.AirbyteJSONLoader\\\",\\n \\\"defaultFor\\\": [\\\"jsonl\\\"],\\n \\\"allowdTypes\\\": [\\\"jsonl\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"JSONLoader\\\",\\n \\\"name\\\": \\\"JSON (.json)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.JSONLoader\\\",\\n \\\"defaultFor\\\": [\\\"json\\\"],\\n \\\"allowdTypes\\\": [\\\"json\\\"],\\n \\\"kwargs\\\": {\\\"jq_schema\\\": \\\".\\\", \\\"text_content\\\": False}\\n },\\n {\\n \\\"loader\\\": \\\"BSHTMLLoader\\\",\\n \\\"name\\\": \\\"BeautifulSoup4 HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.BSHTMLLoader\\\",\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"CSVLoader\\\",\\n \\\"name\\\": \\\"CSV (.csv)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.CSVLoader\\\",\\n \\\"defaultFor\\\": [\\\"csv\\\"],\\n \\\"allowdTypes\\\": [\\\"csv\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"CoNLLULoader\\\",\\n \\\"name\\\": \\\"CoNLL-U (.conllu)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.CoNLLULoader\\\",\\n \\\"defaultFor\\\": [\\\"conllu\\\"],\\n \\\"allowdTypes\\\": [\\\"conllu\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"EverNoteLoader\\\",\\n \\\"name\\\": \\\"EverNote (.enex)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.EverNoteLoader\\\",\\n \\\"defaultFor\\\": [\\\"enex\\\"],\\n \\\"allowdTypes\\\": [\\\"enex\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"FacebookChatLoader\\\",\\n \\\"name\\\": \\\"Facebook Chat (.json)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.FacebookChatLoader\\\",\\n \\\"allowdTypes\\\": [\\\"json\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"OutlookMessageLoader\\\",\\n \\\"name\\\": \\\"Outlook Message (.msg)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.OutlookMessageLoader\\\",\\n \\\"defaultFor\\\": [\\\"msg\\\"],\\n \\\"allowdTypes\\\": [\\\"msg\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"PyPDFLoader\\\",\\n \\\"name\\\": \\\"PyPDF (.pdf)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.PyPDFLoader\\\",\\n \\\"defaultFor\\\": [\\\"pdf\\\"],\\n \\\"allowdTypes\\\": [\\\"pdf\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"STRLoader\\\",\\n \\\"name\\\": \\\"Subtitle (.str)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.STRLoader\\\",\\n \\\"defaultFor\\\": [\\\"str\\\"],\\n \\\"allowdTypes\\\": [\\\"str\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"TextLoader\\\",\\n \\\"name\\\": \\\"Text (.txt)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.TextLoader\\\",\\n \\\"defaultFor\\\": [\\\"txt\\\"],\\n \\\"allowdTypes\\\": [\\\"txt\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredEmailLoader\\\",\\n \\\"name\\\": \\\"Unstructured Email (.eml)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredEmailLoader\\\",\\n \\\"defaultFor\\\": [\\\"eml\\\"],\\n \\\"allowdTypes\\\": [\\\"eml\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredHTMLLoader\\\",\\n \\\"name\\\": \\\"Unstructured HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredHTMLLoader\\\",\\n \\\"defaultFor\\\": [\\\"html\\\", \\\"htm\\\"],\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredMarkdownLoader\\\",\\n \\\"name\\\": \\\"Unstructured Markdown (.md)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredMarkdownLoader\\\",\\n \\\"defaultFor\\\": [\\\"md\\\"],\\n \\\"allowdTypes\\\": [\\\"md\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredPowerPointLoader\\\",\\n \\\"name\\\": \\\"Unstructured PowerPoint (.pptx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredPowerPointLoader\\\",\\n \\\"defaultFor\\\": [\\\"pptx\\\"],\\n \\\"allowdTypes\\\": [\\\"pptx\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredWordLoader\\\",\\n \\\"name\\\": \\\"Unstructured Word (.docx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredWordLoader\\\",\\n \\\"defaultFor\\\": [\\\"docx\\\"],\\n \\\"allowdTypes\\\": [\\\"docx\\\"],\\n },\\n]\\n\\n\\n def build_config(self):\\n loader_options = [\\\"Automatic\\\"] + [\\n loader_info[\\\"name\\\"] for loader_info in self.loaders_info\\n ]\\n\\n file_types = []\\n suffixes = []\\n\\n for loader_info in self.loaders_info:\\n if \\\"allowedTypes\\\" in loader_info:\\n file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n return {\\n \\\"directory_path\\\": {\\n \\\"display_name\\\": \\\"Directory Path\\\",\\n \\\"required\\\": True,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": loader_options,\\n \\\"value\\\": \\\"Automatic\\\",\\n },\\n }\\n\\n def build(self, directory_path: str, loader: str) -> Document:\\n # Verifique se o diretório existe\\n if not os.path.exists(directory_path):\\n raise ValueError(f\\\"Directory not found: {directory_path}\\\")\\n\\n files = glob.glob(directory_path + \\\"/*.*\\\")\\n\\n\\n loader_info = None\\n if loader == \\\"Automatic\\\":\\n for file in files:\\n file_type = file.split(\\\".\\\")[-1]\\n\\n\\n for info in self.loaders_info:\\n if \\\"defaultFor\\\" in info:\\n if file_type in info[\\\"defaultFor\\\"]:\\n loader_info = info\\n break\\n if loader_info:\\n break\\n\\n if not loader_info:\\n raise ValueError(\\n \\\"No default loader found for any file in the directory\\\"\\n )\\n\\n else:\\n for info in self.loaders_info:\\n if info[\\\"name\\\"] == loader:\\n loader_info = info\\n break\\n\\n if not loader_info:\\n raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n loader_import = loader_info[\\\"import\\\"]\\n module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n try:\\n # Importe o loader dinamicamente\\n loader_module = __import__(module_name, fromlist=[class_name])\\n loader_instance = getattr(loader_module, class_name)\\n except ImportError as e:\\n raise ValueError(\\n f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{loader_info}\\\"\\n ) from e\\n\\n results = []\\n for file in files:\\n file_path = os.path.join(directory_path, file)\\n kwargs = loader_info.get(\\\"kwargs\\\", {})\\n result = loader_instance(file_path=file_path, **kwargs).load()\\n results.append(result)\\n self.status = results\\n return results\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"directory_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"directory_path\",\"display_name\":\"Directory Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"/Users/ogabrielluiz/Projects/langflow2/docs\"},\"loader\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true}},\"description\":\"Generic File Loader\",\"base_classes\":[\"Document\"],\"display_name\":\"Directory Loader\",\"custom_fields\":{\"directory_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Ip6tG\"},\"id\":\"CustomComponent-Ip6tG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:38.570303\",\"folder\":null,\"id\":\"5fe4debc-b6a7-45d4-a694-c02d8aa93b08\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Whisper Transcriber\",\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, List, Dict, Union\\nfrom langflow.field_typing import (\\n AgentExecutor,\\n BaseChatMemory,\\n BaseLanguageModel,\\n BaseLLM,\\n BaseLoader,\\n BaseMemory,\\n BaseOutputParser,\\n BasePromptTemplate,\\n BaseRetriever,\\n Callable,\\n Chain,\\n ChatPromptTemplate,\\n Data,\\n Document,\\n Embeddings,\\n NestedDict,\\n Object,\\n PromptTemplate,\\n TextSplitter,\\n Tool,\\n VectorStore,\\n)\\n\\nfrom openai import OpenAI\\nclass Component(CustomComponent):\\n display_name: str = \\\"Whisper Transcriber\\\"\\n description: str = \\\"Converts audio to text using OpenAI's Whisper.\\\"\\n\\n def build_config(self):\\n return {\\\"audio\\\":{\\\"field_type\\\":\\\"file\\\",\\\"suffixes\\\":[\\\".mp3\\\", \\\".mp4\\\", \\\".m4a\\\"]},\\\"OpenAIKey\\\":{\\\"field_type\\\":\\\"str\\\",\\\"password\\\":True}}\\n\\n def build(self, audio:str, OpenAIKey:str) -> str:\\n \\n # TODO: if output path, persist & load it instead\\n \\n client = OpenAI(api_key=OpenAIKey)\\n \\n audio_file= open(audio, \\\"rb\\\")\\n transcript = client.audio.transcriptions.create(\\n model=\\\"whisper-1\\\", \\n file=audio_file,\\n response_format=\\\"text\\\"\\n )\\n \\n \\n self.status = transcript\\n return transcript\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"OpenAIKey\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"OpenAIKey\",\"display_name\":\"OpenAIKey\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"audio\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"suffixes\":[\".mp3\",\".mp4\",\".m4a\"],\"password\":false,\"name\":\"audio\",\"display_name\":\"audio\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"file_path\":\"/Users/rodrigonader/Library/Caches/langflow/19b3e1c9-90bf-405f-898a-e982f47adf76/a3308ce7e9b10088fcd985aabb6d17b012c6b6e81ff8806356574474c9d10229.m4a\",\"value\":\"Audio Recording 2023-11-29 at 12.12.04.m4a\"}},\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"base_classes\":[\"str\"],\"display_name\":\"Whisper Transcriber\",\"custom_fields\":{\"OpenAIKey\":null,\"audio\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-GJRrs\"},\"id\":\"CustomComponent-GJRrs\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:39.710502\",\"folder\":null,\"id\":\"bd9e911d-46bd-429f-aa75-dd740430695e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Youtube Conversation\",\"description\":\"Craft Meaningful Interactions, Generate Value.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":589,\"id\":\"CustomComponent-h0NSI\",\"type\":\"genericNode\",\"position\":{\"x\":714.9531293402755,\"y\":0.4994374160582993},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import requests\\nfrom langflow import CustomComponent\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.schema import Document\\nfrom langchain.document_loaders import YoutubeLoader\\n\\n# Example usage:\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"YouTube Transcript\\\"\\n description: str = \\\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\\\"\\n\\n def build_config(self):\\n return {\\\"url\\\": {\\\"multiline\\\": True, \\\"required\\\": True}}\\n\\n def build(self, url: str, llm: BaseLLM, dependencies: Document, language: str = \\\"en\\\") -> Document:\\n dependencies\\n response = requests.get(url)\\n loader = YoutubeLoader.from_youtube_url(url, add_video_info=True, language=[language])\\n result = loader.load()\\n self.repr_value = str(result)\\n return result\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"dependencies\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"dependencies\",\"display_name\":\"dependencies\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"language\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"language\",\"display_name\":\"language\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\",\"base_classes\":[\"Document\"],\"display_name\":\"YouTube Transcript\",\"custom_fields\":{\"dependencies\":null,\"language\":null,\"llm\":null,\"url\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-h0NSI\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":714.9531293402755,\"y\":0.4994374160582993}},{\"width\":384,\"height\":629,\"id\":\"ChatOpenAI-q61Y2\",\"type\":\"genericNode\",\"position\":{\"x\":18,\"y\":625.5521045590924},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-16k\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k-0613\",\"gpt-3.5-turbo-16k\",\"gpt-4-0613\",\"gpt-4-32k-0613\",\"gpt-4\",\"gpt-4-32k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-q61Y2\"},\"selected\":false,\"positionAbsolute\":{\"x\":18,\"y\":625.5521045590924},\"dragging\":false},{\"width\":384,\"height\":539,\"id\":\"Chroma-gVhy7\",\"type\":\"genericNode\",\"position\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"data\":{\"type\":\"Chroma\",\"node\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.Client\",\"list\":false},\"client_settings\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client_settings\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.config.Setting\",\"list\":true},\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"chroma_server_cors_allow_origins\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Chroma Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"chroma_server_grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Chroma Server GRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Chroma Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_http_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_http_port\",\"display_name\":\"Chroma Server HTTP Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_ssl_enabled\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Chroma Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"collection_metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"collection_metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"collection_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"persist\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"persist\",\"display_name\":\"Persist\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"persist_directory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"persist_directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"_type\":\"Chroma\"},\"description\":\"Create a Chroma vectorstore from a raw documents.\",\"base_classes\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Chroma\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma\",\"beta\":false,\"error\":null},\"id\":\"Chroma-gVhy7\"},\"selected\":false,\"positionAbsolute\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"dragging\":false},{\"width\":384,\"height\":501,\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"type\":\"genericNode\",\"position\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"data\":{\"type\":\"RecursiveCharacterTextSplitter\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.utils.util import build_loader_repr_from_documents\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chunk_overlap\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"50\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"400\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"type\":\"int\",\"list\":false},\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"type\":\"Document\",\"list\":true},\"separators\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"type\":\"str\",\"list\":true,\"value\":[\" \"]}},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separators\":null},\"output_types\":[\"RecursiveCharacterTextSplitter\"],\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"beta\":true,\"error\":null},\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"selected\":false,\"positionAbsolute\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"dragging\":false},{\"width\":384,\"height\":367,\"id\":\"OpenAIEmbeddings-cduOO\",\"type\":\"genericNode\",\"position\":{\"x\":1405.1176670984544,\"y\":1029.459061269321},\"data\":{\"type\":\"OpenAIEmbeddings\",\"node\":{\"template\":{\"allowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[],\"password\":false,\"name\":\"allowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"disallowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"all\",\"password\":false,\"name\":\"disallowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"deployment\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"embedding_ctx_length\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":8191,\"password\":false,\"name\":\"embedding_ctx_length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"{'Authorization': 'Bearer '}\",\"password\":false,\"name\":\"headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"model\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_api_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_version\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"show_progress_bar\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"show_progress_bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"skip_empty\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"skip_empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"_type\":\"OpenAIEmbeddings\"},\"description\":\"OpenAI embedding models.\",\"base_classes\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"display_name\":\"OpenAIEmbeddings\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"OpenAIEmbeddings-cduOO\"},\"selected\":false,\"positionAbsolute\":{\"x\":1405.1176670984544,\"y\":1029.459061269321}},{\"width\":384,\"height\":339,\"id\":\"RetrievalQA-4PmTB\",\"type\":\"genericNode\",\"position\":{\"x\":2711.7786966415715,\"y\":709.4450828605463},\"data\":{\"type\":\"RetrievalQA\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"combine_documents_chain\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"combine_documents_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseCombineDocumentsChain\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"list\":false},\"input_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"query\",\"password\":false,\"name\":\"input_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"output_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"result\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"return_source_documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_source_documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"RetrievalQA\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"RetrievalQA\",\"Chain\",\"BaseRetrievalQA\",\"function\"],\"display_name\":\"RetrievalQA\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/vector_db_qa\",\"beta\":false,\"error\":null},\"id\":\"RetrievalQA-4PmTB\"},\"selected\":false,\"positionAbsolute\":{\"x\":2711.7786966415715,\"y\":709.4450828605463}},{\"width\":384,\"height\":333,\"id\":\"CombineDocsChain-yk0JF\",\"type\":\"genericNode\",\"position\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"data\":{\"type\":\"CombineDocsChain\",\"node\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"chain_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"BaseCombineDocumentsChain\",\"function\"],\"display_name\":\"CombineDocsChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"id\":\"CombineDocsChain-yk0JF\"},\"selected\":false,\"positionAbsolute\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"dragging\":false},{\"width\":384,\"height\":417,\"id\":\"CustomComponent-y6Wg0\",\"type\":\"genericNode\",\"position\":{\"x\":39.400034102832365,\"y\":-499.03551482195707},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nimport subprocess\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\nfrom typing import List\\n\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"PIP Install\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n\\n def build(self, libs: List[str]) -> Document:\\n def install_package(package_name):\\n subprocess.check_call([\\\"pip\\\", \\\"install\\\", package_name])\\n for lib in libs:\\n install_package(lib)\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"libs\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"libs\",\"display_name\":\"libs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"requests\",\"pytube\"]}},\"description\":\"Create any custom component you want!\",\"base_classes\":[\"Document\"],\"display_name\":\"PIP Install\",\"custom_fields\":{\"libs\":null},\"output_types\":[],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-y6Wg0\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":39.400034102832365,\"y\":-499.03551482195707}}],\"edges\":[{\"source\":\"ChatOpenAI-q61Y2\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CustomComponent-h0NSI{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"BaseLLM\"}}},{\"source\":\"CustomComponent-y6Wg0\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CustomComponent-y6Wg0{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}-CustomComponent-h0NSI{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-y6Wg0\"},\"targetHandle\":{\"fieldName\":\"dependencies\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"Document\"}}},{\"source\":\"CustomComponent-h0NSI\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-h0NSIœ}\",\"target\":\"RecursiveCharacterTextSplitter-1eaOX\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 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stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-OpenAIEmbeddings-cduOO{œbaseClassesœ:[œEmbeddingsœ,œOpenAIEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-cduOOœ}-Chroma-gVhy7{œfieldNameœ:œembeddingœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"dataType\":\"OpenAIEmbeddings\",\"id\":\"OpenAIEmbeddings-cduOO\"},\"targetHandle\":{\"fieldName\":\"embedding\",\"id\":\"Chroma-gVhy7\",\"inputTypes\":null,\"type\":\"Embeddings\"}}},{\"source\":\"RecursiveCharacterTextSplitter-1eaOX\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ}\",\"target\":\"Chroma-gVhy7\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 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stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CombineDocsChain-yk0JF{œfieldNameœ:œllmœ,œidœ:œCombineDocsChain-yk0JFœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CombineDocsChain-yk0JF\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"}}},{\"source\":\"CombineDocsChain-yk0JF\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CombineDocsChain-yk0JF{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}-RetrievalQA-4PmTB{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"BaseCombineDocumentsChain\",\"function\"],\"dataType\":\"CombineDocsChain\",\"id\":\"CombineDocsChain-yk0JF\"},\"targetHandle\":{\"fieldName\":\"combine_documents_chain\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseCombineDocumentsChain\"}}},{\"source\":\"Chroma-gVhy7\",\"sourceHandle\":\"{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-Chroma-gVhy7{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}-RetrievalQA-4PmTB{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"dataType\":\"Chroma\",\"id\":\"Chroma-gVhy7\"},\"targetHandle\":{\"fieldName\":\"retriever\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseRetriever\"}}}],\"viewport\":{\"x\":189.54413265004274,\"y\":259.89949657174975,\"zoom\":0.291027508374689}},\"is_component\":false,\"updated_at\":\"2023-12-04T22:59:08.830269\",\"folder\":null,\"id\":\"bc7eb94b-6fc6-49cb-9b19-35347ba51afa\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Web Scraper - Content & Links\",\"description\":\"Fetch and parse text and links from a given URL.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":338,\"id\":\"LLMChain-H7PBy\",\"type\":\"genericNode\",\"position\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-H7PBy\"},\"selected\":false,\"positionAbsolute\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"dragging\":false},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-VSAdc\",\"type\":\"genericNode\",\"position\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"sk-...\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-VSAdc\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859}},{\"width\":384,\"height\":374,\"data\":{\"id\":\"GroupNode-WXPMk\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"give me links\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-f6nOg\"},\"selected\":true,\"positionAbsolute\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"dragging\":false},{\"width\":384,\"height\":375,\"id\":\"CustomComponent-FGzJJ\",\"type\":\"genericNode\",\"position\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse HTML\",\"custom_fields\":{\"html_content\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-0XtUN\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":214.00059169497604,\"y\":177.27071061129823}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-ODIcp\",\"type\":\"genericNode\",\"position\":{\"x\":845.0502195222412,\"y\":366.54344452019404},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract Hyperlinks\",\"custom_fields\":{\"soup\":null},\"output_types\":[\"list\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-ODIcp\"},\"selected\":true,\"positionAbsolute\":{\"x\":845.0502195222412,\"y\":366.54344452019404},\"dragging\":false},{\"width\":384,\"height\":657,\"id\":\"PromptTemplate-H9Udy\",\"type\":\"genericNode\",\"position\":{\"x\":2230.389721706283,\"y\":584.4905083765256},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_markdown\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_markdown\",\"display_name\":\"html_markdown\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_links\",\"display_name\":\"html_links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"query\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"name\":\"\",\"display_name\":\"PromptTemplate\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"custom_fields\":{\"\":[\"url\",\"html_markdown\",\"html_links\",\"query\"]},\"output_types\":[],\"full_path\":null,\"field_formatters\":{},\"beta\":false,\"error\":null},\"id\":\"PromptTemplate-H9Udy\"},\"selected\":true,\"positionAbsolute\":{\"x\":2230.389721706283,\"y\":584.4905083765256},\"dragging\":false},{\"width\":384,\"height\":347,\"id\":\"CustomComponent-OACE0\",\"type\":\"genericNode\",\"position\":{\"x\":-1155.9497945157625,\"y\":198.13583204151553},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":null,\"base_classes\":[\"str\"],\"display_name\":\"URL 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markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"items\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"items\",\"display_name\":\"items\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"list\",\"list\":true}},\"description\":\"Converts a Python list into Markdown bullet points.\",\"base_classes\":[\"str\"],\"display_name\":\"List to 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Ptolemy\",\"description\":\"\",\"id\":\"W5oNW\"}}},\"id\":\"GroupNode-WXPMk\",\"position\":{\"x\":873.0737834322758,\"y\":598.8401015776466},\"type\":\"genericNode\",\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":873.0737834322758,\"y\":598.8401015776466}}],\"edges\":[{\"source\":\"ChatOpenAI-VSAdc\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseLanguageModelœ,œChatOpenAIœ,œBaseChatModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-VSAdcœ}\",\"target\":\"LLMChain-H7PBy\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œLLMChain-H7PByœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"LLMChain-H7PBy\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"},\"sourceHandle\":{\"baseClasses\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-VSAdc\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-VSAdc{œbaseClassesœ:[œBaseLanguageModelœ,œChatOpenAIœ,œBaseChatModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-VSAdcœ}-LLMChain-H7PBy{œfieldNameœ:œllmœ,œidœ:œLLMChain-H7PByœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\"},{\"source\":\"GroupNode-WXPMk\",\"sourceHandle\":\"{œbaseClassesœ:[œStringPromptTemplateœ,œBasePromptTemplateœ,œPromptTemplateœ],œdataTypeœ:œPromptTemplateœ,œidœ:œGroupNode-WXPMkœ}\",\"target\":\"LLMChain-H7PBy\",\"targetHandle\":\"{œfieldNameœ:œpromptœ,œidœ:œLLMChain-H7PByœ,œinputTypesœ:null,œtypeœ:œBasePromptTemplateœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"prompt\",\"id\":\"LLMChain-H7PBy\",\"inputTypes\":null,\"type\":\"BasePromptTemplate\"},\"sourceHandle\":{\"baseClasses\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"dataType\":\"PromptTemplate\",\"id\":\"GroupNode-WXPMk\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-GroupNode-WXPMk{œbaseClassesœ:[œStringPromptTemplateœ,œBasePromptTemplateœ,œPromptTemplateœ],œdataTypeœ:œPromptTemplateœ,œidœ:œGroupNode-WXPMkœ}-LLMChain-H7PBy{œfieldNameœ:œpromptœ,œidœ:œLLMChain-H7PByœ,œinputTypesœ:null,œtypeœ:œBasePromptTemplateœ}\"}],\"viewport\":{\"x\":-348.6161822813733,\"y\":121.40545291211492,\"zoom\":0.6801854262029781}},\"is_component\":false,\"updated_at\":\"2023-12-04T23:22:27.784647\",\"folder\":null,\"id\":\"efc3bf27-3cf1-4561-9a83-3df347572440\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Sad Joliot\",\"description\":\"Navigate the Networks of Conversation.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":338,\"id\":\"LLMChain-RQsU1\",\"type\":\"genericNode\",\"position\":{\"x\":514.4440482813261,\"y\":528.164086188516},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-RQsU1\"},\"selected\":false,\"positionAbsolute\":{\"x\":514.4440482813261,\"y\":528.164086188516}},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-NTTcv\",\"type\":\"genericNode\",\"position\":{\"x\":-221.33853765781578,\"y\":-35.48749923706055},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"sk-...\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-NTTcv\"},\"selected\":false,\"positionAbsolute\":{\"x\":-221.33853765781578,\"y\":-35.48749923706055}},{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-VELMV\",\"type\":\"genericNode\",\"position\":{\"x\":-222,\"y\":682.560723386973},\"data\":{\"id\":\"PromptTemplate-VELMV\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-f6nOg\"},\"selected\":true,\"positionAbsolute\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"dragging\":false},{\"width\":384,\"height\":375,\"id\":\"CustomComponent-FGzJJ\",\"type\":\"genericNode\",\"position\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse 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links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract 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is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser 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LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-cHel8\"},\"selected\":false,\"positionAbsolute\":{\"x\":547.3876889720291,\"y\":299.2057833881307},\"dragging\":false},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-LA6y0\",\"type\":\"genericNode\",\"position\":{\"x\":-272.94405331278074,\"y\":-603.148171441675},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"sk-...\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-LA6y0\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":-272.94405331278074,\"y\":-603.148171441675}},{\"width\":384,\"height\":404,\"id\":\"CustomComponent-ZNoRM\",\"type\":\"genericNode\",\"position\":{\"x\":-103.65741264289085,\"y\":134.62332404764658},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-ZNoRM\"},\"selected\":false,\"positionAbsolute\":{\"x\":-103.65741264289085,\"y\":134.62332404764658},\"dragging\":false},{\"width\":384,\"height\":374,\"id\":\"CustomComponent-zNSTv\",\"type\":\"genericNode\",\"position\":{\"x\":-1233.3963469933499,\"y\":280.77850809220325},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-zNSTv\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":-1233.3963469933499,\"y\":280.77850809220325}},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-Ihm2o\",\"type\":\"genericNode\",\"position\":{\"x\":-554.9404152016002,\"y\":683.6763775860567},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse HTML\",\"custom_fields\":{\"html_content\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-Ihm2o\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":-554.9404152016002,\"y\":683.6763775860567}},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-6ST9l\",\"type\":\"genericNode\",\"position\":{\"x\":76.10921262566495,\"y\":872.9491114949525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract 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is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"display_name\":\"template\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"display_name\":\"template_format\"},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"display_name\":\"validate_template\"},\"_type\":\"PromptTemplate\",\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_markdown\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_markdown\",\"display_name\":\"html_markdown\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_links\",\"display_name\":\"html_links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"query\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A 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Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"https://paperswithcode.com/\"}},\"description\":null,\"base_classes\":[\"str\"],\"display_name\":\"URL 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markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"items\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"items\",\"display_name\":\"items\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"list\",\"list\":true}},\"description\":\"Converts a Python list into Markdown bullet points.\",\"base_classes\":[\"str\"],\"display_name\":\"List to 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PromptRunner(CustomComponent):\\n display_name: str = \\\"Prompt Runner\\\"\\n description: str = \\\"Run a Chain with the given PromptTemplate\\\"\\n beta: bool = True\\n field_config = {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt Template\\\",\\n \\\"info\\\": \\\"Make sure the prompt has all variables filled.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}) -> Document:\\n chain = prompt | llm\\n # The input is an empty dict because the prompt is already filled\\n result = chain.invoke(input=inputs)\\n if hasattr(result, \\\"content\\\"):\\n result = result.content\\n self.repr_value = result\\n return 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(1)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-dMB5d\"},\"id\":\"CustomComponent-dMB5d\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:43.668665\",\"folder\":null,\"id\":\"912265df-9b87-4b30-a585-1ca59b944391\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search (2)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-ipifC\"},\"id\":\"CustomComponent-ipifC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:49.799612\",\"folder\":null,\"id\":\"ca83ee08-2f93-427d-9897-80fef6dd5447\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include 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embedding models.\",\"base_classes\":[\"CohereEmbeddings\",\"Embeddings\"],\"display_name\":\"CohereEmbeddings\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/cohere\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CohereEmbeddings-HFUAf\"},\"id\":\"CohereEmbeddings-HFUAf\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:50:46.172344\",\"folder\":null,\"id\":\"662d8040-d47c-40db-bda1-66489a1c9d24\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (1)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-3wrib\"},\"id\":\"CSVLoader-3wrib\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:56:11.662526\",\"folder\":null,\"id\":\"4fae6aec-ddbd-498d-a4d1-ca0290f6a5ad\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (2)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-VEjyx\"},\"id\":\"CSVLoader-VEjyx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:57:37.560784\",\"folder\":null,\"id\":\"d80635ee-966c-41f2-981c-afa2c388ac6e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (3)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-PIhOc\"},\"id\":\"CSVLoader-PIhOc\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:00:27.991966\",\"folder\":null,\"id\":\"c5cc4c32-77c8-4889-a9f8-2632466b7366\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (4)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-deB43\"},\"id\":\"CSVLoader-deB43\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:00:42.509243\",\"folder\":null,\"id\":\"0ab59938-ccf4-4bb9-b285-1f5da38648f0\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-mdkLm\"},\"id\":\"CSVLoader-mdkLm\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:02:17.458354\",\"folder\":null,\"id\":\"f127b7e7-4149-492e-8998-6b1b35ec4153\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (5)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-FRc6Y\"},\"id\":\"CSVLoader-FRc6Y\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:02:26.958867\",\"folder\":null,\"id\":\"a870a096-725c-4914-add0-8d57d710b7e5\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (2)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-Z0uol\"},\"id\":\"AirbyteJSONLoader-Z0uol\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:33.764117\",\"folder\":null,\"id\":\"2a37c76c-65c8-4c01-a72d-eb46f3d41a56\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-28ASv\"},\"id\":\"AmazonBedrockEmbeddings-28ASv\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:41.066618\",\"folder\":null,\"id\":\"850ba4e6-96ca-49a7-9b0c-4b7048fd52c8\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"ConversationBufferMemory\",\"description\":\"Buffer for storing conversation memory.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"ConversationBufferMemory\",\"node\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"memory_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\"],\"display_name\":\"ConversationBufferMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"ConversationBufferMemory-WaoLx\"},\"id\":\"ConversationBufferMemory-WaoLx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:04:46.058568\",\"folder\":null,\"id\":\"be650940-0449-4eb0-a708-056310f924fd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (1)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\"},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:05:50.570800\",\"folder\":null,\"id\":\"53ad1fe2-f3af-4354-86e0-eb141ce12859\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (2)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\"},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:06:11.415088\",\"folder\":null,\"id\":\"e9ed1c90-146e-452d-8ccd-ebf2e0da4331\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (3)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\"},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:07:06.411108\",\"folder\":null,\"id\":\"83783c57-64e3-41a6-aa7e-ed82f80f1e53\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (6)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"z2b\":\"z9\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-2pn2o\"},\"id\":\"CSVLoader-2pn2o\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:38:30.706258\",\"folder\":null,\"id\":\"c4ae9de6-9df3-4d3c-afc9-1752af4fecd7\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-MJrAC\"},\"id\":\"AgentInitializer-MJrAC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:42:54.715163\",\"folder\":null,\"id\":\"ff84b5f9-5680-4084-a9f1-7d94fe675486\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer (1)\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-3lcZ4\"},\"id\":\"AgentInitializer-3lcZ4\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:43:20.819934\",\"folder\":null,\"id\":\"97f5db9f-d6cc-4555-88fb-3be102c67814\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Exuberant Banach\",\"description\":\"Unfolding Linguistic Possibilities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T14:52:49.951416\",\"folder\":null,\"id\":\"a600acd1-213a-4ce7-8648-ab2ee59f5918\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (3)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-JhQtx\"},\"id\":\"AirbyteJSONLoader-JhQtx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:44:49.587337\",\"folder\":null,\"id\":\"07c52ad7-ca52-4cdd-ab40-74a91dbad0dd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (4)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-bIvDc\"},\"id\":\"AirbyteJSONLoader-bIvDc\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:45:13.458500\",\"folder\":null,\"id\":\"53e4d256-3653-444b-b8e0-fb97b3ae5c7e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (5)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json 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import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": 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}\\n\\n def build(self, prompt:str,api_key:str,model:str,file_name:str,output_format:str,width:int,height:int):\\n client = OpenAI(api_key=api_key)\\n cache_dir = Path(user_cache_dir(\\\"langflow\\\"))\\n images_dir = cache_dir / \\\"images\\\"\\n images_dir.mkdir(parents=True, exist_ok=True)\\n image_path = images_dir / f\\\"{file_name}.{output_format}\\\"\\n response = client.images.generate(\\n model=model,\\n prompt=prompt,\\n size=f\\\"{height}x{width}\\\",\\n response_format=\\\"b64_json\\\",\\n n=1,\\n )\\n # Decode base64-encoded image string\\n binary_data = base64.b64decode(response.data[0].b64_json)\\n # Create PIL Image object from binary image data\\n image_pil = Image.open(BytesIO(binary_data))\\n image_pil.save(image_path, format=output_format.upper())\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"api_key\",\"display_name\":\"OpenAI API key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"file_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"file_name\",\"display_name\":\"File Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"album\"},\"height\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1024,\"password\":false,\"name\":\"height\",\"display_name\":\"Height\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"model\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"dall-e-3\",\"password\":false,\"options\":[\"dall-e-2\",\"dall-e-3\"],\"name\":\"model\",\"display_name\":\"Model name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"output_format\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"jpeg\",\"password\":false,\"options\":[\"jpeg\",\"png\"],\"name\":\"output_format\",\"display_name\":\"Output format\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"input_types\":[\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"width\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1024,\"password\":false,\"name\":\"width\",\"display_name\":\"Width\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false}},\"description\":\"generate images using Dall-E\",\"base_classes\":[\"Data\"],\"display_name\":\"Image generator\",\"custom_fields\":{\"api_key\":null,\"file_name\":null,\"height\":null,\"model\":null,\"output_format\":null,\"prompt\":null,\"width\":null},\"output_types\":[\"Data\"],\"documentation\":\"http://docs.langflow.org/components/custom\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-IEIUl\"},\"selected\":false,\"positionAbsolute\":{\"x\":2364.865919667005,\"y\":88.43094097025096}},{\"width\":384,\"height\":338,\"id\":\"LLMChain-KlJb3\",\"type\":\"genericNode\",\"position\":{\"x\":1242.9851164540805,\"y\":-139.74634696823108},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-KlJb3\"},\"selected\":false,\"positionAbsolute\":{\"x\":1242.9851164540805,\"y\":-139.74634696823108},\"dragging\":false},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-bCuc0\",\"type\":\"genericNode\",\"position\":{\"x\":1747.8107777342625,\"y\":319.13729017446667},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Custom Component\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n def build(self, param: Chain) -> str:\\n result = param.run({})\\n self.status = result\\n return result\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"param\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"param\",\"display_name\":\"Parameter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Chain\",\"list\":false}},\"description\":\"Create any custom component you want!\",\"base_classes\":[\"str\"],\"display_name\":\"Custom Component\",\"custom_fields\":{\"param\":null},\"output_types\":[\"str\"],\"documentation\":\"http://docs.langflow.org/components/custom\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-bCuc0\"},\"selected\":false,\"positionAbsolute\":{\"x\":1747.8107777342625,\"y\":319.13729017446667}}],\"edges\":[{\"source\":\"LLMChain-KlJb3\",\"target\":\"CustomComponent-bCuc0\",\"sourceHandle\":\"{œbaseClassesœ:[œChainœ,œCallableœ],œdataTypeœ:œLLMChainœ,œidœ:œLLMChain-KlJb3œ}\",\"targetHandle\":\"{œfieldNameœ:œparamœ,œidœ:œCustomComponent-bCuc0œ,œinputTypesœ:null,œtypeœ:œChainœ}\",\"id\":\"reactflow__edge-LLMChain-KlJb3{œbaseClassesœ:[œChainœ,œCallableœ],œdataTypeœ:œLLMChainœ,œidœ:œLLMChain-KlJb3œ}-CustomComponent-bCuc0{œfieldNameœ:œparamœ,œidœ:œCustomComponent-bCuc0œ,œinputTypesœ:null,œtypeœ:œChainœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"param\",\"id\":\"CustomComponent-bCuc0\",\"inputTypes\":null,\"type\":\"Chain\"},\"sourceHandle\":{\"baseClasses\":[\"Chain\",\"Callable\"],\"dataType\":\"LLMChain\",\"id\":\"LLMChain-KlJb3\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"selected\":false},{\"source\":\"ChatOpenAI-urapv\",\"target\":\"LLMChain-KlJb3\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-urapvœ}\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œLLMChain-KlJb3œ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"id\":\"reactflow__edge-ChatOpenAI-urapv{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-urapvœ}-LLMChain-KlJb3{œfieldNameœ:œllmœ,œidœ:œLLMChain-KlJb3œ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"LLMChain-KlJb3\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"},\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-urapv\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"selected\":false},{\"source\":\"PromptTemplate-m2yFu\",\"target\":\"LLMChain-KlJb3\",\"sourceHandle\":\"{œbaseClassesœ:[œPromptTemplateœ,œStringPromptTemplateœ,œBasePromptTemplateœ],œdataTypeœ:œPromptTemplateœ,œidœ:œPromptTemplate-m2yFuœ}\",\"targetHandle\":\"{œfieldNameœ:œpromptœ,œidœ:œLLMChain-KlJb3œ,œinputTypesœ:null,œtypeœ:œBasePromptTemplateœ}\",\"id\":\"reactflow__edge-PromptTemplate-m2yFu{œbaseClassesœ:[œPromptTemplateœ,œStringPromptTemplateœ,œBasePromptTemplateœ],œdataTypeœ:œPromptTemplateœ,œidœ:œPromptTemplate-m2yFuœ}-LLMChain-KlJb3{œfieldNameœ:œpromptœ,œidœ:œLLMChain-KlJb3œ,œinputTypesœ:null,œtypeœ:œBasePromptTemplateœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"prompt\",\"id\":\"LLMChain-KlJb3\",\"inputTypes\":null,\"type\":\"BasePromptTemplate\"},\"sourceHandle\":{\"baseClasses\":[\"PromptTemplate\",\"StringPromptTemplate\",\"BasePromptTemplate\"],\"dataType\":\"PromptTemplate\",\"id\":\"PromptTemplate-m2yFu\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"selected\":false},{\"source\":\"CustomComponent-bCuc0\",\"target\":\"CustomComponent-IEIUl\",\"sourceHandle\":\"{œbaseClassesœ:[œstrœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-bCuc0œ}\",\"targetHandle\":\"{œfieldNameœ:œpromptœ,œidœ:œCustomComponent-IEIUlœ,œinputTypesœ:[œstrœ],œtypeœ:œstrœ}\",\"id\":\"reactflow__edge-CustomComponent-bCuc0{œbaseClassesœ:[œstrœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-bCuc0œ}-CustomComponent-IEIUl{œfieldNameœ:œpromptœ,œidœ:œCustomComponent-IEIUlœ,œinputTypesœ:[œstrœ],œtypeœ:œstrœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"prompt\",\"id\":\"CustomComponent-IEIUl\",\"inputTypes\":[\"str\"],\"type\":\"str\"},\"sourceHandle\":{\"baseClasses\":[\"str\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-bCuc0\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"selected\":false}],\"viewport\":{\"x\":92.23454077990459,\"y\":183.8125619056221,\"zoom\":0.6070974421975234}},\"is_component\":false,\"updated_at\":\"2023-12-08T21:33:18.503954\",\"folder\":null,\"id\":\"fe142ce5-32dc-4955-b186-672ced662f13\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Goofy Darwin\",\"description\":\"Conversation Catalyst Engine.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":626,\"id\":\"OpenAI-3ZVDh\",\"type\":\"genericNode\",\"position\":{\"x\":-4.0041891741949485,\"y\":-114.02615182719649},\"data\":{\"type\":\"OpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"allowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":[],\"password\":false,\"name\":\"allowed_special\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"batch_size\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":20,\"password\":false,\"name\":\"batch_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"best_of\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"best_of\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"disallowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"all\",\"password\":false,\"name\":\"disallowed_special\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"frequency_penalty\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":0,\"password\":false,\"name\":\"frequency_penalty\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"logit_bias\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"logit_bias\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":256,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-davinci-003\",\"password\":false,\"options\":[\"text-davinci-003\",\"text-davinci-002\",\"text-curie-001\",\"text-babbage-001\",\"text-ada-001\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"presence_penalty\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":0,\"password\":false,\"name\":\"presence_penalty\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"top_p\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"top_p\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"OpenAI\"},\"description\":\"OpenAI large language models.\",\"base_classes\":[\"OpenAI\",\"BaseLLM\",\"BaseOpenAI\",\"BaseLanguageModel\"],\"display_name\":\"OpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"OpenAI-3ZVDh\"},\"selected\":true,\"positionAbsolute\":{\"x\":-4.0041891741949485,\"y\":-114.02615182719649},\"dragging\":false},{\"width\":384,\"height\":338,\"id\":\"LLMChain-RFYXY\",\"type\":\"genericNode\",\"position\":{\"x\":586.672100458868,\"y\":10.967049167706678},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against 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The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\\n\\nCurrent conversation:\\n{history}\\nHuman: 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to have a conversation and load context from memory.\",\"base_classes\":[\"ConversationChain\",\"Chain\",\"LLMChain\",\"function\"],\"display_name\":\"ConversationChain\",\"documentation\":\"\"},\"id\":\"ConversationChain-bVNex\",\"value\":null},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":806,\"y\":554}}],\"edges\":[{\"source\":\"ChatOpenAI-fBcfh\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseChatModelœ,œBaseLanguageModelœ,œChatOpenAIœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-fBcfhœ}\",\"target\":\"ConversationChain-bVNex\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œConversationChain-bVNexœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"className\":\"stroke-gray-900 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File Loader\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import os\\n\\nfrom langchain.schema import Document\\nfrom langflow import CustomComponent\\nimport glob\\n\\nclass DirectoryLoaderComponent(CustomComponent):\\n display_name: str = \\\"Directory Loader\\\"\\n description: str = \\\"Generic File Loader\\\"\\n beta = True\\n loaders_info = [\\n {\\n \\\"loader\\\": \\\"AirbyteJSONLoader\\\",\\n \\\"name\\\": \\\"Airbyte JSON (.jsonl)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.AirbyteJSONLoader\\\",\\n \\\"defaultFor\\\": [\\\"jsonl\\\"],\\n \\\"allowdTypes\\\": [\\\"jsonl\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"JSONLoader\\\",\\n \\\"name\\\": \\\"JSON (.json)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.JSONLoader\\\",\\n \\\"defaultFor\\\": [\\\"json\\\"],\\n \\\"allowdTypes\\\": [\\\"json\\\"],\\n \\\"kwargs\\\": {\\\"jq_schema\\\": \\\".\\\", \\\"text_content\\\": False}\\n },\\n {\\n \\\"loader\\\": \\\"BSHTMLLoader\\\",\\n \\\"name\\\": \\\"BeautifulSoup4 HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.BSHTMLLoader\\\",\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"CSVLoader\\\",\\n \\\"name\\\": \\\"CSV (.csv)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.CSVLoader\\\",\\n \\\"defaultFor\\\": [\\\"csv\\\"],\\n \\\"allowdTypes\\\": [\\\"csv\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"CoNLLULoader\\\",\\n \\\"name\\\": \\\"CoNLL-U (.conllu)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.CoNLLULoader\\\",\\n \\\"defaultFor\\\": [\\\"conllu\\\"],\\n \\\"allowdTypes\\\": [\\\"conllu\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"EverNoteLoader\\\",\\n \\\"name\\\": \\\"EverNote (.enex)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.EverNoteLoader\\\",\\n \\\"defaultFor\\\": [\\\"enex\\\"],\\n \\\"allowdTypes\\\": [\\\"enex\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"FacebookChatLoader\\\",\\n \\\"name\\\": \\\"Facebook Chat (.json)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.FacebookChatLoader\\\",\\n \\\"allowdTypes\\\": [\\\"json\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"OutlookMessageLoader\\\",\\n \\\"name\\\": \\\"Outlook Message (.msg)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.OutlookMessageLoader\\\",\\n \\\"defaultFor\\\": [\\\"msg\\\"],\\n \\\"allowdTypes\\\": [\\\"msg\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"PyPDFLoader\\\",\\n \\\"name\\\": \\\"PyPDF (.pdf)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.PyPDFLoader\\\",\\n \\\"defaultFor\\\": [\\\"pdf\\\"],\\n \\\"allowdTypes\\\": [\\\"pdf\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"STRLoader\\\",\\n \\\"name\\\": \\\"Subtitle (.str)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.STRLoader\\\",\\n \\\"defaultFor\\\": [\\\"str\\\"],\\n \\\"allowdTypes\\\": [\\\"str\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"TextLoader\\\",\\n \\\"name\\\": \\\"Text (.txt)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.TextLoader\\\",\\n \\\"defaultFor\\\": [\\\"txt\\\"],\\n \\\"allowdTypes\\\": [\\\"txt\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredEmailLoader\\\",\\n \\\"name\\\": \\\"Unstructured Email (.eml)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredEmailLoader\\\",\\n \\\"defaultFor\\\": [\\\"eml\\\"],\\n \\\"allowdTypes\\\": [\\\"eml\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredHTMLLoader\\\",\\n \\\"name\\\": \\\"Unstructured HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredHTMLLoader\\\",\\n \\\"defaultFor\\\": [\\\"html\\\", \\\"htm\\\"],\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredMarkdownLoader\\\",\\n \\\"name\\\": \\\"Unstructured Markdown (.md)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredMarkdownLoader\\\",\\n \\\"defaultFor\\\": [\\\"md\\\"],\\n \\\"allowdTypes\\\": [\\\"md\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredPowerPointLoader\\\",\\n \\\"name\\\": \\\"Unstructured PowerPoint (.pptx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredPowerPointLoader\\\",\\n \\\"defaultFor\\\": [\\\"pptx\\\"],\\n \\\"allowdTypes\\\": [\\\"pptx\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredWordLoader\\\",\\n \\\"name\\\": \\\"Unstructured Word (.docx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredWordLoader\\\",\\n \\\"defaultFor\\\": [\\\"docx\\\"],\\n \\\"allowdTypes\\\": [\\\"docx\\\"],\\n },\\n]\\n\\n\\n def build_config(self):\\n loader_options = [\\\"Automatic\\\"] + [\\n loader_info[\\\"name\\\"] for loader_info in self.loaders_info\\n ]\\n\\n file_types = []\\n suffixes = []\\n\\n for loader_info in self.loaders_info:\\n if \\\"allowedTypes\\\" in loader_info:\\n file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n return {\\n \\\"directory_path\\\": {\\n \\\"display_name\\\": \\\"Directory Path\\\",\\n \\\"required\\\": True,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": loader_options,\\n \\\"value\\\": \\\"Automatic\\\",\\n },\\n }\\n\\n def build(self, directory_path: str, loader: str) -> Document:\\n # Verifique se o diretório existe\\n if not os.path.exists(directory_path):\\n raise ValueError(f\\\"Directory not found: {directory_path}\\\")\\n\\n files = glob.glob(directory_path + \\\"/*.*\\\")\\n\\n\\n loader_info = None\\n if loader == \\\"Automatic\\\":\\n for file in files:\\n file_type = file.split(\\\".\\\")[-1]\\n\\n\\n for info in self.loaders_info:\\n if \\\"defaultFor\\\" in info:\\n if file_type in info[\\\"defaultFor\\\"]:\\n loader_info = info\\n break\\n if loader_info:\\n break\\n\\n if not loader_info:\\n raise ValueError(\\n \\\"No default loader found for any file in the directory\\\"\\n )\\n\\n else:\\n for info in self.loaders_info:\\n if info[\\\"name\\\"] == loader:\\n loader_info = info\\n break\\n\\n if not loader_info:\\n raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n loader_import = loader_info[\\\"import\\\"]\\n module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n try:\\n # Importe o loader dinamicamente\\n loader_module = __import__(module_name, fromlist=[class_name])\\n loader_instance = getattr(loader_module, class_name)\\n except ImportError as e:\\n raise ValueError(\\n f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{loader_info}\\\"\\n ) from e\\n\\n results = []\\n for file in files:\\n file_path = os.path.join(directory_path, file)\\n kwargs = loader_info.get(\\\"kwargs\\\", {})\\n result = loader_instance(file_path=file_path, 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Whisper.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, List, Dict, Union\\nfrom langflow.field_typing import (\\n AgentExecutor,\\n BaseChatMemory,\\n BaseLanguageModel,\\n BaseLLM,\\n BaseLoader,\\n BaseMemory,\\n BaseOutputParser,\\n BasePromptTemplate,\\n BaseRetriever,\\n Callable,\\n Chain,\\n ChatPromptTemplate,\\n Data,\\n Document,\\n Embeddings,\\n NestedDict,\\n Object,\\n PromptTemplate,\\n TextSplitter,\\n Tool,\\n VectorStore,\\n)\\n\\nfrom openai import OpenAI\\nclass Component(CustomComponent):\\n display_name: str = \\\"Whisper Transcriber\\\"\\n description: str = \\\"Converts audio to text using OpenAI's Whisper.\\\"\\n\\n def build_config(self):\\n return {\\\"audio\\\":{\\\"field_type\\\":\\\"file\\\",\\\"suffixes\\\":[\\\".mp3\\\", \\\".mp4\\\", \\\".m4a\\\"]},\\\"OpenAIKey\\\":{\\\"field_type\\\":\\\"str\\\",\\\"password\\\":True}}\\n\\n def build(self, audio:str, OpenAIKey:str) -> str:\\n \\n # TODO: if output path, persist & load it instead\\n \\n client = OpenAI(api_key=OpenAIKey)\\n \\n audio_file= open(audio, \\\"rb\\\")\\n transcript = client.audio.transcriptions.create(\\n model=\\\"whisper-1\\\", \\n file=audio_file,\\n response_format=\\\"text\\\"\\n )\\n \\n \\n self.status = transcript\\n return transcript\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"OpenAIKey\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"OpenAIKey\",\"display_name\":\"OpenAIKey\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"audio\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"suffixes\":[\".mp3\",\".mp4\",\".m4a\"],\"password\":false,\"name\":\"audio\",\"display_name\":\"audio\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"file_path\":\"/Users/rodrigonader/Library/Caches/langflow/19b3e1c9-90bf-405f-898a-e982f47adf76/a3308ce7e9b10088fcd985aabb6d17b012c6b6e81ff8806356574474c9d10229.m4a\",\"value\":\"Audio Recording 2023-11-29 at 12.12.04.m4a\"}},\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"base_classes\":[\"str\"],\"display_name\":\"Whisper Transcriber\",\"custom_fields\":{\"OpenAIKey\":null,\"audio\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-GJRrs\"},\"id\":\"CustomComponent-GJRrs\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:39.710502\",\"folder\":null,\"id\":\"bd9e911d-46bd-429f-aa75-dd740430695e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Youtube Conversation\",\"description\":\"Craft Meaningful Interactions, Generate Value.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":589,\"id\":\"CustomComponent-h0NSI\",\"type\":\"genericNode\",\"position\":{\"x\":714.9531293402755,\"y\":0.4994374160582993},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import requests\\nfrom langflow import CustomComponent\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.schema import Document\\nfrom langchain.document_loaders import YoutubeLoader\\n\\n# Example usage:\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"YouTube Transcript\\\"\\n description: str = \\\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\\\"\\n\\n def build_config(self):\\n return {\\\"url\\\": {\\\"multiline\\\": True, \\\"required\\\": True}}\\n\\n def build(self, url: str, llm: BaseLLM, dependencies: Document, language: str = \\\"en\\\") -> Document:\\n dependencies\\n response = requests.get(url)\\n loader = YoutubeLoader.from_youtube_url(url, add_video_info=True, language=[language])\\n result = loader.load()\\n self.repr_value = str(result)\\n return result\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"dependencies\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"dependencies\",\"display_name\":\"dependencies\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"language\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"language\",\"display_name\":\"language\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\",\"base_classes\":[\"Document\"],\"display_name\":\"YouTube Transcript\",\"custom_fields\":{\"dependencies\":null,\"language\":null,\"llm\":null,\"url\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-h0NSI\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":714.9531293402755,\"y\":0.4994374160582993}},{\"width\":384,\"height\":629,\"id\":\"ChatOpenAI-q61Y2\",\"type\":\"genericNode\",\"position\":{\"x\":18,\"y\":625.5521045590924},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-16k\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k-0613\",\"gpt-3.5-turbo-16k\",\"gpt-4-0613\",\"gpt-4-32k-0613\",\"gpt-4\",\"gpt-4-32k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-q61Y2\"},\"selected\":false,\"positionAbsolute\":{\"x\":18,\"y\":625.5521045590924},\"dragging\":false},{\"width\":384,\"height\":539,\"id\":\"Chroma-gVhy7\",\"type\":\"genericNode\",\"position\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"data\":{\"type\":\"Chroma\",\"node\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.Client\",\"list\":false},\"client_settings\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client_settings\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.config.Setting\",\"list\":true},\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"chroma_server_cors_allow_origins\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Chroma Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"chroma_server_grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Chroma Server GRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Chroma Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_http_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_http_port\",\"display_name\":\"Chroma Server HTTP Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_ssl_enabled\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Chroma Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"collection_metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"collection_metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"collection_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"persist\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"persist\",\"display_name\":\"Persist\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"persist_directory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"persist_directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"_type\":\"Chroma\"},\"description\":\"Create a Chroma vectorstore from a raw documents.\",\"base_classes\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Chroma\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma\",\"beta\":false,\"error\":null},\"id\":\"Chroma-gVhy7\"},\"selected\":false,\"positionAbsolute\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"dragging\":false},{\"width\":384,\"height\":501,\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"type\":\"genericNode\",\"position\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"data\":{\"type\":\"RecursiveCharacterTextSplitter\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.utils.util import build_loader_repr_from_documents\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chunk_overlap\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"50\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"400\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"type\":\"int\",\"list\":false},\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"type\":\"Document\",\"list\":true},\"separators\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"type\":\"str\",\"list\":true,\"value\":[\" \"]}},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separators\":null},\"output_types\":[\"RecursiveCharacterTextSplitter\"],\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"beta\":true,\"error\":null},\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"selected\":false,\"positionAbsolute\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"dragging\":false},{\"width\":384,\"height\":367,\"id\":\"OpenAIEmbeddings-cduOO\",\"type\":\"genericNode\",\"position\":{\"x\":1405.1176670984544,\"y\":1029.459061269321},\"data\":{\"type\":\"OpenAIEmbeddings\",\"node\":{\"template\":{\"allowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[],\"password\":false,\"name\":\"allowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"disallowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"all\",\"password\":false,\"name\":\"disallowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"deployment\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"embedding_ctx_length\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":8191,\"password\":false,\"name\":\"embedding_ctx_length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"{'Authorization': 'Bearer '}\",\"password\":false,\"name\":\"headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"model\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_api_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_version\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI 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embedding 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Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI 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Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-VSAdc\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859}},{\"width\":384,\"height\":374,\"data\":{\"id\":\"GroupNode-WXPMk\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"give me links\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown 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{\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse 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links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract 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is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_markdown\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_markdown\",\"display_name\":\"html_markdown\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_links\",\"display_name\":\"html_links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"query\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A 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Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":null,\"base_classes\":[\"str\"],\"display_name\":\"URL 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URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-f6nOg\"},\"selected\":true,\"positionAbsolute\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"dragging\":false},{\"width\":384,\"height\":375,\"id\":\"CustomComponent-FGzJJ\",\"type\":\"genericNode\",\"position\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse HTML\",\"custom_fields\":{\"html_content\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-0XtUN\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":214.00059169497604,\"y\":177.27071061129823}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-ODIcp\",\"type\":\"genericNode\",\"position\":{\"x\":845.0502195222412,\"y\":366.54344452019404},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract Hyperlinks\",\"custom_fields\":{\"soup\":null},\"output_types\":[\"list\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-ODIcp\"},\"selected\":true,\"positionAbsolute\":{\"x\":845.0502195222412,\"y\":366.54344452019404},\"dragging\":false},{\"width\":384,\"height\":657,\"id\":\"PromptTemplate-H9Udy\",\"type\":\"genericNode\",\"position\":{\"x\":2230.389721706283,\"y\":584.4905083765256},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_markdown\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_markdown\",\"display_name\":\"html_markdown\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_links\",\"display_name\":\"html_links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"query\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"name\":\"\",\"display_name\":\"PromptTemplate\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"custom_fields\":{\"\":[\"url\",\"html_markdown\",\"html_links\",\"query\"]},\"output_types\":[],\"full_path\":null,\"field_formatters\":{},\"beta\":false,\"error\":null},\"id\":\"PromptTemplate-H9Udy\"},\"selected\":true,\"positionAbsolute\":{\"x\":2230.389721706283,\"y\":584.4905083765256},\"dragging\":false},{\"width\":384,\"height\":347,\"id\":\"CustomComponent-OACE0\",\"type\":\"genericNode\",\"position\":{\"x\":-1155.9497945157625,\"y\":198.13583204151553},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":null,\"base_classes\":[\"str\"],\"display_name\":\"URL 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markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"items\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"items\",\"display_name\":\"items\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"list\",\"list\":true}},\"description\":\"Converts a Python list into Markdown bullet points.\",\"base_classes\":[\"str\"],\"display_name\":\"List to 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Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI 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Transformation.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-08T21:23:07.792245\",\"folder\":null,\"id\":\"b6444376-4162-436b-8b40-f5a6afc850db\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Sad Bhabha\",\"description\":\"Empowering Enterprises with Intelligent Interactions.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-08T21:23:07.890349\",\"folder\":null,\"id\":\"d90af439-fb34-4d27-98f2-06f7f9a9ed8c\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Spirited Hoover\",\"description\":\"The Pinnacle of Prompt Generation.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-08T21:23:07.905750\",\"folder\":null,\"id\":\"31597ce2-de3c-490b-9ead-3f702f63cfd9\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Trusting Davinci\",\"description\":\"Design, Develop, Dialogize.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-08T21:23:08.001400\",\"folder\":null,\"id\":\"b43c63e9-a257-4a53-8acc-049e13706ac2\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"23\",\"description\":\"23\",\"data\":{\"nodes\":[{\"width\":384,\"height\":467,\"id\":\"PromptTemplate-K7xiS\",\"type\":\"genericNode\",\"position\":{\"x\":-658.2250903773149,\"y\":809.352046606987},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"dasdas\",\"dasdasd\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"{dasdas}\\n{dasdasd}\\n\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"dasdas\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"dasdas\",\"display_name\":\"dasdas\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"dasdasd\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"dasdasd\",\"display_name\":\"dasdasd\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt template for a language 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local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-DXfcM\"},\"selected\":false,\"positionAbsolute\":{\"x\":-1110.8267574563533,\"y\":569.1107380883907},\"dragging\":false},{\"width\":384,\"height\":376,\"id\":\"PromptRunner-ckWMH\",\"type\":\"genericNode\",\"position\":{\"x\":-1149.4746387825978,\"y\":992.3970573758324},\"data\":{\"type\":\"PromptRunner\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\n\\nclass PromptRunner(CustomComponent):\\n display_name: str = \\\"Prompt Runner\\\"\\n description: str = \\\"Run a Chain with the given PromptTemplate\\\"\\n beta: bool = True\\n field_config = {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt Template\\\",\\n \\\"info\\\": \\\"Make sure the prompt has all variables filled.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}) -> Document:\\n chain = prompt | llm\\n # The input is an empty dict because the prompt is already filled\\n result = chain.invoke(input=inputs)\\n if hasattr(result, \\\"content\\\"):\\n result = result.content\\n self.repr_value = result\\n return Document(page_content=str(result))\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"inputs\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"inputs\",\"display_name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt Template\",\"advanced\":false,\"dynamic\":false,\"info\":\"Make sure the prompt has all variables filled.\",\"type\":\"PromptTemplate\",\"list\":false}},\"description\":\"Run a Chain with the given PromptTemplate\",\"base_classes\":[\"Document\"],\"display_name\":\"Prompt 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stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-PromptRunner-ckWMH{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œPromptRunnerœ,œidœ:œPromptRunner-ckWMHœ}-PromptTemplate-K7xiS{œfieldNameœ:œdasdasdœ,œidœ:œPromptTemplate-K7xiSœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ],œtypeœ:œstrœ}\"},{\"source\":\"AirbyteJSONLoader-DXfcM\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œAirbyteJSONLoaderœ,œidœ:œAirbyteJSONLoader-DXfcMœ}\",\"target\":\"PromptTemplate-K7xiS\",\"targetHandle\":\"{œfieldNameœ:œdasdasœ,œidœ:œPromptTemplate-K7xiSœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ],œtypeœ:œstrœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"dasdas\",\"id\":\"PromptTemplate-K7xiS\",\"inputTypes\":[\"Document\",\"BaseOutputParser\"],\"type\":\"str\"},\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"AirbyteJSONLoader\",\"id\":\"AirbyteJSONLoader-DXfcM\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-AirbyteJSONLoader-DXfcM{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œAirbyteJSONLoaderœ,œidœ:œAirbyteJSONLoader-DXfcMœ}-PromptTemplate-K7xiS{œfieldNameœ:œdasdasœ,œidœ:œPromptTemplate-K7xiSœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ],œtypeœ:œstrœ}\"}],\"viewport\":{\"x\":721.09842496776,\"y\":-303.59762799439625,\"zoom\":0.6417129487814537}},\"is_component\":false,\"updated_at\":\"2023-12-08T22:52:14.560323\",\"folder\":null,\"id\":\"8533c46e-21fd-4b92-b68e-1086ea86c72d\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Twinkly Stonebraker\",\"description\":\"Navigate the Linguistic Landscape, Discover Opportunities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T13:26:42.332360\",\"folder\":null,\"id\":\"92bc0875-4a73-44f2-9410-3b8342e404bf\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search (1)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-dMB5d\"},\"id\":\"CustomComponent-dMB5d\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:43.668665\",\"folder\":null,\"id\":\"912265df-9b87-4b30-a585-1ca59b944391\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search (2)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-ipifC\"},\"id\":\"CustomComponent-ipifC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:49.799612\",\"folder\":null,\"id\":\"ca83ee08-2f93-427d-9897-80fef6dd5447\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per 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Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Y4qL7\"},\"id\":\"CustomComponent-Y4qL7\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:53.719960\",\"folder\":null,\"id\":\"5847602b-769a-4a82-82e8-a70f54a59929\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Lively Williams\",\"description\":\"Conversational Cartography Unlocked.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T13:27:45.691254\",\"folder\":null,\"id\":\"0e3bdba9-127a-4399-81a4-2865b70a4a47\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Shared Component\",\"description\":\"Conversational Cartography Unlocked.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":328,\"id\":\"CSVAgent-TK9Ea\",\"type\":\"genericNode\",\"position\":{\"x\":251.25514772667083,\"y\":160.7424529887874},\"data\":{\"type\":\"CSVAgent\",\"node\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"_type\":\"csv_agent\"},\"description\":\"Construct a CSV agent from a CSV and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"CSVAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/csv\",\"beta\":false,\"error\":null},\"id\":\"CSVAgent-TK9Ea\"},\"positionAbsolute\":{\"x\":251.25514772667083,\"y\":160.7424529887874}}],\"edges\":[],\"viewport\":{\"x\":104.85568116317398,\"y\":88.26375874183478,\"zoom\":0.7169776240079145}},\"is_component\":false,\"updated_at\":\"2023-12-09T13:28:34.119070\",\"folder\":null,\"id\":\"29c1a247-47b0-457b-8666-7c0a67dc72b0\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"teste 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Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile 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Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-28ASv\"},\"id\":\"AmazonBedrockEmbeddings-28ASv\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:41.066618\",\"folder\":null,\"id\":\"850ba4e6-96ca-49a7-9b0c-4b7048fd52c8\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"ConversationBufferMemory\",\"description\":\"Buffer for storing conversation memory.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"ConversationBufferMemory\",\"node\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"memory_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\"],\"display_name\":\"ConversationBufferMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"ConversationBufferMemory-WaoLx\"},\"id\":\"ConversationBufferMemory-WaoLx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:04:46.058568\",\"folder\":null,\"id\":\"be650940-0449-4eb0-a708-056310f924fd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (1)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\"},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:05:50.570800\",\"folder\":null,\"id\":\"53ad1fe2-f3af-4354-86e0-eb141ce12859\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (2)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\"},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:06:11.415088\",\"folder\":null,\"id\":\"e9ed1c90-146e-452d-8ccd-ebf2e0da4331\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (3)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\"},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:07:06.411108\",\"folder\":null,\"id\":\"83783c57-64e3-41a6-aa7e-ed82f80f1e53\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (6)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"z2b\":\"z9\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-2pn2o\"},\"id\":\"CSVLoader-2pn2o\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:38:30.706258\",\"folder\":null,\"id\":\"c4ae9de6-9df3-4d3c-afc9-1752af4fecd7\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-MJrAC\"},\"id\":\"AgentInitializer-MJrAC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:42:54.715163\",\"folder\":null,\"id\":\"ff84b5f9-5680-4084-a9f1-7d94fe675486\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer (1)\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent 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import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile 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Language.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.813144\",\"folder\":null,\"id\":\"8d8b80f9-4b74-4cd5-bc5c-08a32c4d2b95\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Awesome Einstein\",\"description\":\"The Power of Language at Your Fingertips.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:19.518262\",\"folder\":null,\"id\":\"21808fd7-a492-4e29-8863-301c2785f542\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Funky Swanson\",\"description\":\"Sculpting Language with 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Fingertips.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.283929\",\"folder\":null,\"id\":\"0092d077-6d31-401f-a739-b164476b90ed\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Grinning Bhabha\",\"description\":\"Your Passport to Linguistic Landscapes.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.467739\",\"folder\":null,\"id\":\"4a395211-712d-4dd2-b3bb-e6b19200f15d\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Mirthful Babbage\",\"description\":\"Design Dialogues with 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Mastered.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-m2yFu\",\"type\":\"genericNode\",\"position\":{\"x\":462.6456058272081,\"y\":1033.9314297130313},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"transcription\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"create an image prompt based on the song's lyrics to be used as the album cover of this song:\\n\\nlyrics:\\n{transcription}\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"transcription\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"transcription\",\"display_name\":\"transcription\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt 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OpenAI\\nfrom pathlib import Path\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Image generator\\\"\\n description: str = \\\"generate images using Dall-E\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\",\\\"input_types\\\":[\\\"str\\\"]},\\n \\\"api_key\\\":{\\\"display_name\\\":\\\"OpenAI API key\\\",\\\"password\\\":True},\\n \\\"model\\\":{\\\"display_name\\\":\\\"Model name\\\",\\\"advanced\\\":True,\\\"options\\\":[\\\"dall-e-2\\\",\\\"dall-e-3\\\"], \\\"value\\\":\\\"dall-e-3\\\"},\\n \\\"file_name\\\":{\\\"display_name\\\":\\\"File Name\\\"},\\n \\\"output_format\\\":{\\\"display_name\\\":\\\"Output format\\\",\\\"options\\\":[\\\"jpeg\\\",\\\"png\\\"],\\\"value\\\":\\\"jpeg\\\"},\\n \\\"width\\\":{\\\"display_name\\\":\\\"Width\\\" ,\\\"value\\\":1024},\\n \\\"height\\\":{\\\"display_name\\\":\\\"Height\\\", \\\"value\\\":1024}\\n }\\n\\n def build(self, prompt:str,api_key:str,model:str,file_name:str,output_format:str,width:int,height:int):\\n client = OpenAI(api_key=api_key)\\n cache_dir = Path(user_cache_dir(\\\"langflow\\\"))\\n images_dir = cache_dir / \\\"images\\\"\\n images_dir.mkdir(parents=True, exist_ok=True)\\n image_path = images_dir / f\\\"{file_name}.{output_format}\\\"\\n response = client.images.generate(\\n model=model,\\n prompt=prompt,\\n size=f\\\"{height}x{width}\\\",\\n response_format=\\\"b64_json\\\",\\n n=1,\\n )\\n # Decode base64-encoded image string\\n binary_data = base64.b64decode(response.data[0].b64_json)\\n # Create PIL Image object from binary image data\\n image_pil = Image.open(BytesIO(binary_data))\\n image_pil.save(image_path, format=output_format.upper())\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"api_key\",\"display_name\":\"OpenAI API key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"file_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"file_name\",\"display_name\":\"File 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generator\",\"custom_fields\":{\"api_key\":null,\"file_name\":null,\"height\":null,\"model\":null,\"output_format\":null,\"prompt\":null,\"width\":null},\"output_types\":[\"Data\"],\"documentation\":\"http://docs.langflow.org/components/custom\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-IEIUl\"},\"selected\":false,\"positionAbsolute\":{\"x\":2364.865919667005,\"y\":88.43094097025096}},{\"width\":384,\"height\":338,\"id\":\"LLMChain-KlJb3\",\"type\":\"genericNode\",\"position\":{\"x\":1242.9851164540805,\"y\":-139.74634696823108},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-KlJb3\"},\"selected\":false,\"positionAbsolute\":{\"x\":1242.9851164540805,\"y\":-139.74634696823108},\"dragging\":false},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-bCuc0\",\"type\":\"genericNode\",\"position\":{\"x\":1747.8107777342625,\"y\":319.13729017446667},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Custom Component\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n def build(self, param: Chain) -> str:\\n result = param.run({})\\n self.status = result\\n return result\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"param\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"param\",\"display_name\":\"Parameter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Chain\",\"list\":false}},\"description\":\"Create any custom component you want!\",\"base_classes\":[\"str\"],\"display_name\":\"Custom Component\",\"custom_fields\":{\"param\":null},\"output_types\":[\"str\"],\"documentation\":\"http://docs.langflow.org/components/custom\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-bCuc0\"},\"selected\":false,\"positionAbsolute\":{\"x\":1747.8107777342625,\"y\":319.13729017446667}}],\"edges\":[{\"source\":\"LLMChain-KlJb3\",\"target\":\"CustomComponent-bCuc0\",\"sourceHandle\":\"{œbaseClassesœ:[œChainœ,œCallableœ],œdataTypeœ:œLLMChainœ,œidœ:œLLMChain-KlJb3œ}\",\"targetHandle\":\"{œfieldNameœ:œparamœ,œidœ:œCustomComponent-bCuc0œ,œinputTypesœ:null,œtypeœ:œChainœ}\",\"id\":\"reactflow__edge-LLMChain-KlJb3{œbaseClassesœ:[œChainœ,œCallableœ],œdataTypeœ:œLLMChainœ,œidœ:œLLMChain-KlJb3œ}-CustomComponent-bCuc0{œfieldNameœ:œparamœ,œidœ:œCustomComponent-bCuc0œ,œinputTypesœ:null,œtypeœ:œChainœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"param\",\"id\":\"CustomComponent-bCuc0\",\"inputTypes\":null,\"type\":\"Chain\"},\"sourceHandle\":{\"baseClasses\":[\"Chain\",\"Callable\"],\"dataType\":\"LLMChain\",\"id\":\"LLMChain-KlJb3\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 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[\\\"str\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"TextLoader\\\",\\n \\\"name\\\": \\\"Text (.txt)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.TextLoader\\\",\\n \\\"defaultFor\\\": [\\\"txt\\\"],\\n \\\"allowdTypes\\\": [\\\"txt\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredEmailLoader\\\",\\n \\\"name\\\": \\\"Unstructured Email (.eml)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredEmailLoader\\\",\\n \\\"defaultFor\\\": [\\\"eml\\\"],\\n \\\"allowdTypes\\\": [\\\"eml\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredHTMLLoader\\\",\\n \\\"name\\\": \\\"Unstructured HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredHTMLLoader\\\",\\n \\\"defaultFor\\\": [\\\"html\\\", \\\"htm\\\"],\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredMarkdownLoader\\\",\\n \\\"name\\\": \\\"Unstructured Markdown (.md)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredMarkdownLoader\\\",\\n \\\"defaultFor\\\": [\\\"md\\\"],\\n \\\"allowdTypes\\\": [\\\"md\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredPowerPointLoader\\\",\\n \\\"name\\\": \\\"Unstructured PowerPoint (.pptx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredPowerPointLoader\\\",\\n \\\"defaultFor\\\": [\\\"pptx\\\"],\\n \\\"allowdTypes\\\": [\\\"pptx\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredWordLoader\\\",\\n \\\"name\\\": \\\"Unstructured Word (.docx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredWordLoader\\\",\\n \\\"defaultFor\\\": [\\\"docx\\\"],\\n \\\"allowdTypes\\\": [\\\"docx\\\"],\\n },\\n]\\n\\n\\n def build_config(self):\\n loader_options = [\\\"Automatic\\\"] + [\\n loader_info[\\\"name\\\"] for loader_info in self.loaders_info\\n ]\\n\\n file_types = []\\n suffixes = []\\n\\n for loader_info in self.loaders_info:\\n if \\\"allowedTypes\\\" in loader_info:\\n file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n return {\\n \\\"directory_path\\\": {\\n \\\"display_name\\\": \\\"Directory Path\\\",\\n \\\"required\\\": True,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": loader_options,\\n \\\"value\\\": \\\"Automatic\\\",\\n },\\n }\\n\\n def build(self, directory_path: str, loader: str) -> Document:\\n # Verifique se o diretório existe\\n if not os.path.exists(directory_path):\\n raise ValueError(f\\\"Directory not found: {directory_path}\\\")\\n\\n files = glob.glob(directory_path + \\\"/*.*\\\")\\n\\n\\n loader_info = None\\n if loader == \\\"Automatic\\\":\\n for file in files:\\n file_type = file.split(\\\".\\\")[-1]\\n\\n\\n for info in self.loaders_info:\\n if \\\"defaultFor\\\" in info:\\n if file_type in info[\\\"defaultFor\\\"]:\\n loader_info = info\\n break\\n if loader_info:\\n break\\n\\n if not loader_info:\\n raise ValueError(\\n \\\"No default loader found for any file in the directory\\\"\\n )\\n\\n else:\\n for info in self.loaders_info:\\n if info[\\\"name\\\"] == loader:\\n loader_info = info\\n break\\n\\n if not loader_info:\\n raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n loader_import = loader_info[\\\"import\\\"]\\n module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n try:\\n # Importe o loader dinamicamente\\n loader_module = __import__(module_name, fromlist=[class_name])\\n loader_instance = getattr(loader_module, class_name)\\n except ImportError as e:\\n raise ValueError(\\n f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{loader_info}\\\"\\n ) from e\\n\\n results = []\\n for file in files:\\n file_path = os.path.join(directory_path, file)\\n kwargs = loader_info.get(\\\"kwargs\\\", {})\\n result = loader_instance(file_path=file_path, **kwargs).load()\\n results.append(result)\\n self.status = results\\n return results\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"directory_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"directory_path\",\"display_name\":\"Directory Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"/Users/ogabrielluiz/Projects/langflow2/docs\"},\"loader\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true}},\"description\":\"Generic File Loader\",\"base_classes\":[\"Document\"],\"display_name\":\"Directory Loader\",\"custom_fields\":{\"directory_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Ip6tG\"},\"id\":\"CustomComponent-Ip6tG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:38.570303\",\"folder\":null,\"id\":\"5fe4debc-b6a7-45d4-a694-c02d8aa93b08\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Whisper Transcriber\",\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, List, Dict, Union\\nfrom langflow.field_typing import (\\n AgentExecutor,\\n BaseChatMemory,\\n BaseLanguageModel,\\n BaseLLM,\\n BaseLoader,\\n BaseMemory,\\n BaseOutputParser,\\n BasePromptTemplate,\\n BaseRetriever,\\n Callable,\\n Chain,\\n ChatPromptTemplate,\\n Data,\\n Document,\\n Embeddings,\\n NestedDict,\\n Object,\\n PromptTemplate,\\n TextSplitter,\\n Tool,\\n VectorStore,\\n)\\n\\nfrom openai import OpenAI\\nclass Component(CustomComponent):\\n display_name: str = \\\"Whisper Transcriber\\\"\\n description: str = \\\"Converts audio to text using OpenAI's Whisper.\\\"\\n\\n def build_config(self):\\n return {\\\"audio\\\":{\\\"field_type\\\":\\\"file\\\",\\\"suffixes\\\":[\\\".mp3\\\", \\\".mp4\\\", \\\".m4a\\\"]},\\\"OpenAIKey\\\":{\\\"field_type\\\":\\\"str\\\",\\\"password\\\":True}}\\n\\n def build(self, audio:str, OpenAIKey:str) -> str:\\n \\n # TODO: if output path, persist & load it instead\\n \\n client = OpenAI(api_key=OpenAIKey)\\n \\n audio_file= open(audio, \\\"rb\\\")\\n transcript = client.audio.transcriptions.create(\\n model=\\\"whisper-1\\\", \\n file=audio_file,\\n response_format=\\\"text\\\"\\n )\\n \\n \\n self.status = transcript\\n return transcript\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"OpenAIKey\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"OpenAIKey\",\"display_name\":\"OpenAIKey\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"audio\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"suffixes\":[\".mp3\",\".mp4\",\".m4a\"],\"password\":false,\"name\":\"audio\",\"display_name\":\"audio\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"file_path\":\"/Users/rodrigonader/Library/Caches/langflow/19b3e1c9-90bf-405f-898a-e982f47adf76/a3308ce7e9b10088fcd985aabb6d17b012c6b6e81ff8806356574474c9d10229.m4a\",\"value\":\"Audio Recording 2023-11-29 at 12.12.04.m4a\"}},\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"base_classes\":[\"str\"],\"display_name\":\"Whisper Transcriber\",\"custom_fields\":{\"OpenAIKey\":null,\"audio\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-GJRrs\"},\"id\":\"CustomComponent-GJRrs\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:39.710502\",\"folder\":null,\"id\":\"bd9e911d-46bd-429f-aa75-dd740430695e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Youtube Conversation\",\"description\":\"Craft Meaningful Interactions, Generate Value.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":589,\"id\":\"CustomComponent-h0NSI\",\"type\":\"genericNode\",\"position\":{\"x\":714.9531293402755,\"y\":0.4994374160582993},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import requests\\nfrom langflow import CustomComponent\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.schema import Document\\nfrom langchain.document_loaders import YoutubeLoader\\n\\n# Example usage:\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"YouTube Transcript\\\"\\n description: str = \\\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\\\"\\n\\n def build_config(self):\\n return {\\\"url\\\": {\\\"multiline\\\": True, \\\"required\\\": True}}\\n\\n def build(self, url: str, llm: BaseLLM, dependencies: Document, language: str = \\\"en\\\") -> Document:\\n dependencies\\n response = requests.get(url)\\n loader = YoutubeLoader.from_youtube_url(url, add_video_info=True, language=[language])\\n result = loader.load()\\n self.repr_value = str(result)\\n return result\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"dependencies\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"dependencies\",\"display_name\":\"dependencies\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"language\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"language\",\"display_name\":\"language\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\",\"base_classes\":[\"Document\"],\"display_name\":\"YouTube Transcript\",\"custom_fields\":{\"dependencies\":null,\"language\":null,\"llm\":null,\"url\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-h0NSI\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":714.9531293402755,\"y\":0.4994374160582993}},{\"width\":384,\"height\":629,\"id\":\"ChatOpenAI-q61Y2\",\"type\":\"genericNode\",\"position\":{\"x\":18,\"y\":625.5521045590924},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-16k\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k-0613\",\"gpt-3.5-turbo-16k\",\"gpt-4-0613\",\"gpt-4-32k-0613\",\"gpt-4\",\"gpt-4-32k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-q61Y2\"},\"selected\":false,\"positionAbsolute\":{\"x\":18,\"y\":625.5521045590924},\"dragging\":false},{\"width\":384,\"height\":539,\"id\":\"Chroma-gVhy7\",\"type\":\"genericNode\",\"position\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"data\":{\"type\":\"Chroma\",\"node\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.Client\",\"list\":false},\"client_settings\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client_settings\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.config.Setting\",\"list\":true},\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"chroma_server_cors_allow_origins\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Chroma Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"chroma_server_grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Chroma Server GRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Chroma Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_http_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_http_port\",\"display_name\":\"Chroma Server HTTP Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_ssl_enabled\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Chroma Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"collection_metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"collection_metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"collection_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"persist\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"persist\",\"display_name\":\"Persist\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"persist_directory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"persist_directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"_type\":\"Chroma\"},\"description\":\"Create a Chroma vectorstore from a raw documents.\",\"base_classes\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Chroma\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma\",\"beta\":false,\"error\":null},\"id\":\"Chroma-gVhy7\"},\"selected\":false,\"positionAbsolute\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"dragging\":false},{\"width\":384,\"height\":501,\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"type\":\"genericNode\",\"position\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"data\":{\"type\":\"RecursiveCharacterTextSplitter\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.utils.util import build_loader_repr_from_documents\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chunk_overlap\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"50\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"400\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"type\":\"int\",\"list\":false},\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"type\":\"Document\",\"list\":true},\"separators\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"type\":\"str\",\"list\":true,\"value\":[\" \"]}},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separators\":null},\"output_types\":[\"RecursiveCharacterTextSplitter\"],\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"beta\":true,\"error\":null},\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"selected\":false,\"positionAbsolute\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"dragging\":false},{\"width\":384,\"height\":367,\"id\":\"OpenAIEmbeddings-cduOO\",\"type\":\"genericNode\",\"position\":{\"x\":1405.1176670984544,\"y\":1029.459061269321},\"data\":{\"type\":\"OpenAIEmbeddings\",\"node\":{\"template\":{\"allowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[],\"password\":false,\"name\":\"allowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"disallowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"all\",\"password\":false,\"name\":\"disallowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"deployment\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"embedding_ctx_length\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":8191,\"password\":false,\"name\":\"embedding_ctx_length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"{'Authorization': 'Bearer '}\",\"password\":false,\"name\":\"headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"model\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_api_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_version\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"show_progress_bar\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"show_progress_bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"skip_empty\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"skip_empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"_type\":\"OpenAIEmbeddings\"},\"description\":\"OpenAI embedding models.\",\"base_classes\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"display_name\":\"OpenAIEmbeddings\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"OpenAIEmbeddings-cduOO\"},\"selected\":false,\"positionAbsolute\":{\"x\":1405.1176670984544,\"y\":1029.459061269321}},{\"width\":384,\"height\":339,\"id\":\"RetrievalQA-4PmTB\",\"type\":\"genericNode\",\"position\":{\"x\":2711.7786966415715,\"y\":709.4450828605463},\"data\":{\"type\":\"RetrievalQA\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"combine_documents_chain\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"combine_documents_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseCombineDocumentsChain\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"list\":false},\"input_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"query\",\"password\":false,\"name\":\"input_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"output_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"result\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"return_source_documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_source_documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"RetrievalQA\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"RetrievalQA\",\"Chain\",\"BaseRetrievalQA\",\"function\"],\"display_name\":\"RetrievalQA\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/vector_db_qa\",\"beta\":false,\"error\":null},\"id\":\"RetrievalQA-4PmTB\"},\"selected\":false,\"positionAbsolute\":{\"x\":2711.7786966415715,\"y\":709.4450828605463}},{\"width\":384,\"height\":333,\"id\":\"CombineDocsChain-yk0JF\",\"type\":\"genericNode\",\"position\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"data\":{\"type\":\"CombineDocsChain\",\"node\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"chain_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"BaseCombineDocumentsChain\",\"function\"],\"display_name\":\"CombineDocsChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"id\":\"CombineDocsChain-yk0JF\"},\"selected\":false,\"positionAbsolute\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"dragging\":false},{\"width\":384,\"height\":417,\"id\":\"CustomComponent-y6Wg0\",\"type\":\"genericNode\",\"position\":{\"x\":39.400034102832365,\"y\":-499.03551482195707},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nimport subprocess\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\nfrom typing import List\\n\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"PIP Install\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n\\n def build(self, libs: List[str]) -> Document:\\n def install_package(package_name):\\n subprocess.check_call([\\\"pip\\\", \\\"install\\\", package_name])\\n for lib in libs:\\n install_package(lib)\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"libs\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"libs\",\"display_name\":\"libs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"requests\",\"pytube\"]}},\"description\":\"Create any custom component you want!\",\"base_classes\":[\"Document\"],\"display_name\":\"PIP Install\",\"custom_fields\":{\"libs\":null},\"output_types\":[],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-y6Wg0\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":39.400034102832365,\"y\":-499.03551482195707}}],\"edges\":[{\"source\":\"ChatOpenAI-q61Y2\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CustomComponent-h0NSI{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"BaseLLM\"}}},{\"source\":\"CustomComponent-y6Wg0\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CustomComponent-y6Wg0{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}-CustomComponent-h0NSI{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-y6Wg0\"},\"targetHandle\":{\"fieldName\":\"dependencies\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"Document\"}}},{\"source\":\"CustomComponent-h0NSI\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-h0NSIœ}\",\"target\":\"RecursiveCharacterTextSplitter-1eaOX\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CustomComponent-h0NSI{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-h0NSIœ}-RecursiveCharacterTextSplitter-1eaOX{œfieldNameœ:œdocumentsœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-h0NSI\"},\"targetHandle\":{\"fieldName\":\"documents\",\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"inputTypes\":null,\"type\":\"Document\"}},\"selected\":false},{\"source\":\"OpenAIEmbeddings-cduOO\",\"sourceHandle\":\"{œbaseClassesœ:[œEmbeddingsœ,œOpenAIEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-cduOOœ}\",\"target\":\"Chroma-gVhy7\",\"targetHandle\":\"{œfieldNameœ:œembeddingœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-OpenAIEmbeddings-cduOO{œbaseClassesœ:[œEmbeddingsœ,œOpenAIEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-cduOOœ}-Chroma-gVhy7{œfieldNameœ:œembeddingœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"dataType\":\"OpenAIEmbeddings\",\"id\":\"OpenAIEmbeddings-cduOO\"},\"targetHandle\":{\"fieldName\":\"embedding\",\"id\":\"Chroma-gVhy7\",\"inputTypes\":null,\"type\":\"Embeddings\"}}},{\"source\":\"RecursiveCharacterTextSplitter-1eaOX\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ}\",\"target\":\"Chroma-gVhy7\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-RecursiveCharacterTextSplitter-1eaOX{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ}-Chroma-gVhy7{œfieldNameœ:œdocumentsœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"RecursiveCharacterTextSplitter\",\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"targetHandle\":{\"fieldName\":\"documents\",\"id\":\"Chroma-gVhy7\",\"inputTypes\":null,\"type\":\"Document\"}}},{\"source\":\"ChatOpenAI-q61Y2\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}\",\"target\":\"CombineDocsChain-yk0JF\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œCombineDocsChain-yk0JFœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CombineDocsChain-yk0JF{œfieldNameœ:œllmœ,œidœ:œCombineDocsChain-yk0JFœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CombineDocsChain-yk0JF\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"}}},{\"source\":\"CombineDocsChain-yk0JF\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CombineDocsChain-yk0JF{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}-RetrievalQA-4PmTB{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"BaseCombineDocumentsChain\",\"function\"],\"dataType\":\"CombineDocsChain\",\"id\":\"CombineDocsChain-yk0JF\"},\"targetHandle\":{\"fieldName\":\"combine_documents_chain\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseCombineDocumentsChain\"}}},{\"source\":\"Chroma-gVhy7\",\"sourceHandle\":\"{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-Chroma-gVhy7{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}-RetrievalQA-4PmTB{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"dataType\":\"Chroma\",\"id\":\"Chroma-gVhy7\"},\"targetHandle\":{\"fieldName\":\"retriever\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseRetriever\"}}}],\"viewport\":{\"x\":189.54413265004274,\"y\":259.89949657174975,\"zoom\":0.291027508374689}},\"is_component\":false,\"updated_at\":\"2023-12-04T22:59:08.830269\",\"folder\":null,\"id\":\"bc7eb94b-6fc6-49cb-9b19-35347ba51afa\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Web Scraper - Content & Links\",\"description\":\"Fetch and parse text and links from a given URL.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":338,\"id\":\"LLMChain-H7PBy\",\"type\":\"genericNode\",\"position\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-H7PBy\"},\"selected\":false,\"positionAbsolute\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"dragging\":false},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-VSAdc\",\"type\":\"genericNode\",\"position\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"sk-...\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-VSAdc\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859}},{\"width\":384,\"height\":374,\"data\":{\"id\":\"GroupNode-WXPMk\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"give me links\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown 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{\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch 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is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser 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Conversation.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":338,\"id\":\"LLMChain-RQsU1\",\"type\":\"genericNode\",\"position\":{\"x\":514.4440482813261,\"y\":528.164086188516},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-RQsU1\"},\"selected\":false,\"positionAbsolute\":{\"x\":514.4440482813261,\"y\":528.164086188516}},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-NTTcv\",\"type\":\"genericNode\",\"position\":{\"x\":-221.33853765781578,\"y\":-35.48749923706055},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"sk-...\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-NTTcv\"},\"selected\":false,\"positionAbsolute\":{\"x\":-221.33853765781578,\"y\":-35.48749923706055}},{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-VELMV\",\"type\":\"genericNode\",\"position\":{\"x\":-222,\"y\":682.560723386973},\"data\":{\"id\":\"PromptTemplate-VELMV\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-f6nOg\"},\"selected\":true,\"positionAbsolute\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"dragging\":false},{\"width\":384,\"height\":375,\"id\":\"CustomComponent-FGzJJ\",\"type\":\"genericNode\",\"position\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse 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LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, 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LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-cHel8\"},\"selected\":false,\"positionAbsolute\":{\"x\":547.3876889720291,\"y\":299.2057833881307},\"dragging\":false},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-LA6y0\",\"type\":\"genericNode\",\"position\":{\"x\":-272.94405331278074,\"y\":-603.148171441675},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"sk-...\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-LA6y0\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":-272.94405331278074,\"y\":-603.148171441675}},{\"width\":384,\"height\":404,\"id\":\"CustomComponent-ZNoRM\",\"type\":\"genericNode\",\"position\":{\"x\":-103.65741264289085,\"y\":134.62332404764658},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-ZNoRM\"},\"selected\":false,\"positionAbsolute\":{\"x\":-103.65741264289085,\"y\":134.62332404764658},\"dragging\":false},{\"width\":384,\"height\":374,\"id\":\"CustomComponent-zNSTv\",\"type\":\"genericNode\",\"position\":{\"x\":-1233.3963469933499,\"y\":280.77850809220325},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-zNSTv\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":-1233.3963469933499,\"y\":280.77850809220325}},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-Ihm2o\",\"type\":\"genericNode\",\"position\":{\"x\":-554.9404152016002,\"y\":683.6763775860567},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse HTML\",\"custom_fields\":{\"html_content\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-Ihm2o\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":-554.9404152016002,\"y\":683.6763775860567}},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-6ST9l\",\"type\":\"genericNode\",\"position\":{\"x\":76.10921262566495,\"y\":872.9491114949525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract Hyperlinks\",\"custom_fields\":{\"soup\":null},\"output_types\":[\"list\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-6ST9l\"},\"selected\":false,\"positionAbsolute\":{\"x\":76.10921262566495,\"y\":872.9491114949525},\"dragging\":false},{\"width\":384,\"height\":654,\"id\":\"PromptTemplate-l35W1\",\"type\":\"genericNode\",\"position\":{\"x\":1461.4487148097069,\"y\":1090.896175351284},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"display_name\":\"output_parser\"},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"display_name\":\"input_types\"},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"display_name\":\"input_variables\"},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"display_name\":\"partial_variables\"},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"display_name\":\"template\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"display_name\":\"template_format\"},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"display_name\":\"validate_template\"},\"_type\":\"PromptTemplate\",\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_markdown\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_markdown\",\"display_name\":\"html_markdown\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_links\",\"display_name\":\"html_links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"query\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"name\":\"\",\"display_name\":\"PromptTemplate\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"custom_fields\":{\"\":[\"url\",\"html_markdown\",\"html_links\",\"query\"]},\"output_types\":[],\"full_path\":null,\"field_formatters\":{},\"beta\":false,\"error\":null},\"id\":\"PromptTemplate-l35W1\"},\"selected\":false,\"positionAbsolute\":{\"x\":1461.4487148097069,\"y\":1090.896175351284},\"dragging\":false},{\"width\":384,\"height\":346,\"id\":\"CustomComponent-iTLf4\",\"type\":\"genericNode\",\"position\":{\"x\":-1924.8908014123388,\"y\":704.5414990162741},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"https://paperswithcode.com/\"}},\"description\":null,\"base_classes\":[\"str\"],\"display_name\":\"URL Input\",\"custom_fields\":{\"url\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-iTLf4\"},\"selected\":false,\"positionAbsolute\":{\"x\":-1924.8908014123388,\"y\":704.5414990162741},\"dragging\":false},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-jWLUz\",\"type\":\"genericNode\",\"position\":{\"x\":627.6164007410564,\"y\":1201.2365384322047},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"items\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"items\",\"display_name\":\"items\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"list\",\"list\":true}},\"description\":\"Converts a Python list into Markdown bullet points.\",\"base_classes\":[\"str\"],\"display_name\":\"List to Bullets\",\"custom_fields\":{\"items\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-jWLUz\"},\"selected\":false,\"positionAbsolute\":{\"x\":627.6164007410564,\"y\":1201.2365384322047},\"dragging\":false}],\"edges\":[{\"source\":\"ChatOpenAI-LA6y0\",\"target\":\"LLMChain-cHel8\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseLanguageModelœ,œChatOpenAIœ,œBaseChatModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-LA6y0œ}\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œLLMChain-cHel8œ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"id\":\"reactflow__edge-ChatOpenAI-LA6y0{œbaseClassesœ:[œBaseLanguageModelœ,œChatOpenAIœ,œBaseChatModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-LA6y0œ}-LLMChain-cHel8{œfieldNameœ:œllmœ,œidœ:œLLMChain-cHel8œ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"LLMChain-cHel8\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"},\"sourceHandle\":{\"baseClasses\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-LA6y0\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 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local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-DXfcM\"},\"selected\":false,\"positionAbsolute\":{\"x\":-1110.8267574563533,\"y\":569.1107380883907},\"dragging\":false},{\"width\":384,\"height\":376,\"id\":\"PromptRunner-ckWMH\",\"type\":\"genericNode\",\"position\":{\"x\":-1149.4746387825978,\"y\":992.3970573758324},\"data\":{\"type\":\"PromptRunner\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\n\\nclass PromptRunner(CustomComponent):\\n display_name: str = \\\"Prompt Runner\\\"\\n description: str = \\\"Run a Chain with the given PromptTemplate\\\"\\n beta: bool = True\\n field_config = {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt Template\\\",\\n \\\"info\\\": \\\"Make sure the prompt has all variables filled.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}) -> Document:\\n chain = prompt | llm\\n # The input is an empty dict because the prompt is already filled\\n result = chain.invoke(input=inputs)\\n if hasattr(result, \\\"content\\\"):\\n result = result.content\\n self.repr_value = result\\n return Document(page_content=str(result))\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"inputs\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"inputs\",\"display_name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt Template\",\"advanced\":false,\"dynamic\":false,\"info\":\"Make sure the prompt has all variables filled.\",\"type\":\"PromptTemplate\",\"list\":false}},\"description\":\"Run a Chain with the given PromptTemplate\",\"base_classes\":[\"Document\"],\"display_name\":\"Prompt Runner\",\"custom_fields\":{\"inputs\":null,\"llm\":null,\"prompt\":null},\"output_types\":[\"PromptRunner\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"PromptRunner-ckWMH\"},\"selected\":false,\"positionAbsolute\":{\"x\":-1149.4746387825978,\"y\":992.3970573758324},\"dragging\":false}],\"edges\":[{\"source\":\"PromptRunner-ckWMH\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œPromptRunnerœ,œidœ:œPromptRunner-ckWMHœ}\",\"target\":\"PromptTemplate-K7xiS\",\"targetHandle\":\"{œfieldNameœ:œdasdasdœ,œidœ:œPromptTemplate-K7xiSœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ],œtypeœ:œstrœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"dasdasd\",\"id\":\"PromptTemplate-K7xiS\",\"inputTypes\":[\"Document\",\"BaseOutputParser\"],\"type\":\"str\"},\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"PromptRunner\",\"id\":\"PromptRunner-ckWMH\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-PromptRunner-ckWMH{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œPromptRunnerœ,œidœ:œPromptRunner-ckWMHœ}-PromptTemplate-K7xiS{œfieldNameœ:œdasdasdœ,œidœ:œPromptTemplate-K7xiSœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ],œtypeœ:œstrœ}\"},{\"source\":\"AirbyteJSONLoader-DXfcM\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œAirbyteJSONLoaderœ,œidœ:œAirbyteJSONLoader-DXfcMœ}\",\"target\":\"PromptTemplate-K7xiS\",\"targetHandle\":\"{œfieldNameœ:œdasdasœ,œidœ:œPromptTemplate-K7xiSœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ],œtypeœ:œstrœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"dasdas\",\"id\":\"PromptTemplate-K7xiS\",\"inputTypes\":[\"Document\",\"BaseOutputParser\"],\"type\":\"str\"},\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"AirbyteJSONLoader\",\"id\":\"AirbyteJSONLoader-DXfcM\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 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(1)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-dMB5d\"},\"id\":\"CustomComponent-dMB5d\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:43.668665\",\"folder\":null,\"id\":\"912265df-9b87-4b30-a585-1ca59b944391\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search (2)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-ipifC\"},\"id\":\"CustomComponent-ipifC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:49.799612\",\"folder\":null,\"id\":\"ca83ee08-2f93-427d-9897-80fef6dd5447\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Y4qL7\"},\"id\":\"CustomComponent-Y4qL7\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:53.719960\",\"folder\":null,\"id\":\"5847602b-769a-4a82-82e8-a70f54a59929\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Lively Williams\",\"description\":\"Conversational Cartography Unlocked.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T13:27:45.691254\",\"folder\":null,\"id\":\"0e3bdba9-127a-4399-81a4-2865b70a4a47\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Shared Component\",\"description\":\"Conversational Cartography 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Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-VEjyx\"},\"id\":\"CSVLoader-VEjyx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:57:37.560784\",\"folder\":null,\"id\":\"d80635ee-966c-41f2-981c-afa2c388ac6e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (3)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-PIhOc\"},\"id\":\"CSVLoader-PIhOc\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:00:27.991966\",\"folder\":null,\"id\":\"c5cc4c32-77c8-4889-a9f8-2632466b7366\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (4)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-deB43\"},\"id\":\"CSVLoader-deB43\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:00:42.509243\",\"folder\":null,\"id\":\"0ab59938-ccf4-4bb9-b285-1f5da38648f0\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-mdkLm\"},\"id\":\"CSVLoader-mdkLm\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:02:17.458354\",\"folder\":null,\"id\":\"f127b7e7-4149-492e-8998-6b1b35ec4153\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (5)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-FRc6Y\"},\"id\":\"CSVLoader-FRc6Y\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:02:26.958867\",\"folder\":null,\"id\":\"a870a096-725c-4914-add0-8d57d710b7e5\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (2)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-Z0uol\"},\"id\":\"AirbyteJSONLoader-Z0uol\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:33.764117\",\"folder\":null,\"id\":\"2a37c76c-65c8-4c01-a72d-eb46f3d41a56\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-28ASv\"},\"id\":\"AmazonBedrockEmbeddings-28ASv\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:41.066618\",\"folder\":null,\"id\":\"850ba4e6-96ca-49a7-9b0c-4b7048fd52c8\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"ConversationBufferMemory\",\"description\":\"Buffer for storing conversation memory.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"ConversationBufferMemory\",\"node\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"memory_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\"],\"display_name\":\"ConversationBufferMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"ConversationBufferMemory-WaoLx\"},\"id\":\"ConversationBufferMemory-WaoLx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:04:46.058568\",\"folder\":null,\"id\":\"be650940-0449-4eb0-a708-056310f924fd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (1)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\"},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:05:50.570800\",\"folder\":null,\"id\":\"53ad1fe2-f3af-4354-86e0-eb141ce12859\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (2)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\"},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:06:11.415088\",\"folder\":null,\"id\":\"e9ed1c90-146e-452d-8ccd-ebf2e0da4331\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (3)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\"},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:07:06.411108\",\"folder\":null,\"id\":\"83783c57-64e3-41a6-aa7e-ed82f80f1e53\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (6)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"z2b\":\"z9\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-2pn2o\"},\"id\":\"CSVLoader-2pn2o\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:38:30.706258\",\"folder\":null,\"id\":\"c4ae9de6-9df3-4d3c-afc9-1752af4fecd7\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-MJrAC\"},\"id\":\"AgentInitializer-MJrAC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:42:54.715163\",\"folder\":null,\"id\":\"ff84b5f9-5680-4084-a9f1-7d94fe675486\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer (1)\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-3lcZ4\"},\"id\":\"AgentInitializer-3lcZ4\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:43:20.819934\",\"folder\":null,\"id\":\"97f5db9f-d6cc-4555-88fb-3be102c67814\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Exuberant Banach\",\"description\":\"Unfolding Linguistic Possibilities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T14:52:49.951416\",\"folder\":null,\"id\":\"a600acd1-213a-4ce7-8648-ab2ee59f5918\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (3)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-JhQtx\"},\"id\":\"AirbyteJSONLoader-JhQtx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:44:49.587337\",\"folder\":null,\"id\":\"07c52ad7-ca52-4cdd-ab40-74a91dbad0dd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (4)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-bIvDc\"},\"id\":\"AirbyteJSONLoader-bIvDc\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:45:13.458500\",\"folder\":null,\"id\":\"53e4d256-3653-444b-b8e0-fb97b3ae5c7e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (5)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-2BLS8\"},\"id\":\"AirbyteJSONLoader-2BLS8\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:45:44.461699\",\"folder\":null,\"id\":\"b5158cc1-c6b8-4e25-907a-bc7e7637aa38\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (4)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile 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Construct, id:string, props: RdsProps){ + constructor(scope: Construct, id: string, props: RdsProps) { super(scope, id); - const {vpc, dbSG} = props - const instanceType = ec2.InstanceType.of(ec2.InstanceClass.BURSTABLE4_GRAVITON, ec2.InstanceSize.MEDIUM) + const { vpc, dbSG } = props; + const instanceType = ec2.InstanceType.of( + ec2.InstanceClass.BURSTABLE4_GRAVITON, + ec2.InstanceSize.MEDIUM, + ); // RDSのパスワードを自動生成してSecrets Managerに格納 - const rdsCredentials = rds.Credentials.fromGeneratedSecret('db_user',{ - secretName: 'langflow-DbSecret', - }) - + const rdsCredentials = rds.Credentials.fromGeneratedSecret("db_user", { + secretName: "langflow-DbSecret", + }); + // DB クラスターのパラメータグループ作成 - const clusterParameterGroup = new rds.ParameterGroup(scope, 'ClusterParameterGroup',{ - engine: rds.DatabaseClusterEngine.auroraMysql({ - version: rds.AuroraMysqlEngineVersion.VER_3_02_0 - }), - description: 'for-langflow', - }) - clusterParameterGroup.bindToCluster({}) + const clusterParameterGroup = new rds.ParameterGroup( + scope, + "ClusterParameterGroup", + { + engine: rds.DatabaseClusterEngine.auroraMysql({ + version: rds.AuroraMysqlEngineVersion.of( + "8.0.mysql_aurora.3.05.2", + "8.0", + ), + }), + description: "for-langflow", + }, + ); + clusterParameterGroup.bindToCluster({}); // DB インスタンスのパラメタグループ作成 - const instanceParameterGroup = new rds.ParameterGroup(scope, 'InstanceParameterGroup',{ - engine: rds.DatabaseClusterEngine.auroraMysql({ - version: rds.AuroraMysqlEngineVersion.VER_3_02_0, - }), - description: 'for-langflow', - }) - instanceParameterGroup.bindToInstance({}) + const instanceParameterGroup = new rds.ParameterGroup( + scope, + "InstanceParameterGroup", + { + engine: rds.DatabaseClusterEngine.auroraMysql({ + version: rds.AuroraMysqlEngineVersion.of( + "8.0.mysql_aurora.3.05.2", + "8.0", + ), + }), + description: "for-langflow", + }, + ); + instanceParameterGroup.bindToInstance({}); - this.rdsCluster = new rds.DatabaseCluster(scope, 'LangflowDbCluster', { + this.rdsCluster = new rds.DatabaseCluster(scope, "LangflowDbCluster", { engine: rds.DatabaseClusterEngine.auroraMysql({ - version: rds.AuroraMysqlEngineVersion.VER_3_02_0, + version: rds.AuroraMysqlEngineVersion.of( + "8.0.mysql_aurora.3.05.2", + "8.0", + ), }), storageEncrypted: true, credentials: rdsCredentials, - instanceIdentifierBase: 'langflow-instance', - vpc:vpc, - vpcSubnets:vpc.selectSubnets({ - subnetGroupName: 'langflow-Isolated', + instanceIdentifierBase: "langflow-instance", + vpc: vpc, + vpcSubnets: vpc.selectSubnets({ + subnetGroupName: "langflow-Isolated", }), - securityGroups:[dbSG], + securityGroups: [dbSG], writer: rds.ClusterInstance.provisioned("WriterInstance", { instanceType: instanceType, enablePerformanceInsights: true, - parameterGroup:instanceParameterGroup, + parameterGroup: instanceParameterGroup, }), - // 2台目以降はreaders:で設定 + // 2台目以降はreaders:で設定 parameterGroup: clusterParameterGroup, - defaultDatabaseName: 'langflow', - }) + defaultDatabaseName: "langflow", + }); } } diff --git a/scripts/aws/package-lock.json b/scripts/aws/package-lock.json index 4deefa752..c1be6a071 100644 --- a/scripts/aws/package-lock.json +++ b/scripts/aws/package-lock.json @@ -8,35 +8,35 @@ "name": "cdk", "version": "0.1.0", "dependencies": { - "@aws-solutions-constructs/aws-cloudfront-s3": "^2.49.0", - "aws-cdk-lib": "^2.124.0", - "cdk-ecr-deployment": "^2.5.30", - "constructs": "^10.0.0", - "deploy-time-build": "^0.3.12", - "dotenv": "^16.3.1", + "@aws-solutions-constructs/aws-cloudfront-s3": "^2.57.0", + "aws-cdk-lib": "^2.141.0", + "cdk-ecr-deployment": "^3.0.55", + "constructs": "^10.3.0", + "deploy-time-build": "^0.3.21", + "dotenv": "^16.4.5", "source-map-support": "^0.5.21" }, "bin": { "cdk": "bin/cdk.js" }, "devDependencies": { - "@types/jest": "^29.5.1", - "@types/node": "20.1.7", - "aws-cdk": "^2.86.0", - "jest": "^29.5.0", - "ts-jest": "^29.1.0", - "ts-node": "^10.9.1", - "typescript": "~5.1.3" + "@types/jest": "^29.5.12", + "@types/node": 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"node_modules/@aws-cdk/integ-tests-alpha": { + "version": "2.138.0-alpha.0", + "resolved": "https://registry.npmjs.org/@aws-cdk/integ-tests-alpha/-/integ-tests-alpha-2.138.0-alpha.0.tgz", + "integrity": "sha512-TqLcJbC9VX05X2Py2GpFZ8quO7Mj3i1NuerekzqM8RV8AiU3aPMcZFGRK+MdXHNIiGzYx9ZGfMPERVT4Qqwn6A==", + "engines": { + "node": ">= 14.15.0" }, "peerDependencies": { - "@aws-solutions-constructs/core": "2.49.0", - "@aws-solutions-constructs/resources": "2.49.0", - "aws-cdk-lib": "^2.118.0", + "aws-cdk-lib": "^2.138.0", + "constructs": "^10.0.0" + } + }, + "node_modules/@aws-solutions-constructs/aws-cloudfront-s3": { + "version": "2.57.0", + "resolved": "https://registry.npmjs.org/@aws-solutions-constructs/aws-cloudfront-s3/-/aws-cloudfront-s3-2.57.0.tgz", + "integrity": "sha512-ReL7pk7UE2Xgg0mw6v8TmMC6Jhkmv1R4s3UZqM9w4RMXzp3LceS78YZLJHBT6m8AAT6wxY4kAFqJNoUZJ7fFKg==", + "dependencies": { + "@aws-cdk/integ-tests-alpha": "2.138.0-alpha.0", + "@aws-solutions-constructs/core": "2.57.0", + 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true, - "license": "MIT", - "engines": { - "node": ">=0.10.0" + "license": "ISC", + "bin": { + "color-support": "bin.js" } }, "node_modules/@aws-solutions-constructs/core/node_modules/console-control-strings": { @@ -126,11 +141,6 @@ "inBundle": true, "license": "ISC" }, - "node_modules/@aws-solutions-constructs/core/node_modules/core-util-is": { - "version": "1.0.3", - "inBundle": true, - "license": "MIT" - }, "node_modules/@aws-solutions-constructs/core/node_modules/deep-diff": { "version": "1.0.2", "inBundle": true, @@ -144,29 +154,27 @@ "node": ">=0.10.0" } }, - "node_modules/@aws-solutions-constructs/core/node_modules/delegates": { - "version": "1.0.0", - "inBundle": true, - "license": "MIT" - }, "node_modules/@aws-solutions-constructs/core/node_modules/emoji-regex": { "version": "8.0.0", "inBundle": true, "license": "MIT" }, "node_modules/@aws-solutions-constructs/core/node_modules/gauge": { - "version": "2.7.4", + "version": "5.0.2", "inBundle": true, "license": "ISC", 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"dependencies": { - "yallist": "^4.0.0" - }, - "engines": { - "node": ">=10" - } - }, "node_modules/ts-jest/node_modules/semver": { - "version": "7.5.4", - "resolved": "https://registry.npmjs.org/semver/-/semver-7.5.4.tgz", - "integrity": "sha512-1bCSESV6Pv+i21Hvpxp3Dx+pSD8lIPt8uVjRrxAUt/nbswYc+tK6Y2btiULjd4+fnq15PX+nqQDC7Oft7WkwcA==", + "version": "7.6.2", + "resolved": "https://registry.npmjs.org/semver/-/semver-7.6.2.tgz", + "integrity": "sha512-FNAIBWCx9qcRhoHcgcJ0gvU7SN1lYU2ZXuSfl04bSC5OpvDHFyJCjdNHomPXxjQlCBU67YW64PzY7/VIEH7F2w==", "dev": true, - "dependencies": { - "lru-cache": "^6.0.0" - }, "bin": { "semver": "bin/semver.js" }, @@ -4669,16 +4472,10 @@ "node": ">=10" } }, - "node_modules/ts-jest/node_modules/yallist": { - "version": "4.0.0", - "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz", - "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==", - "dev": true - }, "node_modules/ts-node": { - "version": "10.9.1", - "resolved": "https://registry.npmjs.org/ts-node/-/ts-node-10.9.1.tgz", - "integrity": "sha512-NtVysVPkxxrwFGUUxGYhfux8k78pQB3JqYBXlLRZgdGUqTO5wU/UyHop5p70iEbGhB7q5KmiZiU0Y3KlJrScEw==", + "version": "10.9.2", + "resolved": "https://registry.npmjs.org/ts-node/-/ts-node-10.9.2.tgz", + "integrity": "sha512-f0FFpIdcHgn8zcPSbf1dRevwt047YMnaiJM3u2w2RewrB+fob/zePZcrOyQoLMMO7aBIddLcQIEK5dYjkLnGrQ==", "dev": true, "dependencies": { "@cspotcode/source-map-support": "^0.8.0", @@ -4740,9 +4537,9 @@ } }, "node_modules/typescript": { - "version": "5.1.6", - "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.1.6.tgz", - "integrity": "sha512-zaWCozRZ6DLEWAWFrVDz1H6FVXzUSfTy5FUMWsQlU8Ym5JP9eO4xkTIROFCQvhQf61z6O/G6ugw3SgAnvvm+HA==", + "version": "5.4.5", + "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.4.5.tgz", + "integrity": "sha512-vcI4UpRgg81oIRUFwR0WSIHKt11nJ7SAVlYNIu+QpqeyXP+gpQJy/Z4+F0aGxSE4MqwjyXvW/TzgkLAx2AGHwQ==", "dev": true, "bin": { "tsc": "bin/tsc", @@ -4752,10 +4549,16 @@ "node": ">=14.17" } }, + "node_modules/undici-types": { + "version": "5.26.5", + "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz", + "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==", + "dev": true + }, "node_modules/update-browserslist-db": { - "version": "1.0.13", - "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.0.13.tgz", - "integrity": "sha512-xebP81SNcPuNpPP3uzeW1NYXxI3rxyJzF3pD6sH4jE7o/IX+WtSpwnVU+qIsDPyk0d3hmFQ7mjqc6AtV604hbg==", + "version": "1.0.16", + "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.0.16.tgz", + "integrity": "sha512-KVbTxlBYlckhF5wgfyZXTWnMn7MMZjMu9XG8bPlliUOP9ThaF4QnhP8qrjrH7DRzHfSk0oQv1wToW+iA5GajEQ==", "dev": true, "funding": [ { @@ -4772,8 +4575,8 @@ } ], "dependencies": { - "escalade": "^3.1.1", - "picocolors": "^1.0.0" + "escalade": "^3.1.2", + "picocolors": "^1.0.1" }, "bin": { "update-browserslist-db": "cli.js" @@ -4789,9 +4592,9 @@ "dev": true }, "node_modules/v8-to-istanbul": { - "version": "9.1.3", - "resolved": "https://registry.npmjs.org/v8-to-istanbul/-/v8-to-istanbul-9.1.3.tgz", - "integrity": "sha512-9lDD+EVI2fjFsMWXc6dy5JJzBsVTcQ2fVkfBvncZ6xJWG9wtBhOldG+mHkSL0+V1K/xgZz0JDO5UT5hFwHUghg==", + "version": "9.2.0", + "resolved": "https://registry.npmjs.org/v8-to-istanbul/-/v8-to-istanbul-9.2.0.tgz", + "integrity": "sha512-/EH/sDgxU2eGxajKdwLCDmQ4FWq+kpi3uCmBGpw1xJtnAxEjlD8j8PEiGWpCIMIs3ciNAgH0d3TTJiUkYzyZjA==", "dev": true, "dependencies": { "@jridgewell/trace-mapping": "^0.3.12", diff --git a/scripts/aws/package.json b/scripts/aws/package.json index c74884aea..29a02b756 100644 --- a/scripts/aws/package.json +++ b/scripts/aws/package.json @@ -11,21 +11,21 @@ "cdk": "cdk" }, "devDependencies": { - "@types/jest": "^29.5.1", - "@types/node": "20.1.7", - "aws-cdk": "^2.86.0", - "jest": "^29.5.0", - "ts-jest": "^29.1.0", - "ts-node": "^10.9.1", - "typescript": "~5.1.3" + "@types/jest": "^29.5.12", + "@types/node": "^20.12.12", + "aws-cdk": "^2.141.0", + "jest": "^29.7.0", + "ts-jest": "^29.1.2", + "ts-node": "^10.9.2", + "typescript": "^5.4.5" }, "dependencies": { - "@aws-solutions-constructs/aws-cloudfront-s3": "^2.49.0", - "aws-cdk-lib": "^2.124.0", - "cdk-ecr-deployment": "^2.5.30", - "constructs": "^10.0.0", - "deploy-time-build": "^0.3.12", - "dotenv": "^16.3.1", + "@aws-solutions-constructs/aws-cloudfront-s3": "^2.57.0", + "aws-cdk-lib": "^2.141.0", + "cdk-ecr-deployment": "^3.0.55", + "constructs": "^10.3.0", + "deploy-time-build": "^0.3.21", + "dotenv": "^16.4.5", "source-map-support": "^0.5.21" } } diff --git a/scripts/gcp/deploy_langflow_gcp.sh b/scripts/gcp/deploy_langflow_gcp.sh index cae2d0c93..cdb8e209f 100644 --- a/scripts/gcp/deploy_langflow_gcp.sh +++ b/scripts/gcp/deploy_langflow_gcp.sh @@ -48,8 +48,10 @@ apt -y upgrade # Install Python 3 pip, Langflow, and Nginx apt -y install python3-pip -pip install langflow -langflow --host 0.0.0.0 --port 7860 +pip3 install pip -U +apt -y update +pip3 install langflow +langflow run --host 0.0.0.0 --port 7860 EOF ) diff --git a/src/backend/.gitignore b/src/backend/.gitignore index 9af18a35f..ac0cc6c6d 100644 --- a/src/backend/.gitignore +++ b/src/backend/.gitignore @@ -131,3 +131,4 @@ dmypy.json # Pyre type checker .pyre/ +*.db \ No newline at end of file diff --git a/src/backend/base/langflow/__main__.py b/src/backend/base/langflow/__main__.py index 925e6a7a1..dfb784c90 100644 --- a/src/backend/base/langflow/__main__.py +++ b/src/backend/base/langflow/__main__.py @@ -2,6 +2,7 @@ import platform import socket import sys import time +import warnings from pathlib import Path from typing import Optional @@ -16,8 +17,10 @@ from rich import print as rprint from rich.console import Console from rich.panel import Panel from rich.table import Table +from sqlmodel import select from langflow.main import setup_app +from langflow.services.database.models.folder.utils import create_default_folder_if_it_doesnt_exist from langflow.services.database.utils import session_getter from langflow.services.deps import get_db_service from langflow.services.utils import initialize_services @@ -431,17 +434,57 @@ def superuser( # Verify that the superuser was created from langflow.services.database.models.user.model import User - user: User = session.query(User).filter(User.username == username).first() + user: User = session.exec(select(User).where(User.username == username)).first() if user is None or not user.is_superuser: typer.echo("Superuser creation failed.") return - + # Now create the first folder for the user + result = create_default_folder_if_it_doesnt_exist(session, user.id) + if result: + typer.echo("Default folder created successfully.") + else: + raise RuntimeError("Could not create default folder.") typer.echo("Superuser created successfully.") else: typer.echo("Superuser creation failed.") +# command to copy the langflow database from the cache to the current directory +# because now the database is stored per installation +@app.command() +def copy_db(): + """ + Copy the database files to the current directory. + + This function copies the 'langflow.db' and 'langflow-pre.db' files from the cache directory to the current directory. + If the files exist in the cache directory, they will be copied to the same directory as this script (__main__.py). + + Returns: + None + """ + import shutil + + from platformdirs import user_cache_dir + + cache_dir = Path(user_cache_dir("langflow")) + db_path = cache_dir / "langflow.db" + pre_db_path = cache_dir / "langflow-pre.db" + # It should be copied to the current directory + # this file is __main__.py and it should be in the same directory as the database + destination_folder = Path(__file__).parent + if db_path.exists(): + shutil.copy(db_path, destination_folder) + typer.echo(f"Database copied to {destination_folder}") + else: + typer.echo("Database not found in the cache directory.") + if pre_db_path.exists(): + shutil.copy(pre_db_path, destination_folder) + typer.echo(f"Pre-release database copied to {destination_folder}") + else: + typer.echo("Pre-release database not found in the cache directory.") + + @app.command() def migration( test: bool = typer.Option(True, help="Run migrations in test mode."), @@ -468,7 +511,9 @@ def migration( def main(): - app() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + app() if __name__ == "__main__": diff --git a/src/backend/base/langflow/alembic/versions/012fb73ac359_add_folder_table.py b/src/backend/base/langflow/alembic/versions/012fb73ac359_add_folder_table.py new file mode 100644 index 000000000..b9ebc2ca2 --- /dev/null +++ b/src/backend/base/langflow/alembic/versions/012fb73ac359_add_folder_table.py @@ -0,0 +1,78 @@ +"""Add Folder table + +Revision ID: 012fb73ac359 +Revises: c153816fd85f +Create Date: 2024-05-07 12:52:16.954691 + +""" + +from typing import Sequence, Union + +import sqlalchemy as sa +import sqlmodel +from alembic import op +from sqlalchemy.engine.reflection import Inspector + +# revision identifiers, used by Alembic. +revision: str = "012fb73ac359" +down_revision: Union[str, None] = "c153816fd85f" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + conn = op.get_bind() + inspector = Inspector.from_engine(conn) # type: ignore + table_names = inspector.get_table_names() + # ### commands auto generated by Alembic - please adjust! ### + if "folder" not in table_names: + op.create_table( + "folder", + sa.Column("name", sqlmodel.sql.sqltypes.AutoString(), nullable=False), + sa.Column("description", sqlmodel.sql.sqltypes.AutoString(), nullable=True), + sa.Column("id", sqlmodel.sql.sqltypes.GUID(), nullable=False), + sa.Column("parent_id", sqlmodel.sql.sqltypes.GUID(), nullable=True), + sa.Column("user_id", sqlmodel.sql.sqltypes.GUID(), nullable=True), + sa.ForeignKeyConstraint( + ["parent_id"], + ["folder.id"], + ), + sa.ForeignKeyConstraint( + ["user_id"], + ["user.id"], + ), + sa.PrimaryKeyConstraint("id"), + ) + indexes = inspector.get_indexes("folder") + if "ix_folder_name" not in [index["name"] for index in indexes]: + with op.batch_alter_table("folder", schema=None) as batch_op: + batch_op.create_index(batch_op.f("ix_folder_name"), ["name"], unique=False) + + if "folder_id" not in inspector.get_columns("flow"): + with op.batch_alter_table("flow", schema=None) as batch_op: + batch_op.add_column(sa.Column("folder_id", sqlmodel.sql.sqltypes.GUID(), nullable=True)) + batch_op.create_foreign_key("flow_folder_id_fkey", "folder", ["folder_id"], ["id"]) + batch_op.drop_column("folder") + + # ### end Alembic commands ### + + +def downgrade() -> None: + conn = op.get_bind() + inspector = Inspector.from_engine(conn) # type: ignore + table_names = inspector.get_table_names() + # ### commands auto generated by Alembic - please adjust! ### + if "folder_id" in inspector.get_columns("flow"): + with op.batch_alter_table("flow", schema=None) as batch_op: + batch_op.add_column(sa.Column("folder", sa.VARCHAR(), nullable=True)) + batch_op.drop_constraint("flow_folder_id_fkey", type_="foreignkey") + batch_op.drop_column("folder_id") + + indexes = inspector.get_indexes("folder") + if "ix_folder_name" in [index["name"] for index in indexes]: + with op.batch_alter_table("folder", schema=None) as batch_op: + batch_op.drop_index(batch_op.f("ix_folder_name")) + + if "folder" in table_names: + op.drop_table("folder") + # ### end Alembic commands ### diff --git a/src/backend/base/langflow/alembic/versions/29fe8f1f806b_add_missing_index.py b/src/backend/base/langflow/alembic/versions/29fe8f1f806b_add_missing_index.py new file mode 100644 index 000000000..9a2518df2 --- /dev/null +++ b/src/backend/base/langflow/alembic/versions/29fe8f1f806b_add_missing_index.py @@ -0,0 +1,43 @@ +"""Add missing index + +Revision ID: 29fe8f1f806b +Revises: 012fb73ac359 +Create Date: 2024-05-21 09:23:48.772367 + +""" + +from typing import Sequence, Union + +from alembic import op +from sqlalchemy.engine.reflection import Inspector + +revision: str = "29fe8f1f806b" +down_revision: Union[str, None] = "012fb73ac359" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + conn = op.get_bind() + inspector = Inspector.from_engine(conn) # type: ignore + # ### commands auto generated by Alembic - please adjust! ### + indexes = inspector.get_indexes("flow") + with op.batch_alter_table("flow", schema=None) as batch_op: + indexes_names = [index["name"] for index in indexes] + if "ix_flow_folder_id" not in indexes_names: + batch_op.create_index(batch_op.f("ix_flow_folder_id"), ["folder_id"], unique=False) + + # ### end Alembic commands ### + + +def downgrade() -> None: + conn = op.get_bind() + inspector = Inspector.from_engine(conn) # type: ignore + # ### commands auto generated by Alembic - please adjust! ### + indexes = inspector.get_indexes("flow") + with op.batch_alter_table("flow", schema=None) as batch_op: + indexes_names = [index["name"] for index in indexes] + if "ix_flow_folder_id" in indexes_names: + batch_op.drop_index(batch_op.f("ix_flow_folder_id")) + + # ### end Alembic commands ### diff --git a/src/backend/base/langflow/api/router.py b/src/backend/base/langflow/api/router.py index 569014acf..d5d709738 100644 --- a/src/backend/base/langflow/api/router.py +++ b/src/backend/base/langflow/api/router.py @@ -13,6 +13,7 @@ from langflow.api.v1 import ( users_router, validate_router, variables_router, + folders_router, ) router = APIRouter( @@ -29,3 +30,4 @@ router.include_router(login_router) router.include_router(variables_router) router.include_router(files_router) router.include_router(monitor_router) +router.include_router(folders_router) diff --git a/src/backend/base/langflow/api/v1/__init__.py b/src/backend/base/langflow/api/v1/__init__.py index 0342c76c8..efc55cba8 100644 --- a/src/backend/base/langflow/api/v1/__init__.py +++ b/src/backend/base/langflow/api/v1/__init__.py @@ -9,6 +9,7 @@ from langflow.api.v1.store import router as store_router from langflow.api.v1.users import router as users_router from langflow.api.v1.validate import router as validate_router from langflow.api.v1.variable import router as variables_router +from langflow.api.v1.folders import router as folders_router __all__ = [ "chat_router", @@ -22,4 +23,5 @@ __all__ = [ "variables_router", "monitor_router", "files_router", + "folders_router", ] diff --git a/src/backend/base/langflow/api/v1/chat.py b/src/backend/base/langflow/api/v1/chat.py index 549e0c2b7..40290afa7 100644 --- a/src/backend/base/langflow/api/v1/chat.py +++ b/src/backend/base/langflow/api/v1/chat.py @@ -29,7 +29,7 @@ from langflow.services.deps import get_chat_service, get_session, get_session_se from langflow.services.monitor.utils import log_vertex_build if TYPE_CHECKING: - from langflow.graph.vertex.types import ChatVertex + from langflow.graph.vertex.types import InterfaceVertex from langflow.services.session.service import SessionService router = APIRouter(tags=["Chat"]) @@ -288,7 +288,7 @@ async def build_vertex_stream( if not graph: raise ValueError(f"No graph found for {flow_id}.") - vertex: "ChatVertex" = graph.get_vertex(vertex_id) + vertex: "InterfaceVertex" = graph.get_vertex(vertex_id) if not hasattr(vertex, "stream"): raise ValueError(f"Vertex {vertex_id} does not support streaming") if isinstance(vertex._built_result, str) and vertex._built_result: diff --git a/src/backend/base/langflow/api/v1/endpoints.py b/src/backend/base/langflow/api/v1/endpoints.py index e13d4333d..e206935c4 100644 --- a/src/backend/base/langflow/api/v1/endpoints.py +++ b/src/backend/base/langflow/api/v1/endpoints.py @@ -4,7 +4,7 @@ from typing import Annotated, List, Optional, Union import sqlalchemy as sa from fastapi import APIRouter, Body, Depends, HTTPException, UploadFile, status from loguru import logger -from sqlmodel import Session, select +from sqlmodel import Session, col, select from langflow.api.utils import update_frontend_node_with_template_values from langflow.api.v1.schemas import ( diff --git a/src/backend/base/langflow/api/v1/flows.py b/src/backend/base/langflow/api/v1/flows.py index bd055f120..2b8e4ee37 100644 --- a/src/backend/base/langflow/api/v1/flows.py +++ b/src/backend/base/langflow/api/v1/flows.py @@ -1,18 +1,20 @@ -from datetime import datetime +from datetime import datetime, timezone from typing import List from uuid import UUID +from langflow.services.database.models.folder.constants import DEFAULT_FOLDER_NAME import orjson from fastapi import APIRouter, Depends, File, HTTPException, UploadFile from fastapi.encoders import jsonable_encoder from loguru import logger -from sqlmodel import Session, select +from sqlmodel import Session, col, select from langflow.api.utils import remove_api_keys, validate_is_component -from langflow.api.v1.schemas import FlowListCreate, FlowListRead +from langflow.api.v1.schemas import FlowListCreate, FlowListIds, FlowListRead from langflow.initial_setup.setup import STARTER_FOLDER_NAME from langflow.services.auth.utils import get_current_active_user from langflow.services.database.models.flow import Flow, FlowCreate, FlowRead, FlowUpdate +from langflow.services.database.models.folder.model import Folder from langflow.services.database.models.user.model import User from langflow.services.deps import get_session, get_settings_service from langflow.services.settings.service import SettingsService @@ -33,7 +35,12 @@ def create_flow( flow.user_id = current_user.id db_flow = Flow.model_validate(flow, from_attributes=True) - db_flow.updated_at = datetime.utcnow() + db_flow.updated_at = datetime.now(timezone.utc) + + if db_flow.folder_id is None: + default_folder = session.exec(select(Folder).where(Folder.name == DEFAULT_FOLDER_NAME)).first() + if default_folder: + db_flow.folder_id = default_folder.id session.add(db_flow) session.commit() @@ -67,7 +74,7 @@ def read_flows( example_flows = session.exec( select(Flow).where( Flow.user_id == None, # noqa - Flow.folder == STARTER_FOLDER_NAME, + Flow.folder.has(Folder.name == STARTER_FOLDER_NAME), ) ).all() for example_flow in example_flows: @@ -128,7 +135,11 @@ def update_flow( for key, value in flow_data.items(): if value is not None: setattr(db_flow, key, value) - db_flow.updated_at = datetime.utcnow() + db_flow.updated_at = datetime.now(timezone.utc) + if db_flow.folder_id is None: + default_folder = session.exec(select(Folder).where(Folder.name == DEFAULT_FOLDER_NAME)).first() + if default_folder: + db_flow.folder_id = default_folder.id session.add(db_flow) session.commit() session.refresh(db_flow) @@ -208,3 +219,31 @@ async def download_file( """Download all flows as a file.""" flows = read_flows(current_user=current_user, session=session, settings_service=settings_service) return FlowListRead(flows=flows) + + +@router.post("/multiple_delete/") +async def delete_multiple_flows( + flow_ids: FlowListIds, user: User = Depends(get_current_active_user), db: Session = Depends(get_session) +): + """ + Delete multiple flows by their IDs. + + Args: + flow_ids (List[str]): The list of flow IDs to delete. + user (User, optional): The user making the request. Defaults to the current active user. + + Returns: + dict: A dictionary containing the number of flows deleted. + + """ + try: + deleted_flows = db.exec( + select(Flow).where(col(Flow.id).in_(flow_ids.flow_ids)).where(Flow.user_id == user.id) + ).all() + for flow in deleted_flows: + db.delete(flow) + db.commit() + return {"deleted": len(deleted_flows)} + except Exception as exc: + logger.exception(exc) + raise HTTPException(status_code=500, detail=str(exc)) from exc diff --git a/src/backend/base/langflow/api/v1/folders.py b/src/backend/base/langflow/api/v1/folders.py new file mode 100644 index 000000000..9e598b1ea --- /dev/null +++ b/src/backend/base/langflow/api/v1/folders.py @@ -0,0 +1,243 @@ +from typing import List +from uuid import UUID + +from fastapi import APIRouter, Depends, File, HTTPException, Response, UploadFile, status +from langflow.api.v1.flows import create_flows +from langflow.api.v1.schemas import FlowListCreate, FlowListReadWithFolderName +from langflow.initial_setup.setup import STARTER_FOLDER_NAME +from langflow.services.database.models.flow.model import Flow, FlowCreate, FlowRead +from langflow.services.database.models.folder.constants import DEFAULT_FOLDER_NAME +import orjson +from sqlalchemy import update +from sqlmodel import Session, select + +from langflow.services.auth.utils import get_current_active_user +from langflow.services.database.models.folder.model import ( + Folder, + FolderCreate, + FolderRead, + FolderReadWithFlows, + FolderUpdate, +) +from langflow.services.database.models.user.model import User +from langflow.services.deps import get_session + +router = APIRouter(prefix="/folders", tags=["Folders"]) + + +@router.post("/", response_model=FolderRead, status_code=201) +def create_folder( + *, + session: Session = Depends(get_session), + folder: FolderCreate, + current_user: User = Depends(get_current_active_user), +): + try: + new_folder = Folder.model_validate(folder, from_attributes=True) + new_folder.user_id = current_user.id + session.add(new_folder) + session.commit() + session.refresh(new_folder) + + if folder.components_list.__len__() > 0: + update_statement_components = ( + update(Flow).where(Flow.id.in_(folder.components_list)).values(folder_id=new_folder.id) + ) + session.exec(update_statement_components) + session.commit() + + if folder.flows_list.__len__() > 0: + update_statement_flows = update(Flow).where(Flow.id.in_(folder.flows_list)).values(folder_id=new_folder.id) + session.exec(update_statement_flows) + session.commit() + + return new_folder + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.get("/", response_model=List[FolderRead], status_code=200) +def read_folders( + *, + session: Session = Depends(get_session), + current_user: User = Depends(get_current_active_user), +): + try: + folders = session.exec(select(Folder).where(Folder.user_id == current_user.id)).all() + return folders + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.get("/starter-projects", response_model=FolderReadWithFlows, status_code=200) +def read_starter_folders(*, session: Session = Depends(get_session)): + try: + folders = session.exec(select(Folder).where(Folder.name == STARTER_FOLDER_NAME)).first() + return folders + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.get("/{folder_id}", response_model=FolderReadWithFlows, status_code=200) +def read_folder( + *, + session: Session = Depends(get_session), + folder_id: UUID, + current_user: User = Depends(get_current_active_user), +): + try: + folder = session.exec(select(Folder).where(Folder.id == folder_id, Folder.user_id == current_user.id)).first() + if not folder: + raise HTTPException(status_code=404, detail="Folder not found") + folder.flows = session.exec(select(Flow).where(Flow.folder_id == folder_id)).all() + return folder + except Exception as e: + if "No result found" in str(e): + raise HTTPException(status_code=404, detail="Folder not found") + raise HTTPException(status_code=500, detail=str(e)) + + +@router.patch("/{folder_id}", response_model=FolderRead, status_code=200) +def update_folder( + *, + session: Session = Depends(get_session), + folder_id: UUID, + folder: FolderUpdate, # Assuming FolderUpdate is a Pydantic model defining updatable fields + current_user: User = Depends(get_current_active_user), +): + try: + existing_folder = session.exec( + select(Folder).where(Folder.id == folder_id, Folder.user_id == current_user.id) + ).first() + if not existing_folder: + raise HTTPException(status_code=404, detail="Folder not found") + folder_data = folder.model_dump(exclude_unset=True) + for key, value in folder_data.items(): + if key != "components" and key != "flows": + setattr(existing_folder, key, value) + session.add(existing_folder) + session.commit() + session.refresh(existing_folder) + + concat_folder_components = folder.components + folder.flows + + flows_ids = session.exec(select(Flow.id).where(Flow.folder_id == existing_folder.id)).all() + + excluded_flows = list(set(flows_ids) - set(concat_folder_components)) + + my_collection_folder = session.exec(select(Folder).where(Folder.name == DEFAULT_FOLDER_NAME)).first() + if my_collection_folder: + update_statement_my_collection = ( + update(Flow).where(Flow.id.in_(excluded_flows)).values(folder_id=my_collection_folder.id) + ) + session.exec(update_statement_my_collection) + session.commit() + + if concat_folder_components.__len__() > 0: + update_statement_components = ( + update(Flow).where(Flow.id.in_(concat_folder_components)).values(folder_id=existing_folder.id) + ) + session.exec(update_statement_components) + session.commit() + + return existing_folder + + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.delete("/{folder_id}", status_code=204) +def delete_folder( + *, + session: Session = Depends(get_session), + folder_id: UUID, + current_user: User = Depends(get_current_active_user), +): + try: + folder = session.exec(select(Folder).where(Folder.id == folder_id, Folder.user_id == current_user.id)).first() + if not folder: + raise HTTPException(status_code=404, detail="Folder not found") + session.delete(folder) + session.commit() + flows = session.exec(select(Flow).where(Flow.folder_id == folder_id, Folder.user_id == current_user.id)).all() + for flow in flows: + session.delete(flow) + session.commit() + return Response(status_code=status.HTTP_204_NO_CONTENT) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.get("/download/{folder_id}", response_model=FlowListReadWithFolderName, status_code=200) +async def download_file( + *, + session: Session = Depends(get_session), + folder_id: UUID, + current_user: User = Depends(get_current_active_user), +): + """Download all flows from folder.""" + try: + flows = session.exec( + select(Flow).distinct().join(Folder).where(Flow.folder_id == folder_id, Folder.user_id == current_user.id) + ).all() + folder_name = ( + session.exec(select(Folder).where(Folder.id == folder_id, Folder.user_id == current_user.id)).first().name + ) + folder_description = ( + session.exec(select(Folder).where(Folder.id == folder_id, Folder.user_id == current_user.id)) + .first() + .description + ) + if not flows: + flows = [] + return FlowListReadWithFolderName(flows=flows, folder_name=folder_name, folder_description=folder_description) + except Exception as e: + if "No result found" in str(e): + raise HTTPException(status_code=404, detail="Folder not found") + raise HTTPException(status_code=500, detail=str(e)) + + +@router.post("/upload/", response_model=List[FlowRead], status_code=201) +async def upload_file( + *, + session: Session = Depends(get_session), + file: UploadFile = File(...), + current_user: User = Depends(get_current_active_user), +): + """Upload flows from a file.""" + contents = await file.read() + data = orjson.loads(contents) + + if data.__len__() == 0: + raise HTTPException(status_code=400, detail="No flows found in the file") + + folder_results = session.exec( + select(Folder).where(Folder.name.like(f"{data['folder_name']}%"), Folder.user_id == current_user.id) + ) + existing_folder_names = [folder.name for folder in folder_results] + + if existing_folder_names.__len__() > 0: + data["folder_name"] = f"{data['folder_name']} ({existing_folder_names.__len__() + 1})" + + folder = FolderCreate(name=data["folder_name"], description=data["folder_description"]) + + new_folder = Folder.model_validate(folder, from_attributes=True) + new_folder.id = None + new_folder.user_id = current_user.id + session.add(new_folder) + session.commit() + session.refresh(new_folder) + + del data["folder_name"] + del data["folder_description"] + + if "flows" in data: + flow_list = FlowListCreate(flows=[FlowCreate(**flow) for flow in data["flows"]]) + else: + raise HTTPException(status_code=400, detail="No flows found in the data") + # Now we set the user_id for all flows + for flow in flow_list.flows: + flow.user_id = current_user.id + flow.folder_id = new_folder.id + + return create_flows(session=session, flow_list=flow_list, current_user=current_user) diff --git a/src/backend/base/langflow/api/v1/login.py b/src/backend/base/langflow/api/v1/login.py index 8c9fb5bd4..69a1d5876 100644 --- a/src/backend/base/langflow/api/v1/login.py +++ b/src/backend/base/langflow/api/v1/login.py @@ -1,5 +1,7 @@ from fastapi import APIRouter, Depends, HTTPException, Request, Response, status from fastapi.security import OAuth2PasswordRequestForm +from sqlmodel import Session + from langflow.api.v1.schemas import Token from langflow.services.auth.utils import ( authenticate_user, @@ -7,14 +9,10 @@ from langflow.services.auth.utils import ( create_user_longterm_token, create_user_tokens, ) -from langflow.services.deps import ( - get_session, - get_settings_service, - get_variable_service, -) +from langflow.services.database.models.folder.utils import create_default_folder_if_it_doesnt_exist +from langflow.services.deps import get_session, get_settings_service, get_variable_service from langflow.services.settings.manager import SettingsService from langflow.services.variable.service import VariableService -from sqlmodel import Session router = APIRouter(tags=["Login"]) @@ -58,6 +56,8 @@ async def login_to_get_access_token( expires=auth_settings.ACCESS_TOKEN_EXPIRE_SECONDS, ) variable_service.initialize_user_variables(user.id, db) + # Create default folder for user if it doesn't exist + create_default_folder_if_it_doesnt_exist(db, user.id) return tokens else: raise HTTPException( @@ -86,6 +86,7 @@ async def auto_login( expires=None, # Set to None to make it a session cookie ) variable_service.initialize_user_variables(user_id, db) + create_default_folder_if_it_doesnt_exist(db, user_id) return tokens raise HTTPException( @@ -139,4 +140,3 @@ async def logout(response: Response): response.delete_cookie("refresh_token_lf") response.delete_cookie("access_token_lf") return {"message": "Logout successful"} - return {"message": "Logout successful"} diff --git a/src/backend/base/langflow/api/v1/monitor.py b/src/backend/base/langflow/api/v1/monitor.py index dbefd1c3b..05fee6f03 100644 --- a/src/backend/base/langflow/api/v1/monitor.py +++ b/src/backend/base/langflow/api/v1/monitor.py @@ -1,10 +1,13 @@ -from typing import Optional +from typing import List, Optional from fastapi import APIRouter, Depends, HTTPException, Query - from langflow.services.deps import get_monitor_service -from langflow.services.monitor.schema import VertexBuildMapModel +from langflow.services.monitor.schema import ( + MessageModelResponse, + TransactionModelResponse, + VertexBuildMapModel, +) from langflow.services.monitor.service import MonitorService router = APIRouter(prefix="/monitor", tags=["Monitor"]) @@ -40,8 +43,9 @@ async def delete_vertex_builds( raise HTTPException(status_code=500, detail=str(e)) -@router.get("/messages") +@router.get("/messages", response_model=List[MessageModelResponse]) async def get_messages( + flow_id: Optional[str] = Query(None), session_id: Optional[str] = Query(None), sender: Optional[str] = Query(None), sender_name: Optional[str] = Query(None), @@ -49,25 +53,32 @@ async def get_messages( monitor_service: MonitorService = Depends(get_monitor_service), ): try: - return monitor_service.get_messages( + df = monitor_service.get_messages( + flow_id=flow_id, sender=sender, sender_name=sender_name, session_id=session_id, order_by=order_by, ) + dicts = df.to_dict(orient="records") + return [MessageModelResponse(**d) for d in dicts] except Exception as e: raise HTTPException(status_code=500, detail=str(e)) -@router.get("/transactions") +@router.get("/transactions", response_model=List[TransactionModelResponse]) async def get_transactions( source: Optional[str] = Query(None), target: Optional[str] = Query(None), status: Optional[str] = Query(None), order_by: Optional[str] = Query("timestamp"), + flow_id: Optional[str] = Query(None), monitor_service: MonitorService = Depends(get_monitor_service), ): try: - return monitor_service.get_transactions(source=source, target=target, status=status, order_by=order_by) + dicts = monitor_service.get_transactions( + source=source, target=target, status=status, order_by=order_by, flow_id=flow_id + ) + return [TransactionModelResponse(**d) for d in dicts] except Exception as e: raise HTTPException(status_code=500, detail=str(e)) diff --git a/src/backend/base/langflow/api/v1/schemas.py b/src/backend/base/langflow/api/v1/schemas.py index 1a68dcc8f..1d93cc0e2 100644 --- a/src/backend/base/langflow/api/v1/schemas.py +++ b/src/backend/base/langflow/api/v1/schemas.py @@ -1,4 +1,4 @@ -from datetime import datetime +from datetime import datetime, timezone from enum import Enum from pathlib import Path from typing import Any, Dict, List, Optional, Union @@ -139,10 +139,20 @@ class FlowListCreate(BaseModel): flows: List[FlowCreate] +class FlowListIds(BaseModel): + flow_ids: List[str] + + class FlowListRead(BaseModel): flows: List[FlowRead] +class FlowListReadWithFolderName(BaseModel): + flows: List[FlowRead] + folder_name: str + folder_description: str + + class InitResponse(BaseModel): flowId: str @@ -250,7 +260,7 @@ class VertexBuildResponse(BaseModel): """JSON string of the params.""" data: ResultDataResponse """Mapping of vertex ids to result dict containing the param name and result value.""" - timestamp: Optional[datetime] = Field(default_factory=datetime.utcnow) + timestamp: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc)) """Timestamp of the build.""" diff --git a/src/backend/base/langflow/api/v1/users.py b/src/backend/base/langflow/api/v1/users.py index 7a6d4b7c6..f7261e9d2 100644 --- a/src/backend/base/langflow/api/v1/users.py +++ b/src/backend/base/langflow/api/v1/users.py @@ -13,6 +13,7 @@ from langflow.services.auth.utils import ( get_password_hash, verify_password, ) +from langflow.services.database.models.folder.utils import create_default_folder_if_it_doesnt_exist from langflow.services.database.models.user import User, UserCreate, UserRead, UserUpdate from langflow.services.database.models.user.crud import get_user_by_id, update_user from langflow.services.deps import get_session, get_settings_service @@ -36,6 +37,9 @@ def add_user( session.add(new_user) session.commit() session.refresh(new_user) + folder = create_default_folder_if_it_doesnt_exist(session, new_user.id) + if not folder: + raise HTTPException(status_code=500, detail="Error creating default folder") except IntegrityError as e: session.rollback() raise HTTPException(status_code=400, detail="This username is unavailable.") from e diff --git a/src/backend/base/langflow/base/data/utils.py b/src/backend/base/langflow/base/data/utils.py index 09169393b..7026d4968 100644 --- a/src/backend/base/langflow/base/data/utils.py +++ b/src/backend/base/langflow/base/data/utils.py @@ -67,23 +67,25 @@ def retrieve_file_paths( return file_paths -def partition_file_to_record(file_path: str, silent_errors: bool) -> Optional[Record]: - # Use the partition function to load the file - from unstructured.partition.auto import partition # type: ignore +# ! Removing unstructured dependency until +# ! 3.12 is supported +# def partition_file_to_record(file_path: str, silent_errors: bool) -> Optional[Record]: +# # Use the partition function to load the file +# from unstructured.partition.auto import partition # type: ignore - try: - elements = partition(file_path) - except Exception as e: - if not silent_errors: - raise ValueError(f"Error loading file {file_path}: {e}") from e - return None +# try: +# elements = partition(file_path) +# except Exception as e: +# if not silent_errors: +# raise ValueError(f"Error loading file {file_path}: {e}") from e +# return None - # Create a Record - text = "\n\n".join([Text(el) for el in elements]) - metadata = elements.metadata if hasattr(elements, "metadata") else {} - metadata["file_path"] = file_path - record = Record(text=text, data=metadata) - return record +# # Create a Record +# text = "\n\n".join([Text(el) for el in elements]) +# metadata = elements.metadata if hasattr(elements, "metadata") else {} +# metadata["file_path"] = file_path +# record = Record(text=text, data=metadata) +# return record def read_text_file(file_path: str) -> str: @@ -131,18 +133,20 @@ def parse_text_file_to_record(file_path: str, silent_errors: bool) -> Optional[R return record -def get_elements( - file_paths: List[str], - silent_errors: bool, - max_concurrency: int, - use_multithreading: bool, -) -> List[Optional[Record]]: - if use_multithreading: - records = parallel_load_records(file_paths, silent_errors, max_concurrency) - else: - records = [partition_file_to_record(file_path, silent_errors) for file_path in file_paths] - records = list(filter(None, records)) - return records +# ! Removing unstructured dependency until +# ! 3.12 is supported +# def get_elements( +# file_paths: List[str], +# silent_errors: bool, +# max_concurrency: int, +# use_multithreading: bool, +# ) -> List[Optional[Record]]: +# if use_multithreading: +# records = parallel_load_records(file_paths, silent_errors, max_concurrency) +# else: +# records = [partition_file_to_record(file_path, silent_errors) for file_path in file_paths] +# records = list(filter(None, records)) +# return records def parallel_load_records( diff --git a/src/backend/base/langflow/base/io/chat.py b/src/backend/base/langflow/base/io/chat.py index b3cb7c01d..14a09b3f5 100644 --- a/src/backend/base/langflow/base/io/chat.py +++ b/src/backend/base/langflow/base/io/chat.py @@ -49,12 +49,12 @@ class ChatComponent(CustomComponent): sender: Optional[str] = None, sender_name: Optional[str] = None, ) -> list[Record]: - records = store_message( message, session_id=session_id, sender=sender, sender_name=sender_name, + flow_id=self.graph.flow_id, ) self.status = records diff --git a/src/backend/base/langflow/base/models/model.py b/src/backend/base/langflow/base/models/model.py index 977090818..b38d275f9 100644 --- a/src/backend/base/langflow/base/models/model.py +++ b/src/backend/base/langflow/base/models/model.py @@ -57,7 +57,7 @@ class LCModelComponent(CustomComponent): prompt_tokens = token_usage["prompt_tokens"] total_tokens = token_usage["total_tokens"] finish_reason = response_metadata["finish_reason"] - status_message = f"Tokens:\n- Input: {prompt_tokens}\nOutput: {completion_tokens}\nTotal Tokens: {total_tokens}\nStop Reason: {finish_reason}\nResponse: {content}" + status_message = f"Tokens:\nInput: {prompt_tokens}\nOutput: {completion_tokens}\nTotal Tokens: {total_tokens}\nStop Reason: {finish_reason}\nResponse: {content}" elif all(key in response_metadata for key in anthropic_keys) and all( key in response_metadata["usage"] for key in inner_anthropic_keys ): @@ -65,7 +65,7 @@ class LCModelComponent(CustomComponent): input_tokens = usage["input_tokens"] output_tokens = usage["output_tokens"] stop_reason = response_metadata["stop_reason"] - status_message = f"Tokens:\n- Input: {input_tokens}\n- Output: {output_tokens}\nStop Reason: {stop_reason}\nResponse: {content}" + status_message = f"Tokens:\nInput: {input_tokens}\nOutput: {output_tokens}\nStop Reason: {stop_reason}\nResponse: {content}" else: status_message = f"Response: {content}" else: diff --git a/src/backend/base/langflow/base/models/openai_constants.py b/src/backend/base/langflow/base/models/openai_constants.py index 89ad8425e..e846229cc 100644 --- a/src/backend/base/langflow/base/models/openai_constants.py +++ b/src/backend/base/langflow/base/models/openai_constants.py @@ -1 +1 @@ -MODEL_NAMES = ["gpt-4-turbo", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-3.5-turbo-0125"] +MODEL_NAMES = ["gpt-4o", "gpt-4-turbo", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-3.5-turbo-0125"] diff --git a/src/backend/base/langflow/base/prompts/utils.py b/src/backend/base/langflow/base/prompts/utils.py index fbf46dd71..2270035af 100644 --- a/src/backend/base/langflow/base/prompts/utils.py +++ b/src/backend/base/langflow/base/prompts/utils.py @@ -1,3 +1,6 @@ +from copy import deepcopy + + from langchain_core.documents import Document from langflow.schema import Record @@ -27,19 +30,20 @@ def dict_values_to_string(d: dict) -> dict: dict: The dictionary with values converted to strings. """ # Do something similar to the above - for key, value in d.items(): + d_copy = deepcopy(d) + for key, value in d_copy.items(): # it could be a list of records or documents or strings if isinstance(value, list): for i, item in enumerate(value): if isinstance(item, Record): - d[key][i] = record_to_string(item) + d_copy[key][i] = record_to_string(item) elif isinstance(item, Document): - d[key][i] = document_to_string(item) + d_copy[key][i] = document_to_string(item) elif isinstance(value, Record): - d[key] = record_to_string(value) + d_copy[key] = record_to_string(value) elif isinstance(value, Document): - d[key] = document_to_string(value) - return d + d_copy[key] = document_to_string(value) + return d_copy def document_to_string(document: Document) -> str: diff --git a/src/backend/base/langflow/components/agents/OpenAIConversationalAgent.py b/src/backend/base/langflow/components/agents/OpenAIConversationalAgent.py index 0edf8ea55..032704d9c 100644 --- a/src/backend/base/langflow/components/agents/OpenAIConversationalAgent.py +++ b/src/backend/base/langflow/components/agents/OpenAIConversationalAgent.py @@ -8,7 +8,7 @@ from langchain.prompts import SystemMessagePromptTemplate from langchain.prompts.chat import MessagesPlaceholder from langchain.schema.memory import BaseMemory from langchain.tools import Tool -from langchain_community.chat_models import ChatOpenAI +from langchain_openai import ChatOpenAI from langflow.field_typing.range_spec import RangeSpec from langflow.interface.custom.custom_component import CustomComponent diff --git a/src/backend/base/langflow/components/agents/SQLAgent.py b/src/backend/base/langflow/components/agents/SQLAgent.py index 68f65d18f..abde4ab94 100644 --- a/src/backend/base/langflow/components/agents/SQLAgent.py +++ b/src/backend/base/langflow/components/agents/SQLAgent.py @@ -1,7 +1,7 @@ from typing import Callable, Union from langchain.agents import AgentExecutor -from langchain.sql_database import SQLDatabase +from langchain_community.utilities import SQLDatabase from langchain_community.agent_toolkits import SQLDatabaseToolkit from langchain_community.agent_toolkits.sql.base import create_sql_agent diff --git a/src/backend/base/langflow/components/embeddings/MistalAIEmbeddings.py b/src/backend/base/langflow/components/embeddings/MistalAIEmbeddings.py new file mode 100644 index 000000000..23d80132f --- /dev/null +++ b/src/backend/base/langflow/components/embeddings/MistalAIEmbeddings.py @@ -0,0 +1,64 @@ +from pydantic.v1 import SecretStr + +from langchain_mistralai.embeddings import MistralAIEmbeddings +from langflow.interface.custom.custom_component import CustomComponent +from langflow.field_typing import Embeddings + + +class MistralAIEmbeddingsComponent(CustomComponent): + display_name = "MistralAI Embeddings" + description = "Generate embeddings using MistralAI models." + + def build_config(self): + return { + "model": { + "display_name": "Model", + "advanced": False, + "options": ["mistral-embed"], + "value": "mistral-embed", + }, + "mistral_api_key": { + "display_name": "Mistral API Key", + "password": True, + "advanced": False, + }, + "max_concurrent_requests": { + "display_name": "Max Concurrent Requests", + "advanced": True, + "value": 64, + }, + "max_retries": { + "display_name": "Max Retries", + "advanced": True, + "value": 5, + }, + "timeout": { + "display_name": "Request Timeout", + "advanced": True, + "value": 120, + }, + "endpoint": {"display_name": "API Endpoint", "advanced": True, "value": "https://api.mistral.ai/v1/"}, + } + + def build( + self, + mistral_api_key: str, + model: str = "mistral-embed", + max_concurrent_requests: int = 64, + max_retries: int = 5, + timeout: int = 120, + endpoint: str = "https://api.mistral.ai/v1/", + ) -> Embeddings: + if mistral_api_key: + api_key = SecretStr(mistral_api_key) + else: + api_key = None + + return MistralAIEmbeddings( + api_key=api_key, + model=model, + endpoint=endpoint, + max_concurrent_requests=max_concurrent_requests, + max_retries=max_retries, + timeout=timeout, + ) diff --git a/src/backend/base/langflow/components/experimental/Pass.py b/src/backend/base/langflow/components/experimental/Pass.py new file mode 100644 index 000000000..9528f0591 --- /dev/null +++ b/src/backend/base/langflow/components/experimental/Pass.py @@ -0,0 +1,29 @@ +from typing import Union +from langflow.interface.custom.custom_component import CustomComponent +from langflow.schema import Record +from langflow.field_typing import Text + + +class PassComponent(CustomComponent): + display_name = "Pass" + description = "A pass-through component that forwards the second input while ignoring the first, used for controlling workflow direction." + field_order = ["ignored_input", "forwarded_input"] + + def build_config(self) -> dict: + return { + "ignored_input": { + "display_name": "Ignored Input", + "info": "This input is ignored. It's used to control the flow in the graph.", + "input_types": ["Text", "Record"], + }, + "forwarded_input": { + "display_name": "Input", + "info": "This input is forwarded by the component.", + "input_types": ["Text", "Record"], + }, + } + + def build(self, ignored_input: Text, forwarded_input: Text) -> Union[Text, Record]: + # The ignored_input is not used in the logic, it's just there for graph flow control + self.status = forwarded_input + return forwarded_input diff --git a/src/backend/base/langflow/components/experimental/SQLExecutor.py b/src/backend/base/langflow/components/experimental/SQLExecutor.py index 2289514a6..a62ac187b 100644 --- a/src/backend/base/langflow/components/experimental/SQLExecutor.py +++ b/src/backend/base/langflow/components/experimental/SQLExecutor.py @@ -1,5 +1,5 @@ from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool -from langchain_experimental.sql.base import SQLDatabase +from langchain_community.utilities import SQLDatabase from langflow.field_typing import Text from langflow.interface.custom.custom_component import CustomComponent diff --git a/src/backend/base/langflow/components/experimental/SplitText.py b/src/backend/base/langflow/components/experimental/SplitText.py new file mode 100644 index 000000000..bd2bc921f --- /dev/null +++ b/src/backend/base/langflow/components/experimental/SplitText.py @@ -0,0 +1,49 @@ +from typing import Optional + +from langflow.field_typing import Text +from langflow.interface.custom.custom_component import CustomComponent +from langflow.schema import Record +from langflow.utils.util import unescape_string + + +class SplitTextComponent(CustomComponent): + display_name: str = "Split Text" + description: str = "Split text into chunks of a specified length." + + def build_config(self): + return { + "inputs": { + "display_name": "Inputs", + "info": "Texts to split.", + "input_types": ["Record", "Text"], + }, + "separator": { + "display_name": "Separator", + "info": 'The character to split on. Defaults to " ".', + }, + "truncate_size": { + "display_name": "Truncate Size", + "info": "The maximum length (in number of characters) of each chunk to keep. Defaults to 0 (no truncation).", + }, + } + + def build( + self, + inputs: list[Text], + separator: str = " ", + truncate_size: Optional[int] = 0, + ) -> list[Record]: + separator = unescape_string(separator) + + outputs = [] + for text in inputs: + chunks = text.split(separator) + + if truncate_size: + chunks = [chunk[:truncate_size] for chunk in chunks] + + for chunk in chunks: + outputs.append(Record(text=chunk, data={"parent": text})) + + self.status = outputs + return outputs diff --git a/src/backend/base/langflow/components/experimental/StoreMessage.py b/src/backend/base/langflow/components/experimental/StoreMessage.py index ac50b78b4..9f3aa60e2 100644 --- a/src/backend/base/langflow/components/experimental/StoreMessage.py +++ b/src/backend/base/langflow/components/experimental/StoreMessage.py @@ -32,7 +32,6 @@ class StoreMessageComponent(CustomComponent): session_id: Optional[str] = None, message: str = "", ) -> List[Record]: - store_message( sender=sender, sender_name=sender_name, diff --git a/src/backend/base/langflow/components/experimental/SubFlow.py b/src/backend/base/langflow/components/experimental/SubFlow.py index 72e07c8e8..80e15c6ad 100644 --- a/src/backend/base/langflow/components/experimental/SubFlow.py +++ b/src/backend/base/langflow/components/experimental/SubFlow.py @@ -35,7 +35,7 @@ class SubFlowComponent(CustomComponent): build_config["flow_name"]["options"] = self.get_flow_names() # Clean up the build config for key in list(build_config.keys()): - if key not in self.field_order + ["code", "_type"]: + if key not in self.field_order + ["code", "_type", "get_final_results_only"]: del build_config[key] if field_value is not None and field_name == "flow_name": try: @@ -85,20 +85,29 @@ class SubFlowComponent(CustomComponent): "display_name": "Tweaks", "info": "Tweaks to apply to the flow.", }, + "get_final_results_only": { + "display_name": "Get Final Results Only", + "info": "If False, the output will contain all outputs from the flow.", + "advanced": True, + }, } - def build_records_from_result_data(self, result_data: ResultData) -> List[Record]: + def build_records_from_result_data(self, result_data: ResultData, get_final_results_only: bool) -> List[Record]: messages = result_data.messages if not messages: return [] records = [] for message in messages: message_dict = message if isinstance(message, dict) else message.model_dump() - record = Record(data={"result": result_data.model_dump(), "message": message_dict.get("message", "")}) + if get_final_results_only: + result_data_dict = result_data.model_dump() + results = result_data_dict.get("results", {}) + inner_result = results.get("result", {}) + record = Record(data={"result": inner_result, "message": message_dict}, text_key="result") records.append(record) return records - async def build(self, flow_name: str, **kwargs) -> List[Record]: + async def build(self, flow_name: str, get_final_results_only: bool = True, **kwargs) -> List[Record]: tweaks = {key: {"input_value": value} for key, value in kwargs.items()} run_outputs: List[Optional[RunOutputs]] = await self.run_flow( tweaks=tweaks, @@ -112,7 +121,7 @@ class SubFlowComponent(CustomComponent): if run_output is not None: for output in run_output.outputs: if output: - records.extend(self.build_records_from_result_data(output)) + records.extend(self.build_records_from_result_data(output, get_final_results_only)) self.status = records logger.debug(records) diff --git a/src/backend/base/langflow/components/experimental/TextOperator.py b/src/backend/base/langflow/components/experimental/TextOperator.py index d21d7b7cb..89fba13b6 100644 --- a/src/backend/base/langflow/components/experimental/TextOperator.py +++ b/src/backend/base/langflow/components/experimental/TextOperator.py @@ -1,7 +1,10 @@ +from typing import Optional, Union + from langflow.interface.custom.custom_component import CustomComponent from langflow.schema import Record from langflow.field_typing import Text + class TextOperatorComponent(CustomComponent): display_name = "Text Operator" description = "Compares two text inputs based on a specified condition such as equality or inequality, with optional case sensitivity." @@ -19,17 +22,29 @@ class TextOperatorComponent(CustomComponent): "operator": { "display_name": "Operator", "info": "The operator to apply for comparing the texts.", - "options": ["equals", "not equals", "contains", "starts with", "ends with"], + "options": ["equals", "not equals", "contains", "starts with", "ends with", "exists"], }, "case_sensitive": { "display_name": "Case Sensitive", "info": "If true, the comparison will be case sensitive.", "field_type": "bool", "default": False, - } + }, + "true_output": { + "display_name": "Output", + "info": "The output to return or display when the comparison is true.", + "input_types": ["Text", "Record"], # Allow both text and record types + }, } - def build(self, input_text: Text, match_text: Text, operator: Text, case_sensitive: bool = False) -> Text: + def build( + self, + input_text: Text, + match_text: Text, + operator: Text, + case_sensitive: bool = False, + true_output: Optional[Text] = "", + ) -> Union[Text, Record]: if not input_text or not match_text: raise ValueError("Both 'input_text' and 'match_text' must be provided and non-empty.") @@ -49,7 +64,13 @@ class TextOperatorComponent(CustomComponent): elif operator == "ends with": result = input_text.endswith(match_text) - if not result: + output_record = true_output if true_output else input_text + + if result: + self.status = output_record + return output_record + else: + self.status = "Comparison failed, stopping execution." self.stop() - self.status = f"{result} \n\n {input_text}" - return input_text + + return output_record diff --git a/src/backend/base/langflow/components/helpers/SplitText.py b/src/backend/base/langflow/components/helpers/SplitText.py deleted file mode 100644 index 565ee1ffc..000000000 --- a/src/backend/base/langflow/components/helpers/SplitText.py +++ /dev/null @@ -1,87 +0,0 @@ -from typing import Optional, Union - -from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter -from langchain_core.documents import Document - -from langflow.field_typing import Text -from langflow.interface.custom.custom_component import CustomComponent -from langflow.schema import Record -from langflow.utils.util import unescape_string - - -class SplitTextComponent(CustomComponent): - display_name: str = "Split Text" - description: str = "Split text into chunks of a specified length." - - def build_config(self): - return { - "inputs": { - "display_name": "Inputs", - "info": "Texts to split.", - "input_types": ["Record", "Text"], - }, - "separators": { - "display_name": "Separators", - "info": 'The characters to split on. Defaults to [" "].', - "is_list": True, - }, - "chunk_size": { - "display_name": "Max Chunk Size", - "info": "The maximum length (in number of characters) of each chunk.", - "field_type": "int", - "value": 1000, - }, - "chunk_overlap": { - "display_name": "Chunk Overlap", - "info": "The amount of character overlap between chunks.", - "field_type": "int", - "value": 200, - }, - "recursive": { - "display_name": "Recursive", - }, - "code": {"show": False}, - } - - def build( - self, - inputs: list[Text], - separators: Optional[list[str]] = [" "], - chunk_size: Optional[int] = 1000, - chunk_overlap: Optional[int] = 200, - recursive: bool = False, - ) -> list[Record]: - if separators is None: - separators = [] - separators = [unescape_string(x) for x in separators] - - # Make sure chunk_size and chunk_overlap are ints - if isinstance(chunk_size, str): - chunk_size = int(chunk_size) - if isinstance(chunk_overlap, str): - chunk_overlap = int(chunk_overlap) - splitter: Optional[Union[CharacterTextSplitter, RecursiveCharacterTextSplitter]] = None - if recursive: - splitter = RecursiveCharacterTextSplitter( - separators=separators, - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - ) - - else: - splitter = CharacterTextSplitter( - separator=separators[0], - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - ) - - documents = [] - for _input in inputs: - if isinstance(_input, Record): - documents.append(_input.to_lc_document()) - else: - documents.append(Document(page_content=_input)) - - records = self.to_records(splitter.split_documents(documents)) - self.status = records - return records diff --git a/src/backend/base/langflow/components/memories/ZepMessageReader.py b/src/backend/base/langflow/components/memories/ZepMessageReader.py index 603e41972..bac6e9f1a 100644 --- a/src/backend/base/langflow/components/memories/ZepMessageReader.py +++ b/src/backend/base/langflow/components/memories/ZepMessageReader.py @@ -54,6 +54,10 @@ class ZepMessageReaderComponent(BaseMemoryComponent): "info": "Limit of search results.", "advanced": True, }, + "api_base_path": { + "display_name": "API Base Path", + "options": ["api/v1", "api/v2"], + }, } def get_messages(self, **kwargs) -> list[Record]: @@ -108,6 +112,7 @@ class ZepMessageReaderComponent(BaseMemoryComponent): def build( self, session_id: Text, + api_base_path: str = "api/v1", url: Optional[Text] = None, api_key: Optional[Text] = None, query: Optional[Text] = None, @@ -118,12 +123,21 @@ class ZepMessageReaderComponent(BaseMemoryComponent): try: from zep_python import ZepClient from zep_python.langchain import ZepChatMessageHistory + + # Monkeypatch API_BASE_PATH to + # avoid 404 + # This is a workaround for the local Zep instance + # cloud Zep works with v2 + import zep_python.zep_client + + zep_python.zep_client.API_BASE_PATH = api_base_path except ImportError: raise ImportError( "Could not import zep-python package. " "Please install it with `pip install zep-python`." ) if url == "": url = None + zep_client = ZepClient(api_url=url, api_key=api_key) memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client) records = self.get_messages( diff --git a/src/backend/base/langflow/components/memories/ZepMessageWriter.py b/src/backend/base/langflow/components/memories/ZepMessageWriter.py index 248dfd810..b062f66bf 100644 --- a/src/backend/base/langflow/components/memories/ZepMessageWriter.py +++ b/src/backend/base/langflow/components/memories/ZepMessageWriter.py @@ -1,5 +1,4 @@ -from typing import Optional, TYPE_CHECKING - +from typing import TYPE_CHECKING, Optional from langflow.base.memory.memory import BaseMemoryComponent from langflow.field_typing import Text @@ -39,6 +38,10 @@ class ZepMessageWriterComponent(BaseMemoryComponent): "display_name": "Input Record", "info": "Record to write to Zep.", }, + "api_base_path": { + "display_name": "API Base Path", + "options": ["api/v1", "api/v2"], + }, } def add_message( @@ -77,18 +80,27 @@ class ZepMessageWriterComponent(BaseMemoryComponent): self, input_value: Record, session_id: Text, + api_base_path: str = "api/v1", url: Optional[Text] = None, api_key: Optional[Text] = None, ) -> Record: try: + # Monkeypatch API_BASE_PATH to + # avoid 404 + # This is a workaround for the local Zep instance + # cloud Zep works with v2 + import zep_python.zep_client from zep_python import ZepClient from zep_python.langchain import ZepChatMessageHistory + + zep_python.zep_client.API_BASE_PATH = api_base_path except ImportError: raise ImportError( "Could not import zep-python package. " "Please install it with `pip install zep-python`." ) if url == "": url = None + zep_client = ZepClient(api_url=url, api_key=api_key) memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client) self.add_message(**input_value.data, memory=memory) diff --git a/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py b/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py index 21ed13e70..cc4ba3b9f 100644 --- a/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py +++ b/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py @@ -1,6 +1,6 @@ from typing import Optional -from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint +from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint from langflow.interface.custom.custom_component import CustomComponent from langflow.field_typing import BaseLanguageModel diff --git a/src/backend/base/langflow/components/model_specs/ChatMistralSpecs.py b/src/backend/base/langflow/components/model_specs/ChatMistralSpecs.py new file mode 100644 index 000000000..90f94aacd --- /dev/null +++ b/src/backend/base/langflow/components/model_specs/ChatMistralSpecs.py @@ -0,0 +1,87 @@ +from typing import Optional + +from langchain_mistralai import ChatMistralAI +from pydantic.v1 import SecretStr + +from langflow.custom import CustomComponent +from langflow.field_typing import BaseLanguageModel + + +class MistralAIModelComponent(CustomComponent): + display_name: str = "MistralAI" + description: str = "Generate text using MistralAI LLMs." + icon = "MistralAI" + + field_order = [ + "model", + "mistral_api_key", + "max_tokens", + "temperature", + "mistral_api_base", + ] + + def build_config(self): + return { + "model": { + "display_name": "Model Name", + "options": [ + "open-mistral-7b", + "open-mixtral-8x7b", + "open-mixtral-8x22b", + "mistral-small-latest", + "mistral-medium-latest", + "mistral-large-latest", + ], + "info": "Name of the model to use.", + "required": True, + "value": "open-mistral-7b", + }, + "mistral_api_key": { + "display_name": "Mistral API Key", + "required": True, + "password": True, + "info": "Your Mistral API key.", + }, + "max_tokens": { + "display_name": "Max Tokens", + "field_type": "int", + "advanced": True, + "value": 256, + }, + "temperature": { + "display_name": "Temperature", + "field_type": "float", + "value": 0.1, + }, + "mistral_api_base": { + "display_name": "Mistral API Base", + "advanced": True, + "info": "Endpoint of the Mistral API. Defaults to 'https://api.mistral.ai' if not specified.", + }, + "code": {"show": False}, + } + + def build( + self, + model: str, + temperature: float = 0.1, + mistral_api_key: Optional[str] = None, + max_tokens: Optional[int] = None, + mistral_api_base: Optional[str] = None, + ) -> BaseLanguageModel: + # Set default API endpoint if not provided + if not mistral_api_base: + mistral_api_base = "https://api.mistral.ai" + + try: + output = ChatMistralAI( + model_name=model, + api_key=(SecretStr(mistral_api_key) if mistral_api_key else None), + max_tokens=max_tokens, + temperature=temperature, + endpoint=mistral_api_base, + ) + except Exception as e: + raise ValueError("Could not connect to Mistral API.") from e + + return output diff --git a/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py b/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py index e37eacc77..d6560580b 100644 --- a/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py +++ b/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py @@ -1,6 +1,6 @@ from typing import Optional -from langchain_community.chat_models.openai import ChatOpenAI +from langchain_openai import ChatOpenAI from langflow.base.models.openai_constants import MODEL_NAMES from langflow.field_typing import BaseLanguageModel, NestedDict @@ -52,7 +52,7 @@ class ChatOpenAIComponent(CustomComponent): self, max_tokens: Optional[int] = 256, model_kwargs: NestedDict = {}, - model_name: str = "gpt-4-1106-preview", + model_name: str = "gpt-4o", openai_api_base: Optional[str] = None, openai_api_key: Optional[str] = None, temperature: float = 0.7, diff --git a/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py b/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py index c16fb2c2c..c145105ea 100644 --- a/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py +++ b/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py @@ -1,7 +1,7 @@ from typing import Optional from langflow.field_typing import BaseLanguageModel -from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint +from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint from langflow.interface.custom.custom_component import CustomComponent diff --git a/src/backend/base/langflow/components/models/MistralModel.py b/src/backend/base/langflow/components/models/MistralModel.py new file mode 100644 index 000000000..b8834b314 --- /dev/null +++ b/src/backend/base/langflow/components/models/MistralModel.py @@ -0,0 +1,141 @@ +from typing import Optional + +from langchain_mistralai import ChatMistralAI +from pydantic.v1 import SecretStr + +from langflow.base.constants import STREAM_INFO_TEXT +from langflow.base.models.model import LCModelComponent +from langflow.field_typing import Text + + +class MistralAIModelComponent(LCModelComponent): + display_name = "MistralAI" + description = "Generates text using MistralAI LLMs." + icon = "MistralAI" + + field_order = [ + "max_tokens", + "model_kwargs", + "model_name", + "mistral_api_base", + "mistral_api_key", + "temperature", + "input_value", + "system_message", + "stream", + ] + + def build_config(self): + return { + "input_value": {"display_name": "Input"}, + "max_tokens": { + "display_name": "Max Tokens", + "advanced": True, + }, + "model_name": { + "display_name": "Model Name", + "advanced": False, + "options": [ + "open-mistral-7b", + "open-mixtral-8x7b", + "open-mixtral-8x22b", + "mistral-small-latest", + "mistral-medium-latest", + "mistral-large-latest", + ], + "value": "open-mistral-7b", + }, + "mistral_api_base": { + "display_name": "Mistral API Base", + "advanced": True, + "info": ( + "The base URL of the Mistral API. Defaults to https://api.mistral.ai.\n\n" + "You can change this to use other APIs like JinaChat, LocalAI and Prem." + ), + }, + "mistral_api_key": { + "display_name": "Mistral API Key", + "info": "The Mistral API Key to use for the Mistral model.", + "advanced": False, + "password": True, + }, + "temperature": { + "display_name": "Temperature", + "advanced": False, + "value": 0.1, + }, + "stream": { + "display_name": "Stream", + "info": STREAM_INFO_TEXT, + "advanced": True, + }, + "system_message": { + "display_name": "System Message", + "info": "System message to pass to the model.", + "advanced": True, + }, + "max_retries": { + "display_name": "Max Retries", + "advanced": True, + }, + "timeout": { + "display_name": "Timeout", + "advanced": True, + }, + "max_concurrent_requests": { + "display_name": "Max Concurrent Requests", + "advanced": True, + }, + "top_p": { + "display_name": "Top P", + "advanced": True, + }, + "random_seed": { + "display_name": "Random Seed", + "advanced": True, + }, + "safe_mode": { + "display_name": "Safe Mode", + "advanced": True, + }, + } + + def build( + self, + input_value: Text, + mistral_api_key: str, + model_name: str, + temperature: float = 0.1, + max_tokens: Optional[int] = 256, + mistral_api_base: Optional[str] = None, + stream: bool = False, + system_message: Optional[str] = None, + max_retries: int = 5, + timeout: int = 120, + max_concurrent_requests: int = 64, + top_p: float = 1, + random_seed: Optional[int] = None, + safe_mode: bool = False, + ) -> Text: + if not mistral_api_base: + mistral_api_base = "https://api.mistral.ai" + if mistral_api_key: + api_key = SecretStr(mistral_api_key) + else: + api_key = None + + chat_model = ChatMistralAI( + max_tokens=max_tokens, + model_name=model_name, + endpoint=mistral_api_base, + api_key=api_key, + temperature=temperature, + max_retries=max_retries, + timeout=timeout, + max_concurrent_requests=max_concurrent_requests, + top_p=top_p, + random_seed=random_seed, + safe_mode=safe_mode, + ) + + return self.get_chat_result(chat_model, stream, input_value, system_message) diff --git a/src/backend/base/langflow/components/models/OpenAIModel.py b/src/backend/base/langflow/components/models/OpenAIModel.py index a37caefc3..149c93ad3 100644 --- a/src/backend/base/langflow/components/models/OpenAIModel.py +++ b/src/backend/base/langflow/components/models/OpenAIModel.py @@ -41,7 +41,6 @@ class OpenAIModelComponent(LCModelComponent): "display_name": "Model Name", "advanced": False, "options": MODEL_NAMES, - "value": "gpt-4-turbo-preview", }, "openai_api_base": { "display_name": "OpenAI API Base", @@ -79,7 +78,7 @@ class OpenAIModelComponent(LCModelComponent): input_value: Text, openai_api_key: str, temperature: float, - model_name: str, + model_name: str = "gpt-4o", max_tokens: Optional[int] = 256, model_kwargs: NestedDict = {}, openai_api_base: Optional[str] = None, diff --git a/src/backend/base/langflow/components/outputs/RecordsOutput.py b/src/backend/base/langflow/components/outputs/RecordsOutput.py new file mode 100644 index 000000000..bdd8a85e2 --- /dev/null +++ b/src/backend/base/langflow/components/outputs/RecordsOutput.py @@ -0,0 +1,10 @@ +from langflow.base.io.text import TextComponent +from langflow.schema import Record + + +class RecordsOutput(TextComponent): + display_name = "Records Output" + description = "Display Records as a Table" + + def build(self, input_value: Record) -> Record: + return input_value diff --git a/src/backend/base/langflow/config.yaml b/src/backend/base/langflow/config.yaml index ac9a7456b..561fac48d 100644 --- a/src/backend/base/langflow/config.yaml +++ b/src/backend/base/langflow/config.yaml @@ -224,11 +224,6 @@ wrappers: documentation: "" SQLDatabase: documentation: "" -output_parsers: - StructuredOutputParser: - documentation: "https://python.langchain.com/docs/modules/model_io/output_parsers/structured" - ResponseSchema: - documentation: "https://python.langchain.com/docs/modules/model_io/output_parsers/structured" custom_components: CustomComponent: documentation: "https://docs.langflow.org/guidelines/custom-component" diff --git a/src/backend/base/langflow/graph/edge/base.py b/src/backend/base/langflow/graph/edge/base.py index ba7fe05e8..dad8469c0 100644 --- a/src/backend/base/langflow/graph/edge/base.py +++ b/src/backend/base/langflow/graph/edge/base.py @@ -3,9 +3,8 @@ from typing import TYPE_CHECKING, Any, List, Optional from loguru import logger from pydantic import BaseModel, Field -from langflow.graph.edge.utils import build_clean_params +from langflow.graph.vertex.utils import log_transaction from langflow.schema.schema import INPUT_FIELD_NAME -from langflow.services.deps import get_monitor_service from langflow.services.monitor.utils import log_message if TYPE_CHECKING: @@ -157,26 +156,9 @@ class ContractEdge(Edge): message=target.params.get(INPUT_FIELD_NAME, {}), session_id=target.params.get("session_id", ""), artifacts=target.artifacts, + flow_id=target.graph.flow_id, ) return self.result def __repr__(self) -> str: return f"{self.source_id} -[{self.target_param}]-> {self.target_id}" - - -def log_transaction(edge: ContractEdge, source: "Vertex", target: "Vertex", status, error=None): - try: - monitor_service = get_monitor_service() - clean_params = build_clean_params(target) - data = { - "source": source.vertex_type, - "target": target.vertex_type, - "target_args": clean_params, - "timestamp": monitor_service.get_timestamp(), - "status": status, - "error": error, - } - monitor_service.add_row(table_name="transactions", data=data) - except Exception as e: - logger.error(f"Error logging transaction: {e}") - logger.error(f"Error logging transaction: {e}") diff --git a/src/backend/base/langflow/graph/edge/utils.py b/src/backend/base/langflow/graph/edge/utils.py index 0f69e4b2d..e69de29bb 100644 --- a/src/backend/base/langflow/graph/edge/utils.py +++ b/src/backend/base/langflow/graph/edge/utils.py @@ -1,19 +0,0 @@ -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from langflow.graph.vertex.base import Vertex - - -def build_clean_params(target: "Vertex") -> dict: - """ - Cleans the parameters of the target vertex. - """ - # Removes all keys that the values aren't python types like str, int, bool, etc. - params = { - key: value for key, value in target.params.items() if isinstance(value, (str, int, bool, float, list, dict)) - } - # if it is a list we need to check if the contents are python types - for key, value in params.items(): - if isinstance(value, list): - params[key] = [item for item in value if isinstance(item, (str, int, bool, float, list, dict))] - return params diff --git a/src/backend/base/langflow/graph/graph/base.py b/src/backend/base/langflow/graph/graph/base.py index 01bbab251..bd17d43f8 100644 --- a/src/backend/base/langflow/graph/graph/base.py +++ b/src/backend/base/langflow/graph/graph/base.py @@ -14,7 +14,7 @@ from langflow.graph.graph.state_manager import GraphStateManager from langflow.graph.graph.utils import process_flow from langflow.graph.schema import InterfaceComponentTypes, RunOutputs from langflow.graph.vertex.base import Vertex -from langflow.graph.vertex.types import ChatVertex, FileToolVertex, LLMVertex, StateVertex, ToolkitVertex +from langflow.graph.vertex.types import FileToolVertex, InterfaceVertex, LLMVertex, StateVertex, ToolkitVertex from langflow.interface.tools.constants import FILE_TOOLS from langflow.schema import Record from langflow.schema.schema import INPUT_FIELD_NAME, InputType @@ -242,6 +242,7 @@ class Graph: outputs: list[str], stream: bool, session_id: str, + fallback_to_env_vars: bool, ) -> List[Optional["ResultData"]]: """ Runs the graph with the given inputs. @@ -289,7 +290,7 @@ class Graph: start_component_id = next( (vertex_id for vertex_id in self._is_input_vertices if "chat" in vertex_id.lower()), None ) - await self.process(start_component_id=start_component_id) + await self.process(start_component_id=start_component_id, fallback_to_env_vars=fallback_to_env_vars) self.increment_run_count() except Exception as exc: logger.exception(exc) @@ -315,6 +316,7 @@ class Graph: outputs: Optional[list[str]] = None, session_id: Optional[str] = None, stream: bool = False, + fallback_to_env_vars: bool = False, ) -> List[RunOutputs]: """ Run the graph with the given inputs and return the outputs. @@ -340,6 +342,7 @@ class Graph: outputs=outputs, session_id=session_id, stream=stream, + fallback_to_env_vars=fallback_to_env_vars, ) try: @@ -362,6 +365,7 @@ class Graph: outputs: Optional[list[str]] = None, session_id: Optional[str] = None, stream: bool = False, + fallback_to_env_vars: bool = False, ) -> List[RunOutputs]: """ Runs the graph with the given inputs. @@ -403,6 +407,7 @@ class Graph: outputs=outputs or [], stream=stream, session_id=session_id or "", + fallback_to_env_vars=fallback_to_env_vars, ) run_output_object = RunOutputs(inputs=run_inputs, outputs=run_outputs) logger.debug(f"Run outputs: {run_output_object}") @@ -468,9 +473,9 @@ class Graph: """Marks a branch of the graph.""" if visited is None: visited = set() - visited.add(vertex_id) if vertex_id in visited: return + visited.add(vertex_id) self.mark_vertex(vertex_id, state) @@ -712,6 +717,7 @@ class Graph: vertex_id: str, inputs_dict: Optional[Dict[str, str]] = None, user_id: Optional[str] = None, + fallback_to_env_vars: bool = False, ): """ Builds a vertex in the graph. @@ -733,7 +739,7 @@ class Graph: vertex = self.get_vertex(vertex_id) try: if not vertex.frozen or not vertex._built: - await vertex.build(user_id=user_id, inputs=inputs_dict) + await vertex.build(user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars) if vertex.result is not None: params = vertex._built_object_repr() @@ -796,7 +802,7 @@ class Graph: vertices.append(vertex) return vertices - async def process(self, start_component_id: Optional[str] = None) -> "Graph": + async def process(self, fallback_to_env_vars: bool, start_component_id: Optional[str] = None) -> "Graph": """Processes the graph with vertices in each layer run in parallel.""" first_layer = self.sort_vertices(start_component_id=start_component_id) @@ -821,6 +827,7 @@ class Graph: vertex_id=vertex_id, user_id=self.user_id, inputs_dict={}, + fallback_to_env_vars=fallback_to_env_vars, ), name=f"{vertex.display_name} Run {vertex_task_run_count.get(vertex_id, 0)}", ) @@ -987,8 +994,8 @@ class Graph: """Returns the node class based on the node type.""" # First we check for the node_base_type node_name = node_id.split("-")[0] - if node_name in ["ChatOutput", "ChatInput"]: - return ChatVertex + if node_name in InterfaceComponentTypes: + return InterfaceVertex elif node_name in ["SharedState", "Notify", "Listen"]: return StateVertex elif node_base_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP: diff --git a/src/backend/base/langflow/graph/graph/constants.py b/src/backend/base/langflow/graph/graph/constants.py index f948a0753..2ed42fbff 100644 --- a/src/backend/base/langflow/graph/graph/constants.py +++ b/src/backend/base/langflow/graph/graph/constants.py @@ -1,3 +1,4 @@ +from langflow.graph.schema import CHAT_COMPONENTS from langflow.graph.vertex import types from langflow.interface.agents.base import agent_creator from langflow.interface.custom.base import custom_component_creator @@ -5,7 +6,6 @@ from langflow.interface.document_loaders.base import documentloader_creator from langflow.interface.embeddings.base import embedding_creator from langflow.interface.llms.base import llm_creator from langflow.interface.memories.base import memory_creator -from langflow.interface.output_parsers.base import output_parser_creator from langflow.interface.prompts.base import prompt_creator from langflow.interface.retrievers.base import retriever_creator from langflow.interface.text_splitters.base import textsplitter_creator @@ -14,8 +14,6 @@ from langflow.interface.tools.base import tool_creator from langflow.interface.wrappers.base import wrapper_creator from langflow.utils.lazy_load import LazyLoadDictBase -CHAT_COMPONENTS = ["ChatInput", "ChatOutput", "TextInput", "SessionID"] - class VertexTypesDict(LazyLoadDictBase): def __init__(self): @@ -46,10 +44,9 @@ class VertexTypesDict(LazyLoadDictBase): # **{t: types.VectorStoreVertex for t in vectorstore_creator.to_list()}, **{t: types.DocumentLoaderVertex for t in documentloader_creator.to_list()}, **{t: types.TextSplitterVertex for t in textsplitter_creator.to_list()}, - **{t: types.OutputParserVertex for t in output_parser_creator.to_list()}, **{t: types.CustomComponentVertex for t in custom_component_creator.to_list()}, **{t: types.RetrieverVertex for t in retriever_creator.to_list()}, - **{t: types.ChatVertex for t in CHAT_COMPONENTS}, + **{t: types.InterfaceVertex for t in CHAT_COMPONENTS}, } def get_custom_component_vertex_type(self): diff --git a/src/backend/base/langflow/graph/schema.py b/src/backend/base/langflow/graph/schema.py index 0bbdfa720..18010de4c 100644 --- a/src/backend/base/langflow/graph/schema.py +++ b/src/backend/base/langflow/graph/schema.py @@ -30,6 +30,7 @@ class InterfaceComponentTypes(str, Enum, metaclass=ContainsEnumMeta): ChatOutput = "ChatOutput" TextInput = "TextInput" TextOutput = "TextOutput" + RecordsOutput = "RecordsOutput" def __contains__(cls, item): try: @@ -40,6 +41,8 @@ class InterfaceComponentTypes(str, Enum, metaclass=ContainsEnumMeta): return True +CHAT_COMPONENTS = [InterfaceComponentTypes.ChatInput, InterfaceComponentTypes.ChatOutput] +RECORDS_COMPONENTS = [InterfaceComponentTypes.RecordsOutput] INPUT_COMPONENTS = [ InterfaceComponentTypes.ChatInput, InterfaceComponentTypes.TextInput, diff --git a/src/backend/base/langflow/graph/vertex/base.py b/src/backend/base/langflow/graph/vertex/base.py index e250e9419..e0dd0f4bf 100644 --- a/src/backend/base/langflow/graph/vertex/base.py +++ b/src/backend/base/langflow/graph/vertex/base.py @@ -10,7 +10,7 @@ from loguru import logger from langflow.graph.schema import INPUT_COMPONENTS, OUTPUT_COMPONENTS, InterfaceComponentTypes, ResultData from langflow.graph.utils import UnbuiltObject, UnbuiltResult -from langflow.graph.vertex.utils import generate_result +from langflow.graph.vertex.utils import generate_result, log_transaction from langflow.interface.initialize import loading from langflow.interface.listing import lazy_load_dict from langflow.schema.schema import INPUT_FIELD_NAME @@ -315,7 +315,11 @@ class Vertex: params[field_name] = full_path elif field.get("required"): field_display_name = field.get("display_name") - raise ValueError(f"File path not found for {field_display_name} in component {self.display_name}") + logger.warning( + f"File path not found for {field_display_name} in component {self.display_name}. Setting to None." + ) + params[field_name] = None + elif field.get("type") in DIRECT_TYPES and params.get(field_name) is None: val = field.get("value") if field.get("type") == "code": @@ -390,13 +394,17 @@ class Vertex: self.params = self._raw_params.copy() self.updated_raw_params = True - async def _build(self, user_id=None): + async def _build( + self, + fallback_to_env_vars, + user_id=None, + ): """ Initiate the build process. """ logger.debug(f"Building {self.display_name}") await self._build_each_vertex_in_params_dict(user_id) - await self._get_and_instantiate_class(user_id) + await self._get_and_instantiate_class(user_id, fallback_to_env_vars) self._validate_built_object() self._built = True @@ -432,7 +440,11 @@ class Vertex: # to the frontend self.set_artifacts() artifacts = self.artifacts - messages = self.extract_messages_from_artifacts(artifacts) + if isinstance(artifacts, dict): + messages = self.extract_messages_from_artifacts(artifacts) + else: + messages = [] + result_dict = ResultData( results=result_dict, artifacts=artifacts, @@ -500,7 +512,7 @@ class Vertex: if not self._is_vertex(value): self.params[key][sub_key] = value else: - result = await value.get_result() + result = await value.get_result(self) self.params[key][sub_key] = result def _is_vertex(self, value): @@ -515,9 +527,7 @@ class Vertex: """ return all(self._is_vertex(vertex) for vertex in value) - async def get_result( - self, - ) -> Any: + async def get_result(self, requester: "Vertex") -> Any: """ Retrieves the result of the vertex. @@ -527,9 +537,9 @@ class Vertex: The result of the vertex. """ async with self._lock: - return await self._get_result() + return await self._get_result(requester) - async def _get_result(self) -> Any: + async def _get_result(self, requester: "Vertex") -> Any: """ Retrieves the result of the built component. @@ -539,15 +549,19 @@ class Vertex: The built result if use_result is True, else the built object. """ if not self._built: + log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="error") raise ValueError(f"Component {self.display_name} has not been built yet") - return self._built_result if self.use_result else self._built_object + + result = self._built_result if self.use_result else self._built_object + log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="success") + return result async def _build_vertex_and_update_params(self, key, vertex: "Vertex"): """ Builds a given vertex and updates the params dictionary accordingly. """ - result = await vertex.get_result() + result = await vertex.get_result(self) self._handle_func(key, result) if isinstance(result, list): self._extend_params_list_with_result(key, result) @@ -563,7 +577,7 @@ class Vertex: """ self.params[key] = [] for vertex in vertices: - result = await vertex.get_result() + result = await vertex.get_result(self) # Weird check to see if the params[key] is a list # because sometimes it is a Record and breaks the code if not isinstance(self.params[key], list): @@ -606,7 +620,7 @@ class Vertex: if isinstance(self.params[key], list): self.params[key].extend(result) - async def _get_and_instantiate_class(self, user_id=None): + async def _get_and_instantiate_class(self, user_id=None, fallback_to_env_vars=False): """ Gets the class from a dictionary and instantiates it with the params. """ @@ -615,6 +629,7 @@ class Vertex: try: result = await loading.instantiate_class( user_id=user_id, + fallback_to_env_vars=fallback_to_env_vars, vertex=self, ) self._update_built_object_and_artifacts(result) diff --git a/src/backend/base/langflow/graph/vertex/types.py b/src/backend/base/langflow/graph/vertex/types.py index 87f4856b2..941f06036 100644 --- a/src/backend/base/langflow/graph/vertex/types.py +++ b/src/backend/base/langflow/graph/vertex/types.py @@ -6,14 +6,14 @@ import yaml from langchain_core.messages import AIMessage from loguru import logger -from langflow.graph.schema import InterfaceComponentTypes +from langflow.graph.schema import CHAT_COMPONENTS, RECORDS_COMPONENTS, InterfaceComponentTypes from langflow.graph.utils import UnbuiltObject, flatten_list, serialize_field from langflow.graph.vertex.base import Vertex from langflow.interface.utils import extract_input_variables_from_prompt from langflow.schema import Record from langflow.schema.schema import INPUT_FIELD_NAME from langflow.services.monitor.utils import log_vertex_build -from langflow.utils.schemas import ChatOutputResponse +from langflow.utils.schemas import ChatOutputResponse, RecordOutputResponse from langflow.utils.util import unescape_string @@ -300,11 +300,6 @@ class PromptVertex(Vertex): return str(self._built_object) -class OutputParserVertex(Vertex): - def __init__(self, data: Dict, graph): - super().__init__(data, graph=graph, base_type="output_parsers") - - class CustomComponentVertex(Vertex): def __init__(self, data: Dict, graph): super().__init__(data, graph=graph, base_type="custom_components") @@ -314,7 +309,7 @@ class CustomComponentVertex(Vertex): return self.artifacts["repr"] or super()._built_object_repr() -class ChatVertex(Vertex): +class InterfaceVertex(Vertex): def __init__(self, data: Dict, graph): super().__init__(data, graph=graph, base_type="custom_components", is_task=True) self.steps = [self._build, self._run] @@ -330,56 +325,131 @@ class ChatVertex(Vertex): return f"Task {self.task_id} is not running" if self.artifacts: # dump as a yaml string - artifacts = {k.title().replace("_", " "): v for k, v in self.artifacts.items() if v is not None} + if isinstance(self.artifacts, dict): + _artifacts = [self.artifacts] + elif hasattr(self.artifacts, "records"): + _artifacts = self.artifacts.records + else: + _artifacts = self.artifacts + artifacts = [] + for artifact in _artifacts: + # artifacts = {k.title().replace("_", " "): v for k, v in self.artifacts.items() if v is not None} + artifact = {k.title().replace("_", " "): v for k, v in artifact.items() if v is not None} + artifacts.append(artifact) yaml_str = yaml.dump(artifacts, default_flow_style=False, allow_unicode=True) return yaml_str return super()._built_object_repr() + def _process_chat_component(self): + """ + Process the chat component and return the message. + + This method processes the chat component by extracting the necessary parameters + such as sender, sender_name, and message from the `params` dictionary. It then + performs additional operations based on the type of the `_built_object` attribute. + If `_built_object` is an instance of `AIMessage`, it creates a `ChatOutputResponse` + object using the `from_message` method. If `_built_object` is not an instance of + `UnbuiltObject`, it checks the type of `_built_object` and performs specific + operations accordingly. If `_built_object` is a dictionary, it converts it into a + code block. If `_built_object` is an instance of `Record`, it assigns the `text` + attribute to the `message` variable. If `message` is an instance of `AsyncIterator` + or `Iterator`, it builds a stream URL and sets `message` to an empty string. If + `_built_object` is not a string, it converts it to a string. If `message` is a + generator or iterator, it assigns it to the `message` variable. Finally, it creates + a `ChatOutputResponse` object using the extracted parameters and assigns it to the + `artifacts` attribute. If `artifacts` is not None, it calls the `model_dump` method + on it and assigns the result to the `artifacts` attribute. It then returns the + `message` variable. + + Returns: + str: The processed message. + """ + artifacts = None + sender = self.params.get("sender", None) + sender_name = self.params.get("sender_name", None) + message = self.params.get(INPUT_FIELD_NAME, None) + if isinstance(message, str): + message = unescape_string(message) + stream_url = None + if isinstance(self._built_object, AIMessage): + artifacts = ChatOutputResponse.from_message( + self._built_object, + sender=sender, + sender_name=sender_name, + ) + elif not isinstance(self._built_object, UnbuiltObject): + if isinstance(self._built_object, dict): + # Turn the dict into a pleasing to + # read JSON inside a code block + message = dict_to_codeblock(self._built_object) + elif isinstance(self._built_object, Record): + message = self._built_object.text + elif isinstance(message, (AsyncIterator, Iterator)): + stream_url = self.build_stream_url() + message = "" + elif not isinstance(self._built_object, str): + message = str(self._built_object) + # if the message is a generator or iterator + # it means that it is a stream of messages + else: + message = self._built_object + + artifacts = ChatOutputResponse( + message=message, + sender=sender, + sender_name=sender_name, + stream_url=stream_url, + ) + + self.will_stream = stream_url is not None + if artifacts: + self.artifacts = artifacts.model_dump(exclude_none=True) + + return message + + def _process_record_component(self): + """ + Process the record component of the vertex. + + If the built object is an instance of `Record`, it calls the `model_dump` method + and assigns the result to the `artifacts` attribute. + + If the built object is a list, it iterates over each element and checks if it is + an instance of `Record`. If it is, it calls the `model_dump` method and appends + the result to the `artifacts` list. If it is not, it raises a `ValueError` if the + `ignore_errors` parameter is set to `False`, or logs an error message if it is set + to `True`. + + Returns: + The built object. + + Raises: + ValueError: If an element in the list is not an instance of `Record` and + `ignore_errors` is set to `False`. + """ + if isinstance(self._built_object, Record): + artifacts = [self._built_object.data] + elif isinstance(self._built_object, list): + artifacts = [] + ignore_errors = self.params.get("ignore_errors", False) + for record in self._built_object: + if isinstance(record, Record): + artifacts.append(record.data) + elif ignore_errors: + logger.error(f"Record expected, but got {record} of type {type(record)}") + else: + raise ValueError(f"Record expected, but got {record} of type {type(record)}") + self.artifacts = RecordOutputResponse(records=artifacts) + return self._built_object + async def _run(self, *args, **kwargs): if self.is_interface_component: - if self.vertex_type in ["ChatOutput", "ChatInput"]: - artifacts = None - sender = self.params.get("sender", None) - sender_name = self.params.get("sender_name", None) - message = self.params.get(INPUT_FIELD_NAME, None) - if isinstance(message, str): - message = unescape_string(message) - stream_url = None - if isinstance(self._built_object, AIMessage): - artifacts = ChatOutputResponse.from_message( - self._built_object, - sender=sender, - sender_name=sender_name, - ) - elif not isinstance(self._built_object, UnbuiltObject): - if isinstance(self._built_object, dict): - # Turn the dict into a pleasing to - # read JSON inside a code block - message = dict_to_codeblock(self._built_object) - elif isinstance(self._built_object, Record): - message = self._built_object.text - elif isinstance(message, (AsyncIterator, Iterator)): - stream_url = self.build_stream_url() - message = "" - elif not isinstance(self._built_object, str): - message = str(self._built_object) - # if the message is a generator or iterator - # it means that it is a stream of messages - else: - message = self._built_object - - artifacts = ChatOutputResponse( - message=message, - sender=sender, - sender_name=sender_name, - stream_url=stream_url, - ) - - self.will_stream = stream_url is not None - if artifacts: - self.artifacts = artifacts.model_dump(exclude_none=True) + if self.vertex_type in CHAT_COMPONENTS: + message = self._process_chat_component() + elif self.vertex_type in RECORDS_COMPONENTS: + message = self._process_record_component() if isinstance(self._built_object, (AsyncIterator, Iterator)): - if self.params["return_record"]: + if self.params.get("return_record", False): self._built_object = Record(text=message, data=self.artifacts) else: self._built_object = message diff --git a/src/backend/base/langflow/graph/vertex/utils.py b/src/backend/base/langflow/graph/vertex/utils.py index ea2693e29..b978424f2 100644 --- a/src/backend/base/langflow/graph/vertex/utils.py +++ b/src/backend/base/langflow/graph/vertex/utils.py @@ -1,11 +1,15 @@ -from typing import Any, Optional, Union +from typing import Any, Optional, Union, TYPE_CHECKING from langchain_core.messages import BaseMessage from langchain_core.runnables import Runnable from loguru import logger +from langflow.services.deps import get_monitor_service from langflow.utils.constants import PYTHON_BASIC_TYPES +if TYPE_CHECKING: + from langflow.graph.vertex.base import Vertex + def is_basic_type(obj): return type(obj) in PYTHON_BASIC_TYPES @@ -63,3 +67,49 @@ async def generate_result(built_object: Any, inputs: dict, has_external_output: else: result = built_object return result + + +def build_clean_params(target: "Vertex") -> dict: + """ + Cleans the parameters of the target vertex. + """ + # Removes all keys that the values aren't python types like str, int, bool, etc. + params = { + key: value for key, value in target.params.items() if isinstance(value, (str, int, bool, float, list, dict)) + } + # if it is a list we need to check if the contents are python types + for key, value in params.items(): + if isinstance(value, list): + params[key] = [item for item in value if isinstance(item, (str, int, bool, float, list, dict))] + return params + + +def log_transaction(source: "Vertex", target: "Vertex", flow_id, status, error=None): + """ + Logs a transaction between two vertices. + + Args: + source (Vertex): The source vertex of the transaction. + target (Vertex): The target vertex of the transaction. + status: The status of the transaction. + error (Optional): Any error associated with the transaction. + + Raises: + Exception: If there is an error while logging the transaction. + + """ + try: + monitor_service = get_monitor_service() + clean_params = build_clean_params(target) + data = { + "source": source.vertex_type, + "target": target.vertex_type, + "target_args": clean_params, + "timestamp": monitor_service.get_timestamp(), + "status": status, + "error": error, + "flow_id": flow_id, + } + monitor_service.add_row(table_name="transactions", data=data) + except Exception as e: + logger.error(f"Error logging transaction: {e}") diff --git a/src/backend/base/langflow/initial_setup/setup.py b/src/backend/base/langflow/initial_setup/setup.py index ca2bc230a..3c976cb94 100644 --- a/src/backend/base/langflow/initial_setup/setup.py +++ b/src/backend/base/langflow/initial_setup/setup.py @@ -11,10 +11,11 @@ from sqlmodel import select from langflow.base.constants import FIELD_FORMAT_ATTRIBUTES, NODE_FORMAT_ATTRIBUTES from langflow.interface.types import get_all_components from langflow.services.database.models.flow.model import Flow, FlowCreate +from langflow.services.database.models.folder.model import Folder, FolderCreate from langflow.services.deps import get_settings_service, session_scope STARTER_FOLDER_NAME = "Starter Projects" - +STARTER_FOLDER_DESCRIPTION = "Starter projects to help you get started in Langflow." # In the folder ./starter_projects we have a few JSON files that represent # starter projects. We want to load these into the database so that users @@ -158,6 +159,7 @@ def create_new_project( project_data, project_icon, project_icon_bg_color, + new_folder_id ): logger.debug(f"Creating starter project {project_name}") new_project = FlowCreate( @@ -168,33 +170,41 @@ def create_new_project( data=project_data, is_component=project_is_component, updated_at=updated_at_datetime, - folder=STARTER_FOLDER_NAME, + folder_id=new_folder_id, ) db_flow = Flow.model_validate(new_project, from_attributes=True) session.add(db_flow) -def get_all_flows_similar_to_project(session, project_name): - flows = session.exec( - select(Flow).where( - Flow.name == project_name, - Flow.folder == STARTER_FOLDER_NAME, - ) - ).all() +def get_all_flows_similar_to_project(session, folder_id): + flows = session.exec(select(Folder).where(Folder.id == folder_id)).first().flows return flows -def delete_start_projects(session): - flows = session.exec( - select(Flow).where( - Flow.folder == STARTER_FOLDER_NAME, - ) - ).all() +def delete_start_projects(session, folder_id): + flows = session.exec(select(Folder).where(Folder.id == folder_id)).first().flows for flow in flows: session.delete(flow) session.commit() +def folder_exists(session, folder_name): + folder = session.exec(select(Folder).where(Folder.name == folder_name)).first() + return folder is not None + + +def create_starter_folder(session): + if not folder_exists(session, STARTER_FOLDER_NAME): + new_folder = FolderCreate(name=STARTER_FOLDER_NAME, description=STARTER_FOLDER_DESCRIPTION) + db_folder = Folder.model_validate(new_folder, from_attributes=True) + session.add(db_folder) + session.commit() + session.refresh(db_folder) + return db_folder + else: + return session.exec(select(Folder).where(Folder.name == STARTER_FOLDER_NAME)).first() + + def create_or_update_starter_projects(): components_paths = get_settings_service().settings.COMPONENTS_PATH try: @@ -203,8 +213,9 @@ def create_or_update_starter_projects(): logger.exception(f"Error loading components: {e}") raise e with session_scope() as session: + new_folder = create_starter_folder(session) starter_projects = load_starter_projects() - delete_start_projects(session) + delete_start_projects(session, new_folder.id) for project_path, project in starter_projects: ( project_name, @@ -224,7 +235,7 @@ def create_or_update_starter_projects(): update_project_file(project_path, project, updated_project_data) if project_name and project_data: - for existing_project in get_all_flows_similar_to_project(session, project_name): + for existing_project in get_all_flows_similar_to_project(session, new_folder.id): session.delete(existing_project) create_new_project( @@ -236,4 +247,5 @@ def create_or_update_starter_projects(): project_data, project_icon, project_icon_bg_color, + new_folder.id ) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index ae82881aa..b8693e13f 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -1,885 +1,800 @@ { - "id": "c091a57f-43a7-4a5e-b352-035ae8d8379c", - "data": { - "nodes": [ - { - "id": "Prompt-uxBqP", - "type": "genericNode", - "position": { - "x": 53.588791333410654, - "y": -107.07318910019967 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: ", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "user_input": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "user_input", - "display_name": "user_input", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "user_input" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-uxBqP", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": true, - "width": 384, - "height": 383, - "dragging": false, - "positionAbsolute": { - "x": 53.588791333410654, - "y": -107.07318910019967 - } + "id": "c091a57f-43a7-4a5e-b352-035ae8d8379c", + "data": { + "nodes": [ + { + "id": "Prompt-uxBqP", + "type": "genericNode", + "position": { + "x": 53.588791333410654, + "y": -107.07318910019967 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "user_input": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "user_input", + "display_name": "user_input", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "OpenAIModel-k39HS", - "type": "genericNode", - "position": { - "x": 634.8148772766217, - "y": 27.035057029045305 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-k39HS", - "description": "Generates text using OpenAI LLMs.", - "display_name": "OpenAI" - }, - "selected": false, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 634.8148772766217, - "y": 27.035057029045305 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": ["object", "str", "Text"], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": ["user_input"] }, - { - "id": "ChatOutput-njtka", - "type": "genericNode", - "position": { - "x": 1193.250417197867, - "y": 71.88476890163852 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "Record", - "Text", - "str", - "object" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-njtka" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 1193.250417197867, - "y": 71.88476890163852 - }, - "dragging": false - }, - { - "id": "ChatInput-P3fgL", - "type": "genericNode", - "position": { - "x": -495.2223093083827, - "y": -232.56998443685862 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "hi" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "object", - "Record", - "str", - "Text" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-P3fgL" - }, - "selected": false, - "width": 384, - "height": 375, - "positionAbsolute": { - "x": -495.2223093083827, - "y": -232.56998443685862 - }, - "dragging": false - 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} - ], - "viewport": { - "x": 260.58251815500563, - "y": 318.2261172111936, - "zoom": 0.43514115784696294 + "output_types": ["Text"], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-uxBqP", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": true, + "width": 384, + "height": 383, + "dragging": false, + "positionAbsolute": { + "x": 53.588791333410654, + "y": -107.07318910019967 } - }, - "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ", - "name": "Basic Prompting (Hello, World)", - "last_tested_version": "1.0.0a4", - "is_component": false + }, + { + "id": "OpenAIModel-k39HS", + "type": "genericNode", + "position": { + "x": 634.8148772766217, + "y": 27.035057029045305 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": ["object", "Text", "str"], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-k39HS", + "description": "Generates text using OpenAI LLMs.", + "display_name": "OpenAI" + }, + "selected": false, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 634.8148772766217, + "y": 27.035057029045305 + }, + "dragging": false + }, + { + "id": "ChatOutput-njtka", + "type": "genericNode", + "position": { + "x": 1193.250417197867, + "y": 71.88476890163852 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["Record", "Text", "str", "object"], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null + }, + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-njtka" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 1193.250417197867, + "y": 71.88476890163852 + }, + "dragging": false + }, + { + "id": "ChatInput-P3fgL", + "type": "genericNode", + "position": { + "x": -495.2223093083827, + "y": -232.56998443685862 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "hi" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": ["object", "Record", "str", "Text"], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null + }, + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-P3fgL" + }, + "selected": false, + "width": 384, + "height": 375, + "positionAbsolute": { + "x": -495.2223093083827, + "y": -232.56998443685862 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "OpenAIModel-k39HS", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}", + "target": "ChatOutput-njtka", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-njtka", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "Text", "str"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-k39HS" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-k39HS{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}-ChatOutput-njtka{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-uxBqP", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}", + "target": "OpenAIModel-k39HS", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-k39HS", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "str", "Text"], + "dataType": "Prompt", + "id": "Prompt-uxBqP" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-uxBqP{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}-OpenAIModel-k39HS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "ChatInput-P3fgL", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}", + "target": "Prompt-uxBqP", + "targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "user_input", + "id": "Prompt-uxBqP", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "Record", "str", "Text"], + "dataType": "ChatInput", + "id": "ChatInput-P3fgL" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-P3fgL{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}-Prompt-uxBqP{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": 260.58251815500563, + "y": 318.2261172111936, + "zoom": 0.43514115784696294 + } + }, + "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ", + "name": "Basic Prompting (Hello, World)", + "last_tested_version": "1.0.0a4", + "is_component": false } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index 92384be99..d2e4986ab 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -1,1095 +1,987 @@ { - "id": "6ad5559d-fb66-4fdc-8f98-96f4ac12799d", - "data": { - "nodes": [ - { - "id": "Prompt-Rse03", - "type": "genericNode", - "position": { - "x": 1331.381712783371, - "y": 535.0279854229713 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Reference 1:\n\n{reference_1}\n\n---\n\nReference 2:\n\n{reference_2}\n\n---\n\n{instructions}\n\nBlog: \n\n\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "reference_1": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "reference_1", - "display_name": "reference_1", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "reference_2": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "reference_2", - "display_name": "reference_2", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "instructions": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "instructions", - "display_name": "instructions", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "Text", - "str" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "reference_1", - "reference_2", - "instructions" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-Rse03", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 571, - "dragging": false, - "positionAbsolute": { - "x": 1331.381712783371, - "y": 535.0279854229713 - } + "id": "6ad5559d-fb66-4fdc-8f98-96f4ac12799d", + "data": { + "nodes": [ + { + "id": "Prompt-Rse03", + "type": "genericNode", + "position": { + "x": 1331.381712783371, + "y": 535.0279854229713 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Reference 1:\n\n{reference_1}\n\n---\n\nReference 2:\n\n{reference_2}\n\n---\n\n{instructions}\n\nBlog: \n\n\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "reference_1": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "reference_1", + "display_name": "reference_1", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "reference_2": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "reference_2", + "display_name": "reference_2", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "instructions": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "instructions", + "display_name": "instructions", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "URL-HYPkR", - "type": "genericNode", - "position": { - "x": 568.2971412887712, - "y": 700.9983368007821 - }, - "data": { - "type": "URL", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "urls": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "urls", - "display_name": "URL", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": [ - "https://www.promptingguide.ai/techniques/prompt_chaining" - ] - }, - "_type": "CustomComponent" - }, - "description": "Fetch content from one or more URLs.", - "icon": "layout-template", - "base_classes": [ - "Record" - ], - "display_name": "URL", - "documentation": "", - "custom_fields": { - "urls": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "URL-HYPkR" - }, - "selected": false, - "width": 384, - "height": 281, - "positionAbsolute": { - "x": 568.2971412887712, - "y": 700.9983368007821 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": ["object", "Text", "str"], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": ["reference_1", "reference_2", "instructions"] }, - { - "id": "ChatOutput-JPlxl", - "type": "genericNode", - "position": { - "x": 2503.8617424688505, - "y": 789.3005578928434 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "Text", - "Record", - "object", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-JPlxl" - }, - "selected": false, - "width": 384, - "height": 383 - }, - { - "id": "OpenAIModel-gi29P", - "type": "genericNode", - "position": { - "x": 1917.7089968570963, - "y": 575.9186499244129 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "1024", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo-0125", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "0.1", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-gi29P" - }, - "selected": false, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 1917.7089968570963, - "y": 575.9186499244129 - }, - "dragging": false - }, - { - "id": "URL-2cX90", - "type": "genericNode", - "position": { - "x": 573.961301764604, - "y": 336.41463436122086 - }, - "data": { - "type": "URL", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "urls": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "urls", - "display_name": "URL", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": [ - "https://www.promptingguide.ai/introduction/basics" - ] - }, - "_type": "CustomComponent" - }, - "description": "Fetch content from one or more URLs.", - "icon": "layout-template", - "base_classes": [ - "Record" - ], - "display_name": "URL", - "documentation": "", - "custom_fields": { - "urls": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "URL-2cX90" - }, - "selected": false, - "width": 384, - "height": 281, - "positionAbsolute": { - "x": 573.961301764604, - "y": 336.41463436122086 - }, - "dragging": false - }, - { - "id": "TextInput-og8Or", - "type": "genericNode", - "position": { - "x": 569.9387927203336, - "y": 1095.3352160671316 - }, - "data": { - "type": "TextInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[str] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Use the references above for style to write a new blog/tutorial about prompt engineering techniques. Suggest non-covered topics.", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as input.", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get text inputs from the Playground.", - "icon": "type", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "Instructions", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextInput-og8Or" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 569.9387927203336, - "y": 1095.3352160671316 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "URL-HYPkR", - "target": "Prompt-Rse03", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153URL\u0153,\u0153id\u0153:\u0153URL-HYPkR\u0153}", - "targetHandle": 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variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 571, + "dragging": false, + "positionAbsolute": { + "x": 1331.381712783371, + "y": 535.0279854229713 } - }, - "description": "This flow can be used to create a blog post following instructions from the user, using two other blogs as reference.", - "name": "Blog Writer", - "last_tested_version": "1.0.0a0", - "is_component": false + }, + { + "id": "URL-HYPkR", + "type": "genericNode", + "position": { + "x": 568.2971412887712, + "y": 700.9983368007821 + }, + "data": { + "type": "URL", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "urls": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "urls", + "display_name": "URL", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": [ + "https://www.promptingguide.ai/techniques/prompt_chaining" + ] + }, + "_type": "CustomComponent" + }, + "description": "Fetch content from one or more URLs.", + "icon": "layout-template", + "base_classes": ["Record"], + "display_name": "URL", + "documentation": "", + "custom_fields": { + "urls": null + }, + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "URL-HYPkR" + }, + "selected": false, + "width": 384, + "height": 281, + "positionAbsolute": { + "x": 568.2971412887712, + "y": 700.9983368007821 + }, + "dragging": false + }, + { + "id": "ChatOutput-JPlxl", + "type": "genericNode", + "position": { + "x": 2503.8617424688505, + "y": 789.3005578928434 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["Text", "Record", "object", "str"], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null + }, + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-JPlxl" + }, + "selected": false, + "width": 384, + "height": 383 + }, + { + "id": "OpenAIModel-gi29P", + "type": "genericNode", + "position": { + "x": 1917.7089968570963, + "y": 575.9186499244129 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "1024", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo-0125", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "0.1", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": ["str", "Text", "object"], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-gi29P" + }, + "selected": false, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 1917.7089968570963, + "y": 575.9186499244129 + }, + "dragging": false + }, + { + "id": "URL-2cX90", + "type": "genericNode", + "position": { + "x": 573.961301764604, + "y": 336.41463436122086 + }, + "data": { + "type": "URL", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "urls": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "urls", + "display_name": "URL", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": ["https://www.promptingguide.ai/introduction/basics"] + }, + "_type": "CustomComponent" + }, + "description": "Fetch content from one or more URLs.", + "icon": "layout-template", + "base_classes": ["Record"], + "display_name": "URL", + "documentation": "", + "custom_fields": { + "urls": null + }, + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "URL-2cX90" + }, + "selected": false, + "width": 384, + "height": 281, + "positionAbsolute": { + "x": 573.961301764604, + "y": 336.41463436122086 + }, + "dragging": false + }, + { + "id": "TextInput-og8Or", + "type": "genericNode", + "position": { + "x": 569.9387927203336, + "y": 1095.3352160671316 + }, + "data": { + "type": "TextInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[str] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Use the references above for style to write a new blog/tutorial about prompt engineering techniques. Suggest non-covered topics.", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": ["Record", "Text"], + "dynamic": false, + "info": "Text or Record to be passed as input.", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Get text inputs from the Playground.", + "icon": "type", + "base_classes": ["object", "Text", "str"], + "display_name": "Instructions", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null + }, + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextInput-og8Or" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 569.9387927203336, + "y": 1095.3352160671316 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "URL-HYPkR", + "target": "Prompt-Rse03", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-HYPkRœ}", + "targetHandle": "{œfieldNameœ:œreference_2œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "id": "reactflow__edge-URL-HYPkR{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-HYPkRœ}-Prompt-Rse03{œfieldNameœ:œreference_2œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "reference_2", + "id": "Prompt-Rse03", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Record"], + "dataType": "URL", + "id": "URL-HYPkR" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "OpenAIModel-gi29P", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-gi29Pœ}", + "target": "ChatOutput-JPlxl", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-JPlxlœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-JPlxl", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-gi29P" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-gi29P{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-gi29Pœ}-ChatOutput-JPlxl{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-JPlxlœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "URL-2cX90", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-2cX90œ}", + "target": "Prompt-Rse03", + "targetHandle": "{œfieldNameœ:œreference_1œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "reference_1", + "id": "Prompt-Rse03", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Record"], + "dataType": "URL", + "id": "URL-2cX90" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-URL-2cX90{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-2cX90œ}-Prompt-Rse03{œfieldNameœ:œreference_1œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "TextInput-og8Or", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-og8Orœ}", + "target": "Prompt-Rse03", + "targetHandle": "{œfieldNameœ:œinstructionsœ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "instructions", + "id": "Prompt-Rse03", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "Text", "str"], + "dataType": "TextInput", + "id": "TextInput-og8Or" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-TextInput-og8Or{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-og8Orœ}-Prompt-Rse03{œfieldNameœ:œinstructionsœ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-Rse03", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-Rse03œ}", + "target": "OpenAIModel-gi29P", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-gi29Pœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-gi29P", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "Text", "str"], + "dataType": "Prompt", + "id": "Prompt-Rse03" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-Rse03{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-Rse03œ}-OpenAIModel-gi29P{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-gi29Pœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "selected": false + } + ], + "viewport": { + "x": -214.14726025721177, + "y": -35.83855793844168, + "zoom": 0.47344308394045925 + } + }, + "description": "This flow can be used to create a blog post following instructions from the user, using two other blogs as reference.", + "name": "Blog Writer", + "last_tested_version": "1.0.0a0", + "is_component": false } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index 744e28289..cde05c847 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -1,1028 +1,933 @@ { - "id": "fecbce42-6f11-454c-8ab2-db6eddbbbb0f", - "data": { - "nodes": [ - { - "id": "Prompt-tHwPf", - "type": "genericNode", - "position": { - "x": 585.7906101139403, - "y": 117.52115876762832 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "Document": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "Document", - "display_name": "Document", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "Question": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "Question", - "display_name": "Question", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "Document", - "Question" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-tHwPf", - "description": "A component for creating prompt templates using dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 479, - "positionAbsolute": { - "x": 585.7906101139403, - "y": 117.52115876762832 - }, - "dragging": false + "id": "fecbce42-6f11-454c-8ab2-db6eddbbbb0f", + "data": { + "nodes": [ + { + "id": "Prompt-tHwPf", + "type": "genericNode", + "position": { + "x": 585.7906101139403, + "y": 117.52115876762832 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "Document": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "Document", + "display_name": "Document", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "Question": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "Question", + "display_name": "Question", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "File-6TEsD", - "type": "genericNode", - "position": { - "x": -18.636536329280602, - "y": 3.951948774836353 - }, - "data": { - "type": "File", - "node": { - "template": { - "path": { - "type": "file", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [ - ".txt", - ".md", - ".mdx", - ".csv", - ".json", - ".yaml", - ".yml", - ".xml", - ".html", - ".htm", - ".pdf", - ".docx" - ], - "password": false, - "name": "path", - "display_name": "Path", - "advanced": false, - "dynamic": false, - "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"Files\"\n description = \"A generic file loader.\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "silent_errors": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "silent_errors", - "display_name": "Silent Errors", - "advanced": true, - "dynamic": false, - "info": "If true, errors will not raise an exception.", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "A generic file loader.", - "base_classes": [ - "Record" - ], - "display_name": "Files", - "documentation": "", - "custom_fields": { - "path": null, - "silent_errors": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "File-6TEsD" - }, - "selected": false, - "width": 384, - "height": 282, - "positionAbsolute": { - "x": -18.636536329280602, - "y": 3.951948774836353 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": ["object", "str", "Text"], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": ["Document", "Question"] }, - { - "id": "ChatInput-MsSJ9", - "type": "genericNode", - "position": { - "x": -28.80036300619821, - "y": 379.81180230285355 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "str", - "Record", - "Text", - "object" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-MsSJ9" - }, - "selected": true, - "width": 384, - "height": 377, - "positionAbsolute": { - "x": -28.80036300619821, - "y": 379.81180230285355 - }, - "dragging": false + "output_types": ["Text"], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-tHwPf", + "description": "A component for creating prompt templates using dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 479, + "positionAbsolute": { + "x": 585.7906101139403, + "y": 117.52115876762832 + }, + "dragging": false + }, + { + "id": "File-6TEsD", + "type": "genericNode", + "position": { + "x": -18.636536329280602, + "y": 3.951948774836353 + }, + "data": { + "type": "File", + "node": { + "template": { + "path": { + "type": "file", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [ + ".txt", + ".md", + ".mdx", + ".csv", + ".json", + ".yaml", + ".yml", + ".xml", + ".html", + ".htm", + ".pdf", + ".docx" + ], + "password": false, + "name": "path", + "display_name": "Path", + "advanced": false, + "dynamic": false, + "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"Files\"\n description = \"A generic file loader.\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "silent_errors": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "silent_errors", + "display_name": "Silent Errors", + "advanced": true, + "dynamic": false, + "info": "If true, errors will not raise an exception.", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "id": "ChatOutput-F5Awj", - "type": "genericNode", - "position": { - "x": 1733.3012915204283, - "y": 168.76098809939327 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "str", - "Record", - "Text", - "object" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-F5Awj" - }, - "selected": false, - "width": 384, - "height": 385, - "positionAbsolute": { - "x": 1733.3012915204283, - "y": 168.76098809939327 - }, - "dragging": false + "description": "A generic file loader.", + "base_classes": ["Record"], + "display_name": "Files", + "documentation": "", + "custom_fields": { + "path": null, + "silent_errors": null }, - { - "id": "OpenAIModel-Bt067", - "type": "genericNode", - "position": { - "x": 1137.6078582863759, - "y": -14.41920034020356 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-turbo-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": false, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "str", - "Text" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-Bt067" - }, - "selected": false, - "width": 384, - "height": 642, - "positionAbsolute": { - "x": 1137.6078582863759, - "y": -14.41920034020356 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "ChatInput-MsSJ9", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Record\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-MsSJ9\u0153}", - "target": "Prompt-tHwPf", - "targetHandle": "{\u0153fieldName\u0153:\u0153Question\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "Question", - "id": "Prompt-tHwPf", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Record", - "Text", - "object" - ], - "dataType": "ChatInput", - "id": "ChatInput-MsSJ9" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-MsSJ9{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Record\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-MsSJ9\u0153}-Prompt-tHwPf{\u0153fieldName\u0153:\u0153Question\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "File-6TEsD" + }, + "selected": false, + "width": 384, + "height": 282, + "positionAbsolute": { + "x": -18.636536329280602, + "y": 3.951948774836353 + }, + "dragging": false + }, + { + "id": "ChatInput-MsSJ9", + "type": "genericNode", + "position": { + "x": -28.80036300619821, + "y": 379.81180230285355 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "source": "File-6TEsD", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-6TEsD\u0153}", - "target": "Prompt-tHwPf", - "targetHandle": "{\u0153fieldName\u0153:\u0153Document\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "Document", - "id": "Prompt-tHwPf", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "File", - "id": "File-6TEsD" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-File-6TEsD{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-6TEsD\u0153}-Prompt-tHwPf{\u0153fieldName\u0153:\u0153Document\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": ["str", "Record", "Text", "object"], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "source": "Prompt-tHwPf", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153}", - "target": "OpenAIModel-Bt067", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-Bt067", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-tHwPf" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-tHwPf{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153}-OpenAIModel-Bt067{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-MsSJ9" + }, + "selected": true, + "width": 384, + "height": 377, + "positionAbsolute": { + "x": -28.80036300619821, + "y": 379.81180230285355 + }, + "dragging": false + }, + { + "id": "ChatOutput-F5Awj", + "type": "genericNode", + "position": { + "x": 1733.3012915204283, + "y": 168.76098809939327 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-Bt067", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153}", - "target": "ChatOutput-F5Awj", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-F5Awj\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-F5Awj", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-Bt067" - } + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["str", "Record", "Text", "object"], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null + }, + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-F5Awj" + }, + "selected": false, + "width": 384, + "height": 385, + "positionAbsolute": { + "x": 1733.3012915204283, + "y": 168.76098809939327 + }, + "dragging": false + }, + { + "id": "OpenAIModel-Bt067", + "type": "genericNode", + "position": { + "x": 1137.6078582863759, + "y": -14.41920034020356 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-turbo-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": false, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-Bt067{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153}-ChatOutput-F5Awj{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-F5Awj\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - } - ], - "viewport": { - "x": 352.20899206064655, - "y": 56.054900898593075, - "zoom": 0.9023391400011 - } - }, - "description": "This flow integrates PDF reading with a language model to answer document-specific questions. 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Ideal for small-scale texts, it facilitates direct queries with immediate insights.", + "name": "Document QA", + "last_tested_version": "1.0.0a0", + "is_component": false } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index bd937a2c7..a20f7bc19 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -1,1271 +1,1137 @@ { - "id": "08d5cccf-d098-4367-b14b-1078429c9ed9", - "icon": "\ud83e\udd16", - "icon_bg_color": "#FFD700", - "data": { - "nodes": [ - { - "id": "ChatInput-t7F8v", - "type": "genericNode", - "position": { - "x": 1283.2700598313072, - "y": 982.5953650473145 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": false, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "MySessionID" - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "Text", - "object", - "Record", - "str" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-t7F8v" - }, - "selected": false, - "width": 384, - "height": 469, - "positionAbsolute": { - "x": 1283.2700598313072, - "y": 982.5953650473145 - }, - "dragging": false + "id": "08d5cccf-d098-4367-b14b-1078429c9ed9", + "icon": "🤖", + "icon_bg_color": "#FFD700", + "data": { + "nodes": [ + { + "id": "ChatInput-t7F8v", + "type": "genericNode", + "position": { + "x": 1283.2700598313072, + "y": 982.5953650473145 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": false, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "MySessionID" + }, + "_type": "CustomComponent" }, - { - "id": "ChatOutput-P1jEe", - "type": "genericNode", - "position": { - "x": 3154.916355514023, - "y": 851.051882666333 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": false, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "MySessionID" - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "Text", - "object", - "Record", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-P1jEe" - }, - "selected": false, - "width": 384, - "height": 477, - "dragging": false, - "positionAbsolute": { - "x": 3154.916355514023, - "y": 851.051882666333 - } + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": ["Text", "object", "Record", "str"], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "id": "MemoryComponent-cdA1J", - "type": "genericNode", - "position": { - "x": 1289.9606870058817, - "y": 442.16804561053766 - }, - "data": { - "type": "MemoryComponent", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.schema import Record\n\n\nclass MemoryComponent(BaseMemoryComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def get_messages(self, **kwargs) -> list[Record]:\n # Validate kwargs by checking if it contains the correct keys\n if \"sender\" not in kwargs:\n kwargs[\"sender\"] = None\n if \"sender_name\" not in kwargs:\n kwargs[\"sender_name\"] = None\n if \"session_id\" not in kwargs:\n kwargs[\"session_id\"] = None\n if \"limit\" not in kwargs:\n kwargs[\"limit\"] = 5\n if \"order\" not in kwargs:\n kwargs[\"order\"] = \"Descending\"\n\n kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n if kwargs[\"sender\"] == \"Machine and User\":\n kwargs[\"sender\"] = None\n return get_messages(**kwargs)\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n messages = self.get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template or \"\", records=messages)\n self.status = messages_str\n return messages_str\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "n_messages": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 5, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "n_messages", - "display_name": "Number of Messages", - "advanced": false, - "dynamic": false, - "info": "Number of messages to retrieve.", - "load_from_db": false, - "title_case": false - }, - "order": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Descending", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Ascending", - "Descending" - ], - "name": "order", - "display_name": "Order", - "advanced": true, - "dynamic": false, - "info": "Order of the messages.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{sender_name}: {text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine and User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User", - "Machine and User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "Session ID of the chat history.", - "load_from_db": false, - "title_case": false, - "value": "MySessionID" - }, - "_type": "CustomComponent" - }, - "description": "Retrieves stored chat messages given a specific Session ID.", - "icon": "history", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "Chat Memory", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "session_id": null, - "n_messages": null, - "order": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": true - }, - "id": "MemoryComponent-cdA1J", - "description": "Retrieves stored chat messages given a specific Session ID.", - "display_name": "Chat Memory" - }, - "selected": false, - "width": 384, - "height": 489, - "dragging": false, - "positionAbsolute": { - "x": 1289.9606870058817, - "y": 442.16804561053766 - } + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-t7F8v" + }, + "selected": false, + "width": 384, + "height": 469, + "positionAbsolute": { + "x": 1283.2700598313072, + "y": 982.5953650473145 + }, + "dragging": false + }, + { + "id": "ChatOutput-P1jEe", + "type": "genericNode", + "position": { + "x": 3154.916355514023, + "y": 851.051882666333 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": false, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "MySessionID" + }, + "_type": "CustomComponent" }, - { - "id": "Prompt-ODkUx", - "type": "genericNode", - "position": { - "x": 1894.594426342426, - "y": 753.3797365481901 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "{context}\n\nUser: {user_message}\nAI: ", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "context": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "context", - "display_name": "context", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "user_message": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "user_message", - "display_name": "user_message", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "Text", - "str", - "object" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "context", - "user_message" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-ODkUx", - "description": "A component for creating prompt templates using dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 477, - "dragging": false, - "positionAbsolute": { - "x": 1894.594426342426, - "y": 753.3797365481901 - } + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["Text", "object", "Record", "str"], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "id": "OpenAIModel-9RykF", - "type": "genericNode", - "position": { - "x": 2561.5850334731617, - "y": 553.2745131130916 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-1106-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "0.2", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "str", - "object", - "Text" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-9RykF" - }, - "selected": false, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 2561.5850334731617, - "y": 553.2745131130916 - }, - "dragging": false - }, - { - "id": "TextOutput-vrs6T", - "type": "genericNode", - "position": { - "x": 1911.4785906252087, - "y": 247.39079954376987 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Playground.", - "icon": "type", - "base_classes": [ - "str", - "object", - "Text" - ], - "display_name": "Inspect Memory", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-vrs6T" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 1911.4785906252087, - "y": 247.39079954376987 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "MemoryComponent-cdA1J", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}", - 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}, - { - "source": "MemoryComponent-cdA1J", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}", - "target": "TextOutput-vrs6T", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-vrs6T\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-vrs6T", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "MemoryComponent", - "id": "MemoryComponent-cdA1J" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-MemoryComponent-cdA1J{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}-TextOutput-vrs6T{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-vrs6T\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - } - ], - "viewport": { - "x": -569.862554459756, - "y": -42.08339711050985, - "zoom": 0.4868590524514978 + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-P1jEe" + }, + "selected": false, + "width": 384, + "height": 477, + "dragging": false, + "positionAbsolute": { + "x": 3154.916355514023, + "y": 851.051882666333 } - }, - "description": "This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.", - "name": "Memory Chatbot", - "last_tested_version": "1.0.0a0", - "is_component": false + }, + { + "id": "MemoryComponent-cdA1J", + "type": "genericNode", + "position": { + "x": 1289.9606870058817, + "y": 442.16804561053766 + }, + "data": { + "type": "MemoryComponent", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.schema import Record\n\n\nclass MemoryComponent(BaseMemoryComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def get_messages(self, **kwargs) -> list[Record]:\n # Validate kwargs by checking if it contains the correct keys\n if \"sender\" not in kwargs:\n kwargs[\"sender\"] = None\n if \"sender_name\" not in kwargs:\n kwargs[\"sender_name\"] = None\n if \"session_id\" not in kwargs:\n kwargs[\"session_id\"] = None\n if \"limit\" not in kwargs:\n kwargs[\"limit\"] = 5\n if \"order\" not in kwargs:\n kwargs[\"order\"] = \"Descending\"\n\n kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n if kwargs[\"sender\"] == \"Machine and User\":\n kwargs[\"sender\"] = None\n return get_messages(**kwargs)\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n messages = self.get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template or \"\", records=messages)\n self.status = messages_str\n return messages_str\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "n_messages": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 5, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "n_messages", + "display_name": "Number of Messages", + "advanced": false, + "dynamic": false, + "info": "Number of messages to retrieve.", + "load_from_db": false, + "title_case": false + }, + "order": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Descending", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Ascending", "Descending"], + "name": "order", + "display_name": "Order", + "advanced": true, + "dynamic": false, + "info": "Order of the messages.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{sender_name}: {text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine and User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User", "Machine and User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "Session ID of the chat history.", + "load_from_db": false, + "title_case": false, + "value": "MySessionID" + }, + "_type": "CustomComponent" + }, + "description": "Retrieves stored chat messages given a specific Session ID.", + "icon": "history", + "base_classes": ["str", "Text", "object"], + "display_name": "Chat Memory", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "session_id": null, + "n_messages": null, + "order": null, + "record_template": null + }, + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": true + }, + "id": "MemoryComponent-cdA1J", + "description": "Retrieves stored chat messages given a specific Session ID.", + "display_name": "Chat Memory" + }, + "selected": false, + "width": 384, + "height": 489, + "dragging": false, + "positionAbsolute": { + "x": 1289.9606870058817, + "y": 442.16804561053766 + } + }, + { + "id": "Prompt-ODkUx", + "type": "genericNode", + "position": { + "x": 1894.594426342426, + "y": 753.3797365481901 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "{context}\n\nUser: {user_message}\nAI: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "context": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "context", + "display_name": "context", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "user_message": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "user_message", + "display_name": "user_message", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } + }, + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": ["Text", "str", "object"], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": ["context", "user_message"] + }, + "output_types": ["Text"], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-ODkUx", + "description": "A component for creating prompt templates using dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 477, + "dragging": false, + "positionAbsolute": { + "x": 1894.594426342426, + "y": 753.3797365481901 + } + }, + { + "id": "OpenAIModel-9RykF", + "type": "genericNode", + "position": { + "x": 2561.5850334731617, + "y": 553.2745131130916 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-1106-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "0.2", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": ["str", "object", "Text"], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-9RykF" + }, + "selected": false, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 2561.5850334731617, + "y": 553.2745131130916 + }, + "dragging": false + }, + { + "id": "TextOutput-vrs6T", + "type": "genericNode", + "position": { + "x": 1911.4785906252087, + "y": 247.39079954376987 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": ["Record", "Text"], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Display a text output in the Playground.", + "icon": "type", + "base_classes": ["str", "object", "Text"], + "display_name": "Inspect Memory", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null + }, + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-vrs6T" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 1911.4785906252087, + "y": 247.39079954376987 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "MemoryComponent-cdA1J", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", + "target": "Prompt-ODkUx", + "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "context", + "type": "str", + "id": "Prompt-ODkUx", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"] + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "MemoryComponent", + "id": "MemoryComponent-cdA1J" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-MemoryComponent-cdA1J{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}-Prompt-ODkUx{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "selected": false + }, + { + "source": "ChatInput-t7F8v", + "sourceHandle": "{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}", + "target": "Prompt-ODkUx", + "targetHandle": "{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "user_message", + "type": "str", + "id": "Prompt-ODkUx", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"] + }, + "sourceHandle": { + "baseClasses": ["Text", "object", "Record", "str"], + "dataType": "ChatInput", + "id": "ChatInput-t7F8v" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-t7F8v{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}-Prompt-ODkUx{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "selected": false + }, + { + "source": "Prompt-ODkUx", + "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}", + "target": "OpenAIModel-9RykF", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-9RykF", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Text", "str", "object"], + "dataType": "Prompt", + "id": "Prompt-ODkUx" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-ODkUx{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}-OpenAIModel-9RykF{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "OpenAIModel-9RykF", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}", + "target": "ChatOutput-P1jEe", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-P1jEe", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "object", "Text"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-9RykF" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-9RykF{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}-ChatOutput-P1jEe{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "MemoryComponent-cdA1J", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", + "target": "TextOutput-vrs6T", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "TextOutput-vrs6T", + "inputTypes": ["Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "MemoryComponent", + "id": "MemoryComponent-cdA1J" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-foreground stroke-connection", + "id": "reactflow__edge-MemoryComponent-cdA1J{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}-TextOutput-vrs6T{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": -569.862554459756, + "y": -42.08339711050985, + "zoom": 0.4868590524514978 + } + }, + "description": "This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.", + "name": "Memory Chatbot", + "last_tested_version": "1.0.0a0", + "is_component": false } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index a11153155..38dda79d5 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -1,1767 +1,1586 @@ { - "id": "85392e54-20f3-4ab5-a179-cb4bef16f639", - "data": { - "nodes": [ - { - "id": "Prompt-amqBu", - "type": "genericNode", - "position": { - "x": 2191.5837146441663, - "y": 1047.9307944451873 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "You are a helpful assistant. Given a long document, your task is to create a concise summary that captures the main points and key details. The summary should be clear, accurate, and succinct. Please provide the summary in the format below:\n####\n{document}\n####\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "document": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "document", - "display_name": "document", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "document" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-amqBu", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 385, - "positionAbsolute": { - "x": 2191.5837146441663, - "y": 1047.9307944451873 - }, - "dragging": false + "id": "85392e54-20f3-4ab5-a179-cb4bef16f639", + "data": { + "nodes": [ + { + "id": "Prompt-amqBu", + "type": "genericNode", + "position": { + "x": 2191.5837146441663, + "y": 1047.9307944451873 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "You are a helpful assistant. Given a long document, your task is to create a concise summary that captures the main points and key details. The summary should be clear, accurate, and succinct. Please provide the summary in the format below:\n####\n{document}\n####\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "document": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "document", + "display_name": "document", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "Prompt-gTNiz", - "type": "genericNode", - "position": { - "x": 3731.0813766902447, - "y": 799.631909121391 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Given a summary of an article, please create two multiple-choice questions that cover the key points and details mentioned. Ensure the questions are clear and provide three options (A, B, C), with one correct answer.\n####\n{summary}\n####", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "summary": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "summary", - "display_name": "summary", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "summary" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-gTNiz", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 385, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": ["object", "str", "Text"], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": ["document"] }, - { - "id": "ChatOutput-EJkG3", - "type": "genericNode", - "position": { - "x": 3722.1747844849388, - "y": 1283.413553222214 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Summarizer", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "object", - "Record", - "Text", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-EJkG3" - }, - "selected": false, - "width": 384, - "height": 385, - "dragging": false + "output_types": ["Text"], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-amqBu", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 385, + "positionAbsolute": { + "x": 2191.5837146441663, + "y": 1047.9307944451873 + }, + "dragging": false + }, + { + "id": "Prompt-gTNiz", + "type": "genericNode", + "position": { + "x": 3731.0813766902447, + "y": 799.631909121391 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Given a summary of an article, please create two multiple-choice questions that cover the key points and details mentioned. Ensure the questions are clear and provide three options (A, B, C), with one correct answer.\n####\n{summary}\n####", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "summary": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "summary", + "display_name": "summary", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "ChatOutput-DNmvg", - "type": "genericNode", - "position": { - "x": 5077.71285886074, - "y": 1232.9152769735522 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Question Generator", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "object", - "Record", - "Text", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-DNmvg" - }, - "selected": false, - "width": 384, - "height": 385 + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": ["object", "str", "Text"], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": ["summary"] }, - { - "id": "TextInput-sptaH", - "type": "genericNode", - "position": { - "x": 1700.5624822024752, - "y": 1039.603088937466 - }, - "data": { - "type": "TextInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[Text] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Revolutionary Nano-Battery Technology Unveiled In a groundbreaking announcement yesterday, researchers from the fictional Tech Innovations Institute revealed the development of a new nano-battery technology that promises to revolutionize energy storage. The new battery, dubbed the \"EnerGCell\", uses advanced nanomaterials to achieve unprecedented efficiency and storage capacities. According to lead researcher Dr. Ada Byron, the EnerGCell can store up to ten times more energy than the best lithium-ion batteries available today, while charging in just a fraction of the time. \"We're talking about charging your electric vehicle in just five minutes for a range of over 1,000 miles,\" Dr. Byron stated during the press conference. The technology behind the EnerGCell involves a complex arrangement of nanostructured electrodes that allow for rapid ion transfer and extremely high energy density. This breakthrough was achieved after a decade of research into nanomaterials and their applications in energy storage. The implications of this technology are vast, promising to accelerate the adoption of renewable energy by making it more practical and affordable to store wind and solar power. It could also lead to significant advancements in electric vehicles, mobile devices, and any other technology that relies on batteries. Despite the excitement, some experts are calling for patience, noting that the EnerGCell is still in its early stages of development and may take several years before it's commercially available. However, the potential impact of such a technology on the environment and the global economy is undeniable. Tech Innovations Institute plans to continue refining the EnerGCell and begin pilot projects with select partners in the coming year. If successful, this nano-battery technology could indeed be the breakthrough needed to usher in a new era of clean energy and technology.", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as input.", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get text inputs from the Playground.", - "icon": "type", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "Text Input", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextInput-sptaH" - }, - "selected": false, - "width": 384, - "height": 290, - "positionAbsolute": { - "x": 1700.5624822024752, - "y": 1039.603088937466 - }, - "dragging": false + "output_types": ["Text"], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-gTNiz", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 385, + "dragging": false + }, + { + "id": "ChatOutput-EJkG3", + "type": "genericNode", + "position": { + "x": 3722.1747844849388, + "y": 1283.413553222214 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Summarizer", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "id": "TextOutput-2MS4a", - "type": "genericNode", - "position": { - "x": 2917.216113690115, - "y": 513.0058511435552 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Playground.", - "icon": "type", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "First Prompt", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-2MS4a" - }, - "selected": false, - "width": 384, - "height": 290, - "positionAbsolute": { - "x": 2917.216113690115, - "y": 513.0058511435552 - }, - "dragging": false + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["object", "Record", "Text", "str"], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null }, - { - "id": "OpenAIModel-uYXZJ", - "type": "genericNode", - "position": { - "x": 2925.784767523062, - "y": 933.6465680967775 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-turbo-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-uYXZJ" - }, - "selected": false, - "width": 384, - "height": 565, - "positionAbsolute": { - "x": 2925.784767523062, - "y": 933.6465680967775 - }, - "dragging": false + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-EJkG3" + }, + "selected": false, + "width": 384, + "height": 385, + "dragging": false + }, + { + "id": "ChatOutput-DNmvg", + "type": "genericNode", + "position": { + "x": 5077.71285886074, + "y": 1232.9152769735522 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Question Generator", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "id": "TextOutput-MUDOR", - "type": "genericNode", - "position": { - "x": 4446.064323520379, - "y": 633.833297518702 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Playground.", - "icon": "type", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "Second Prompt", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-MUDOR" - }, - "selected": false, - "width": 384, - "height": 290, - "dragging": false, - "positionAbsolute": { - "x": 4446.064323520379, - "y": 633.833297518702 - } + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["object", "Record", "Text", "str"], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null }, - { - "id": "OpenAIModel-XawYB", - "type": "genericNode", - "position": { - "x": 4500.152018344182, - "y": 1027.7382026227656 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-turbo-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-XawYB" - }, - "selected": false, - "width": 384, - "height": 565, - "positionAbsolute": { - "x": 4500.152018344182, - "y": 1027.7382026227656 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "TextInput-sptaH", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153TextInput\u0153,\u0153id\u0153:\u0153TextInput-sptaH\u0153}", - "target": "Prompt-amqBu", - "targetHandle": "{\u0153fieldName\u0153:\u0153document\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "document", - "id": "Prompt-amqBu", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "TextInput", - "id": "TextInput-sptaH" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-TextInput-sptaH{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153TextInput\u0153,\u0153id\u0153:\u0153TextInput-sptaH\u0153}-Prompt-amqBu{\u0153fieldName\u0153:\u0153document\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-DNmvg" + }, + "selected": false, + "width": 384, + "height": 385 + }, + { + "id": "TextInput-sptaH", + "type": "genericNode", + "position": { + "x": 1700.5624822024752, + "y": 1039.603088937466 + }, + "data": { + "type": "TextInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[Text] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Revolutionary Nano-Battery Technology Unveiled In a groundbreaking announcement yesterday, researchers from the fictional Tech Innovations Institute revealed the development of a new nano-battery technology that promises to revolutionize energy storage. The new battery, dubbed the \"EnerGCell\", uses advanced nanomaterials to achieve unprecedented efficiency and storage capacities. According to lead researcher Dr. Ada Byron, the EnerGCell can store up to ten times more energy than the best lithium-ion batteries available today, while charging in just a fraction of the time. \"We're talking about charging your electric vehicle in just five minutes for a range of over 1,000 miles,\" Dr. Byron stated during the press conference. The technology behind the EnerGCell involves a complex arrangement of nanostructured electrodes that allow for rapid ion transfer and extremely high energy density. This breakthrough was achieved after a decade of research into nanomaterials and their applications in energy storage. The implications of this technology are vast, promising to accelerate the adoption of renewable energy by making it more practical and affordable to store wind and solar power. It could also lead to significant advancements in electric vehicles, mobile devices, and any other technology that relies on batteries. Despite the excitement, some experts are calling for patience, noting that the EnerGCell is still in its early stages of development and may take several years before it's commercially available. However, the potential impact of such a technology on the environment and the global economy is undeniable. Tech Innovations Institute plans to continue refining the EnerGCell and begin pilot projects with select partners in the coming year. If successful, this nano-battery technology could indeed be the breakthrough needed to usher in a new era of clean energy and technology.", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": ["Record", "Text"], + "dynamic": false, + "info": "Text or Record to be passed as input.", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "source": "Prompt-amqBu", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153}", - "target": "TextOutput-2MS4a", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-2MS4a\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-2MS4a", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-amqBu" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-amqBu{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153}-TextOutput-2MS4a{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-2MS4a\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "description": "Get text inputs from the Playground.", + "icon": "type", + "base_classes": ["str", "Text", "object"], + "display_name": "Text Input", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null }, - { - "source": "Prompt-amqBu", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153}", - "target": "OpenAIModel-uYXZJ", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-uYXZJ", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-amqBu" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-amqBu{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153}-OpenAIModel-uYXZJ{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextInput-sptaH" + }, + "selected": false, + "width": 384, + "height": 290, + "positionAbsolute": { + "x": 1700.5624822024752, + "y": 1039.603088937466 + }, + "dragging": false + }, + { + "id": "TextOutput-2MS4a", + "type": "genericNode", + "position": { + "x": 2917.216113690115, + "y": 513.0058511435552 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": ["Record", "Text"], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-uYXZJ", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}", - "target": "Prompt-gTNiz", - "targetHandle": "{\u0153fieldName\u0153:\u0153summary\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "summary", - "id": "Prompt-gTNiz", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-uYXZJ" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-uYXZJ{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}-Prompt-gTNiz{\u0153fieldName\u0153:\u0153summary\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "description": "Display a text output in the Playground.", + "icon": "type", + "base_classes": ["str", "Text", "object"], + "display_name": "First Prompt", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null }, - { - "source": "OpenAIModel-uYXZJ", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}", - "target": "ChatOutput-EJkG3", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-EJkG3\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-EJkG3", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-uYXZJ" - } + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-2MS4a" + }, + "selected": false, + "width": 384, + "height": 290, + "positionAbsolute": { + "x": 2917.216113690115, + "y": 513.0058511435552 + }, + "dragging": false + }, + { + "id": "OpenAIModel-uYXZJ", + "type": "genericNode", + "position": { + "x": 2925.784767523062, + "y": 933.6465680967775 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-turbo-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-uYXZJ{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}-ChatOutput-EJkG3{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-EJkG3\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "source": "Prompt-gTNiz", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}", - "target": "TextOutput-MUDOR", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-MUDOR\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-MUDOR", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-gTNiz" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-gTNiz{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}-TextOutput-MUDOR{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-MUDOR\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": ["str", "Text", "object"], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null }, - { - "source": "Prompt-gTNiz", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}", - "target": "OpenAIModel-XawYB", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-XawYB\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-XawYB", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-gTNiz" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-gTNiz{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}-OpenAIModel-XawYB{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-XawYB\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-uYXZJ" + }, + "selected": false, + "width": 384, + "height": 565, + "positionAbsolute": { + "x": 2925.784767523062, + "y": 933.6465680967775 + }, + "dragging": false + }, + { + "id": "TextOutput-MUDOR", + "type": "genericNode", + "position": { + "x": 4446.064323520379, + "y": 633.833297518702 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": ["Record", "Text"], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-XawYB", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-XawYB\u0153}", - "target": "ChatOutput-DNmvg", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-DNmvg\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-DNmvg", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-XawYB" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-XawYB{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-XawYB\u0153}-ChatOutput-DNmvg{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-DNmvg\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - } - ], - "viewport": { - "x": -383.7251879618552, - "y": 69.19813933800037, - "zoom": 0.3105753483695743 + "description": "Display a text output in the Playground.", + "icon": "type", + "base_classes": ["str", "Text", "object"], + "display_name": "Second Prompt", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null + }, + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-MUDOR" + }, + "selected": false, + "width": 384, + "height": 290, + "dragging": false, + "positionAbsolute": { + "x": 4446.064323520379, + "y": 633.833297518702 } - }, - "description": "The Prompt Chaining flow chains prompts with LLMs, refining outputs through iterative stages.", - "name": "Prompt Chaining", - "last_tested_version": "1.0.0a0", - "is_component": false + }, + { + "id": "OpenAIModel-XawYB", + "type": "genericNode", + "position": { + "x": 4500.152018344182, + "y": 1027.7382026227656 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-turbo-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" 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"input_value", + "id": "ChatOutput-EJkG3", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-uYXZJ" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-ChatOutput-EJkG3{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-gTNiz", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", + "target": "TextOutput-MUDOR", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "TextOutput-MUDOR", + "inputTypes": ["Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "str", "Text"], + "dataType": "Prompt", + "id": "Prompt-gTNiz" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-TextOutput-MUDOR{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-gTNiz", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", + "target": "OpenAIModel-XawYB", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-XawYB", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "str", "Text"], + "dataType": "Prompt", + "id": "Prompt-gTNiz" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-OpenAIModel-XawYB{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "OpenAIModel-XawYB", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}", + "target": "ChatOutput-DNmvg", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-DNmvg", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-XawYB" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-XawYB{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}-ChatOutput-DNmvg{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": -383.7251879618552, + "y": 69.19813933800037, + "zoom": 0.3105753483695743 + } + }, + "description": "The Prompt Chaining flow chains prompts with LLMs, refining outputs through iterative stages.", + "name": "Prompt Chaining", + "last_tested_version": "1.0.0a0", + "is_component": false } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index 02d5a4e92..e13a206c9 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -1,3406 +1,3151 @@ { - "id": "51e2b78a-199b-4054-9f32-e288eef6924c", - "data": { - "nodes": [ - { - "id": "ChatInput-yxMKE", - "type": "genericNode", - "position": { - "x": 1195.5276981160775, - "y": 209.421875 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "what is a line" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "Text", - "str", - "object", - "Record" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-yxMKE" - }, - "selected": false, - "width": 384, - "height": 383 + "id": "51e2b78a-199b-4054-9f32-e288eef6924c", + "data": { + "nodes": [ + { + "id": "ChatInput-yxMKE", + "type": "genericNode", + "position": { + "x": 1195.5276981160775, + "y": 209.421875 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "what is a line" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "id": "TextOutput-BDknO", - "type": "genericNode", - "position": { - "x": 2322.600672827879, - "y": 604.9467307442569 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Playground.", - "icon": "type", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "Extracted Chunks", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-BDknO" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 2322.600672827879, - "y": 604.9467307442569 - }, - "dragging": false + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": ["Text", "str", "object", "Record"], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "id": "OpenAIEmbeddings-ZlOk1", - "type": "genericNode", - "position": { - "x": 1183.667250865064, - "y": 687.3171828430261 - }, - "data": { - "type": "OpenAIEmbeddings", - "node": { - "template": { - "allowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "allowed_special", - "display_name": "Allowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "client", - "display_name": "Client", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_headers": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_headers", - "display_name": "Default Headers", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_query": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_query", - "display_name": "Default Query", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "deployment": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "deployment", - "display_name": "Deployment", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "disallowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [ - "all" - ], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "disallowed_special", - "display_name": "Disallowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "embedding_ctx_length": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 8191, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding_ctx_length", - "display_name": "Embedding Context Length", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_retries": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 6, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_retries", - "display_name": "Max Retries", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "name": "model", - "display_name": "Model", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "openai_api_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_type", - "display_name": "OpenAI API Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_version": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_version", - "display_name": "OpenAI API Version", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_organization": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_organization", - "display_name": "OpenAI Organization", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_proxy": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_proxy", - "display_name": "OpenAI Proxy", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "request_timeout": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "request_timeout", - "display_name": "Request Timeout", - "advanced": true, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "show_progress_bar": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "show_progress_bar", - "display_name": "Show Progress Bar", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "skip_empty": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "skip_empty", - "display_name": "Skip Empty", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_enable": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_enable", - "display_name": "TikToken Enable", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_model_name", - "display_name": "TikToken Model Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Generate embeddings using OpenAI models.", - "base_classes": [ - "Embeddings" - ], - "display_name": "OpenAI Embeddings", - "documentation": "", - "custom_fields": { - "openai_api_key": null, - "default_headers": null, - "default_query": null, - "allowed_special": null, - "disallowed_special": null, - "chunk_size": null, - "client": null, - "deployment": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "output_types": [ - "Embeddings" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "OpenAIEmbeddings-ZlOk1" - }, - "selected": false, - "width": 384, - "height": 383, - "dragging": false + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-yxMKE" + }, + "selected": false, + "width": 384, + "height": 383 + }, + { + "id": "TextOutput-BDknO", + "type": "genericNode", + "position": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": ["Record", "Text"], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "id": "OpenAIModel-EjXlN", - "type": "genericNode", - "position": { - "x": 3410.117202077183, - "y": 431.2038048137648 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-EjXlN" - }, - "selected": true, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 3410.117202077183, - "y": 431.2038048137648 - }, - "dragging": false + "description": "Display a text output in the Playground.", + "icon": "type", + "base_classes": ["object", "Text", "str"], + "display_name": "Extracted Chunks", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null }, - { - "id": "Prompt-xeI6K", - "type": "genericNode", - "position": { - "x": 2969.0261961391298, - "y": 442.1613649809069 + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-BDknO" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-ZlOk1", + "type": "genericNode", + "position": { + "x": 1183.667250865064, + "y": 687.3171828430261 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": ["all"], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "embedding_ctx_length": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 8191, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding_ctx_length", + "display_name": "Embedding Context Length", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_retries": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 6, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_retries", + "display_name": "Max Retries", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-ada-002" + ], + "name": "model", + "display_name": "Model", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "openai_api_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_type", + "display_name": "OpenAI API Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_version": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_version", + "display_name": "OpenAI API Version", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_organization": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_organization", + "display_name": "OpenAI Organization", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_proxy": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_proxy", + "display_name": "OpenAI Proxy", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "request_timeout": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "request_timeout", + "display_name": "Request Timeout", + "advanced": true, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "context": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "context", - "display_name": "context", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "question": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "question", - "display_name": "question", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "Text", - "str" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "context", - "question" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-xeI6K", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 477, - "positionAbsolute": { - "x": 2969.0261961391298, - "y": 442.1613649809069 - }, - "dragging": false + "load_from_db": false, + "title_case": false + }, + "show_progress_bar": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "show_progress_bar", + "display_name": "Show Progress Bar", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "skip_empty": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "skip_empty", + "display_name": "Skip Empty", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_enable": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_enable", + "display_name": "TikToken Enable", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_model_name", + "display_name": "TikToken Model Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "id": "ChatOutput-Q39I8", - "type": "genericNode", - "position": { - "x": 3887.2073667611485, - "y": 588.4801225794856 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "object", - "Text", - "Record", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-Q39I8" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 3887.2073667611485, - "y": 588.4801225794856 - }, - "dragging": false + "description": "Generate embeddings using OpenAI models.", + "base_classes": ["Embeddings"], + "display_name": "OpenAI Embeddings", + "documentation": "", + "custom_fields": { + "openai_api_key": null, + "default_headers": null, + "default_query": null, + "allowed_special": null, + "disallowed_special": null, + "chunk_size": null, + "client": null, + "deployment": null, + "embedding_ctx_length": null, + "max_retries": null, + "model": null, + "model_kwargs": null, + "openai_api_base": null, + "openai_api_type": null, + "openai_api_version": null, + "openai_organization": null, + "openai_proxy": null, + "request_timeout": null, + "show_progress_bar": null, + "skip_empty": null, + "tiktoken_enable": null, + "tiktoken_model_name": null }, - { - "id": "File-t0a6a", - "type": "genericNode", - "position": { - "x": 2257.233450682836, - "y": 1747.5389618367233 + "output_types": ["Embeddings"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "OpenAIEmbeddings-ZlOk1" + }, + "selected": false, + "width": 384, + "height": 383, + "dragging": false + }, + { + "id": "OpenAIModel-EjXlN", + "type": "genericNode", + "position": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "data": { - "type": "File", - "node": { - "template": { - "path": { - "type": "file", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [ - ".txt", - ".md", - ".mdx", - ".csv", - ".json", - ".yaml", - ".yml", - ".xml", - ".html", - ".htm", - ".pdf", - ".docx", - ".py", - ".sh", - ".sql", - ".js", - ".ts", - ".tsx" - ], - "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf", - "password": false, - "name": "path", - "display_name": "Path", - "advanced": false, - "dynamic": false, - "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "silent_errors": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "silent_errors", - "display_name": "Silent Errors", - "advanced": true, - "dynamic": false, - "info": "If true, errors will not raise an exception.", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "A generic file loader.", - "icon": "file-text", - "base_classes": [ - "Record" - ], - "display_name": "File", - "documentation": "", - "custom_fields": { - "path": null, - "silent_errors": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "File-t0a6a" - }, - "selected": false, - "width": 384, - "height": 281, - "positionAbsolute": { - "x": 2257.233450682836, - "y": 1747.5389618367233 - }, - "dragging": false + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "id": "RecursiveCharacterTextSplitter-tR9QM", - "type": "genericNode", - "position": { - "x": 2791.013514133929, - "y": 1462.9588953494142 - }, - "data": { - "type": "RecursiveCharacterTextSplitter", - "node": { - "template": { - "inputs": { - "type": "Document", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "inputs", - "display_name": "Input", - "advanced": false, - "input_types": [ - "Document", - "Record" - ], - "dynamic": false, - "info": "The texts to split.", - "load_from_db": false, - "title_case": false - }, - "chunk_overlap": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 200, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_overlap", - "display_name": "Chunk Overlap", - "advanced": false, - "dynamic": false, - "info": "The amount of overlap between chunks.", - "load_from_db": false, - "title_case": false - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": false, - "dynamic": false, - "info": "The maximum length of each chunk.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "separators": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "separators", - "display_name": "Separators", - "advanced": false, - "dynamic": false, - "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": [ - "" - ] - }, - "_type": "CustomComponent" - }, - "description": "Split text into chunks of a specified length.", - "base_classes": [ - "Record" - ], - "display_name": "Recursive Character Text Splitter", - "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter", - "custom_fields": { - "inputs": null, - "separators": null, - "chunk_size": null, - "chunk_overlap": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "RecursiveCharacterTextSplitter-tR9QM" - }, - "selected": false, - "width": 384, - "height": 501, - "positionAbsolute": { - "x": 2791.013514133929, - "y": 1462.9588953494142 - }, - "dragging": false + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": ["object", "Text", "str"], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null }, - { - "id": "AstraDBSearch-41nRz", - "type": "genericNode", - "position": { - "x": 1723.976434815103, - "y": 277.03317407245913 - }, - "data": { - "type": "AstraDBSearch", - "node": { - "template": { - "embedding": { - "type": "Embeddings", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding", - "display_name": "Embedding", - "advanced": false, - "dynamic": false, - "info": "Embedding to use", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input Value", - "advanced": false, - "dynamic": false, - "info": "Input value to search", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "api_endpoint": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "api_endpoint", - "display_name": "API Endpoint", - "advanced": false, - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "batch_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "batch_size", - "display_name": "Batch Size", - "advanced": true, - "dynamic": false, - "info": "Optional number of records to process in a single batch.", - "load_from_db": false, - "title_case": false - }, - "bulk_delete_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_delete_concurrency", - "display_name": "Bulk Delete Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_batch_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_batch_concurrency", - "display_name": "Bulk Insert Batch Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_overwrite_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_overwrite_concurrency", - "display_name": "Bulk Insert Overwrite Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "collection_indexing_policy": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_indexing_policy", - "display_name": "Collection Indexing Policy", - "advanced": true, - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "load_from_db": false, - "title_case": false - }, - "collection_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_name", - "display_name": "Collection Name", - "advanced": false, - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "langflow" - }, - "metadata_indexing_exclude": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_exclude", - "display_name": "Metadata Indexing Exclude", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metadata_indexing_include": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_include", - "display_name": "Metadata Indexing Include", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metric": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metric", - "display_name": "Metric", - "advanced": true, - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "namespace": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "namespace", - "display_name": "Namespace", - "advanced": true, - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "number_of_results": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 4, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "number_of_results", - "display_name": "Number of Results", - "advanced": true, - "dynamic": false, - "info": "Number of results to return.", - "load_from_db": false, - "title_case": false - }, - "pre_delete_collection": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "pre_delete_collection", - "display_name": "Pre Delete Collection", - "advanced": true, - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "load_from_db": false, - "title_case": false - }, - "search_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Similarity", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Similarity", - "MMR" - ], - "name": "search_type", - "display_name": "Search Type", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "setup_mode": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Sync", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Sync", - "Async", - "Off" - ], - "name": "setup_mode", - "display_name": "Setup Mode", - "advanced": true, - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "token": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "token", - "display_name": "Token", - "advanced": false, - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "_type": "CustomComponent" - }, - "description": "Searches an existing Astra DB Vector Store.", - "icon": "AstraDB", - "base_classes": [ - "Record" - ], - "display_name": "Astra DB Search", - "documentation": "", - "custom_fields": { - "embedding": null, - "collection_name": null, - "input_value": null, - "token": null, - "api_endpoint": null, - "search_type": null, - "number_of_results": null, - "namespace": null, - "metric": null, - "batch_size": null, - "bulk_insert_batch_concurrency": null, - "bulk_insert_overwrite_concurrency": null, - "bulk_delete_concurrency": null, - "setup_mode": null, - "pre_delete_collection": null, - "metadata_indexing_include": null, - "metadata_indexing_exclude": null, - "collection_indexing_policy": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "token", - "api_endpoint", - "collection_name", - "input_value", - "embedding" - ], - "beta": false - }, - "id": "AstraDBSearch-41nRz" - }, - "selected": false, - "width": 384, - "height": 713, - "dragging": false, - "positionAbsolute": { - "x": 1723.976434815103, - "y": 277.03317407245913 - } + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-EjXlN" + }, + "selected": true, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "dragging": false + }, + { + "id": "Prompt-xeI6K", + "type": "genericNode", + "position": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "context": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "context", + "display_name": "context", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "question": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "question", + "display_name": "question", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "AstraDB-eUCSS", - "type": "genericNode", - "position": { - "x": 3372.04958055989, - "y": 1611.0742035495277 - }, - "data": { - "type": "AstraDB", - "node": { - "template": { - "embedding": { - "type": "Embeddings", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding", - "display_name": "Embedding", - "advanced": false, - "dynamic": false, - "info": "Embedding to use", - "load_from_db": false, - "title_case": false - }, - "inputs": { - "type": "Record", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "inputs", - "display_name": "Inputs", - "advanced": false, - "dynamic": false, - "info": "Optional list of records to be processed and stored in the vector store.", - "load_from_db": false, - "title_case": false - }, - "api_endpoint": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "api_endpoint", - "display_name": "API Endpoint", - "advanced": false, - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "batch_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "batch_size", - "display_name": "Batch Size", - "advanced": true, - "dynamic": false, - "info": "Optional number of records to process in a single batch.", - "load_from_db": false, - "title_case": false - }, - "bulk_delete_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_delete_concurrency", - "display_name": "Bulk Delete Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_batch_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_batch_concurrency", - "display_name": "Bulk Insert Batch Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_overwrite_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_overwrite_concurrency", - "display_name": "Bulk Insert Overwrite Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import List, Optional, Union\n\nfrom langchain.schema import BaseRetriever\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> Union[VectorStore, BaseRetriever]:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "collection_indexing_policy": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_indexing_policy", - "display_name": "Collection Indexing Policy", - "advanced": true, - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "load_from_db": false, - "title_case": false - }, - "collection_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_name", - "display_name": "Collection Name", - "advanced": false, - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "langflow" - }, - "metadata_indexing_exclude": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_exclude", - "display_name": "Metadata Indexing Exclude", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metadata_indexing_include": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_include", - "display_name": "Metadata Indexing Include", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metric": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metric", - "display_name": "Metric", - "advanced": true, - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "namespace": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "namespace", - "display_name": "Namespace", - "advanced": true, - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "pre_delete_collection": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "pre_delete_collection", - "display_name": "Pre Delete Collection", - "advanced": true, - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "load_from_db": false, - "title_case": false - }, - "setup_mode": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Sync", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Sync", - "Async", - "Off" - ], - "name": "setup_mode", - "display_name": "Setup Mode", - "advanced": true, - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "token": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "token", - "display_name": "Token", - "advanced": false, - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "_type": "CustomComponent" - }, - "description": "Builds or loads an Astra DB Vector Store.", - "icon": "AstraDB", - "base_classes": [ - "VectorStore" - ], - "display_name": "Astra DB", - "documentation": "", - "custom_fields": { - "embedding": null, - "token": null, - "api_endpoint": null, - "collection_name": null, - "inputs": null, - "namespace": null, - "metric": null, - "batch_size": null, - "bulk_insert_batch_concurrency": null, - "bulk_insert_overwrite_concurrency": null, - "bulk_delete_concurrency": null, - "setup_mode": null, - "pre_delete_collection": null, - "metadata_indexing_include": null, - "metadata_indexing_exclude": null, - "collection_indexing_policy": null - }, - "output_types": [ - "VectorStore" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "token", - "api_endpoint", - "collection_name", - "inputs", - "embedding" - ], - "beta": false - }, - "id": "AstraDB-eUCSS" - }, - "selected": false, - "width": 384, - "height": 573, - "positionAbsolute": { - "x": 3372.04958055989, - "y": 1611.0742035495277 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": ["object", "Text", "str"], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": ["context", "question"] }, - { - "id": "OpenAIEmbeddings-9TPjc", - "type": "genericNode", - "position": { - "x": 2814.0402191223047, - "y": 1955.9268168273086 - }, - "data": { - "type": "OpenAIEmbeddings", - "node": { - "template": { - "allowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "allowed_special", - "display_name": "Allowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "client", - "display_name": "Client", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_headers": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_headers", - "display_name": "Default Headers", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_query": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_query", - "display_name": "Default Query", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "deployment": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "deployment", - "display_name": "Deployment", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "disallowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [ - "all" - ], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "disallowed_special", - "display_name": "Disallowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "embedding_ctx_length": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 8191, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding_ctx_length", - "display_name": "Embedding Context Length", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_retries": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 6, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_retries", - "display_name": "Max Retries", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "name": "model", - "display_name": "Model", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "openai_api_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_type", - "display_name": "OpenAI API Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_version": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_version", - "display_name": "OpenAI API Version", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_organization": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_organization", - "display_name": "OpenAI Organization", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_proxy": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_proxy", - "display_name": "OpenAI Proxy", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "request_timeout": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "request_timeout", - "display_name": "Request Timeout", - "advanced": true, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "show_progress_bar": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "show_progress_bar", - "display_name": "Show Progress Bar", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "skip_empty": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "skip_empty", - "display_name": "Skip Empty", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_enable": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_enable", - "display_name": "TikToken Enable", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_model_name", - "display_name": "TikToken Model Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Generate embeddings using OpenAI models.", - "base_classes": [ - "Embeddings" - ], - "display_name": "OpenAI Embeddings", - "documentation": "", - "custom_fields": { - "openai_api_key": null, - "default_headers": null, - "default_query": null, - "allowed_special": null, - "disallowed_special": null, - "chunk_size": null, - "client": null, - "deployment": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "output_types": [ - "Embeddings" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "OpenAIEmbeddings-9TPjc" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 2814.0402191223047, - "y": 1955.9268168273086 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "TextOutput-BDknO", - "target": "Prompt-xeI6K", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-TextOutput-BDknO{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}-Prompt-xeI6K{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "context", - "id": "Prompt-xeI6K", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "TextOutput", - "id": "TextOutput-BDknO" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": ["Text"], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-xeI6K", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 477, + "positionAbsolute": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "dragging": false + }, + { + "id": "ChatOutput-Q39I8", + "type": "genericNode", + "position": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "source": "ChatInput-yxMKE", - "target": "Prompt-xeI6K", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153question\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-Prompt-xeI6K{\u0153fieldName\u0153:\u0153question\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "question", - "id": "Prompt-xeI6K", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object", - "Record" - ], - "dataType": "ChatInput", - "id": "ChatInput-yxMKE" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["object", "Text", "Record", "str"], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null }, - { - "source": "Prompt-xeI6K", - "target": "OpenAIModel-EjXlN", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-Prompt-xeI6K{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153}-OpenAIModel-EjXlN{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-EjXlN", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "Prompt", - "id": "Prompt-xeI6K" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-Q39I8" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "dragging": false + }, + { + "id": "File-t0a6a", + "type": "genericNode", + "position": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "data": { + "type": "File", + "node": { + "template": { + "path": { + "type": "file", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [ + ".txt", + ".md", + ".mdx", + ".csv", + ".json", + ".yaml", + ".yml", + ".xml", + ".html", + ".htm", + ".pdf", + ".docx", + ".py", + ".sh", + ".sql", + ".js", + ".ts", + ".tsx" + ], + "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf", + "password": false, + "name": "path", + "display_name": "Path", + "advanced": false, + "dynamic": false, + "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "silent_errors": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "silent_errors", + "display_name": "Silent Errors", + "advanced": true, + "dynamic": false, + "info": "If true, errors will not raise an exception.", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-EjXlN", - "target": "ChatOutput-Q39I8", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-Q39I8\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-OpenAIModel-EjXlN{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153}-ChatOutput-Q39I8{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-Q39I8\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-Q39I8", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-EjXlN" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "description": "A generic file loader.", + "icon": "file-text", + "base_classes": ["Record"], + "display_name": "File", + "documentation": "", + "custom_fields": { + "path": null, + "silent_errors": null }, - { - "source": "File-t0a6a", - "target": "RecursiveCharacterTextSplitter-tR9QM", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}", - "id": "reactflow__edge-File-t0a6a{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}-RecursiveCharacterTextSplitter-tR9QM{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}", - "data": { - "targetHandle": { - "fieldName": "inputs", - "id": "RecursiveCharacterTextSplitter-tR9QM", - "inputTypes": [ - "Document", - "Record" - ], - "type": "Document" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "File", - "id": "File-t0a6a" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "File-t0a6a" + }, + "selected": false, + "width": 384, + "height": 281, + "positionAbsolute": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "dragging": false + }, + { + "id": "RecursiveCharacterTextSplitter-tR9QM", + "type": "genericNode", + "position": { + "x": 2791.013514133929, + "y": 1462.9588953494142 + }, + "data": { + "type": "RecursiveCharacterTextSplitter", + "node": { + "template": { + "inputs": { + "type": "Document", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Input", + "advanced": false, + "input_types": ["Document", "Record"], + "dynamic": false, + "info": "The texts to split.", + "load_from_db": false, + "title_case": false + }, + "chunk_overlap": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 200, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_overlap", + "display_name": "Chunk Overlap", + "advanced": false, + "dynamic": false, + "info": "The amount of overlap between chunks.", + "load_from_db": false, + "title_case": false + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": false, + "dynamic": false, + "info": "The maximum length of each chunk.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "separators": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "separators", + "display_name": "Separators", + "advanced": false, + "dynamic": false, + "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": [""] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIEmbeddings-ZlOk1", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}", - "target": "AstraDBSearch-41nRz", - "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", - "data": { - "targetHandle": { - "fieldName": "embedding", - "id": "AstraDBSearch-41nRz", - "inputTypes": null, - "type": "Embeddings" - }, - "sourceHandle": { - "baseClasses": [ - "Embeddings" - ], - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-ZlOk1" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}" + "description": "Split text into chunks of a specified length.", + "base_classes": ["Record"], + "display_name": "Recursive Character Text Splitter", + "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter", + "custom_fields": { + "inputs": null, + "separators": null, + "chunk_size": null, + "chunk_overlap": null }, - { - "source": "ChatInput-yxMKE", - "sourceHandle": 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"type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within Astra DB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within Astra DB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "number_of_results": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 4, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "number_of_results", + "display_name": "Number of Results", + "advanced": true, + "dynamic": false, + "info": "Number of results to return.", + "load_from_db": false, + "title_case": false + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "search_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Similarity", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Similarity", "MMR"], + "name": "search_type", + "display_name": "Search Type", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Sync", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Sync", "Async", "Off"], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "token": { + "type": "str", + 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"Embedding to use", + "load_from_db": false, + "title_case": false + }, + "inputs": { + "type": "Record", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Inputs", + "advanced": false, + "dynamic": false, + "info": "Optional list of records to be processed and stored in the vector store.", + "load_from_db": false, + "title_case": false + }, + "api_endpoint": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "api_endpoint", + "display_name": "API Endpoint", + "advanced": false, + "dynamic": false, + "info": "API endpoint URL for the Astra DB service.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "batch_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "batch_size", + "display_name": "Batch Size", + "advanced": true, + "dynamic": false, + "info": "Optional number of records to process in a single batch.", + "load_from_db": false, + "title_case": false + }, + "bulk_delete_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_delete_concurrency", + "display_name": "Bulk Delete Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk delete operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_batch_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_batch_concurrency", + "display_name": "Bulk Insert Batch Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_overwrite_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_overwrite_concurrency", + "display_name": "Bulk Insert Overwrite Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional, Union\n\nfrom langchain.schema import BaseRetriever\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> Union[VectorStore, BaseRetriever]:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within Astra DB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within Astra DB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Sync", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Sync", "Async", "Off"], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "token": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "token", + "display_name": "Token", + "advanced": false, + "dynamic": false, + "info": "Authentication token for accessing Astra DB.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "_type": "CustomComponent" + }, + "description": "Builds or loads an Astra DB Vector Store.", + "icon": "AstraDB", + "base_classes": ["VectorStore"], + "display_name": "Astra DB", + "documentation": "", + "custom_fields": { + "embedding": null, + "token": null, + "api_endpoint": null, + "collection_name": null, + "inputs": null, + "namespace": null, + "metric": null, + "batch_size": null, + "bulk_insert_batch_concurrency": null, + "bulk_insert_overwrite_concurrency": null, + "bulk_delete_concurrency": null, + "setup_mode": null, + "pre_delete_collection": null, + "metadata_indexing_include": null, + "metadata_indexing_exclude": null, + "collection_indexing_policy": null + }, + "output_types": ["VectorStore"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "token", + "api_endpoint", + "collection_name", + "inputs", + "embedding" + ], + "beta": false + }, + "id": "AstraDB-eUCSS" + }, + "selected": false, + "width": 384, + "height": 573, + "positionAbsolute": { + "x": 3372.04958055989, + "y": 1611.0742035495277 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-9TPjc", + "type": "genericNode", + "position": { + "x": 2814.0402191223047, + "y": 1955.9268168273086 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": ["all"], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "embedding_ctx_length": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 8191, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding_ctx_length", + "display_name": "Embedding Context Length", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_retries": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 6, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_retries", + "display_name": "Max Retries", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "text-embedding-3-small", + "text-embedding-3-large", + 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Ingestion and RAG in a single file. 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"output_parsers": import_output_parser, "retrievers": import_retriever, } if _type == "models": @@ -57,11 +56,6 @@ def import_by_type(_type: str, name: str) -> Any: return loaded_func(name) -def import_output_parser(output_parser: str) -> Any: - """Import output parser from output parser name""" - return import_module(f"from langchain.output_parsers import {output_parser}") - - def import_chat_llm(llm: str) -> BaseChatModel: """Import chat llm from llm name""" return import_class(f"langchain_community.chat_models.{llm}") diff --git a/src/backend/base/langflow/interface/initialize/loading.py b/src/backend/base/langflow/interface/initialize/loading.py index fcec59cae..6001258bc 100644 --- a/src/backend/base/langflow/interface/initialize/loading.py +++ b/src/backend/base/langflow/interface/initialize/loading.py @@ -1,8 +1,8 @@ import inspect import json +import os from typing import TYPE_CHECKING, Any, Callable, Dict, Sequence, Type - import orjson from langchain.agents import agent as agent_module from langchain.agents.agent import AgentExecutor @@ -20,7 +20,6 @@ from langflow.interface.importing.utils import import_by_type from langflow.interface.initialize.llm import initialize_vertexai from langflow.interface.initialize.utils import handle_format_kwargs, handle_node_type, handle_partial_variables from langflow.interface.initialize.vector_store import vecstore_initializer -from langflow.interface.output_parsers.base import output_parser_creator from langflow.interface.retrievers.base import retriever_creator from langflow.interface.toolkits.base import toolkits_creator from langflow.interface.utils import load_file_into_dict @@ -36,6 +35,7 @@ if TYPE_CHECKING: async def instantiate_class( vertex: "Vertex", + fallback_to_env_vars, user_id=None, ) -> Any: """Instantiate class from module type and key, and params""" @@ -58,7 +58,7 @@ async def instantiate_class( if not base_type: raise ValueError("No base type provided for vertex") if base_type == "custom_components": - return await instantiate_custom_component(params, user_id, vertex) + return await instantiate_custom_component(params, user_id, vertex, fallback_to_env_vars=fallback_to_env_vars) class_object = import_by_type(_type=base_type, name=vertex_type) return await instantiate_based_on_type( class_object=class_object, @@ -67,6 +67,7 @@ async def instantiate_class( params=params, user_id=user_id, vertex=vertex, + fallback_to_env_vars=fallback_to_env_vars, ) @@ -94,14 +95,7 @@ def convert_kwargs(params): return params -async def instantiate_based_on_type( - class_object, - base_type, - node_type, - params, - user_id, - vertex, -): +async def instantiate_based_on_type(class_object, base_type, node_type, params, user_id, vertex, fallback_to_env_vars): if base_type == "agents": return instantiate_agent(node_type, class_object, params) elif base_type == "prompts": @@ -126,8 +120,6 @@ async def instantiate_based_on_type( return instantiate_utility(node_type, class_object, params) elif base_type == "chains": return instantiate_chains(node_type, class_object, params) - elif base_type == "output_parsers": - return instantiate_output_parser(node_type, class_object, params) elif base_type == "models": return instantiate_llm(node_type, class_object, params) elif base_type == "retrievers": @@ -135,33 +127,49 @@ async def instantiate_based_on_type( elif base_type == "memory": return instantiate_memory(node_type, class_object, params) elif base_type == "custom_components": - return await instantiate_custom_component( - params, - user_id, - vertex, - ) + return await instantiate_custom_component(params, user_id, vertex, fallback_to_env_vars=fallback_to_env_vars) elif base_type == "wrappers": return instantiate_wrapper(node_type, class_object, params) else: return class_object(**params) -def update_params_with_load_from_db_fields(custom_component: "CustomComponent", params, load_from_db_fields): +def update_params_with_load_from_db_fields( + custom_component: "CustomComponent", params, load_from_db_fields, fallback_to_env_vars=False +): # For each field in load_from_db_fields, we will check if it's in the params # and if it is, we will get the value from the custom_component.keys(name) # and update the params with the value for field in load_from_db_fields: if field in params: try: - key = custom_component.variables(params[field]) - params[field] = key if key else params[field] + key = None + try: + key = custom_component.variables(params[field]) + except ValueError as e: + # check if "User id is not set" is in the error message + if "User id is not set" in str(e) and not fallback_to_env_vars: + raise e + logger.debug(str(e)) + if fallback_to_env_vars and key is None: + var = os.getenv(params[field]) + if var is None: + raise ValueError(f"Environment variable {params[field]} is not set.") + key = var + logger.info(f"Using environment variable {params[field]} for {field}") + if key is None: + logger.warning(f"Could not get value for {field}. Setting it to None.") + params[field] = key + except Exception as exc: - logger.error(f"Failed to get value for {field} from custom component. Error: {exc}") - pass + logger.error(f"Failed to get value for {field} from custom component. Setting it to None. Error: {exc}") + + params[field] = None + return params -async def instantiate_custom_component(params, user_id, vertex): +async def instantiate_custom_component(params, user_id, vertex, fallback_to_env_vars: bool = False): params_copy = params.copy() class_object: Type["CustomComponent"] = eval_custom_component_code(params_copy.pop("code")) custom_component: "CustomComponent" = class_object( @@ -170,7 +178,9 @@ async def instantiate_custom_component(params, user_id, vertex): vertex=vertex, selected_output_type=vertex.selected_output_type, ) - params_copy = update_params_with_load_from_db_fields(custom_component, params_copy, vertex.load_from_db_fields) + params_copy = update_params_with_load_from_db_fields( + custom_component, params_copy, vertex.load_from_db_fields, fallback_to_env_vars + ) if "retriever" in params_copy and hasattr(params_copy["retriever"], "as_retriever"): params_copy["retriever"] = params_copy["retriever"].as_retriever() @@ -201,15 +211,6 @@ def instantiate_wrapper(node_type, class_object, params): return class_object(**params) -def instantiate_output_parser(node_type, class_object, params): - if node_type in output_parser_creator.from_method_nodes: - method = output_parser_creator.from_method_nodes[node_type] - if class_method := getattr(class_object, method, None): - return class_method(**params) - raise ValueError(f"Method {method} not found in {class_object}") - return class_object(**params) - - def instantiate_llm(node_type, class_object, params: Dict): # This is a workaround so JinaChat works until streaming is implemented # if "openai_api_base" in params and "jina" in params["openai_api_base"]: @@ -514,15 +515,6 @@ def build_prompt_template(prompt, tools): "show": False, "multiline": False, }, - "output_parser": { - "type": "BaseOutputParser", - "required": False, - "placeholder": "", - "list": False, - "show": False, - "multline": False, - "value": None, - }, "template": { "type": "str", "required": True, diff --git a/src/backend/base/langflow/interface/output_parsers/__init__.py b/src/backend/base/langflow/interface/output_parsers/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/backend/base/langflow/interface/output_parsers/base.py b/src/backend/base/langflow/interface/output_parsers/base.py deleted file mode 100644 index b38bba87e..000000000 --- a/src/backend/base/langflow/interface/output_parsers/base.py +++ /dev/null @@ -1,63 +0,0 @@ -from typing import ClassVar, Dict, List, Optional, Type - -from langchain import output_parsers -from langflow.interface.base import LangChainTypeCreator -from langflow.interface.importing.utils import import_class -from langflow.interface.utils import build_template_from_class -from langflow.services.deps import get_settings_service -from langflow.template.frontend_node.output_parsers import OutputParserFrontendNode -from langflow.utils.util import build_template_from_method -from loguru import logger - - -class OutputParserCreator(LangChainTypeCreator): - type_name: str = "output_parsers" - from_method_nodes: ClassVar[Dict] = { - "StructuredOutputParser": "from_response_schemas", - } - - @property - def frontend_node_class(self) -> Type[OutputParserFrontendNode]: - return OutputParserFrontendNode - - @property - def type_to_loader_dict(self) -> Dict: - if self.type_dict is None: - settings_service = get_settings_service() - self.type_dict = { - output_parser_name: import_class(f"langchain.output_parsers.{output_parser_name}") - # if output_parser_name is not lower case it is a class - for output_parser_name in output_parsers.__all__ - } - self.type_dict = { - name: output_parser - for name, output_parser in self.type_dict.items() - if name in settings_service.settings.OUTPUT_PARSERS or settings_service.settings.DEV - } - return self.type_dict - - def get_signature(self, name: str) -> Optional[Dict]: - try: - if name in self.from_method_nodes: - return build_template_from_method( - name, - type_to_cls_dict=self.type_to_loader_dict, - method_name=self.from_method_nodes[name], - ) - else: - return build_template_from_class( - name, - type_to_cls_dict=self.type_to_loader_dict, - ) - except ValueError as exc: - # raise ValueError("OutputParser not found") from exc - logger.error(f"OutputParser {name} not found: {exc}") - except AttributeError as exc: - logger.error(f"OutputParser {name} not loaded: {exc}") - return None - - def to_list(self) -> List[str]: - return list(self.type_to_loader_dict.keys()) - - -output_parser_creator = OutputParserCreator() diff --git a/src/backend/base/langflow/interface/types.py b/src/backend/base/langflow/interface/types.py index 32ab7ff95..46fa44a37 100644 --- a/src/backend/base/langflow/interface/types.py +++ b/src/backend/base/langflow/interface/types.py @@ -8,7 +8,6 @@ from langflow.interface.document_loaders.base import documentloader_creator from langflow.interface.embeddings.base import embedding_creator from langflow.interface.llms.base import llm_creator from langflow.interface.memories.base import memory_creator -from langflow.interface.output_parsers.base import output_parser_creator from langflow.interface.retrievers.base import retriever_creator from langflow.interface.text_splitters.base import textsplitter_creator from langflow.interface.toolkits.base import toolkits_creator @@ -48,7 +47,6 @@ def build_langchain_types_dict(): # sourcery skip: dict-assign-update-to-union documentloader_creator, textsplitter_creator, # utility_creator, - output_parser_creator, retriever_creator, ] diff --git a/src/backend/base/langflow/interface/utils.py b/src/backend/base/langflow/interface/utils.py index aa4e3f7e1..d4271eabf 100644 --- a/src/backend/base/langflow/interface/utils.py +++ b/src/backend/base/langflow/interface/utils.py @@ -106,7 +106,7 @@ def set_langchain_cache(settings): if cache_type := os.getenv("LANGFLOW_LANGCHAIN_CACHE"): try: - cache_class = import_class(f"langchain.cache.{cache_type or settings.LANGCHAIN_CACHE}") + cache_class = import_class(f"langchain_community.cache.{cache_type or settings.LANGCHAIN_CACHE}") logger.debug(f"Setting up LLM caching with {cache_class.__name__}") set_llm_cache(cache_class()) diff --git a/src/backend/base/langflow/memory.py b/src/backend/base/langflow/memory.py index 15cf6d6d8..f44958fdd 100644 --- a/src/backend/base/langflow/memory.py +++ b/src/backend/base/langflow/memory.py @@ -1,5 +1,5 @@ import warnings -from typing import Optional, Union +from typing import List, Optional, Union from loguru import logger @@ -60,7 +60,7 @@ def get_messages( return records -def add_messages(records: Union[list[Record], Record]): +def add_messages(records: Union[list[Record], Record], flow_id: Optional[str] = None): """ Add a message to the monitor service. """ @@ -76,7 +76,8 @@ def add_messages(records: Union[list[Record], Record]): messages: list[MessageModel] = [] for record in records: - messages.append(MessageModel.from_record(record)) + record.timestamp = monitor_service.get_timestamp() + messages.append(MessageModel.from_record(record, flow_id=flow_id)) for message in messages: try: @@ -107,8 +108,24 @@ def store_message( session_id: Optional[str] = None, sender: Optional[str] = None, sender_name: Optional[str] = None, -) -> list[Record]: + flow_id: Optional[str] = None, +) -> List[Record]: + """ + Stores a message in the memory. + Args: + message (Union[str, Record]): The message to be stored. It can be either a string or a Record object. + session_id (Optional[str]): The session ID associated with the message. + sender (Optional[str]): The sender ID associated with the message. + sender_name (Optional[str]): The name of the sender associated with the message. + flow_id (Optional[str]): The flow ID associated with the message. When running from the CustomComponent you can access this using `self.graph.flow_id`. + + Returns: + List[Record]: A list of records containing the stored message. + + Raises: + ValueError: If any of the required parameters (session_id, sender, sender_name) is not provided. + """ if not message: warnings.warn("No message provided.") return [] @@ -135,4 +152,4 @@ def store_message( }, ) - return add_messages([record]) + return add_messages([record], flow_id=flow_id) diff --git a/src/backend/base/langflow/processing/load.py b/src/backend/base/langflow/processing/load.py index cf354c3b3..b56f22c81 100644 --- a/src/backend/base/langflow/processing/load.py +++ b/src/backend/base/langflow/processing/load.py @@ -82,6 +82,7 @@ def run_flow_from_json( env_file: Optional[str] = None, cache: Optional[str] = None, disable_logs: Optional[bool] = True, + fallback_to_env_vars: bool = False, ) -> List[RunOutputs]: """ Run a flow from a JSON file or dictionary. @@ -98,6 +99,7 @@ def run_flow_from_json( env_file (Optional[str], optional): The environment file to load. Defaults to None. cache (Optional[str], optional): The cache directory to use. Defaults to None. disable_logs (Optional[bool], optional): Whether to disable logs. Defaults to True. + fallback_to_env_vars (bool, optional): Whether Global Variables should fallback to environment variables if not found. Defaults to False. Returns: List[RunOutputs]: A list of RunOutputs objects representing the results of running the flow. @@ -127,5 +129,6 @@ def run_flow_from_json( input_type=input_type, output_type=output_type, output_component=output_component, + fallback_to_env_vars=fallback_to_env_vars, ) return result diff --git a/src/backend/base/langflow/processing/process.py b/src/backend/base/langflow/processing/process.py index 2d8356159..d46274b4c 100644 --- a/src/backend/base/langflow/processing/process.py +++ b/src/backend/base/langflow/processing/process.py @@ -175,6 +175,7 @@ def run_graph( input_value: str, input_type: str, output_type: str, + fallback_to_env_vars: bool = False, output_component: Optional[str] = None, ) -> List[RunOutputs]: """ @@ -218,6 +219,7 @@ def run_graph( outputs or [], stream=False, session_id="", + fallback_to_env_vars=fallback_to_env_vars, ) return run_outputs diff --git a/src/backend/base/langflow/schema/schema.py b/src/backend/base/langflow/schema/schema.py index 29ab15ede..921bd65b2 100644 --- a/src/backend/base/langflow/schema/schema.py +++ b/src/backend/base/langflow/schema/schema.py @@ -1,10 +1,10 @@ import copy +import json from typing import Literal, Optional, cast from langchain_core.documents import Document from langchain_core.messages import AIMessage, BaseMessage, HumanMessage from pydantic import BaseModel, model_validator -from langchain_core.messages import HumanMessage, AIMessage class Record(BaseModel): @@ -163,23 +163,15 @@ class Record(BaseModel): # Create a new Record object with a deep copy of the data dictionary return Record(data=copy.deepcopy(self.data, memo), text_key=self.text_key, default_value=self.default_value) - def __str__(self) -> str: - """ - Returns a string representation of the Record, including text and data. - """ - # Assuming a method to dump model data as JSON string exists. - # If it doesn't, you might need to implement it or use json.dumps() directly. - # build the string considering all keys in the data dictionary - prefix = "Record(" - suffix = ")" - text = f"text_key={self.text_key}, " - text += ", ".join([f"{k}={v}" for k, v in self.data.items()]) - return prefix + text + suffix - # check which attributes the Record has by checking the keys in the data dictionary def __dir__(self): return super().__dir__() + list(self.data.keys()) + def __str__(self) -> str: + # return a JSON string representation of the Record atributes + + return json.dumps(self.data) + INPUT_FIELD_NAME = "input_value" diff --git a/src/backend/base/langflow/services/auth/utils.py b/src/backend/base/langflow/services/auth/utils.py index d2290036e..f8396077c 100644 --- a/src/backend/base/langflow/services/auth/utils.py +++ b/src/backend/base/langflow/services/auth/utils.py @@ -1,21 +1,20 @@ from datetime import datetime, timedelta, timezone from typing import Annotated, Coroutine, Optional, Union from uuid import UUID +import warnings from cryptography.fernet import Fernet from fastapi import Depends, HTTPException, Security, status from fastapi.security import APIKeyHeader, APIKeyQuery, OAuth2PasswordBearer + + from jose import JWTError, jwt from sqlmodel import Session from starlette.websockets import WebSocket from langflow.services.database.models.api_key.crud import check_key from langflow.services.database.models.api_key.model import ApiKey -from langflow.services.database.models.user.crud import ( - get_user_by_id, - get_user_by_username, - update_user_last_login_at, -) +from langflow.services.database.models.user.crud import get_user_by_id, get_user_by_username, update_user_last_login_at from langflow.services.database.models.user.model import User from langflow.services.deps import get_session, get_settings_service @@ -111,11 +110,15 @@ async def get_current_user_by_jwt( raise credentials_exception try: - payload = jwt.decode( - token, - settings_service.auth_settings.SECRET_KEY.get_secret_value(), - algorithms=[settings_service.auth_settings.ALGORITHM], - ) + # Ignore warning about datetime.utcnow + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + + payload = jwt.decode( + token, + settings_service.auth_settings.SECRET_KEY.get_secret_value(), + algorithms=[settings_service.auth_settings.ALGORITHM], + ) user_id: UUID = payload.get("sub") # type: ignore token_type: str = payload.get("type") # type: ignore if expires := payload.get("exp", None): @@ -285,11 +288,14 @@ def create_refresh_token(refresh_token: str, db: Session = Depends(get_session)) settings_service = get_settings_service() try: - payload = jwt.decode( - refresh_token, - settings_service.auth_settings.SECRET_KEY.get_secret_value(), - algorithms=[settings_service.auth_settings.ALGORITHM], - ) + # Ignore warning about datetime.utcnow + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + payload = jwt.decode( + refresh_token, + settings_service.auth_settings.SECRET_KEY.get_secret_value(), + algorithms=[settings_service.auth_settings.ALGORITHM], + ) user_id: UUID = payload.get("sub") # type: ignore token_type: str = payload.get("type") # type: ignore @@ -349,3 +355,4 @@ def decrypt_api_key(encrypted_api_key: str, settings_service=Depends(get_setting encoded_bytes = encrypted_api_key decrypted_key = fernet.decrypt(encoded_bytes).decode() return decrypted_key + return decrypted_key diff --git a/src/backend/base/langflow/services/database/models/__init__.py b/src/backend/base/langflow/services/database/models/__init__.py index 1f19435bd..ce12a6fce 100644 --- a/src/backend/base/langflow/services/database/models/__init__.py +++ b/src/backend/base/langflow/services/database/models/__init__.py @@ -1,6 +1,7 @@ from .api_key import ApiKey from .flow import Flow +from .folder import Folder from .user import User from .variable import Variable -__all__ = ["Flow", "User", "ApiKey", "Variable"] +__all__ = ["Flow", "User", "ApiKey", "Variable", "Folder"] diff --git a/src/backend/base/langflow/services/database/models/flow/model.py b/src/backend/base/langflow/services/database/models/flow/model.py index e814d1f3a..17b5e8931 100644 --- a/src/backend/base/langflow/services/database/models/flow/model.py +++ b/src/backend/base/langflow/services/database/models/flow/model.py @@ -1,7 +1,7 @@ # Path: src/backend/langflow/services/database/models/flow/model.py import warnings -from datetime import datetime +from datetime import datetime, timezone from typing import TYPE_CHECKING, Dict, Optional from uuid import UUID, uuid4 @@ -13,6 +13,7 @@ from sqlmodel import JSON, Column, Field, Relationship, SQLModel from langflow.schema.schema import Record if TYPE_CHECKING: + from langflow.services.database.models.folder import Folder from langflow.services.database.models.user import User @@ -23,8 +24,8 @@ class FlowBase(SQLModel): icon_bg_color: Optional[str] = Field(default=None, nullable=True) data: Optional[Dict] = Field(default=None, nullable=True) is_component: Optional[bool] = Field(default=False, nullable=True) - updated_at: Optional[datetime] = Field(default_factory=datetime.utcnow, nullable=True) - folder: Optional[str] = Field(default=None, nullable=True) + updated_at: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc), nullable=True) + folder_id: Optional[UUID] = Field(default=None, nullable=True) @field_validator("icon_bg_color") def validate_icon_bg_color(cls, v): @@ -112,6 +113,8 @@ class Flow(FlowBase, table=True): data: Optional[Dict] = Field(default=None, sa_column=Column(JSON)) user_id: Optional[UUID] = Field(index=True, foreign_key="user.id", nullable=True) user: "User" = Relationship(back_populates="flows") + folder_id: Optional[UUID] = Field(default=None, foreign_key="folder.id", nullable=True, index=True) + folder: Optional["Folder"] = Relationship(back_populates="flows") def to_record(self): serialized = self.model_dump() @@ -128,14 +131,17 @@ class Flow(FlowBase, table=True): class FlowCreate(FlowBase): user_id: Optional[UUID] = None + folder_id: Optional[UUID] = None class FlowRead(FlowBase): id: UUID user_id: Optional[UUID] = Field() + folder_id: Optional[UUID] = Field() class FlowUpdate(SQLModel): name: Optional[str] = None description: Optional[str] = None data: Optional[Dict] = None + folder_id: Optional[UUID] = None diff --git a/src/backend/base/langflow/services/database/models/folder/__init__.py b/src/backend/base/langflow/services/database/models/folder/__init__.py new file mode 100644 index 000000000..e1d7e97b7 --- /dev/null +++ b/src/backend/base/langflow/services/database/models/folder/__init__.py @@ -0,0 +1,3 @@ +from .model import Folder, FolderCreate, FolderRead, FolderUpdate + +__all__ = ["Folder", "FolderCreate", "FolderRead", "FolderUpdate"] diff --git a/src/backend/base/langflow/services/database/models/folder/constants.py b/src/backend/base/langflow/services/database/models/folder/constants.py new file mode 100644 index 000000000..df71e9edf --- /dev/null +++ b/src/backend/base/langflow/services/database/models/folder/constants.py @@ -0,0 +1,2 @@ +DEFAULT_FOLDER_DESCRIPTION = "Manage your personal projects. Download and upload entire collections." +DEFAULT_FOLDER_NAME = "My Projects" diff --git a/src/backend/base/langflow/services/database/models/folder/model.py b/src/backend/base/langflow/services/database/models/folder/model.py new file mode 100644 index 000000000..a7a1c51d0 --- /dev/null +++ b/src/backend/base/langflow/services/database/models/folder/model.py @@ -0,0 +1,55 @@ +from typing import TYPE_CHECKING, List, Optional +from uuid import UUID, uuid4 + +from sqlmodel import Field, Relationship, SQLModel + +from langflow.services.database.models.flow.model import FlowRead + +if TYPE_CHECKING: + from langflow.services.database.models.flow.model import Flow + from langflow.services.database.models.user.model import User + + +class FolderBase(SQLModel): + name: str = Field(index=True) + description: Optional[str] = Field(default=None) + + +class Folder(FolderBase, table=True): + id: Optional[UUID] = Field(default_factory=uuid4, primary_key=True) + parent_id: Optional[UUID] = Field(default=None, foreign_key="folder.id") + + parent: Optional["Folder"] = Relationship( + back_populates="children", + sa_relationship_kwargs=dict(remote_side="Folder.id"), + ) + children: List["Folder"] = Relationship(back_populates="parent") + user_id: Optional[UUID] = Field(default=None, foreign_key="user.id") + user: "User" = Relationship(back_populates="folders") + flows: List["Flow"] = Relationship( + back_populates="folder", sa_relationship_kwargs={"cascade": "all, delete, delete-orphan"} + ) + + +class FolderCreate(FolderBase): + components_list: Optional[List[UUID]] = None + flows_list: Optional[List[UUID]] = None + + +class FolderRead(FolderBase): + id: UUID + parent_id: Optional[UUID] = Field() + + +class FolderReadWithFlows(FolderBase): + id: UUID + parent_id: Optional[UUID] = Field() + flows: List["FlowRead"] = Field(default=[]) + + +class FolderUpdate(SQLModel): + name: Optional[str] = None + description: Optional[str] = None + parent_id: Optional[UUID] = None + components: Optional[List[UUID]] = None + flows: Optional[List[UUID]] = None diff --git a/src/backend/base/langflow/services/database/models/folder/utils.py b/src/backend/base/langflow/services/database/models/folder/utils.py new file mode 100644 index 000000000..19f70819f --- /dev/null +++ b/src/backend/base/langflow/services/database/models/folder/utils.py @@ -0,0 +1,26 @@ +from typing import TYPE_CHECKING +from uuid import UUID + +from langflow.services.database.models.flow.model import Flow +from sqlmodel import Session, select, update + +from .constants import DEFAULT_FOLDER_DESCRIPTION, DEFAULT_FOLDER_NAME +from .model import Folder + +if TYPE_CHECKING: + from langflow.services.database.models.user.model import User + + +def create_default_folder_if_it_doesnt_exist(session: Session, user_id: UUID): + if not session.exec(select(Folder).where(Folder.user_id == user_id)).first(): + folder = Folder(name=DEFAULT_FOLDER_NAME, user_id=user_id, description=DEFAULT_FOLDER_DESCRIPTION) + session.add(folder) + session.commit() + session.refresh(folder) + session.exec( + update(Flow) + .where((Flow.folder_id == None) & (Flow.user_id == user_id)) + .values(folder_id=folder.id) + ) + session.commit() + return None diff --git a/src/backend/base/langflow/services/database/models/user/model.py b/src/backend/base/langflow/services/database/models/user/model.py index fa229b4b3..dfc978c27 100644 --- a/src/backend/base/langflow/services/database/models/user/model.py +++ b/src/backend/base/langflow/services/database/models/user/model.py @@ -1,4 +1,4 @@ -from datetime import datetime +from datetime import datetime, timezone from typing import TYPE_CHECKING, Optional from uuid import UUID, uuid4 @@ -8,6 +8,7 @@ if TYPE_CHECKING: from langflow.services.database.models.api_key import ApiKey from langflow.services.database.models.variable import Variable from langflow.services.database.models.flow import Flow + from langflow.services.database.models.folder import Folder class User(SQLModel, table=True): @@ -17,8 +18,8 @@ class User(SQLModel, table=True): profile_image: Optional[str] = Field(default=None, nullable=True) is_active: bool = Field(default=False) is_superuser: bool = Field(default=False) - create_at: datetime = Field(default_factory=datetime.utcnow) - updated_at: datetime = Field(default_factory=datetime.utcnow) + create_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc)) + updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc)) last_login_at: Optional[datetime] = Field(default=None, nullable=True) api_keys: list["ApiKey"] = Relationship( back_populates="user", @@ -30,6 +31,10 @@ class User(SQLModel, table=True): back_populates="user", sa_relationship_kwargs={"cascade": "delete"}, ) + folders: list["Folder"] = Relationship( + back_populates="user", + sa_relationship_kwargs={"cascade": "delete"}, + ) class UserCreate(SQLModel): diff --git a/src/backend/base/langflow/services/monitor/schema.py b/src/backend/base/langflow/services/monitor/schema.py index 80636a554..e7bc7a963 100644 --- a/src/backend/base/langflow/services/monitor/schema.py +++ b/src/backend/base/langflow/services/monitor/schema.py @@ -2,15 +2,16 @@ import json from datetime import datetime from typing import TYPE_CHECKING, Any, Optional -from pydantic import BaseModel, Field, field_serializer, validator +from pydantic import BaseModel, Field, field_serializer, field_validator if TYPE_CHECKING: from langflow.schema import Record class TransactionModel(BaseModel): - id: Optional[int] = Field(default=None, alias="id") + index: Optional[int] = Field(default=None) timestamp: Optional[datetime] = Field(default_factory=datetime.now, alias="timestamp") + flow_id: str source: str target: str target_args: dict @@ -22,15 +23,53 @@ class TransactionModel(BaseModel): populate_by_name = True # validate target_args in case it is a JSON - @validator("target_args", pre=True) + @field_validator("target_args", mode="before") def validate_target_args(cls, v): if isinstance(v, str): return json.loads(v) return v + @field_serializer("target_args") + def serialize_target_args(v): + if isinstance(v, dict): + return json.dumps(v) + return v + + +class TransactionModelResponse(BaseModel): + index: Optional[int] = Field(default=None) + timestamp: Optional[datetime] = Field(default_factory=datetime.now, alias="timestamp") + flow_id: str + source: str + target: str + target_args: dict + status: str + error: Optional[str] = None + + class Config: + from_attributes = True + populate_by_name = True + + # validate target_args in case it is a JSON + @field_validator("target_args", mode="before") + def validate_target_args(cls, v): + if isinstance(v, str): + return json.loads(v) + return v + + @field_validator("index", mode="before") + def validate_id(cls, v): + if isinstance(v, float): + try: + return int(v) + except ValueError: + return None + return v + class MessageModel(BaseModel): - id: Optional[int] = Field(default=None, alias="id") + index: Optional[int] = Field(default=None) + flow_id: Optional[str] = Field(default=None, alias="flow_id") timestamp: datetime = Field(default_factory=datetime.now) sender: str sender_name: str @@ -42,14 +81,14 @@ class MessageModel(BaseModel): from_attributes = True populate_by_name = True - @validator("artifacts", pre=True) + @field_validator("artifacts", mode="before") def validate_target_args(cls, v): if isinstance(v, str): return json.loads(v) return v @classmethod - def from_record(cls, record: "Record"): + def from_record(cls, record: "Record", flow_id: Optional[str] = None): # first check if the record has all the required fields if not record.data or ("sender" not in record.data and "sender_name" not in record.data): raise ValueError("The record does not have the required fields 'sender' and 'sender_name' in the data.") @@ -59,9 +98,30 @@ class MessageModel(BaseModel): message=record.text, session_id=record.session_id, artifacts=record.artifacts or {}, + timestamp=record.timestamp, + flow_id=flow_id, ) +class MessageModelResponse(MessageModel): + index: Optional[int] = Field(default=None) + + @field_validator("artifacts", mode="before") + def serialize_artifacts(v): + if isinstance(v, str): + return json.loads(v) + return v + + @field_validator("index", mode="before") + def validate_id(cls, v): + if isinstance(v, float): + try: + return int(v) + except ValueError: + return None + return v + + class VertexBuildModel(BaseModel): index: Optional[int] = Field(default=None, alias="index", exclude=True) id: Optional[str] = Field(default=None, alias="id") @@ -86,9 +146,11 @@ class VertexBuildModel(BaseModel): elif isinstance(value, list) and all(isinstance(i, BaseModel) for i in value): v[key] = [i.model_dump() for i in value] return json.dumps(v) + elif isinstance(v, BaseModel): + return v.model_dump_json() return v - @validator("params", pre=True) + @field_validator("params", mode="before") def validate_params(cls, v): if isinstance(v, str): try: @@ -103,16 +165,18 @@ class VertexBuildModel(BaseModel): return json.dumps([i.model_dump() for i in v]) return v - @validator("data", pre=True) + @field_validator("data", mode="before") def validate_data(cls, v): if isinstance(v, str): return json.loads(v) return v - @validator("artifacts", pre=True) + @field_validator("artifacts", mode="before") def validate_artifacts(cls, v): if isinstance(v, str): return json.loads(v) + elif isinstance(v, BaseModel): + return v.model_dump() return v diff --git a/src/backend/base/langflow/services/monitor/service.py b/src/backend/base/langflow/services/monitor/service.py index e3d56db52..7bf8b6e56 100644 --- a/src/backend/base/langflow/services/monitor/service.py +++ b/src/backend/base/langflow/services/monitor/service.py @@ -69,7 +69,7 @@ class MonitorService(Service): valid: Optional[bool] = None, order_by: Optional[str] = "timestamp", ): - query = "SELECT id, flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds" + query = "SELECT index,flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds" conditions = [] if flow_id: conditions.append(f"flow_id = '{flow_id}'") @@ -109,6 +109,7 @@ class MonitorService(Service): def get_messages( self, + flow_id: Optional[str] = None, sender: Optional[str] = None, sender_name: Optional[str] = None, session_id: Optional[str] = None, @@ -116,7 +117,7 @@ class MonitorService(Service): order: Optional[str] = "DESC", limit: Optional[int] = None, ): - query = "SELECT sender_name, sender, session_id, message, artifacts, timestamp FROM messages" + query = "SELECT index, flow_id, sender_name, sender, session_id, message, artifacts, timestamp FROM messages" conditions = [] if sender: conditions.append(f"sender = '{sender}'") @@ -124,6 +125,8 @@ class MonitorService(Service): conditions.append(f"sender_name = '{sender_name}'") if session_id: conditions.append(f"session_id = '{session_id}'") + if flow_id: + conditions.append(f"flow_id = '{flow_id}'") if conditions: query += " WHERE " + " AND ".join(conditions) @@ -146,8 +149,9 @@ class MonitorService(Service): target: Optional[str] = None, status: Optional[str] = None, order_by: Optional[str] = "timestamp", + flow_id: Optional[str] = None, ): - query = "SELECT source, target, target_args, status, error, timestamp FROM transactions" + query = "SELECT index,flow_id, source, target, target_args, status, error, timestamp FROM transactions" conditions = [] if source: conditions.append(f"source = '{source}'") @@ -155,6 +159,8 @@ class MonitorService(Service): conditions.append(f"target = '{target}'") if status: conditions.append(f"status = '{status}'") + if flow_id: + conditions.append(f"flow_id = '{flow_id}'") if conditions: query += " WHERE " + " AND ".join(conditions) diff --git a/src/backend/base/langflow/services/monitor/utils.py b/src/backend/base/langflow/services/monitor/utils.py index 7b6948c1a..aec5ae0c6 100644 --- a/src/backend/base/langflow/services/monitor/utils.py +++ b/src/backend/base/langflow/services/monitor/utils.py @@ -61,7 +61,7 @@ def drop_and_create_table_if_schema_mismatch(db_path: str, table_name: str, mode if current_schema != desired_schema: # If they don't match, drop the existing table and create a new one conn.execute(f"DROP TABLE IF EXISTS {table_name}") - if "id" in desired_schema.keys(): + if INDEX_KEY in desired_schema.keys(): # Create a sequence for the id column try: conn.execute(f"CREATE SEQUENCE seq_{table_name} START 1;") @@ -91,7 +91,7 @@ def add_row_to_table( columns = ", ".join(keys) values_placeholders = ", ".join(["?" for _ in keys]) - values = list(validated_dict.values()) + values = [validated_dict[key] for key in keys] # Create the insert statement insert_sql = f"INSERT INTO {table_name} ({columns}) VALUES ({values_placeholders})" @@ -104,7 +104,7 @@ def add_row_to_table( column_error_message = "" for key, value in validated_dict.items(): logger.error(f"{key}: {type(value)}") - if value in str(e): + if str(value) in str(e): column_error_message = f"Column: {key} Value: {value} Error: {e}" if column_error_message: @@ -119,6 +119,7 @@ async def log_message( message: str, session_id: str, artifacts: Optional[dict] = None, + flow_id: Optional[str] = None, ): try: from langflow.graph.vertex.base import Vertex @@ -134,6 +135,7 @@ async def log_message( "artifacts": artifacts or {}, "session_id": session_id, "timestamp": monitor_service.get_timestamp(), + "flow_id": flow_id, } monitor_service.add_row(table_name="messages", data=row) except Exception as e: diff --git a/src/backend/base/langflow/services/settings/base.py b/src/backend/base/langflow/services/settings/base.py index db6ba1022..97abf2d2b 100644 --- a/src/backend/base/langflow/services/settings/base.py +++ b/src/backend/base/langflow/services/settings/base.py @@ -7,13 +7,12 @@ from typing import Any, List, Optional, Tuple, Type import orjson import yaml +from langflow.services.settings.constants import VARIABLES_TO_GET_FROM_ENVIRONMENT from loguru import logger -from pydantic import field_validator, validator +from pydantic import field_validator from pydantic.fields import FieldInfo from pydantic_settings import BaseSettings, EnvSettingsSource, PydanticBaseSettingsSource, SettingsConfigDict -from langflow.services.settings.constants import VARIABLES_TO_GET_FROM_ENVIRONMENT - # BASE_COMPONENTS_PATH = str(Path(__file__).parent / "components") BASE_COMPONENTS_PATH = str(Path(__file__).parent.parent.parent / "components") @@ -72,11 +71,14 @@ class Settings(BaseSettings): TOOLKITS: dict = {} TEXTSPLITTERS: dict = {} UTILITIES: dict = {} - OUTPUT_PARSERS: dict = {} CUSTOM_COMPONENTS: dict = {} # Define the default LANGFLOW_DIR CONFIG_DIR: Optional[str] = None + # Define if langflow db should be saved in config dir or + # in the langflow directory + SAVE_DB_IN_CONFIG_DIR: bool = False + """Define if langflow database should be saved in LANGFLOW_CONFIG_DIR or in the langflow directory (i.e. in the package directory).""" DEV: bool = False DATABASE_URL: Optional[str] = None @@ -109,12 +111,16 @@ class Settings(BaseSettings): CELERY_ENABLED: bool = False + fallback_to_env_var: bool = True + """If set to True, Global Variables set in the UI will fallback to a environment variable + with the same name in case Langflow fails to retrieve the variable value.""" + store_environment_variables: bool = True """Whether to store environment variables as Global Variables in the database.""" variables_to_get_from_environment: list[str] = VARIABLES_TO_GET_FROM_ENVIRONMENT """List of environment variables to get from the environment and store in the database.""" - @validator("CONFIG_DIR", pre=True, allow_reuse=True) + @field_validator("CONFIG_DIR", mode="before") def set_langflow_dir(cls, value): if not value: from platformdirs import user_cache_dir @@ -137,8 +143,8 @@ class Settings(BaseSettings): return str(value) - @validator("DATABASE_URL", pre=True) - def set_database_url(cls, value, values): + @field_validator("DATABASE_URL", mode="before") + def set_database_url(cls, value, info): if not value: logger.debug("No database_url provided, trying LANGFLOW_DATABASE_URL env variable") if langflow_database_url := os.getenv("LANGFLOW_DATABASE_URL"): @@ -149,29 +155,36 @@ class Settings(BaseSettings): # Originally, we used sqlite:///./langflow.db # so we need to migrate to the new format # if there is a database in that location - if not values["CONFIG_DIR"]: + if not info.data["CONFIG_DIR"]: raise ValueError("CONFIG_DIR not set, please set it or provide a DATABASE_URL") from langflow.version import is_pre_release # type: ignore + if info.data["SAVE_DB_IN_CONFIG_DIR"]: + database_dir = info.data["CONFIG_DIR"] + logger.debug(f"Saving database to CONFIG_DIR: {database_dir}") + else: + database_dir = Path(__file__).parent.parent.parent.resolve() + logger.debug(f"Saving database to langflow directory: {database_dir}") + pre_db_file_name = "langflow-pre.db" db_file_name = "langflow.db" - new_pre_path = f"{values['CONFIG_DIR']}/{pre_db_file_name}" - new_path = f"{values['CONFIG_DIR']}/{db_file_name}" + new_pre_path = f"{database_dir}/{pre_db_file_name}" + new_path = f"{database_dir}/{db_file_name}" final_path = None if is_pre_release: if Path(new_pre_path).exists(): final_path = new_pre_path - elif Path(new_path).exists(): + elif Path(new_path).exists() and info.data["SAVE_DB_IN_CONFIG_DIR"]: # We need to copy the current db to the new location logger.debug("Copying existing database to new location") copy2(new_path, new_pre_path) logger.debug(f"Copied existing database to {new_pre_path}") - elif Path(f"./{db_file_name}").exists(): + elif Path(f"./{db_file_name}").exists() and info.data["SAVE_DB_IN_CONFIG_DIR"]: logger.debug("Copying existing database to new location") copy2(f"./{db_file_name}", new_pre_path) logger.debug(f"Copied existing database to {new_pre_path}") else: - logger.debug(f"Database already exists at {new_pre_path}, using it") + logger.debug(f"Creating new database at {new_pre_path}") final_path = new_pre_path else: if Path(new_path).exists(): @@ -241,7 +254,6 @@ class Settings(BaseSettings): self.VECTORSTORES = new_settings.VECTORSTORES or {} self.DOCUMENTLOADERS = new_settings.DOCUMENTLOADERS or {} self.RETRIEVERS = new_settings.RETRIEVERS or {} - self.OUTPUT_PARSERS = new_settings.OUTPUT_PARSERS or {} self.CUSTOM_COMPONENTS = new_settings.CUSTOM_COMPONENTS or {} self.COMPONENTS_PATH = new_settings.COMPONENTS_PATH or [] self.DEV = dev @@ -313,3 +325,6 @@ def load_settings_from_yaml(file_path: str) -> Settings: logger.debug(f"Loading {len(settings_dict[key])} {key} from {file_path}") return Settings(**settings_dict) + return Settings(**settings_dict) + return Settings(**settings_dict) + return Settings(**settings_dict) diff --git a/src/backend/base/langflow/services/settings/service.py b/src/backend/base/langflow/services/settings/service.py index a66888924..160c266ec 100644 --- a/src/backend/base/langflow/services/settings/service.py +++ b/src/backend/base/langflow/services/settings/service.py @@ -1,11 +1,10 @@ import os import yaml -from loguru import logger - from langflow.services.base import Service from langflow.services.settings.auth import AuthSettings from langflow.services.settings.base import Settings +from loguru import logger class SettingsService(Service): @@ -31,7 +30,7 @@ class SettingsService(Service): for key in settings_dict: if key not in Settings.model_fields.keys(): - raise KeyError(f"Key {key} not found in settings") + logger.warning(f"Key {key} not found in settings") logger.debug(f"Loading {len(settings_dict[key])} {key} from {file_path}") settings = Settings(**settings_dict) diff --git a/src/backend/base/langflow/services/variable/service.py b/src/backend/base/langflow/services/variable/service.py index ea5126d79..84671e0f9 100644 --- a/src/backend/base/langflow/services/variable/service.py +++ b/src/backend/base/langflow/services/variable/service.py @@ -29,17 +29,23 @@ class VariableService(Service): for var in self.settings_service.settings.variables_to_get_from_environment: if var in os.environ: logger.debug(f"Creating {var} variable from environment.") - try: - self.create_variable( - user_id=user_id, - name=var, - value=os.environ[var], - default_fields=[], - _type="Credential", - session=session, - ) - except Exception as e: - logger.error(f"Error creating {var} variable: {e}") + if not session.exec( + select(Variable).where(Variable.user_id == user_id, Variable.name == var) + ).first(): + try: + value = os.environ[var] + if isinstance(value, str): + value = value.strip() + self.create_variable( + user_id=user_id, + name=var, + value=value, + default_fields=[], + _type="Credential", + session=session, + ) + except Exception as e: + logger.error(f"Error creating {var} variable: {e}") else: logger.info("Skipping environment variable storage.") diff --git a/src/backend/base/langflow/settings.py b/src/backend/base/langflow/settings.py index 9cc761b68..3f340df95 100644 --- a/src/backend/base/langflow/settings.py +++ b/src/backend/base/langflow/settings.py @@ -28,7 +28,6 @@ class Settings(BaseSettings): TOOLKITS: dict = {} TEXTSPLITTERS: dict = {} UTILITIES: dict = {} - OUTPUT_PARSERS: dict = {} CUSTOM_COMPONENTS: dict = {} DEV: bool = False @@ -102,7 +101,6 @@ class Settings(BaseSettings): self.VECTORSTORES = new_settings.VECTORSTORES or {} self.DOCUMENTLOADERS = new_settings.DOCUMENTLOADERS or {} self.RETRIEVERS = new_settings.RETRIEVERS or {} - self.OUTPUT_PARSERS = new_settings.OUTPUT_PARSERS or {} self.CUSTOM_COMPONENTS = new_settings.CUSTOM_COMPONENTS or {} self.COMPONENTS_PATH = new_settings.COMPONENTS_PATH or [] self.DEV = dev diff --git a/src/backend/base/langflow/template/field/prompt.py b/src/backend/base/langflow/template/field/prompt.py index 72c57c5db..ccc5d01a0 100644 --- a/src/backend/base/langflow/template/field/prompt.py +++ b/src/backend/base/langflow/template/field/prompt.py @@ -10,5 +10,5 @@ class DefaultPromptField(TemplateField): advanced: bool = False multiline: bool = True - input_types: list[str] = ["Document", "BaseOutputParser", "Record", "Text"] + input_types: list[str] = ["Document", "Record", "Text"] value: str = "" # Set the value to empty string diff --git a/src/backend/base/langflow/template/frontend_node/custom_components.py b/src/backend/base/langflow/template/frontend_node/custom_components.py index 456aa7466..577c89684 100644 --- a/src/backend/base/langflow/template/frontend_node/custom_components.py +++ b/src/backend/base/langflow/template/frontend_node/custom_components.py @@ -14,7 +14,6 @@ from langflow.field_typing import ( BaseLLM, BaseLoader, BaseMemory, - BaseOutputParser, BasePromptTemplate, BaseRetriever, Callable, diff --git a/src/backend/base/langflow/template/frontend_node/output_parsers.py b/src/backend/base/langflow/template/frontend_node/output_parsers.py deleted file mode 100644 index 6b33ee680..000000000 --- a/src/backend/base/langflow/template/frontend_node/output_parsers.py +++ /dev/null @@ -1,12 +0,0 @@ -from typing import Optional - - -from langflow.template.field.base import TemplateField -from langflow.template.frontend_node.base import FrontendNode - - -class OutputParserFrontendNode(FrontendNode): - @staticmethod - def format_field(field: TemplateField, name: Optional[str] = None) -> None: - FrontendNode.format_field(field, name) - field.show = True diff --git a/src/backend/base/langflow/utils/constants.py b/src/backend/base/langflow/utils/constants.py index ac0a03fdf..99ac950d3 100644 --- a/src/backend/base/langflow/utils/constants.py +++ b/src/backend/base/langflow/utils/constants.py @@ -8,6 +8,7 @@ OPENAI_MODELS = [ "text-ada-001", ] CHAT_OPENAI_MODELS = [ + "gpt-4o", "gpt-4-turbo-preview", "gpt-4-0125-preview", "gpt-4-1106-preview", diff --git a/src/backend/base/langflow/utils/schemas.py b/src/backend/base/langflow/utils/schemas.py index 3e6f17a5a..fbbec2429 100644 --- a/src/backend/base/langflow/utils/schemas.py +++ b/src/backend/base/langflow/utils/schemas.py @@ -45,6 +45,12 @@ class ChatOutputResponse(BaseModel): return self +class RecordOutputResponse(BaseModel): + """Record output response schema.""" + + records: List[Optional[Dict]] + + class ContainsEnumMeta(enum.EnumMeta): def __contains__(cls, item): try: diff --git a/src/backend/base/poetry.lock b/src/backend/base/poetry.lock index 1b9cf758c..23665d65f 100644 --- a/src/backend/base/poetry.lock +++ b/src/backend/base/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "aiohttp" @@ -131,13 +131,13 @@ tz = ["backports.zoneinfo"] [[package]] name = "annotated-types" -version = "0.6.0" +version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" files = [ - {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, - {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] [[package]] @@ -517,13 +517,13 @@ test-randomorder = ["pytest-randomly"] [[package]] name = "dataclasses-json" -version = "0.6.5" +version = "0.6.6" description = "Easily serialize dataclasses to and 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"version": "2.4.1", - "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.4.1.tgz", - "integrity": "sha512-pIXzoImaqmfOrL7teGUBt/T7ZDnyeGBWyXQBvOVhLkWLN37GXv8NMLK406UY6dS51JfcQHsmcW5cJ441bHg6Lg==", + "version": "2.4.2", + "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.4.2.tgz", + "integrity": "sha512-B3VqDZ+JAg1nZpaEmWtTXUlBneoGx6CPM9b0TENK6aoSu5t73dItudwdgmi6tHlIZZId4dZ9skcAQ2UbcyAeVA==", "bin": { "yaml": "bin.mjs" }, @@ -13746,6 +13840,31 @@ "node": ">= 14" } }, + "node_modules/yargs": { + "version": "17.7.2", + "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.2.tgz", + "integrity": "sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==", + "dependencies": { + "cliui": "^8.0.1", + "escalade": "^3.1.1", + "get-caller-file": "^2.0.5", + "require-directory": "^2.1.1", + "string-width": "^4.2.3", + "y18n": "^5.0.5", + "yargs-parser": "^21.1.1" + }, + "engines": { + "node": ">=12" + } + }, + "node_modules/yargs-parser": { + "version": "21.1.1", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz", + "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==", + "engines": { + "node": ">=12" + } + }, "node_modules/yocto-queue": { "version": "0.1.0", "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", @@ -13759,9 +13878,9 @@ } }, "node_modules/zod": { - "version": "3.23.4", - "resolved": "https://registry.npmjs.org/zod/-/zod-3.23.4.tgz", - "integrity": "sha512-/AtWOKbBgjzEYYQRNfoGKHObgfAZag6qUJX1VbHo2PRBgS+wfWagEY2mizjfyAPcGesrJOcx/wcl0L9WnVrHFw==", + "version": "3.23.8", + "resolved": "https://registry.npmjs.org/zod/-/zod-3.23.8.tgz", + "integrity": "sha512-XBx9AXhXktjUqnepgTiE5flcKIYWi/rme0Eaj+5Y0lftuGBq+jyRu/md4WnuxqgP1ubdpNCsYEYPxrzVHD8d6g==", "funding": { "url": "https://github.com/sponsors/colinhacks" } diff --git a/src/frontend/package.json b/src/frontend/package.json index 78f0f6132..db4041c2a 100644 --- a/src/frontend/package.json +++ b/src/frontend/package.json @@ -4,7 +4,9 @@ "private": true, "dependencies": { "@headlessui/react": "^1.7.17", + "@hookform/resolvers": "^3.3.4", "@million/lint": "^0.0.73", + "react-hotkeys-hook": "^4.5.0", "@radix-ui/react-accordion": "^1.1.2", "@radix-ui/react-checkbox": "^1.0.4", "@radix-ui/react-dialog": "^1.0.4", @@ -50,7 +52,7 @@ "react-cookie": "^4.1.1", "react-dom": "^18.2.21", "react-error-boundary": "^4.0.11", - "react-hotkeys-hook": "^4.5.0", + "react-hook-form": "^7.51.4", "react-icons": "^5.0.1", "react-laag": "^2.0.5", "react-markdown": "^8.0.7", @@ -69,6 +71,7 @@ "uuid": "^9.0.0", "vite-plugin-svgr": "^3.2.0", "web-vitals": "^2.1.4", + "zod": "^3.23.7", "zustand": "^4.4.7" }, "scripts": { @@ -102,7 +105,7 @@ }, "proxy": "http://127.0.0.1:7860", "devDependencies": { - "@playwright/test": "^1.43.1", + "@playwright/test": "^1.44.0", "@swc/cli": "^0.1.62", "@swc/core": "^1.3.80", "@tailwindcss/typography": "^0.5.9", diff --git a/src/frontend/src/App.css b/src/frontend/src/App.css index 22a596730..a4ff01961 100644 --- a/src/frontend/src/App.css +++ b/src/frontend/src/App.css @@ -11,6 +11,13 @@ body { text-align: center; } +.label { + user-select: none; + -webkit-user-select: none; /* Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; +} + .react-flow__node { width: auto; height: auto; @@ -76,6 +83,25 @@ body { height: 8px !important; border-radius: 10px; } +*::-webkit-scrollbar { + width: 8px !important; + height: 8px !important; + border-radius: 10px; +} + +::-webkit-scrollbar-track { + background-color: #f1f1f1 !important; + border-radius: 10px; +} + +::-webkit-scrollbar-thumb { + background-color: #ccc !important; + border-radius: 999px !important; +} + +::-webkit-scrollbar-thumb:hover { + background-color: #bbb !important; +} .jv-indent::-webkit-scrollbar-track { background-color: #f1f1f1 !important; @@ -111,3 +137,30 @@ body { .json-view-flow .json-view { background-color: #bbb !important; } + +.ag-body-horizontal-scroll-viewport, +.ag-body-vertical-scroll-viewport { + cursor: auto; +} + +.ag-body-horizontal-scroll-viewport::-webkit-scrollbar, +.ag-body-vertical-scroll-viewport::-webkit-scrollbar { + width: 8px; + height: 8px; +} + +.ag-body-horizontal-scroll-viewport::-webkit-scrollbar-track, +.ag-body-vertical-scroll-viewport::-webkit-scrollbar-track { + background-color: #f1f1f1; +} + +.ag-body-horizontal-scroll-viewport::-webkit-scrollbar-thumb, +.ag-body-vertical-scroll-viewport::-webkit-scrollbar-thumb { + background-color: #ccc; + border-radius: 999px; +} + +.ag-body-horizontal-scroll-viewport::-webkit-scrollbar-thumb:hover, +.ag-body-vertical-scroll-viewport::-webkit-scrollbar-thumb:hover { + background-color: #bbb; +} diff --git a/src/frontend/src/App.tsx b/src/frontend/src/App.tsx index 2d8fc3caa..53f085738 100644 --- a/src/frontend/src/App.tsx +++ b/src/frontend/src/App.tsx @@ -19,15 +19,16 @@ import Router from "./routes"; import useAlertStore from "./stores/alertStore"; import { useDarkStore } from "./stores/darkStore"; import useFlowsManagerStore from "./stores/flowsManagerStore"; +import { useFolderStore } from "./stores/foldersStore"; import { useGlobalVariablesStore } from "./stores/globalVariables"; import { useStoreStore } from "./stores/storeStore"; import { useTypesStore } from "./stores/typesStore"; export default function App() { const removeFromTempNotificationList = useAlertStore( - (state) => state.removeFromTempNotificationList + (state) => state.removeFromTempNotificationList, ); const tempNotificationList = useAlertStore( - (state) => state.tempNotificationList + (state) => state.tempNotificationList, ); const [fetchError, setFetchError] = useState(false); const isLoading = useFlowsManagerStore((state) => state.isLoading); @@ -45,15 +46,15 @@ export default function App() { const refreshVersion = useDarkStore((state) => state.refreshVersion); const refreshStars = useDarkStore((state) => state.refreshStars); const setGlobalVariables = useGlobalVariablesStore( - (state) => state.setGlobalVariables - ); - const setUnavailableFields = useGlobalVariablesStore( - (state) => state.setUnavaliableFields + (state) => state.setGlobalVariables, ); const checkHasStore = useStoreStore((state) => state.checkHasStore); const navigate = useNavigate(); const dark = useDarkStore((state) => state.dark); + const getFoldersApi = useFolderStore((state) => state.getFoldersApi); + const loadingFolders = useFolderStore((state) => state.loading); + const [isLoadingHealth, setIsLoadingHealth] = useState(false); useEffect(() => { @@ -76,7 +77,7 @@ export default function App() { setUserData(user); setAutoLogin(true); setLoading(false); - await Promise.all([refreshStars(), refreshVersion(), fetchData()]); + fetchAllData(); } }) .catch(async (error) => { @@ -84,7 +85,7 @@ export default function App() { setAutoLogin(false); if (isAuthenticated && !isLoginPage) { getUser(); - await Promise.all([refreshStars(), refreshVersion(), fetchData()]); + fetchAllData(); } else { setLoading(false); useFlowsManagerStore.setState({ isLoading: false }); @@ -92,14 +93,21 @@ export default function App() { } }); - /* - Abort the request as it isn't needed anymore, the component being + /* + Abort the request as it isn't needed anymore, the component being unmounted. It helps avoid, among other things, the well-known "can't perform a React state update on an unmounted component" warning. */ return () => abortController.abort(); }, []); + const fetchAllData = async () => { + setTimeout(async () => { + await Promise.all([refreshStars(), refreshVersion(), fetchData()]); + getFoldersApi(); + }, 1000); + }; + const fetchData = async () => { return new Promise(async (resolve, reject) => { if (isAuthenticated) { @@ -157,7 +165,7 @@ export default function App() { setFetchError(false); //This condition is necessary to avoid infinite loop on starter page when the application is not healthy if (isLoading === true && window.location.pathname === "/") { - navigate("/flows"); + navigate("/all"); window.location.reload(); } }; @@ -184,7 +192,7 @@ export default function App() { > } - {isLoading ? ( + {isLoading || loadingFolders ? (
diff --git a/src/frontend/src/boilerplate/raw-component/index.tsx b/src/frontend/src/boilerplate/raw-component/index.tsx new file mode 100644 index 000000000..0595c1808 --- /dev/null +++ b/src/frontend/src/boilerplate/raw-component/index.tsx @@ -0,0 +1,9 @@ +type RawComponentProps = {}; +const RawComponent = ({}: RawComponentProps) => { + return ( + <> +
RawComponent
+ + ); +}; +export default RawComponent; diff --git a/src/frontend/src/components/accordionComponent/composite/folderAccordionComponent/index.tsx b/src/frontend/src/components/accordionComponent/composite/folderAccordionComponent/index.tsx new file mode 100644 index 000000000..d4fb95b5c --- /dev/null +++ b/src/frontend/src/components/accordionComponent/composite/folderAccordionComponent/index.tsx @@ -0,0 +1,72 @@ +import { useState } from "react"; +import IconComponent from "../../../../components/genericIconComponent"; +import { AccordionComponentType } from "../../../../types/components"; +import { + Accordion, + AccordionContent, + AccordionItem, + AccordionTrigger, +} from "../../../ui/custom-accordion"; + +export default function FolderAccordionComponent({ + trigger, + open = [], + keyValue, + options, +}: AccordionComponentType): JSX.Element { + const [value, setValue] = useState( + open.length === 0 ? "" : getOpenAccordion(), + ); + + function getOpenAccordion(): string { + let value = ""; + open.forEach((el) => { + if (el == trigger) { + value = trigger; + } + }); + + return value; + } + + function handleClick(): void { + value === "" ? setValue(keyValue!) : setValue(""); + } + + return ( + <> + + + { + handleClick(); + }} + className="px-2" + > + {trigger} + + + {options!.map((option, index) => ( +
+
+ ))} +
+
+
+ + ); +} diff --git a/src/frontend/src/components/accordionComponent/index.tsx b/src/frontend/src/components/accordionComponent/index.tsx index fd9e42580..fdcf8b96c 100644 --- a/src/frontend/src/components/accordionComponent/index.tsx +++ b/src/frontend/src/components/accordionComponent/index.tsx @@ -15,17 +15,16 @@ export default function AccordionComponent({ sideBar, }: AccordionComponentType): JSX.Element { const [value, setValue] = useState( - open.length === 0 ? "" : getOpenAccordion() + open.length === 0 ? "" : getOpenAccordion(), ); function getOpenAccordion(): string { let value = ""; open.forEach((el) => { - if (el == trigger) { - value = trigger; + if (el == keyValue) { + value = keyValue; } }); - return value; } diff --git a/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx b/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx index 2745ba1bf..d9723cea8 100644 --- a/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx +++ b/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx @@ -130,8 +130,8 @@ export default function AddNewVariableButton({ children }): JSX.Element { setFields(value)} selectedOptions={fields} - password={false} options={availableFields()} + password={false} placeholder="Choose a field for the variable..." id={"apply-to-fields"} > diff --git a/src/frontend/src/components/arrayReaderComponent/index.tsx b/src/frontend/src/components/arrayReaderComponent/index.tsx new file mode 100644 index 000000000..bcbfde010 --- /dev/null +++ b/src/frontend/src/components/arrayReaderComponent/index.tsx @@ -0,0 +1,12 @@ +export default function ArrayReader({ array }: { array: any[] }): JSX.Element { + //TODO check array type + return ( +
+
    + {array.map((item, index) => ( +
  • {item}
  • + ))} +
+
+ ); +} diff --git a/src/frontend/src/components/cardComponent/components/dragCardComponent/index.tsx b/src/frontend/src/components/cardComponent/components/dragCardComponent/index.tsx new file mode 100644 index 000000000..e4425c61c --- /dev/null +++ b/src/frontend/src/components/cardComponent/components/dragCardComponent/index.tsx @@ -0,0 +1,39 @@ +import { storeComponent } from "../../../../types/store"; +import { cn } from "../../../../utils/utils"; +import ForwardedIconComponent from "../../../genericIconComponent"; +import ShadTooltip from "../../../shadTooltipComponent"; +import { Card, CardHeader, CardTitle } from "../../../ui/card"; + +export default function DragCardComponent({ data }: { data: storeComponent }) { + return ( + <> + +
+ +
+ + + +
{data.name}
+
+
+
+
+
+ + ); +} diff --git a/src/frontend/src/components/cardComponent/index.tsx b/src/frontend/src/components/cardComponent/index.tsx index e73ead557..10d194db8 100644 --- a/src/frontend/src/components/cardComponent/index.tsx +++ b/src/frontend/src/components/cardComponent/index.tsx @@ -1,4 +1,6 @@ import { useEffect, useState } from "react"; +import { createRoot } from "react-dom/client"; +import { Control } from "react-hook-form"; import { getComponent, postLikeComponent } from "../../controllers/API"; import IOModal from "../../modals/IOModal"; import DeleteConfirmationModal from "../../modals/deleteConfirmationModal"; @@ -6,6 +8,7 @@ import useAlertStore from "../../stores/alertStore"; import useFlowStore from "../../stores/flowStore"; import useFlowsManagerStore from "../../stores/flowsManagerStore"; import { useStoreStore } from "../../stores/storeStore"; +import { FlowType } from "../../types/flow"; import { storeComponent } from "../../types/store"; import cloneFLowWithParent, { getInputsAndOutputs, @@ -21,8 +24,10 @@ import { CardHeader, CardTitle, } from "../ui/card"; +import { Checkbox } from "../ui/checkbox"; +import { FormControl, FormField } from "../ui/form"; import Loading from "../ui/loading"; -import { FlowType } from "../../types/flow"; +import DragCardComponent from "./components/dragCardComponent"; export default function CollectionCardComponent({ data, @@ -32,6 +37,8 @@ export default function CollectionCardComponent({ onClick, onDelete, playground, + control, + is_component, }: { data: storeComponent; authorized?: boolean; @@ -40,6 +47,8 @@ export default function CollectionCardComponent({ button?: JSX.Element; playground?: boolean; onDelete?: () => void; + control?: Control; + is_component?: boolean; }) { const addFlow = useFlowsManagerStore((state) => state.addFlow); const setSuccessData = useAlertStore((state) => state.setSuccessData); @@ -69,6 +78,10 @@ export default function CollectionCardComponent({ ); const [loadingPlayground, setLoadingPlayground] = useState(false); + const selectedFlowsComponentsCards = useFlowsManagerStore( + (state) => state.selectedFlowsComponentsCards, + ); + const name = data.is_component ? "Component" : "Flow"; async function getFlowData() { @@ -82,7 +95,6 @@ export default function CollectionCardComponent({ return false; } const { inputs, outputs } = getInputsAndOutputs(flow?.data?.nodes ?? []); - console.log(inputs, outputs); return inputs.length > 0 || outputs.length > 0; } @@ -177,36 +189,59 @@ export default function CollectionCardComponent({ } } + const isSelectedCard = + selectedFlowsComponentsCards?.includes(data?.id) ?? false; + + function onDragStart(event: React.DragEvent) { + let image: JSX.Element = ; // <== whatever you want here + + var ghost = document.createElement("div"); + ghost.style.transform = "translate(-10000px, -10000px)"; + ghost.style.position = "absolute"; + document.body.appendChild(ghost); + event.dataTransfer.setDragImage(ghost, 0, 0); + const root = createRoot(ghost); + root.render(image); + const flow = getFlowById(data.id); + if (flow) { + event.dataTransfer.setData("flow", JSON.stringify(data)); + } + } + return ( <>
- + + -
{data.name}
+
{data.name}
{data?.metadata !== undefined && ( -
+
{data.private && ( @@ -249,19 +284,29 @@ export default function CollectionCardComponent({
)} - {onDelete && data?.metadata === undefined && ( - +
)}
@@ -368,11 +413,7 @@ export default function CollectionCardComponent({ authorized ? "Delete" : "Please review your API key." } > - { - onDelete(); - }} - > + - -
- )} + + + + )} + {allowExport && ( + +
+ + Export Flow +
+
+ )} + + {Number(activeTab) < 4 && ( + <> + + + + )} + {tabs.map((tab, idx) => ( @@ -210,9 +241,7 @@ export default function CodeTabsComponent({ > {data?.map((node: any, i) => (
- {tweaks?.tweaksList!.current.includes( - node["data"]["id"], - ) && ( + {tweaks?.tweaksList!.includes(node["data"]["id"]) && ( {node["data"]["node"]["display_name"]}
} - open={openAccordion} keyValue={node["data"]["id"]} >
@@ -258,107 +286,71 @@ export default function CodeTabsComponent({
- {node.data.node.template[ - templateField - ].type === "str" && - !node.data.node.template[ - templateField - ].options ? ( -
- {node.data.node.template[ + { - setData((old) => { - let newInputList = - cloneDeep(old); - newInputList![ - i - ].data.node.template[ - templateField - ].value = target; - return newInputList; - }); - tweaks.buildTweakObject!( - node["data"]["id"], - target, - node.data.node.template[ - templateField - ], - ); - }} - /> - ) : node.data.node.template[ + ].options + } + > + - + { - setData((old) => { - let newInputList = - cloneDeep(old); - newInputList![ - i - ].data.node.template[ - templateField - ].value = target; - return newInputList; - }); - tweaks.buildTweakObject!( - node["data"]["id"], - target, - node.data.node - .template[ - templateField - ], - ); - }} - /> -
- ) : ( - { + setData((old) => { + let newInputList = + cloneDeep(old); + newInputList![ + i + ].data.node.template[ + templateField + ].value = target; + return newInputList; + }); + tweaks?.buildTweakObject!( + node["data"]["id"], + target, node.data.node.template[ templateField - ].password ?? false - } + ], + ); + }} + /> + + + +
+ - )} -
- ) : node.data.node.template[ - templateField - ].type === "bool" ? ( +
+ + + + { + setData((old) => { + let newInputList = + cloneDeep(old); + newInputList![ + i + ].data.node.template[ + templateField + ].value = target; + return newInputList; + }); + tweaks?.buildTweakObject!( + node["data"]["id"], + target, + node.data.node.template[ + templateField + ], + ); + }} + /> + + + +
{" "}
- ) : node.data.node.template[ - templateField - ].type === "file" ? ( +
+ +
- ) : node.data.node.template[ - templateField - ].type === "float" ? ( +
+ +
- ) : node.data.node.template[ - templateField - ].type === "str" && - node.data.node.template[ - templateField - ].options ? ( +
+ +
- ) : node.data.node.template[ - templateField - ].type === "int" ? ( +
+ +
- ) : node.data.node.template[ - templateField - ].type === "prompt" ? ( +
+ +
- ) : node.data.node.template[ - templateField - ].type === "code" ? ( +
+ +
- ) : node.data.node.template[ - templateField - ].type === "dict" ? ( +
+ +
- ) : node.data.node.template[ - templateField - ].type === "NestedDict" ? ( +
+ +
- ) : node.data.node.template[ - templateField - ].type === "Any" ? ( - "-" - ) : ( -
- )} +
+ + + <>- +
@@ -795,7 +891,7 @@ export default function CodeTabsComponent({ )} - {tweaks?.tweaksList!.current.length === 0 && ( + {tweaks?.tweaksList!.length === 0 && ( <>
No tweaks are available for this flow. diff --git a/src/frontend/src/components/csvOutputComponent/index.tsx b/src/frontend/src/components/csvOutputComponent/index.tsx index fcf59029c..a98d9c028 100644 --- a/src/frontend/src/components/csvOutputComponent/index.tsx +++ b/src/frontend/src/components/csvOutputComponent/index.tsx @@ -1,7 +1,6 @@ import "ag-grid-community/styles/ag-grid.css"; // Mandatory CSS required by the grid import "ag-grid-community/styles/ag-theme-balham.css"; // Optional Theme applied to the grid -import { AgGridReact } from "ag-grid-react"; -import { useCallback, useEffect, useMemo, useState } from "react"; +import { useEffect, useMemo, useState } from "react"; import { CSVError, CSVNoDataError, @@ -11,6 +10,7 @@ import { useDarkStore } from "../../stores/darkStore"; import { FlowPoolObjectType } from "../../types/chat"; import { NodeType } from "../../types/flow"; import ForwardedIconComponent from "../genericIconComponent"; +import TableComponent from "../tableComponent"; import Loading from "../ui/loading"; import { convertCSVToData } from "./helpers/convert-data-function"; @@ -54,8 +54,6 @@ function CsvOutputComponent({ const [colDefs, setColDefs] = useState([]); const [status, setStatus] = useState("loading"); - var currentRowHeight: number; - var minRowHeight = 25; const defaultColDef = useMemo(() => { return { width: 200, @@ -82,48 +80,6 @@ function CsvOutputComponent({ } }, [separator]); - const getRowHeight = useCallback(() => { - return currentRowHeight; - }, []); - - const onGridReady = useCallback((params: any) => { - minRowHeight = params.api.getSizesForCurrentTheme().rowHeight; - currentRowHeight = minRowHeight; - }, []); - - const updateRowHeight = (params: { api: any }) => { - const bodyViewport = document.querySelector(".ag-body-viewport"); - if (!bodyViewport) { - return; - } - var gridHeight = bodyViewport.clientHeight; - var renderedRowCount = params.api.getDisplayedRowCount(); - - if (renderedRowCount * minRowHeight >= gridHeight) { - if (currentRowHeight !== minRowHeight) { - currentRowHeight = minRowHeight; - params.api.resetRowHeights(); - } - } else { - currentRowHeight = Math.floor(gridHeight / renderedRowCount); - params.api.resetRowHeights(); - } - }; - - const onFirstDataRendered = useCallback( - (params: any) => { - updateRowHeight(params); - }, - [updateRowHeight], - ); - - const onGridSizeChanged = useCallback( - (params: any) => { - updateRowHeight(params); - }, - [updateRowHeight], - ); - return (
{status === "nodata" && ( @@ -158,14 +114,10 @@ function CsvOutputComponent({ className={`${dark ? "ag-theme-balham-dark" : "ag-theme-balham"}`} style={{ height: "100%", width: "100%" }} > - diff --git a/src/frontend/src/components/dateReaderComponent/index.tsx b/src/frontend/src/components/dateReaderComponent/index.tsx new file mode 100644 index 000000000..81c352f5a --- /dev/null +++ b/src/frontend/src/components/dateReaderComponent/index.tsx @@ -0,0 +1,21 @@ +export default function DateReader({ + date: dateString, +}: { + date: string; +}): JSX.Element { + const date = new Date(dateString); + + const year = date.getFullYear(); + const month = String(date.getMonth() + 1).padStart(2, "0"); // Months are 0-indexed in JavaScript + const day = String(date.getDate()).padStart(2, "0"); + + const hours = date.getHours(); + const minutes = String(date.getMinutes()).padStart(2, "0"); + + const ampm = hours >= 12 ? "PM" : "AM"; + const hours12 = hours > 12 ? hours - 12 : hours === 0 ? 12 : hours; // Convert to 12-hour format + + const formattedDate = `${year}-${month}-${day} ${hours12}:${minutes} ${ampm}`; + + return {formattedDate}; +} diff --git a/src/frontend/src/components/dropdownComponent/index.tsx b/src/frontend/src/components/dropdownComponent/index.tsx index b17332268..8402d166e 100644 --- a/src/frontend/src/components/dropdownComponent/index.tsx +++ b/src/frontend/src/components/dropdownComponent/index.tsx @@ -59,7 +59,7 @@ export default function Dropdown({ ? "dropdown-component-outline" : "dropdown-component-false-outline", "w-full justify-between font-normal", - editNode ? "input-edit-node" : "py-2" + editNode ? "input-edit-node" : "py-2", )} > @@ -78,6 +78,8 @@ export default function Dropdown({ )} diff --git a/src/frontend/src/components/floatComponent/index.tsx b/src/frontend/src/components/floatComponent/index.tsx index a4ebf1ae9..8db5b823a 100644 --- a/src/frontend/src/components/floatComponent/index.tsx +++ b/src/frontend/src/components/floatComponent/index.tsx @@ -16,7 +16,7 @@ export default function FloatComponent({ // Clear component state useEffect(() => { if (disabled && value !== "") { - onChange(""); + onChange("", true); } }, [disabled]); diff --git a/src/frontend/src/components/genericIconComponent/index.tsx b/src/frontend/src/components/genericIconComponent/index.tsx index f4e6a49d6..9f7c687a1 100644 --- a/src/frontend/src/components/genericIconComponent/index.tsx +++ b/src/frontend/src/components/genericIconComponent/index.tsx @@ -7,7 +7,7 @@ import Loading from "../ui/loading"; import { useEffect, useState } from "react"; -const ForwardedIconComponent = memo( +export const ForwardedIconComponent = memo( forwardRef( ( { @@ -18,7 +18,7 @@ const ForwardedIconComponent = memo( strokeWidth, id = "", }: IconComponentProps, - ref + ref, ) => { const [showFallback, setShowFallback] = useState(false); @@ -65,8 +65,8 @@ const ForwardedIconComponent = memo( /> ); - } - ) + }, + ), ); export default ForwardedIconComponent; diff --git a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx index 85a586b16..b5a115e1e 100644 --- a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx +++ b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx @@ -12,6 +12,7 @@ import { Node } from "reactflow"; import { UPLOAD_ERROR_ALERT } from "../../../../constants/alerts_constants"; import { SAVED_HOVER } from "../../../../constants/constants"; import ExportModal from "../../../../modals/exportModal"; +import FlowLogsModal from "../../../../modals/flowLogsModal"; import FlowSettingsModal from "../../../../modals/flowSettingsModal"; import useAlertStore from "../../../../stores/alertStore"; import useFlowStore from "../../../../stores/flowStore"; @@ -34,6 +35,7 @@ export const MenuBar = ({ const redo = useFlowsManagerStore((state) => state.redo); const saveLoading = useFlowsManagerStore((state) => state.saveLoading); const [openSettings, setOpenSettings] = useState(false); + const [openLogs, setOpenLogs] = useState(false); const nodes = useFlowStore((state) => state.nodes); const uploadFlow = useFlowsManagerStore((state) => state.uploadFlow); const navigate = useNavigate(); @@ -123,6 +125,18 @@ export const MenuBar = ({ /> Settings + { + setOpenLogs(true); + }} + className="cursor-pointer" + > + + Logs + { @@ -132,7 +146,7 @@ export const MenuBar = ({ title: UPLOAD_ERROR_ALERT, list: [error], }); - } + }, ); }} > @@ -194,6 +208,7 @@ export const MenuBar = ({ open={openSettings} setOpen={setOpenSettings} > +
{(currentFlow.updated_at || saveLoading) && ( {printByBuildStatus()} diff --git a/src/frontend/src/components/headerComponent/index.tsx b/src/frontend/src/components/headerComponent/index.tsx index bc2537ea2..2467fdcfe 100644 --- a/src/frontend/src/components/headerComponent/index.tsx +++ b/src/frontend/src/components/headerComponent/index.tsx @@ -59,7 +59,7 @@ export default function Header(): JSX.Element {
- {/* - - */} + {hasStore && ( + + +
+ <> + {folders.map((item, index) => ( +
onDrop(e, item.id!)} + key={item.id} + data-testid={`sidebar-nav-${item.name}`} + className={cn( + buttonVariants({ variant: "ghost" }), + checkPathName(item.id!) + ? "border border-border bg-muted hover:bg-muted" + : "border hover:bg-transparent lg:border-transparent lg:hover:border-border", + "group flex w-full shrink-0 cursor-pointer gap-2 opacity-100 lg:min-w-full", + )} + onClick={() => handleChangeFolder!(item.id!)} + > +
+ + + {item.name} + +
+ {index > 0 && ( + + )} + {index > 0 && ( + + )} + +
+
+ ))} + +
+ + ); +}; +export default SideBarFoldersButtonsComponent; diff --git a/src/frontend/src/components/sidebarComponent/hooks/use-on-file-drop.tsx b/src/frontend/src/components/sidebarComponent/hooks/use-on-file-drop.tsx new file mode 100644 index 000000000..ce0ad3614 --- /dev/null +++ b/src/frontend/src/components/sidebarComponent/hooks/use-on-file-drop.tsx @@ -0,0 +1,134 @@ +import { + UPLOAD_ALERT_LIST, + WRONG_FILE_ERROR_ALERT, +} from "../../../constants/alerts_constants"; +import { updateFlowInDatabase } from "../../../controllers/API"; +import { uploadFlowsFromFolders } from "../../../pages/MainPage/services"; +import useAlertStore from "../../../stores/alertStore"; +import useFlowsManagerStore from "../../../stores/flowsManagerStore"; +import { useFolderStore } from "../../../stores/foldersStore"; +import { FlowType } from "../../../types/flow"; + +const useFileDrop = (folderId, folderChangeCallback) => { + const setFolderDragging = useFolderStore((state) => state.setFolderDragging); + const setErrorData = useAlertStore((state) => state.setErrorData); + const getFoldersApi = useFolderStore((state) => state.getFoldersApi); + const refreshFlows = useFlowsManagerStore((state) => state.refreshFlows); + const flows = useFlowsManagerStore((state) => state.flows); + + const triggerFolderChange = (folderId) => { + if (folderChangeCallback) { + folderChangeCallback(folderId); + } + }; + const handleFileDrop = async (e) => { + if (e.dataTransfer.types.some((type) => type === "Files")) { + if (e.dataTransfer.files && e.dataTransfer.files.length > 0) { + const firstFile = e.dataTransfer.files[0]; + if (firstFile.type === "application/json") { + uploadFormData(firstFile); + } else { + setErrorData({ + title: WRONG_FILE_ERROR_ALERT, + list: [UPLOAD_ALERT_LIST], + }); + } + } + } + }; + + const dragOver = ( + e: + | React.DragEvent + | React.DragEvent + | React.DragEvent, + ) => { + e.preventDefault(); + + if (e.dataTransfer.types.some((types) => types === "Files")) { + setFolderDragging(true); + } + }; + + const dragEnter = ( + e: + | React.DragEvent + | React.DragEvent + | React.DragEvent, + ) => { + if (e.dataTransfer.types.some((types) => types === "Files")) { + setFolderDragging(true); + } + e.preventDefault(); + }; + + const dragLeave = ( + e: + | React.DragEvent + | React.DragEvent + | React.DragEvent, + ) => { + e.preventDefault(); + if (e.target === e.currentTarget) { + setFolderDragging(false); + } + }; + + const onDrop = ( + e: + | React.DragEvent + | React.DragEvent + | React.DragEvent, + folderId: string, + ) => { + if (e?.dataTransfer?.getData("flow")) { + const data = JSON.parse(e?.dataTransfer?.getData("flow")); + + if (data) { + uploadFromDragCard(data.id, folderId); + return; + } + } + + e.preventDefault(); + handleFileDrop(e); + setFolderDragging(false); + }; + + const uploadFromDragCard = (flowId, folderId) => { + const selectedFlow = flows.find((flow) => flow.id === flowId); + + if (!selectedFlow) { + throw new Error("Flow not found"); + } + + const updatedFlow: FlowType = { + ...selectedFlow, + folder_id: folderId, + }; + updateFlowInDatabase(updatedFlow).then(() => { + getFoldersApi(true); + triggerFolderChange(folderId); + }); + }; + + const uploadFormData = (data) => { + const formData = new FormData(); + formData.append("file", data); + + uploadFlowsFromFolders(formData).then(() => { + getFoldersApi(true); + triggerFolderChange(folderId); + refreshFlows(); + }); + }; + + return { + dragOver, + dragEnter, + dragLeave, + onDrop, + }; +}; + +export default useFileDrop; diff --git a/src/frontend/src/components/sidebarComponent/index.tsx b/src/frontend/src/components/sidebarComponent/index.tsx index edeaee51e..63fec9661 100644 --- a/src/frontend/src/components/sidebarComponent/index.tsx +++ b/src/frontend/src/components/sidebarComponent/index.tsx @@ -1,48 +1,61 @@ -import { Link, useLocation } from "react-router-dom"; +import { useLocation } from "react-router-dom"; +import { FolderType } from "../../pages/MainPage/entities"; +import { useFolderStore } from "../../stores/foldersStore"; import { cn } from "../../utils/utils"; -import { buttonVariants } from "../ui/button"; +import HorizontalScrollFadeComponent from "../horizontalScrollFadeComponent"; +import SideBarButtonsComponent from "./components/sideBarButtons"; +import SideBarFoldersButtonsComponent from "./components/sideBarFolderButtons"; -interface SidebarNavProps extends React.HTMLAttributes { +type SidebarNavProps = { items: { - href: string; + href?: string; title: string; icon: React.ReactNode; }[]; -} + handleOpenNewFolderModal?: () => void; + handleChangeFolder?: (id: string) => void; + handleEditFolder?: (item: FolderType) => void; + handleDeleteFolder?: (item: FolderType) => void; + className?: string; +}; export default function SidebarNav({ className, items, + handleOpenNewFolderModal, + handleChangeFolder, + handleEditFolder, + handleDeleteFolder, ...props }: SidebarNavProps) { const location = useLocation(); const pathname = location.pathname; + const loadingFolders = useFolderStore((state) => state.loading); + const folders = useFolderStore((state) => state.folders); + + const pathValues = ["folder", "components", "flows", "all"]; + const isFolderPath = pathValues.some((value) => pathname.includes(value)); return ( -