diff --git a/.dockerignore b/.dockerignore index 130ca4c2c..7cae75457 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,6 +1,6 @@ .venv/ **/aws -# node_modules +node_modules **/node_modules/ dist/ **/build/ diff --git a/.github/workflows/pre-release-base.yml b/.github/workflows/pre-release-base.yml new file mode 100644 index 000000000..60872def3 --- /dev/null +++ b/.github/workflows/pre-release-base.yml @@ -0,0 +1,66 @@ +name: Langflow Base Pre-release + +on: + pull_request: + types: + - closed + branches: + - dev + paths: + - "pyproject.toml" + workflow_dispatch: + +env: + POETRY_VERSION: "1.8.2" + +jobs: + if_release: + if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'pre-release') }} + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Install poetry + run: pipx install poetry==$POETRY_VERSION + - name: Set up Python 3.10 + uses: actions/setup-python@v5 + with: + python-version: "3.10" + cache: "poetry" + - name: Build project for distribution + run: make build base=true + - name: Check Version + id: check-version + run: | + echo version=$(poetry version --short) >> $GITHUB_OUTPUT + - name: Create Release + uses: ncipollo/release-action@v1 + with: + artifacts: "dist/*" + token: ${{ secrets.GITHUB_TOKEN }} + draft: false + generateReleaseNotes: true + prerelease: true + tag: v${{ steps.check-version.outputs.version }} + commit: dev + - name: Publish to PyPI + env: + POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }} + run: | + poetry publish base=true + - name: Set up QEMU + uses: docker/setup-qemu-action@v3 + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - name: Login to Docker Hub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_TOKEN }} + - name: Build and push + uses: docker/build-push-action@v5 + with: + context: . + push: true + file: ./build_and_push_base.Dockerfile + tags: | + logspace/langflow:base-${{ steps.check-version.outputs.version }} diff --git a/.github/workflows/pre-release.yml b/.github/workflows/pre-release-langflow.yml similarity index 88% rename from .github/workflows/pre-release.yml rename to .github/workflows/pre-release-langflow.yml index 6ca9aa67d..57f3b3e1a 100644 --- a/.github/workflows/pre-release.yml +++ b/.github/workflows/pre-release-langflow.yml @@ -1,4 +1,4 @@ -name: pre-release +name: Langflow Pre-release on: pull_request: @@ -9,6 +9,10 @@ on: paths: - "pyproject.toml" workflow_dispatch: + workflow_run: + workflows: ["pre-release-base"] + types: [completed] + branches: [dev] env: POETRY_VERSION: "1.8.2" @@ -20,14 +24,14 @@ jobs: steps: - uses: actions/checkout@v4 - name: Install poetry - run: pipx install poetry==$POETRY_VERSION && poetry self add poetry-monorepo-dependency-plugin + run: pipx install poetry==$POETRY_VERSION - name: Set up Python 3.10 uses: actions/setup-python@v5 with: python-version: "3.10" cache: "poetry" - name: Build project for distribution - run: make build + run: make build main=true - name: Check Version id: check-version run: | @@ -46,7 +50,7 @@ jobs: env: POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }} run: | - poetry publish + poetry publish main=true - name: Set up QEMU uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx @@ -64,4 +68,3 @@ jobs: file: ./build_and_push.Dockerfile tags: | logspace/langflow:${{ steps.check-version.outputs.version }} - logspace/langflow:latest-dev diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index b373b859b..8004618f6 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -19,7 +19,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Install poetry - run: pipx install poetry==$POETRY_VERSION && poetry self add poetry-monorepo-dependency-plugin + run: pipx install poetry==$POETRY_VERSION - name: Set up Python 3.10 uses: actions/setup-python@v5 with: diff --git a/Makefile b/Makefile index e1407c733..e5d886652 100644 --- a/Makefile +++ b/Makefile @@ -10,7 +10,6 @@ path = src/backend/base/langflow/frontend setup_poetry: pipx install poetry - poetry self add poetry-monorepo-dependency-plugin add: @echo 'Adding dependencies' @@ -168,22 +167,27 @@ build_frontend: build: @echo 'Building the project' @make setup_env - make build_langflow_base - make build_langflow - -build_langflow_base: +ifdef base make install_frontendci make build_frontend - cd src/backend/base && poetry build-rewrite-path-deps --version-pinning-strategy=semver + make build_langflow_base +endif + +ifdef main + make build_langflow +endif + +build_langflow_base: + cd src/backend/base && poetry build rm -rf src/backend/base/langflow/frontend build_langflow_backup: - poetry lock && poetry build-rewrite-path-deps --version-pinning-strategy=semver + poetry lock && poetry build build_langflow: - cd ./scripts && python update_dependencies.py + cd ./scripts && poetry run python update_dependencies.py poetry lock - poetry build-rewrite-path-deps --version-pinning-strategy=semver + poetry build mv pyproject.toml.bak pyproject.toml mv poetry.lock.bak poetry.lock @@ -208,6 +212,7 @@ lock: @echo 'Locking dependencies' cd src/backend/base && poetry lock poetry lock + publish_base: make build_langflow_base cd src/backend/base && poetry publish @@ -217,8 +222,14 @@ publish_langflow: poetry publish publish: - make publish_base - make publish_langflow + @echo 'Publishing the project' +ifdef base + -make publish_base +endif + +ifdef main + -make publish_langflow +endif help: @echo '----' diff --git a/README.md b/README.md index 8d243a0dc..894252501 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ [![GitHub fork](https://img.shields.io/github/forks/logspace-ai/langflow?style=social)](https://github.com/logspace-ai/langflow/fork) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langflow_ai.svg?style=social&label=Follow%20%40langflow_ai)](https://twitter.com/langflow_ai) [![](https://dcbadge.vercel.app/api/server/EqksyE2EX9?compact=true&style=flat)](https://discord.com/invite/EqksyE2EX9) -[![HuggingFace Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md.svg)](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) +[![HuggingFace Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md.svg)](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/logspace-ai/langflow) @@ -62,7 +62,7 @@ langflow run # or langflow --help ### HuggingFace Spaces -You can also check it out on HuggingFace Spaces and run it in your browser for free! [Click here to duplicate the Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) +You can also check it out on HuggingFace Spaces and run it in your browser for free! [Click here to duplicate the Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) # ๐Ÿ–ฅ๏ธ Command Line Interface (CLI) diff --git a/build_and_push.Dockerfile b/build_and_push.Dockerfile index 136f34f9e..1c595e180 100644 --- a/build_and_push.Dockerfile +++ b/build_and_push.Dockerfile @@ -65,8 +65,11 @@ COPY src ./src COPY scripts ./scripts COPY Makefile ./ COPY README.md ./ -RUN make build - +RUN --mount=type=cache,target=/root/.cache \ + curl -sSL https://install.python-poetry.org | python3 - +RUN python -m pip install requests && cd ./scripts && python update_dependencies.py +RUN $POETRY_HOME/bin/poetry lock +RUN $POETRY_HOME/bin/poetry build # Final stage for the application FROM python-base as final diff --git a/build_and_push_base.Dockerfile b/build_and_push_base.Dockerfile new file mode 100644 index 000000000..f5092c81c --- /dev/null +++ b/build_and_push_base.Dockerfile @@ -0,0 +1,91 @@ + + +# syntax=docker/dockerfile:1 +# Keep this syntax directive! It's used to enable Docker BuildKit + +# Based on https://github.com/python-poetry/poetry/discussions/1879?sort=top#discussioncomment-216865 +# but I try to keep it updated (see history) + +################################ +# PYTHON-BASE +# Sets up all our shared environment variables +################################ +FROM python:3.10-slim as python-base + +# python +ENV PYTHONUNBUFFERED=1 \ + # prevents python creating .pyc files + PYTHONDONTWRITEBYTECODE=1 \ + \ + # pip + PIP_DISABLE_PIP_VERSION_CHECK=on \ + PIP_DEFAULT_TIMEOUT=100 \ + \ + # poetry + # https://python-poetry.org/docs/configuration/#using-environment-variables + POETRY_VERSION=1.8.2 \ + # make poetry install to this location + POETRY_HOME="/opt/poetry" \ + # make poetry create the virtual environment in the project's root + # it gets named `.venv` + POETRY_VIRTUALENVS_IN_PROJECT=true \ + # do not ask any interactive question + POETRY_NO_INTERACTION=1 \ + \ + # paths + # this is where our requirements + virtual environment will live + PYSETUP_PATH="/opt/pysetup" \ + VENV_PATH="/opt/pysetup/.venv" + + +# prepend poetry and venv to path +ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH" + + +################################ +# BUILDER-BASE +# Used to build deps + create our virtual environment +################################ +FROM python-base as builder-base +RUN apt-get update \ + && apt-get install --no-install-recommends -y \ + # deps for installing poetry + curl \ + # deps for building python deps + build-essential \ + # npm + npm \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +RUN --mount=type=cache,target=/root/.cache \ + curl -sSL https://install.python-poetry.org | python3 - + +# Now we need to copy the entire project into the image +COPY pyproject.toml poetry.lock ./ +COPY src/frontend/package.json /tmp/package.json +RUN cd /tmp && npm install +WORKDIR /app +COPY src/frontend ./src/frontend +RUN rm -rf src/frontend/node_modules +RUN cp -a /tmp/node_modules /app/src/frontend +COPY scripts ./scripts +COPY Makefile ./ +COPY README.md ./ +RUN cd src/frontend && npm run build +COPY src/backend ./src/backend +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 +COPY --from=builder-base /app/src/backend/base/dist/*.tar.gz ./ + +# Install the package from the .tar.gz +RUN pip install *.tar.gz + +WORKDIR /app +CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"] diff --git a/docs/docs/components/embeddings.mdx b/docs/docs/components/embeddings.mdx index d4ad17542..7ec8351e1 100644 --- a/docs/docs/components/embeddings.mdx +++ b/docs/docs/components/embeddings.mdx @@ -2,19 +2,11 @@ import Admonition from "@theme/Admonition"; # Embeddings - -

- We appreciate your understanding as we polish our documentation โ€“ it may - contain some rough edges. Share your feedback or report issues to help us - improve! ๐Ÿ› ๏ธ๐Ÿ“ -

- - Embeddings are vector representations of text that capture the semantic meaning of the text. They are created using text embedding models and allow us to think about the text in a vector space, enabling us to perform tasks like semantic search, where we look for pieces of text that are most similar in the vector space. --- -### BedrockEmbeddings +### Amazon Bedrock Embeddings Used to load [Amazon Bedrocksโ€™s](https://aws.amazon.com/bedrock/) embedding models. @@ -30,7 +22,7 @@ Used to load [Amazon Bedrocksโ€™s](https://aws.amazon.com/bedrock/) embedding mo --- -### CohereEmbeddings +### Cohere Embeddings Used to load [Cohereโ€™s](https://cohere.com/) embedding models. @@ -44,57 +36,93 @@ Used to load [Cohereโ€™s](https://cohere.com/) embedding models. --- -### HuggingFaceEmbeddings +### Azure OpenAI Embeddings + +Generate embeddings using Azure OpenAI models. + +**Params** + +- **Azure Endpoint:** Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/` +- **Deployment Name:** The name of the deployment. +- **API Version:** The API version to use. (Options: 2022-12-01, 2023-03-15-preview, 2023-05-15, 2023-06-01-preview, 2023-07-01-preview, 2023-08-01-preview) +- **API Key:** The API key to access the Azure OpenAI service. + +--- + +### Hugging Face API Embeddings + +Generate embeddings using Hugging Face Inference API models. + +**Params** + +- **API Key:** API key for accessing the Hugging Face Inference API. (Type: str) +- **API URL:** URL of the Hugging Face Inference API. (Default: http://localhost:8080) +- **Model Name:** Name of the model to use. (Default: BAAI/bge-large-en-v1.5) +- **Cache Folder:** Folder path to cache Hugging Face models. (Advanced) +- **Encode Kwargs:** Additional arguments for the encoding process. (Type: dict, Advanced) +- **Model Kwargs:** Additional arguments for the model. (Type: dict, Advanced) +- **Multi Process:** Whether to use multiple processes. (Default: False, Advanced) + +--- + +### Hugging Face Embeddings Used to load [HuggingFaceโ€™s](https://huggingface.co) embedding models. **Params** -- **cache_folder:** Used to specify the folder where the embeddings will be cached. When embeddings are computed for a text, they can be stored in the cache folder so that they can be reused later without the need to recompute them. This can improve the performance of the application by avoiding redundant computations. - -- **encode_kwargs:** Used to pass additional keyword arguments to the encoding method of the underlying HuggingFace model. These keyword arguments can be used to customize the encoding process, such as specifying the maximum length of the input sequence or enabling truncation or padding. - -- **model_kwargs:** Used to customize the behavior of the model, such as specifying the model architecture, the tokenizer, or any other model-specific configuration options. By using `model_kwargs`, the user can configure the HuggingFace model according to specific needs and preferences. - -- **model_name:** Used to specify the name or identifier of the HuggingFace model that will be used for generating embeddings. It allows users to choose a specific pre-trained model from the Hugging Face model hub โ€” defaults to `sentence-transformers/all-mpnet-base-v2`. +- **Cache Folder:** Folder path to cache HuggingFace models. +- **Encode Kwargs:** Additional arguments for the encoding process. (Type: dict) +- **Model Kwargs:** Additional arguments for the model. (Type: dict) +- **Model Name:** Name of the HuggingFace model to use. (Default: sentence-transformers/all-mpnet-base-v2) +- **Multi Process:** Whether to use multiple processes. (Default: False) --- -### OpenAIEmbeddings +### Ollama Embeddings + +Generate embeddings using Ollama models. + +**Params** + +- **Ollama Model:** Name of the Ollama model to use. (Default: llama2) +- **Ollama Base URL:** Base URL of the Ollama API. (Default: http://localhost:11434) +- **Model Temperature:** Temperature parameter for the model. (Type: float) + +--- + +### OpenAI Embeddings Used to load [OpenAIโ€™s](https://openai.com/) embedding models. **Params** -- **chunk_size:** Determines the maximum size of each chunk of text that is processed for embedding. If any of the incoming text chunks exceeds `chunk_size` characters, it will be split into multiple chunks of size `chunk_size` or less before being embedded โ€” defaults to `1000`. - -- **deployment:** Used to specify the deployment name or identifier of the text embedding model. It allows the user to choose a specific deployment of the model to use for embedding. When the deployment is provided, this can be useful when the user has multiple deployments of the same model with different configurations or versions โ€” defaults to `text-embedding-ada-002`. - -- **embedding_ctx_length:** This parameter determines the maximum context length for the text embedding model. It specifies the number of tokens that the model considers when generating embeddings for a piece of text โ€” defaults to `8191` (this means that the model will consider up to 8191 tokens when generating embeddings). - -- **max_retries:** Determines the maximum number of times to retry a request if the model provider returns an error from their API โ€” defaults to `6`. - -- **model:** Defines which pre-trained text embedding model to use โ€” defaults to `text-embedding-ada-002`. - -- **openai_api_base:** Refers to the base URL for the Azure OpenAI resource. It is used to configure the API to connect to the Azure OpenAI service. The base URL can be found in the Azure portal under the user Azure OpenAI resource. - -- **openai_api_key:** Is used to authenticate and authorize access to the OpenAI service. - -- **openai_api_type:** Is used to specify the type of OpenAI API being used, either the regular OpenAI API or the Azure OpenAI API. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate API service. - -- **openai_api_version:** Is used to specify the version of the OpenAI API being used. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate version of the OpenAI API service. - -- **openai_organization:** Is used to specify the organization associated with the OpenAI API key. If not provided, the default organization associated with the API key will be used. - -- **openai_proxy:** Proxy enables better budgeting and cost management for making OpenAI API calls, including more transparency into pricing. - -- **request_timeout:** Used to specify the maximum amount of time, in milliseconds, to wait for a response from the OpenAI API when generating embeddings for a given text. - -- **tiktoken_model_name:** Used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name. +- **OpenAI API Key:** The API key to use for accessing the OpenAI API. (Type: str) +- **Default Headers:** Default headers for the HTTP requests. (Type: Dict[str, str], Optional) +- **Default Query:** Default query parameters for the HTTP requests. (Type: NestedDict, Optional) +- **Allowed Special:** Special tokens allowed for processing. (Type: List[str], Default: []) +- **Disallowed Special:** Special tokens disallowed for processing. (Type: List[str], Default: ["all"]) +- **Chunk Size:** Chunk size for processing. (Type: int, Default: 1000) +- **Client:** HTTP client for making requests. (Type: Any, Optional) +- **Deployment:** Deployment name for the model. (Type: str, Default: "text-embedding-3-small") +- **Embedding Context Length:** Length of embedding context. (Type: int, Default: 8191) +- **Max Retries:** Maximum number of retries for failed requests. (Type: int, Default: 6) +- **Model:** Name of the model to use. (Type: str, Default: "text-embedding-3-small") +- **Model Kwargs:** Additional keyword arguments for the model. (Type: NestedDict, Optional) +- **OpenAI API Base:** Base URL of the OpenAI API. (Type: str, Optional) +- **OpenAI API Type:** Type of the OpenAI API. (Type: str, Optional) +- **OpenAI API Version:** Version of the OpenAI API. (Type: str, Optional) +- **OpenAI Organization:** Organization associated with the API key. (Type: str, Optional) +- **OpenAI Proxy:** Proxy server for the requests. (Type: str, Optional) +- **Request Timeout:** Timeout for the HTTP requests. (Type: float, Optional) +- **Show Progress Bar:** Whether to show a progress bar for processing. (Type: bool, Default: False) +- **Skip Empty:** Whether to skip empty inputs. (Type: bool, Default: False) +- **TikToken Enable:** Whether to enable TikToken. (Type: bool, Default: True) +- **TikToken Model Name:** Name of the TikToken model. (Type: str, Optional) --- -### VertexAIEmbeddings +### VertexAI Embeddings Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings). @@ -113,11 +141,3 @@ Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). - **top_p:** Tokens are selected from most probable to least until the sum of their โ€“ defaults to `0.95`. - **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored. - **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output โ€“ defaults to `False`. - -### OllamaEmbeddings - -Used to load [Ollamaโ€™s](https://ollama.ai/) embedding models. Wrapper around LangChain's [Ollama API](https://python.langchain.com/docs/integrations/text_embedding/ollama). - -- **model** The name of the Ollama model to use โ€“ defaults to `llama2`. -- **base_url** The base URL for the Ollama API โ€“ defaults to `http://localhost:11434`. -- **temperature** Tunes the degree of randomness in text generations. Should be a non-negative value โ€“ defaults to `0`. diff --git a/docs/docs/components/inputs.mdx b/docs/docs/components/inputs.mdx index 78ca6f0f6..fe8804e43 100644 --- a/docs/docs/components/inputs.mdx +++ b/docs/docs/components/inputs.mdx @@ -1,4 +1,5 @@ -import Admonition from '@theme/Admonition'; +import Admonition from "@theme/Admonition"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; # Inputs @@ -17,42 +18,147 @@ This component is designed to get user input from the chat. - **Session ID:** specifies the session ID of the chat history. If provided, the message will be saved in the Message History. -

- If _`As Record`_ is _`true`_ and the _`Message`_ is a _`Record`_, the data of the _`Record`_ will be updated with the _`Sender`_, _`Sender Name`_, and _`Session ID`_. -

+

+ If _`As Record`_ is _`true`_ and the _`Message`_ is a _`Record`_, the data + of the _`Record`_ will be updated with the _`Sender`_, _`Sender Name`_, and + _`Session ID`_. +

+When you get it from the sidebar, it will look like the image below but that is because some fields are in the advanced section. + + + +If you expose all its fields, it will look like the image below. + + + +One key capability of the Chat Input component is how it transforms the Interaction Panel into a chat window. This feature is particularly useful for scenarios where user input is required to initiate or influence the flow. + + + --- ### Prompt -Create a prompt template with dynamic variables. +Create a prompt template with dynamic variables. This is a very useful component for structuring prompts and passing dynamic data to a language model. **Parameters** -- **Template:** the template for the prompt. +- **Template:** the template for the prompt. This field allows you to create other fields dynamically by using curly brackets `{}`. For example, if you have a template like this: _`"Hello {name}, how are you?"`_, a new field called _`name`_ will be created. -

- Prompt variables can be created with any chosen name inside curly brackets, e.g. `{variable_name}` -

+

+ Prompt variables can be created with any chosen name inside curly brackets, + e.g. `{variable_name}` +

---- +Here is how it looks when you get it from the sidebar. + + +And here when you add a Template with the value _`Hello {name}, how are you?`_. + + + +--- ### Text Input This component is designed for simple text input, allowing users to pass textual data to subsequent components in the workflow. It's particularly useful for scenarios where a brief user input is required to initiate or influence the flow. - **Params** - **Value:** Specifies the text input value. This is where the user can input the text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string. +- **Record Template:** Specifies how a Record should be converted into Text. -

- The `TextInput` component serves as a straightforward means for setting Text input values in the chat window. It ensures that textual data can be seamlessly passed to subsequent components in the flow. -

+

+ The `TextInput` component serves as a straightforward means for setting Text + input values in the chat window. It ensures that textual data can be + seamlessly passed to subsequent components in the flow. +

+It should look like this when dropped directly from the sidebar. + + + +And when you expose all its fields, it will look like the image below. + +The **Record Template** field is used to specify how a Record should be converted into Text. This is particularly useful when you want to extract specific information from a Record and pass it as text to the next component in the sequence. + +For example, if you have a Record with the following structure: + +```json +{ + "name": "John Doe", + "age": 30, + "email": "johndoe@email.com" +} +``` + +You can use a template like this: _`"Name: {name}, Age: {age}"`_ to convert the Record into a text string like this: _`"Name: John Doe, Age: 30"`_, and if you pass more than one Record, the text will be concatenated with a new line separator. + + + +The Text Input component gives you the possibility to add an Input field on the Interaction Panel. This is useful because it allows you to define parameters while running and testing your flow. + + diff --git a/docs/docs/components/vector-stores.mdx b/docs/docs/components/vector-stores.mdx index 0fd1fd89b..23cac871d 100644 --- a/docs/docs/components/vector-stores.mdx +++ b/docs/docs/components/vector-stores.mdx @@ -1,41 +1,42 @@ -import Admonition from '@theme/Admonition'; +import Admonition from "@theme/Admonition"; # Vector Stores -

- We appreciate your understanding as we polish our documentation โ€“ it may contain some rough edges. Share your feedback or report issues to help us improve! ๐Ÿ› ๏ธ๐Ÿ“ -

+

+ We appreciate your understanding as we polish our documentation โ€“ it may + contain some rough edges. Share your feedback or report issues to help us + improve! ๐Ÿ› ๏ธ๐Ÿ“ +

+### Astra DB -### AstraDB - -The `AstraDB` is a component for initializing an AstraDB Vector Store from Records. It facilitates the creation of AstraDB-based vector indexes for efficient document storage and retrieval. +The `Astra DB` is a component for initializing an Astra DB Vector Store from Records. It facilitates the creation of Astra DB-based vector indexes for efficient document storage and retrieval. **Params** - **Input:** The input documents or records. -- **Embedding:** The embedding model used by AstraDB. +- **Embedding:** The embedding model used by Astra DB. -- **Collection Name:** The name of the collection in AstraDB. +- **Collection Name:** The name of the collection in Astra DB. -- **Token:** The token for AstraDB. +- **Token:** The token for Astra DB. -- **API Endpoint:** The API endpoint for AstraDB. +- **API Endpoint:** The API endpoint for Astra DB. -- **Namespace:** The namespace in AstraDB. +- **Namespace:** The namespace in Astra DB. -- **Metric:** The metric to use in AstraDB. +- **Metric:** The metric to use in Astra DB. -- **Batch Size:** The batch size for AstraDB. +- **Batch Size:** The batch size for Astra DB. -- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for AstraDB. +- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for Astra DB. -- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for AstraDB. +- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for Astra DB. -- **Bulk Delete Concurrency:** The bulk delete concurrency for AstraDB. +- **Bulk Delete Concurrency:** The bulk delete concurrency for Astra DB. - **Setup Mode:** The setup mode for the vector store. @@ -49,16 +50,16 @@ The `AstraDB` is a component for initializing an AstraDB Vector Store from Recor

- Ensure that the required AstraDB token and API endpoint are properly configured. + Ensure that the required Astra DB token and API endpoint are properly configured.

--- -### AstraDB Search +### Astra DB Search -The `AstraDBSearch` is a component for searching an existing AstraDB Vector Store for similar documents. It extends the functionality of the `AstraDB` component to provide efficient document retrieval based on similarity metrics. +The `Astra DBSearch` is a component for searching an existing Astra DB Vector Store for similar documents. It extends the functionality of the `Astra DB` component to provide efficient document retrieval based on similarity metrics. **Params** @@ -66,25 +67,25 @@ The `AstraDBSearch` is a component for searching an existing AstraDB Vector Stor - **Input Value:** The input value to search for. -- **Embedding:** The embedding model used by AstraDB. +- **Embedding:** The embedding model used by Astra DB. -- **Collection Name:** The name of the collection in AstraDB. +- **Collection Name:** The name of the collection in Astra DB. -- **Token:** The token for AstraDB. +- **Token:** The token for Astra DB. -- **API Endpoint:** The API endpoint for AstraDB. +- **API Endpoint:** The API endpoint for Astra DB. -- **Namespace:** The namespace in AstraDB. +- **Namespace:** The namespace in Astra DB. -- **Metric:** The metric to use in AstraDB. +- **Metric:** The metric to use in Astra DB. -- **Batch Size:** The batch size for AstraDB. +- **Batch Size:** The batch size for Astra DB. -- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for AstraDB. +- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for Astra DB. -- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for AstraDB. +- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for Astra DB. -- **Bulk Delete Concurrency:** The bulk delete concurrency for AstraDB. +- **Bulk Delete Concurrency:** The bulk delete concurrency for Astra DB. - **Setup Mode:** The setup mode for the vector store. @@ -118,7 +119,6 @@ The `Chroma` is a component designed for implementing a Vector Store using Chrom - **Server SSL Enabled (Optional):** Whether to enable SSL for the Chroma server. - - **Input:** Input data for creating the Vector Store. - **Embedding:** The embeddings to use for the Vector Store. @@ -129,7 +129,6 @@ For detailed documentation and integration guides, please refer to the [Chroma C ### Chroma Search - The `ChromaSearch` is a component designed for searching a Chroma collection for similar documents. This component integrates with Chroma to facilitate efficient document retrieval based on similarity metrics. **Params** @@ -154,7 +153,6 @@ The `ChromaSearch` is a component designed for searching a Chroma collection for - **Server SSL Enabled (Optional):** Whether SSL is enabled for the Chroma server. - --- ### FAISS @@ -171,7 +169,6 @@ The `FAISS` is a component designed for ingesting documents into a FAISS Vector - **Index Name:** The name of the FAISS index. - For detailed documentation and integration guides, please refer to the [FAISS Component Documentation](https://faiss.ai/index.html). --- @@ -190,10 +187,8 @@ The `FAISSSearch` is a component for searching a FAISS Vector Store for similar - **Index Name:** The name of the FAISS index. - --- - ### MongoDB Atlas The `MongoDBAtlas` is a component used to construct a MongoDB Atlas Vector Search vector store from Records. It facilitates the creation of MongoDB Atlas-based vector stores for efficient document storage and retrieval. @@ -214,11 +209,8 @@ The `MongoDBAtlas` is a component used to construct a MongoDB Atlas Vector Searc - **Search Kwargs:** Additional search arguments for MongoDB Atlas. - -

- Ensure that pymongo is installed to use MongoDB Atlas Vector Store. -

+

Ensure that pymongo is installed to use MongoDB Atlas Vector Store.

 --- @@ -245,7 +237,6 @@ The `MongoDBAtlasSearch` is a component for searching a MongoDB Atlas Vector Sto - **Search Kwargs:** Additional search arguments for MongoDB Atlas. - --- ### PGVector @@ -262,14 +253,13 @@ The `PGVector` is a component for implementing a Vector Store using PostgreSQL. - **Table:** The name of the table in the PostgreSQL database. - For detailed documentation and integration guides, please refer to the [PGVector Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/pgvector). - -

- Ensure that the required PostgreSQL server is accessible and properly configured. -

+

+ Ensure that the required PostgreSQL server is accessible and properly + configured. +

--- @@ -290,7 +280,6 @@ The `PGVectorSearch` is a component for searching a PGVector Store for similar d - **Search Type:** The type of search to perform (e.g., "Similarity", "MMR"). - --- ### Pinecone @@ -315,11 +304,11 @@ The `Pinecone` is a component used to construct a Pinecone wrapper from Records. - **Pool Threads:** The number of threads to use for Pinecone. - -

- Ensure that the required Pinecone API key and environment are properly configured. -

+

+ Ensure that the required Pinecone API key and environment are properly + configured. +

--- @@ -348,7 +337,6 @@ The `PineconeSearch` is a component used to search a Pinecone Vector Store for s - **Pool Threads:** The number of threads to use for Pinecone. - --- ### Qdrant @@ -462,9 +450,11 @@ The `Redis` is a component for implementing a Vector Store using Redis. It provi For detailed documentation, please refer to the [Redis Documentation](https://python.langchain.com/docs/integrations/vectorstores/redis). -

- Ensure that the required Redis server connection URL and index name are properly configured. If no documents are provided, a schema must be provided. -

+

+ Ensure that the required Redis server connection URL and index name are + properly configured. If no documents are provided, a schema must be + provided. +

--- @@ -512,9 +502,10 @@ The `Supabase` is a component for initializing a Supabase Vector Store from text - **Table Name:** The name of the table in Supabase (advanced). -

- Ensure that the required Supabase service key, Supabase URL, and table name are properly configured. -

+

+ Ensure that the required Supabase service key, Supabase URL, and table name + are properly configured. +

--- @@ -562,9 +553,10 @@ The `Vectara` is a component for implementing a Vector Store using Vectara. For detailed documentation and integration guides, please refer to the [Vectara Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/vectara). -

- If `inputs` are provided, they will be upserted to the corpus. If `files_url` are provided, Vectara will process the files from the URLs. -

+

+ If `inputs` are provided, they will be upserted to the corpus. If + `files_url` are provided, Vectara will process the files from the URLs. +

--- @@ -586,6 +578,7 @@ The `VectaraSearch` is a component for searching a Vectara Vector Store for simi - **Vectara API Key:** The API key for Vectara. - **Files Url:** The URL(s) of the file(s) to be used for initializing the Vectara Vector Store (optional). + --- ### Weaviate @@ -613,9 +606,14 @@ The `Weaviate` is a component for implementing a Vector Store using Weaviate. For detailed documentation and integration guides, please refer to the [Weaviate Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/weaviate). -

- Before using the Weaviate Vector Store component, ensure that you have a Weaviate instance running and accessible at the specified URL. Additionally, make sure to provide the correct API key for authentication if required. Adjust the index name, text key, and attributes according to your dataset and indexing requirements. Finally, ensure that the provided embeddings are compatible with Weaviate's requirements. -

+

+ Before using the Weaviate Vector Store component, ensure that you have a + Weaviate instance running and accessible at the specified URL. Additionally, + make sure to provide the correct API key for authentication if required. + Adjust the index name, text key, and attributes according to your dataset + and indexing requirements. Finally, ensure that the provided embeddings are + compatible with Weaviate's requirements. +

--- @@ -642,4 +640,4 @@ The `WeaviateSearch` component facilitates searching a Weaviate Vector Store for - **Embedding:** The embedding model used by Weaviate. -- **Attributes:** Additional attributes to consider during indexing (optional). \ No newline at end of file +- **Attributes:** Additional attributes to consider during indexing (optional). diff --git a/docs/docs/components/wrappers.mdx b/docs/docs/components/wrappers.mdx deleted file mode 100644 index 4b1251b60..000000000 --- a/docs/docs/components/wrappers.mdx +++ /dev/null @@ -1,20 +0,0 @@ -import Admonition from '@theme/Admonition'; - -# Wrappers - - -

- We appreciate your understanding as we polish our documentation โ€“ it may contain some rough edges. Share your feedback or report issues to help us improve! ๐Ÿ› ๏ธ๐Ÿ“ -

- - - -### TextRequestsWrapper - -This component is designed to work with the Python Requests module, which is a popular tool for making web requests. Used to fetch data from a particular website. - -**Params** - -- **header:** specifies the headers to be included in the HTTP request. Defaults to `{'Authorization': 'Bearer '}`. - - Headers are key-value pairs that provide additional information about the request or the client making the request. They can be used to send authentication credentials, specify the content type of the request, set cookies, and more. They allow the client and the server to communicate additional information beyond the basic request. \ No newline at end of file diff --git a/docs/docs/examples/buffer-memory.mdx b/docs/docs/examples/buffer-memory.mdx index 3167081a5..b196f9031 100644 --- a/docs/docs/examples/buffer-memory.mdx +++ b/docs/docs/examples/buffer-memory.mdx @@ -16,6 +16,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; light: "img/buffer-memory.png", dark: "img/buffer-memory.png", }} + style={{ + width: "80%", + margin: "20px auto", + display: "flex", + justifyContent: "center", + }} /> #### Download Flow diff --git a/docs/docs/examples/conversation-chain.mdx b/docs/docs/examples/conversation-chain.mdx index 1cd59ca55..294d1b440 100644 --- a/docs/docs/examples/conversation-chain.mdx +++ b/docs/docs/examples/conversation-chain.mdx @@ -22,6 +22,13 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; light: "img/basic-chat.png", dark: "img/basic-chat.png", }} + +style={{ + width: "80%", + margin: "20px auto", + display: "flex", + justifyContent: "center", + }} /> #### Download Flow diff --git a/docs/docs/examples/csv-loader.mdx b/docs/docs/examples/csv-loader.mdx index 351e99440..25f3bb444 100644 --- a/docs/docs/examples/csv-loader.mdx +++ b/docs/docs/examples/csv-loader.mdx @@ -34,6 +34,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; light: "img/csv-loader.png", dark: "img/csv-loader.png", }} + style={{ + width: "80%", + margin: "20px auto", + display: "flex", + justifyContent: "center", + }} /> #### Download Flow diff --git a/docs/docs/examples/flow-runner.mdx b/docs/docs/examples/flow-runner.mdx index 38466e4b3..8a07adb0a 100644 --- a/docs/docs/examples/flow-runner.mdx +++ b/docs/docs/examples/flow-runner.mdx @@ -3,7 +3,6 @@ description: Custom Components hide_table_of_contents: true --- - # FlowRunner Component The CustomComponent class allows us to create components that interact with Langflow itself. In this example, we will make a component that runs other flows available in "My Collection". @@ -16,7 +15,7 @@ The CustomComponent class allows us to create components that interact with Lang }} style={{ width: "30%", - margin: "0 auto", + margin: "20px auto", display: "flex", justifyContent: "center", }} @@ -367,4 +366,3 @@ Done! This is what our script and custom component looks like: import ZoomableImage from "/src/theme/ZoomableImage.js"; import Admonition from "@theme/Admonition"; - diff --git a/docs/docs/examples/how-upload-examples.mdx b/docs/docs/examples/how-upload-examples.mdx deleted file mode 100644 index 4f54558eb..000000000 --- a/docs/docs/examples/how-upload-examples.mdx +++ /dev/null @@ -1,28 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# ๐Ÿ“š How to Upload Examples? - -We welcome all examples that can help our community learn and explore Langflow's capabilities. -Langflow Examples is a repository on [GitHub](https://github.com/logspace-ai/langflow_examples) that contains examples of flows that people can use for inspiration and learning. - -{" "} - - -To upload examples, please follow these steps: - -1. **Create a Flow:** First, create a flow using Langflow. You can use any of the available templates or create a new flow from scratch. - -2. **Export the Flow:** Once you have created a flow, export it as a JSON file. Make sure to give your file a descriptive name and include a brief description of what it does. - -3. **Submit a Pull Request:** Finally, submit a pull request (PR) to the examples repo. Make sure to include your JSON file in the PR. - -If your example uses any third-party libraries or packages, please include them in your PR and make sure that your example follows the [**โ›“๏ธ Langflow Code Of Conduct**](https://github.com/logspace-ai/langflow/blob/dev/CODE_OF_CONDUCT.md). diff --git a/docs/docs/examples/midjourney-prompt-chain.mdx b/docs/docs/examples/midjourney-prompt-chain.mdx deleted file mode 100644 index 9df732026..000000000 --- a/docs/docs/examples/midjourney-prompt-chain.mdx +++ /dev/null @@ -1,46 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# MidJourney Prompt Chain - -The `MidJourneyPromptChain` can be used to generate imaginative and detailed MidJourney prompts. - -For example, type something like: - -```bash -Dragon -``` - -And get a response such as: - -```text -Imagine a mysterious forest, the trees are tall and ancient, their branches reaching up to the sky. Through the darkness, a dragon emerges from the shadows, its scales shimmering in the moonlight. Its wingspan is immense, and its eyes glow with a fierce intensity. It is a majestic and powerful creature, one that commands both respect and fear. -``` - - - Notice that the `ConversationSummaryMemory` stores a summary of the - conversation over time. Try using it to create better prompts as the - conversation goes on. - - -## โ›“๏ธ Langflow Example - -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - - - -#### Download Flow - - - -- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai) -- [`ConversationSummaryMemory`](https://python.langchain.com/docs/modules/memory/types/summary) - - diff --git a/docs/docs/examples/multiple-vectorstores.mdx b/docs/docs/examples/multiple-vectorstores.mdx deleted file mode 100644 index 2e554bbf1..000000000 --- a/docs/docs/examples/multiple-vectorstores.mdx +++ /dev/null @@ -1,58 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Multiple Vector Stores - -The example below shows an agent operating with two vector stores built upon different data sources. - -The `TextLoader` loads a TXT file, while the `WebBaseLoader` pulls text from webpages into a document format to accessed downstream. The `Chroma` vector stores are created analogous to what we have demonstrated in our [CSV Loader](/examples/csv-loader.mdx) example. Finally, the `VectorStoreRouterAgent` constructs an agent that routes between the vector stores. - - - Get the TXT file used - [here](https://github.com/hwchase17/chat-your-data/blob/master/state_of_the_union.txt). - - -URL used by the `WebBaseLoader`: - -```text -https://pt.wikipedia.org/wiki/Harry_Potter -``` - - - When you build the flow, request information about one of the sources. The - agent should be able to use the correct source to generate a response. - - - - Learn more about Multiple Vector Stores - [here](https://python.langchain.com/docs/modules/data_connection/vectorstores/). - - -## โ›“๏ธ Langflow Example - -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - - - -#### Download Flow - - - -- [`WebBaseLoader`](https://python.langchain.com/docs/integrations/document_loaders/web_base) -- [`TextLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/) -- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter) -- [`OpenAIEmbedding`](https://python.langchain.com/docs/integrations/text_embedding/openai) -- [`Chroma`](https://python.langchain.com/docs/integrations/vectorstores/chroma) -- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/) -- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai) -- [`VectorStoreRouterToolkit`](https://js.langchain.com/docs/modules/agents/tools/how_to/agents_with_vectorstores) -- [`VectorStoreRouterAgent`](https://js.langchain.com/docs/modules/agents/tools/how_to/agents_with_vectorstores) - - diff --git a/docs/docs/examples/python-function.mdx b/docs/docs/examples/python-function.mdx index 9eadd7273..2bb4b93e1 100644 --- a/docs/docs/examples/python-function.mdx +++ b/docs/docs/examples/python-function.mdx @@ -43,6 +43,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; light: "img/python-function.png", dark: "img/python-function.png", }} + style={{ + width: "80%", + margin: "20px auto", + display: "flex", + justifyContent: "center", + }} /> #### Download Flow diff --git a/docs/docs/examples/serp-api-tool.mdx b/docs/docs/examples/serp-api-tool.mdx index 7e8d95936..175b6f1be 100644 --- a/docs/docs/examples/serp-api-tool.mdx +++ b/docs/docs/examples/serp-api-tool.mdx @@ -37,6 +37,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; light: "img/serp-api-tool.png", dark: "img/serp-api-tool.png", }} + style={{ + width: "80%", + margin: "20px auto", + display: "flex", + justifyContent: "center", + }} /> #### Download Flow diff --git a/docs/docs/getting-started/basic-prompting.mdx b/docs/docs/getting-started/basic-prompting.mdx new file mode 100644 index 000000000..e69de29bb diff --git a/docs/docs/getting-started/blog-writer.mdx b/docs/docs/getting-started/blog-writer.mdx new file mode 100644 index 000000000..e69de29bb diff --git a/docs/docs/getting-started/creating-flows.mdx b/docs/docs/getting-started/creating-flows.mdx deleted file mode 100644 index 9c16d225f..000000000 --- a/docs/docs/getting-started/creating-flows.mdx +++ /dev/null @@ -1,51 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; - -# ๐ŸŽจ Creating Flows - -## Compose - -Creating flows with Langflow is easy. Drag sidebar components onto the canvas and connect them together to create your pipeline. -Langflow provides a range of Components to choose from, including **Chat Input**, **Chat Output**, **API Request** and **Prompt**. - - - -## Starter Flows - -Langflow provides a range of starter flows to help you get started. These flows are pre-built and can be used as a starting point for your own flows. - -
- -
- -## Defining Inputs and Outputs - -Each flow can have multiple inputs and outputs. These can be defined by placing **Inputs** and **Outputs** components on the canvas. - -The **Inputs** components define the inputs to the flow. -Whenever you place an Input component on the canvas, it will allow you to interactively define change its value -from the Interactive Panel. - -The **Text Input** component allows you to define a text input, and the **Chat Input** component allows you to use the chat input from the Interactive Panel. - -The **Outputs** components define the outputs of the flow and work similarly to the Inputs components. - -Both Inputs and Outputs components can be connected to other components on the canvas and are used to define how the API works too. - - - -
- -
diff --git a/docs/docs/getting-started/document-qa.mdx b/docs/docs/getting-started/document-qa.mdx new file mode 100644 index 000000000..e69de29bb diff --git a/docs/docs/getting-started/hugging-face-spaces.mdx b/docs/docs/getting-started/hugging-face-spaces.mdx deleted file mode 100644 index c61a99f32..000000000 --- a/docs/docs/getting-started/hugging-face-spaces.mdx +++ /dev/null @@ -1,30 +0,0 @@ -# ๐Ÿค— HuggingFace Spaces - -## TLDR; - -A fully featured version of Langflow can be accessed via [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) with no installation required. All you gotta do is [duplicate the Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) and you'll have your own copy to play around with! - ---- - -# ๐Ÿš€ Getting Started - -HuggingFace provides great support for running Langflow in their Spaces environment. This means you can run Langflow without any installation required. - -The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true). - -You'll be greeted with the following screen: - - - -From here, you can rename your Space, define the visibility (Public or Private) and click on the `Duplicate Space` button to start the duplication process. - -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; \ No newline at end of file diff --git a/docs/docs/getting-started/memory-chatbot.mdx b/docs/docs/getting-started/memory-chatbot.mdx new file mode 100644 index 000000000..e69de29bb diff --git a/docs/docs/guides/rag-with-astradb.mdx b/docs/docs/getting-started/rag-with-astradb.mdx similarity index 61% rename from docs/docs/guides/rag-with-astradb.mdx rename to docs/docs/getting-started/rag-with-astradb.mdx index 310f9ea70..01daa7b6f 100644 --- a/docs/docs/guides/rag-with-astradb.mdx +++ b/docs/docs/getting-started/rag-with-astradb.mdx @@ -1,16 +1,15 @@ import ThemedImage from "@theme/ThemedImage"; import useBaseUrl from "@docusaurus/useBaseUrl"; import ZoomableImage from "/src/theme/ZoomableImage.js"; -import DownloadableJsonFile from "/src/theme/DownloadableJsonFile.js"; import Admonition from "@theme/Admonition"; -# ๐ŸŒŸ RAG with AstraDB +# ๐ŸŒŸ RAG with Astra DB -This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **AstraDB** and **Langflow**. +This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **Astra DB** and **Langflow**. -AstraDB is a cloud-native database built on Apache Cassandra that is optimized for the cloud. It is a fully managed database-as-a-service that simplifies operations and reduces costs. AstraDB is built on the same technology that powers the largest Cassandra deployments in the world. +[Astra DB](https://www.datastax.com/products/datastax-astra?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=astradb) is a cloud-native database built on Apache Cassandra that is optimized for the cloud. It is a fully managed database-as-a-service that simplifies operations and reduces costs. Astra DB is built on the same technology that powers the largest Cassandra deployments in the world. -In this guide, we will use AstraDB as a vector store to store and retrieve the documents that will be used by the RAG application to generate responses. +In this guide, we will use Astra DB as a vector store to store and retrieve the documents that will be used by the RAG application to generate responses. This guide assumes that you have Langflow up and running. If you are new to @@ -19,26 +18,23 @@ In this guide, we will use AstraDB as a vector store to store and retrieve the d TLDR; -- Visit the [Astra](https://astra.datastax.com) website and create a free account -- Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) +- [Create a free Astra DB account](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-a-free-astra-db-account) +- Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) - Create a new database, get a **Token** and the **API Endpoint** -- +- Click on the **New Project** button and look for Vector Store RAG. This will create a new project with the necessary components - Import the project into Langflow by dropping it on the Canvas or My Collection page -- Update the **Token** and **API Endpoint** in the **AstraDB** components +- Update the **Token** and **API Endpoint** in the **Astra DB** components - Update the OpenAI API key in the **OpenAI** components -- Run the ingestion flow which is the one that uses the **AstraDB** component +- Run the ingestion flow which is the one that uses the **Astra DB** component - Click on the โšก _Run_ button and start interacting with your RAG application # First things first -## Create an AstraDB Database +## Create an Astra DB Database -To get started, you will need to create an AstraDB database. Visit the [Astra](https://astra.datastax.com) website and create a free account. +To get started, you will need to [create an Astra DB database](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-an-astradb-database). -Once you have created an account, you will be taken to the AstraDB dashboard. Click on the **Create Database** button. +Once you have created an account, you will be taken to the Astra DB dashboard. Click on the **Create Database** button. Now you will need to configure your database. Choose the **Serverless (Vector)** deployment type, and pick a Database name, provider and region. @@ -59,7 +55,7 @@ After you have configured your database, click on the **Create Database** button light: "img/astra-configure-deployment.png", dark: "img/astra-configure-deployment.png", }} - style={{ width: "70%" }} + style={{ width: "80%", margin: "20px auto" }} /> Once your database is initialized, to the right of the page, you will see the _Database Details_ section which contains a button for you to copy the **API Endpoint** and another to generate a **Token**. @@ -70,22 +66,18 @@ Once your database is initialized, to the right of the page, you will see the _D light: "img/astra-generate-token.png", dark: "img/astra-generate-token.png", }} - style={{ width: "50%" }} + style={{ width: "50%", margin: "20px auto" }} /> -Now we are all set to start building our RAG application using AstraDB and Langflow. +Now we are all set to start building our RAG application using Astra DB and Langflow. ## (Optional) Duplicate the Langflow 1.0 HuggingFace Space -If you haven't already, now is the time to launch Langflow. To make things easier, you can duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you. +If you haven't already, now is the time to launch Langflow. To make things easier, you can duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you. -You'll still need to get the Project file and import it so, let's get to that. +## Open the Vector Store RAG Project -## Import AstraDB RAG Flows - -To get started, you will need to . - -Once you have downloaded the project file, you can import it into Langflow by dropping it on the Canvas or My Collection page. +To get started, click on the **New Project** button and look for the **Vector Store RAG** project. This will open a starter project with the necessary components to run a RAG application using Astra DB. -This project consists of two flows. The simpler one is the **Ingestion Flow** which is responsible for ingesting the documents into the AstraDB database. +This project consists of two flows. The simpler one is the **Ingestion Flow** which is responsible for ingesting the documents into the Astra DB database. Your first step should be to understand what each flow does and how they interact with each other. @@ -105,7 +97,7 @@ The ingestion flow consists of: - **Files** component that uploads a text file to Langflow - **Recursive Character Text Splitter** component that splits the text into smaller chunks - **OpenAIEmbeddings** component that generates embeddings for the text chunks -- **AstraDB** component that stores the text chunks in the AstraDB database +- **Astra DB** component that stores the text chunks in the Astra DB database -Now, let's update the **AstraDB** and **AstraDB Search** components with the **Token** and **API Endpoint** that we generated earlier, and the OpenAI Embeddings components with your OpenAI API key. +Now, let's update the **Astra DB** and **Astra DB Search** components with the **Token** and **API Endpoint** that we generated earlier, and the OpenAI Embeddings components with your OpenAI API key. -And run it! This will ingest the Text data from your file into the AstraDB database. +And run it! This will ingest the Text data from your file into the Astra DB database. -Now, on to the **RAG Flow**. This flow is responsible for generating responses to your queries. +Now, on to the **RAG Flow**. This flow is responsible for generating responses to your queries. It will define all of the steps from getting the User's input to generating a response and displaying it in the Interaction Panel. The RAG flow is a bit more complex. It consists of: - **Chat Input** component that defines where to put the user input coming from the Interaction Panel - **OpenAI Embeddings** component that generates embeddings from the user input -- **AstraDB Search** component that retrieves the most relevant Records from the AstraDB database +- **Astra DB Search** component that retrieves the most relevant Records from the Astra DB database - **Text Output** component that turns the Records into Text by concatenating them and also displays it in the Interaction Panel - One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Interaction Panel - **Prompt** component that takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model @@ -157,7 +149,7 @@ The RAG flow is a bit more complex. It consists of: light: "img/astra-rag-flow.png", dark: "img/astra-rag-flow.png", }} - style={{ width: "90%" }} + style={{ width: "80%", margin: "20px auto" }} /> To run it all we have to do is click on the โšก _Run_ button and start interacting with your RAG application. @@ -168,7 +160,7 @@ To run it all we have to do is click on the โšก _Run_ button and start interacti light: "img/astra-rag-flow-run.png", dark: "img/astra-rag-flow-run.png", }} - style={{ width: "90%" }} + style={{ width: "80%", margin: "20px auto" }} /> This opens the Interaction Panel where you can chat your data. @@ -181,7 +173,7 @@ Because this flow has a **Chat Input** and a **Text Output** component, the Pane light: "img/astra-rag-flow-interaction-panel.png", dark: "img/astra-rag-flow-interaction-panel.png", }} - style={{ width: "80%" }} + style={{ width: "80%", margin: "20px auto" }} /> Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved records. @@ -192,11 +184,12 @@ Once we interact with it we get a response and the Extracted Chunks section is u light: "img/astra-rag-flow-interaction-panel-interaction.png", dark: "img/astra-rag-flow-interaction-panel-interaction.png", }} - style={{ width: "80%" }} + style={{ width: "80%", margin: "20px auto" }} /> -And that's it! You have successfully built a RAG application using AstraDB and Langflow. +And that's it! You have successfully ran a RAG application using Astra DB and Langflow. # Conclusion -In this guide, we have learned how to build a RAG application using AstraDB and Langflow. We have seen how to create an AstraDB database, import the AstraDB RAG Flows project into Langflow, and run the ingestion and RAG flows. +In this guide, we have learned how to run a RAG application using Astra DB and Langflow. +We have seen how to create an Astra DB database, import the Astra DB RAG Flows project into Langflow, and run the ingestion and RAG flows. diff --git a/docs/docs/guidelines/api.mdx b/docs/docs/guidelines/api.mdx index 8bba633fb..25dbeb31e 100644 --- a/docs/docs/guidelines/api.mdx +++ b/docs/docs/guidelines/api.mdx @@ -1,5 +1,6 @@ import useBaseUrl from "@docusaurus/useBaseUrl"; import ZoomableImage from "/src/theme/ZoomableImage.js"; +import Admonition from "@theme/Admonition"; # API Keys @@ -7,12 +8,17 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; Langflow offers an API Key functionality that allows users to access their individual components and flows without going through traditional login authentication. The API Key is a user-specific token that can be included in the request's header or query parameter to authenticate API calls. The following documentation outlines how to generate, use, and manage these API Keys in Langflow. + + This feature requires the `LANGFLOW_AUTO_LOGIN` environment variable to be set + to `False`. The default user and password are set using _`LANGFLOW_SUPERUSER`_ + and _`LANGFLOW_SUPERUSER_PASSWORD`_ environment variables. Default values are + _`langflow`_ and _`langflow`_ respectively. + + ## Generating an API Key ### Through Langflow UI -{/* add image img/api-key.png */} - \ + http://localhost:3000/api/v1/run/ \ -H 'Content-Type: application/json'\ -H 'x-api-key: '\ -d '{"inputs": {"text":""}, "tweaks": {}}' diff --git a/docs/docs/guidelines/async-api.mdx b/docs/docs/guidelines/async-api.mdx deleted file mode 100644 index c5473812e..000000000 --- a/docs/docs/guidelines/async-api.mdx +++ /dev/null @@ -1,73 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Asynchronous Processing - -## Introduction - -Starting from version 0.5, Langflow introduces a new feature to its API: the _`sync`_ flag. This flag allows users to opt for asynchronous processing of their flows, freeing up resources and enabling better control over long-running tasks. -This feature supports running tasks in a Celery worker queue and AnyIO task groups for now. - - - This is an experimental feature. The default behavior of the API is still - synchronous processing. The API may change in the future. - - -## The _`sync`_ Flag - -The _`sync`_ flag can be included in the payload of your POST request to the _`/api/v1/process/`_ endpoint. -When set to _`false`_, the API will initiate an asynchronous task instead of processing the flow synchronously. - -### API Request with _`sync`_ flag - -```bash -curl -X POST \ - http://localhost:3000/api/v1/process/ \ - -H 'Content-Type: application/json' \ - -H 'x-api-key: ' \ - -d '{"inputs": {"text": ""}, "tweaks": {}, "sync": false}' -``` - -Response: - -```json -{ - "result": { - "output": "..." - }, - "task": { - "id": "...", - "href": "api/v1/task/" - }, - "session_id": "...", - "backend": "..." // celery or anyio -} -``` - -## Checking Task Status - -You can check the status of an asynchronous task by making a GET request to the `/task/{task_id}` endpoint. - -```bash -curl -X GET \ - http://localhost:3000/api/v1/task/ \ - -H 'x-api-key: ' -``` - -### Response - -The endpoint will return the current status of the task and, if completed, the result of the task. Possible statuses include: - -- _`PENDING`_: The task is waiting for execution. -- _`SUCCESS`_: The task has completed successfully. -- _`FAILURE`_: The task has failed. - -Example response for a completed task: - -```json -{ - "status": "SUCCESS", - "result": { - "output": "..." - } -} -``` diff --git a/docs/docs/guidelines/components.mdx b/docs/docs/guidelines/components.mdx index 32ec00615..16aa83eff 100644 --- a/docs/docs/guidelines/components.mdx +++ b/docs/docs/guidelines/components.mdx @@ -26,13 +26,14 @@ Components are the building blocks of the flows. They are made of inputs, output {" "} +
diff --git a/docs/docs/guidelines/custom-component.mdx b/docs/docs/guidelines/custom-component.mdx index 77a1c5aaa..daf47987f 100644 --- a/docs/docs/guidelines/custom-component.mdx +++ b/docs/docs/guidelines/custom-component.mdx @@ -406,8 +406,4 @@ Langflow will attempt to load all of the components found in the specified direc Once your custom components have been loaded successfully, they will appear in Langflow's sidebar. From there, you can add them to your Langflow canvas for use. However, please note that components with errors will not be available for addition to the canvas. Always ensure your code is error-free before attempting to load components. -Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding!import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - +Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding! diff --git a/docs/docs/guidelines/features.mdx b/docs/docs/guidelines/features.mdx index 6f50abb35..d4998edb6 100644 --- a/docs/docs/guidelines/features.mdx +++ b/docs/docs/guidelines/features.mdx @@ -1,4 +1,3 @@ - # Features
@@ -9,13 +8,14 @@
{" "} +
@@ -63,7 +63,6 @@ The example below shows a Python script making a POST request to a local API end
- import ThemedImage from "@theme/ThemedImage"; import useBaseUrl from "@docusaurus/useBaseUrl"; import ZoomableImage from "/src/theme/ZoomableImage.js"; diff --git a/docs/docs/guidelines/login.mdx b/docs/docs/guidelines/login.mdx index fde7cd09a..1d5a1d031 100644 --- a/docs/docs/guidelines/login.mdx +++ b/docs/docs/guidelines/login.mdx @@ -105,7 +105,7 @@ Users can change their profile settings by clicking on the profile icon in the t light: useBaseUrl("img/my-account.png"), dark: useBaseUrl("img/my-account.png"), }} - style={{ width: "50%", maxWidth: "600px", margin: "0 auto" }} + style={{ width: "50%", maxWidth: "600px", margin: "20px auto" }} /> By clicking on **Profile Settings**, the user is taken to the profile settings page, where they can change their password and their profile picture. @@ -116,10 +116,11 @@ By clicking on **Profile Settings**, the user is taken to the profile settings p light: useBaseUrl("img/profile-settings.png"), dark: useBaseUrl("img/profile-settings.png"), }} - style={{ maxWidth: "600px", margin: "0 auto" }} + style={{ maxWidth: "600px", margin: "20px auto" }} /> -By clicking on **Admin Page**, the superuser is taken to the admin page, where they can manage users and groups. +By clicking on **Admin Page**, the superuser is taken to the admin page, where they +can manage users and groups. - This implementation is still in development. Contributions are welcome! - - -The Async API is an implementation of the Langflow API that uses [Celery](https://docs.celeryproject.org/en/stable/) -to run the tasks asynchronously, using a message broker to send and receive messages, a result backend to store the results and a cache to store the task states and session data. - -### Configuration - -The folder _`./deploy`_ in the [Github repository](https://github.com/logspace-ai/langflow) contains a _`.env.example`_ file that can be used to configure a Langflow deployment. -The file contains the variables required to configure a Celery worker queue, Redis cache and result backend and a RabbitMQ message broker. - -To set it up locally you can copy the file to _`.env`_ and run the following command: - -```bash -docker compose up -d -``` - -This will set up the following containers: - -- Langflow API -- Celery worker -- RabbitMQ message broker -- Redis cache -- PostgreSQL database -- PGAdmin -- Flower -- Traefik -- Grafana -- Prometheus - -### Testing - -To run the tests for the Async API, you can run the following command: - -```bash -docker compose -f docker-compose.with_tests.yml up --exit-code-from tests tests result_backend broker celeryworker db --build -``` diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx index c2c15ec32..9357a8fdf 100644 --- a/docs/docs/index.mdx +++ b/docs/docs/index.mdx @@ -1,10 +1,12 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; + # ๐Ÿ‘‹ Welcome to Langflow Langflow is an easy way to build from simple to complex AI applications. It is a low-code platform that allows you to integrate AI into everything you do. -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; +{" "} {" "} @@ -17,7 +19,6 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; style={{ width: "100%" }} /> - ## ๐Ÿš€ First steps ## Installation @@ -29,19 +30,21 @@ You can install **Langflow** with [pipx](https://pipx.pypa.io/stable/installatio Pipx can fetch the missing Python version for you, but you can also install it manually. ```bash -pipx install langflow --python python3.10 --fetch-missing-python -# or pip install langflow -U +# or +pipx install langflow --python python3.10 --fetch-missing-python ``` Or you can install a pre-release version using: ```bash -pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre" +pip install langflow --pre --force-reinstall # or -pip install langflow --pre -U +pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall" ``` +We recommend using --force-reinstall to ensure you have the latest version of Langflow and its dependencies. + ### โ›“๏ธ Running Langflow Langflow can be run in a variety of ways, including using the command-line interface (CLI) or HuggingFace Spaces. @@ -64,17 +67,15 @@ Remember to use a Chromium-based browser for the best experience. You'll be pres light: "img/duplicate-space.png", dark: "img/duplicate-space.png", }} - style={{ width: "100%" }} + style={{ width: "100%", margin: "20px auto" }} /> - From here, just name your Space, define the visibility (Public or Private), and click on `Duplicate Space` to start the installation process. When that is done, you'll be redirected to the Space's main page to start using Langflow right away! Once you get Langflow running, click on New Project in the top right corner of the screen. Langflow provides a range of example flows to help you get started. To quickly try one of them, open a starter example, set up your API keys and click โšก Run, on the bottom right corner of the canvas. This will open up Langflow's Interaction Panel with the chat console, text inputs, and outputs. - ### ๐Ÿ–ฅ๏ธ Command Line Interface (CLI) Langflow provides a command-line interface (CLI) for easy management and configuration. @@ -91,4 +92,4 @@ Find more information about the available options by running: ```bash langflow --help -``` \ No newline at end of file +``` diff --git a/docs/docs/migration/api.mdx b/docs/docs/migration/api.mdx new file mode 100644 index 000000000..e69de29bb diff --git a/docs/docs/migration/global-variables.mdx b/docs/docs/migration/global-variables.mdx index e69de29bb..ce6d15a5f 100644 --- a/docs/docs/migration/global-variables.mdx +++ b/docs/docs/migration/global-variables.mdx @@ -0,0 +1,65 @@ +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import Admonition from "@theme/Admonition"; + +# Global Variables + +Global Variables are a really useful feature of Langflow. +They allow you to define reusable variables that can be accessed from any Text field in your project. + +The first thing you need to do is find a **Text field** in a Component, so let's talk about what a Text field is. + +## Text Fields + +Text fields are the fields in a Component where you can write text but that does not allow you to open a Text Area. + +The easiest way to find fields that are Text fields, though, is to look for fields that have a ๐ŸŒ button. + + + +## Creating a Global Variable + +To create a Global Variable, you need to click on the ๐ŸŒ button in a Text field and that will open a dropdown showing your currently available variables and at the end of it **+ Add New Variable**. + + + +Click on **+ Add New Variable** and a window will open where you can define your new Global Variable. + +In it, you can define the **Name** of the variable, the optional **Type** of the variable, and the **Value** of the variable. + +The **Name** is the name that you will use to refer to the variable in your Text fields. + +The **Type** is optional for now but will be used in the future to allow for more advanced features. + +The **Value** is the value that the variable will have. +{/* say that all variables are encrypted */} + + + All Global Variables are encrypted and cannot be accessed by anyone but you. + + + + +After you have defined your variable, click on **Save Variable** and your variable will be created. + +After that, once you click on the ๐ŸŒ button in a Text field, you will see your new variable in the dropdown. diff --git a/docs/docs/migration/inputs-and-outputs.mdx b/docs/docs/migration/inputs-and-outputs.mdx index 8a9ce10ed..5db3f3af2 100644 --- a/docs/docs/migration/inputs-and-outputs.mdx +++ b/docs/docs/migration/inputs-and-outputs.mdx @@ -33,4 +33,4 @@ Outputs are components that are used to define where data comes out of your flow The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user. -You can find out more about it and the other Outputs [here](../components/outputs). \ No newline at end of file +You can find out more about it and the other Outputs [here](../components/outputs). diff --git a/docs/docs/whats-new/a-new-chapter-langflow.mdx b/docs/docs/whats-new/a-new-chapter-langflow.mdx index a11b87f50..4ecaf635f 100644 --- a/docs/docs/whats-new/a-new-chapter-langflow.mdx +++ b/docs/docs/whats-new/a-new-chapter-langflow.mdx @@ -27,11 +27,9 @@ This is a big change, but it's also a big improvement. It allows you to define the structure of your conversation and the data that flows through it. This makes it easier to understand and control your conversation. - This change comes with a new way of visualizing your projects. Before 1.0 you would connect Components to ultimately build one final Component that was processed behind the scenes. Now, each step of the process is defined by you, is visible on the canvas, and can be monitored and controlled by you. This makes it so that Composition is now just another way of building in Langflow. **Now data flows through your project more transparently**. - The caveat is existing projects may need some new Components to get them back to their full functionality. [We've made this as easy as possible](../migration/compatibility), and there will be improvements to it as we get feedback in our Discord server and on GitHub. @@ -40,10 +38,8 @@ The caveat is existing projects may need some new Components to get them back to The moment we decided to make this change, we saw the potential to make Langflow even more yours. By having a clear definition of Inputs and Outputs, we could build the experience around that which led us to create the **Interaction Panel**. - When building a project testing and debugging is crucial. The Interaction Panel is a tool that changes dynamically based on the Inputs and Outputs you defined in your project. - For example, let's say you are building a simple RAG application. Generally, you have an Input, some references that come from a Vector Store Search, a Prompt and the answer. Now, you could plug the output of your Prompt into a [Text Output](../components/outputs#Text-Output), rename that to "Prompt Result" and see the output of your Prompt in the Interaction Panel. @@ -65,9 +61,11 @@ We wanted to create start projects that would help you learn about new features For now, we have: -- **Basic Prompting**: A simple project that shows you how to use the Prompt Component. -- **Data Ingestion**: A project that shows you how to ingest files into a Vector Store. -- **RAG**: A project that shows you how to use a Vector Store Search and a Prompt to build a simple RAG application. +- **[Basic Prompting (Ahoy World!)](/getting-started/basic-prompting)**: A simple flow that shows you how to use the Prompt Component and how to talk like a pirate. +- **[Vector Store RAG](/getting-started/rag-with-astradb)**: A flow that shows you how to ingest data into a Vector Store and then use it to run a RAG application. +- **[Memory Chatbot](/getting-started/memory-chatbot)**: This one shows you how to create a simple chatbot that can remember things about the user. +- **[Document QA](/getting-started/document-qa)**: This flow shows you how to build a simple flow that helps you get answers about a document. +- **[Blog Writer](/getting-started/blog-writer)**: Shows you how you can expand on the Prompt variables and be creative about what inputs you add to it. As always, your feedback is invaluable, so please let us know what you think of the new starter projects and what you would like to see in the future. @@ -95,4 +93,4 @@ We also have some experimental features like a State Management System (so cool! ## Reach out -One last time, we want to thank you for being part of the Langflow community. Your feedback is invaluable, and we want to hear from you. \ No newline at end of file +One last time, we want to thank you for being part of the Langflow community. Your feedback is invaluable, and we want to hear from you. diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index a9a8a06e0..979953918 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -43,6 +43,10 @@ module.exports = { path: "docs", // sidebarPath: 'sidebars.js', }, + gtag: { + trackingID: 'G-XHC7G628ZP', + anonymizeIP: true, + }, theme: { customCss: [ require.resolve("@code-hike/mdx/styles.css"), diff --git a/docs/package-lock.json b/docs/package-lock.json index 7e5d0e8b9..c91a4ec06 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -11,6 +11,7 @@ "@babel/preset-react": "^7.22.3", "@code-hike/mdx": "^0.9.0", "@docusaurus/core": "^3.2.0", + "@docusaurus/plugin-google-gtag": "^3.2.0", "@docusaurus/plugin-ideal-image": "^3.2.0", "@docusaurus/preset-classic": "^3.2.0", "@docusaurus/theme-classic": "^3.2.0", diff --git a/docs/package.json b/docs/package.json index ce14c7568..87f3d3d71 100644 --- a/docs/package.json +++ b/docs/package.json @@ -17,6 +17,7 @@ "@babel/preset-react": "^7.22.3", "@code-hike/mdx": "^0.9.0", "@docusaurus/core": "^3.2.0", + "@docusaurus/plugin-google-gtag": "^3.2.0", "@docusaurus/plugin-ideal-image": "^3.2.0", "@docusaurus/preset-classic": "^3.2.0", "@docusaurus/theme-classic": "^3.2.0", diff --git a/docs/sidebars.js b/docs/sidebars.js index 6f8cd3411..ae444d51e 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -7,8 +7,11 @@ module.exports = { items: [ "index", "getting-started/cli", - "getting-started/hugging-face-spaces", - "getting-started/creating-flows", + "getting-started/basic-prompting", + "getting-started/document-qa", + "getting-started/blog-writer", + "getting-started/memory-chatbot", + "getting-started/rag-with-astradb", ], }, { @@ -20,37 +23,26 @@ module.exports = { "whats-new/migrating-to-one-point-zero", ], }, + { type: "category", - label: " Step-by-Step Guides", - collapsed: false, - items: [ - "guides/rag-with-astradb", - "guides/async-tasks", - "guides/loading_document", - "guides/chatprompttemplate_guide", - "guides/langfuse_integration", - ], - }, - { - type: "category", - label: "Migration Guides", + label: " Migration Guides", collapsed: false, items: [ // "migration/flow-of-data", "migration/inputs-and-outputs", // "migration/supported-frameworks", - // "migration/sidebar-and-interaction-panel", - // "migration/new-categories-and-components", - // "migration/text-and-record", + "migration/sidebar-and-interaction-panel", + "migration/new-categories-and-components", + "migration/text-and-record", // "migration/custom-component", "migration/compatibility", - // "migration/multiple-flows", - // "migration/component-status-and-data-passing", + "migration/multiple-flows", + "migration/component-status-and-data-passing", // "migration/connecting-output-components", - // "migration/renaming-and-editing-components", + "migration/renaming-and-editing-components", // "migration/passing-tweaks-and-inputs", - // "migration/global-variables", + "migration/global-variables", // "migration/experimental-components", // "migration/state-management", ], @@ -62,7 +54,6 @@ module.exports = { items: [ "guidelines/login", "guidelines/api", - "guidelines/async-api", "guidelines/components", "guidelines/features", "guidelines/collection", @@ -72,6 +63,12 @@ module.exports = { "guidelines/custom-component", ], }, + { + type: "category", + label: "Step-by-Step Guides", + collapsed: false, + items: ["guides/langfuse_integration"], + }, { type: "category", label: "Core Components", @@ -83,7 +80,7 @@ module.exports = { "components/models", "components/helpers", "components/vector-stores", - "components/embeddings", + "components/embeddings", ], }, { @@ -102,8 +99,6 @@ module.exports = { "components/text-splitters", "components/toolkits", "components/tools", - "components/wrappers", - // "components/prompts", ], }, { @@ -114,13 +109,10 @@ module.exports = { "examples/flow-runner", "examples/conversation-chain", "examples/buffer-memory", - "examples/midjourney-prompt-chain", "examples/csv-loader", "examples/searchapi-tool", "examples/serp-api-tool", - "examples/multiple-vectorstores", "examples/python-function", - "examples/how-upload-examples", ], }, { diff --git a/docs/static/data/AstraDB-RAG-Flows.json b/docs/static/data/AstraDB-RAG-Flows.json index cf96c6841..5706a0fbf 100644 --- a/docs/static/data/AstraDB-RAG-Flows.json +++ b/docs/static/data/AstraDB-RAG-Flows.json @@ -20,7 +20,7 @@ "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 Interaction Panel.\"\n icon = \"ChatInput\"\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(\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", + "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 Interaction Panel.\"\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(\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, @@ -44,9 +44,7 @@ "name": "input_value", "display_name": "Message", "advanced": false, - "input_types": [ - "Text" - ], + "input_types": [], "dynamic": false, "info": "", "load_from_db": false, @@ -360,7 +358,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\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-3-small\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-3-small\",\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\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=openai_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", + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\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\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=openai_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, @@ -415,7 +413,7 @@ "list": false, "show": true, "multiline": false, - "value": "text-embedding-3-small", + "value": "text-embedding-ada-002", "fileTypes": [], "file_path": "", "password": false, @@ -499,7 +497,7 @@ "list": true, "show": true, "multiline": false, - "value": "text-embedding-3-small", + "value": "text-embedding-ada-002", "fileTypes": [], "file_path": "", "password": false, @@ -1489,7 +1487,7 @@ "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", + "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, @@ -1498,8 +1496,7 @@ "dynamic": true, "info": "", "load_from_db": false, - "title_case": false, - "display_name": "code" + "title_case": false }, "silent_errors": { "type": "bool", @@ -1639,8 +1636,7 @@ "dynamic": true, "info": "", "load_from_db": false, - "title_case": false, - "display_name": "code" + "title_case": false }, "separators": { "type": "str", @@ -1763,7 +1759,7 @@ "display_name": "API Endpoint", "advanced": false, "dynamic": false, - "info": "API endpoint URL for the AstraDB service.", + "info": "API endpoint URL for the Astra DB service.", "load_from_db": false, "title_case": false, "input_types": [ @@ -1850,7 +1846,7 @@ "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 = \"AstraDB Search\"\n description = \"Searches an existing AstraDB 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 AstraDB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing AstraDB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the AstraDB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within AstraDB 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 โ€œSyncโ€, โ€œAsyncโ€, or โ€œOffโ€.\",\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 the 'content' field.\"\n )\n else:\n raise e\n", + "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 โ€œSyncโ€, โ€œAsyncโ€, or โ€œOffโ€.\",\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, @@ -1893,7 +1889,7 @@ "display_name": "Collection Name", "advanced": false, "dynamic": false, - "info": "The name of the collection within AstraDB where the vectors will be stored.", + "info": "The name of the collection within Astra DB where the vectors will be stored.", "load_from_db": false, "title_case": false, "input_types": [ @@ -1978,7 +1974,7 @@ "display_name": "Namespace", "advanced": true, "dynamic": false, - "info": "Optional namespace within AstraDB to use for the collection.", + "info": "Optional namespace within Astra DB to use for the collection.", "load_from_db": false, "title_case": false, "input_types": [ @@ -2090,7 +2086,7 @@ "display_name": "Token", "advanced": false, "dynamic": false, - "info": "Authentication token for accessing AstraDB.", + "info": "Authentication token for accessing Astra DB.", "load_from_db": false, "title_case": false, "input_types": [ @@ -2100,12 +2096,12 @@ }, "_type": "CustomComponent" }, - "description": "Searches an existing AstraDB Vector Store.", + "description": "Searches an existing Astra DB Vector Store.", "icon": "AstraDB", "base_classes": [ "Record" ], - "display_name": "AstraDB Search", + "display_name": "Astra DB Search", "documentation": "", "custom_fields": { "embedding": null, @@ -2213,7 +2209,7 @@ "display_name": "API Endpoint", "advanced": false, "dynamic": false, - "info": "API endpoint URL for the AstraDB service.", + "info": "API endpoint URL for the Astra DB service.", "load_from_db": false, "title_case": false, "input_types": [ @@ -2300,7 +2296,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import List, Optional\n\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 = \"AstraDB\"\n description = \"Builds or loads an AstraDB 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 AstraDB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing AstraDB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the AstraDB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within AstraDB 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 โ€œSyncโ€, โ€œAsyncโ€, or โ€œOffโ€.\",\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 = \"Async\",\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 ) -> VectorStore:\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", + "value": "from typing import List, Optional\n\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 โ€œSyncโ€, โ€œAsyncโ€, or โ€œOffโ€.\",\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 = \"Async\",\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 ) -> VectorStore:\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, @@ -2343,7 +2339,7 @@ "display_name": "Collection Name", "advanced": false, "dynamic": false, - "info": "The name of the collection within AstraDB where the vectors will be stored.", + "info": "The name of the collection within Astra DB where the vectors will be stored.", "load_from_db": false, "title_case": false, "input_types": [ @@ -2428,7 +2424,7 @@ "display_name": "Namespace", "advanced": true, "dynamic": false, - "info": "Optional namespace within AstraDB to use for the collection.", + "info": "Optional namespace within Astra DB to use for the collection.", "load_from_db": false, "title_case": false, "input_types": [ @@ -2495,7 +2491,7 @@ "display_name": "Token", "advanced": false, "dynamic": false, - "info": "Authentication token for accessing AstraDB.", + "info": "Authentication token for accessing Astra DB.", "load_from_db": false, "title_case": false, "input_types": [ @@ -2505,12 +2501,12 @@ }, "_type": "CustomComponent" }, - "description": "Builds or loads an AstraDB Vector Store.", + "description": "Builds or loads an Astra DB Vector Store.", "icon": "AstraDB", "base_classes": [ "VectorStore" ], - "display_name": "AstraDB", + "display_name": "Astra DB", "documentation": "", "custom_fields": { "embedding": null, @@ -2632,7 +2628,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\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-3-small\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-3-small\",\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\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=openai_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", + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\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\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=openai_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, @@ -2687,7 +2683,7 @@ "list": false, "show": true, "multiline": false, - "value": "text-embedding-3-small", + "value": "text-embedding-ada-002", "fileTypes": [], "file_path": "", "password": false, @@ -2771,7 +2767,7 @@ "list": true, "show": true, "multiline": false, - "value": "text-embedding-3-small", + "value": "text-embedding-ada-002", "fileTypes": [], "file_path": "", "password": false, @@ -3400,8 +3396,8 @@ "zoom": 0.2687057134854984 } }, - "description": "This project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an AstraDB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the AstraDB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", - "name": "AstraDB RAG Flows", + "description": "Visit https://pre-release.langflow.org/guides/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", + "name": "Vector Store RAG", "last_tested_version": "1.0.0a0", "is_component": false } \ No newline at end of file diff --git a/docs/static/img/add-new-variable.png b/docs/static/img/add-new-variable.png new file mode 100644 index 000000000..178cab67c Binary files /dev/null and b/docs/static/img/add-new-variable.png differ diff --git a/docs/static/img/chat-input-expanded.png b/docs/static/img/chat-input-expanded.png new file mode 100644 index 000000000..befe5afbd Binary files /dev/null and b/docs/static/img/chat-input-expanded.png differ diff --git a/docs/static/img/chat-input.png b/docs/static/img/chat-input.png new file mode 100644 index 000000000..29605c1b1 Binary files /dev/null and b/docs/static/img/chat-input.png differ diff --git a/docs/static/img/create-variable-window.png b/docs/static/img/create-variable-window.png new file mode 100644 index 000000000..75c8842b5 Binary files /dev/null and b/docs/static/img/create-variable-window.png differ diff --git a/docs/static/img/interaction-panel-text-input.png b/docs/static/img/interaction-panel-text-input.png new file mode 100644 index 000000000..77994c924 Binary files /dev/null and b/docs/static/img/interaction-panel-text-input.png differ diff --git a/docs/static/img/interaction-panel-with-chat-input.png b/docs/static/img/interaction-panel-with-chat-input.png new file mode 100644 index 000000000..c5f7c7998 Binary files /dev/null and b/docs/static/img/interaction-panel-with-chat-input.png differ diff --git a/docs/static/img/ollama-gv.png b/docs/static/img/ollama-gv.png new file mode 100644 index 000000000..0ee7540ee Binary files /dev/null and b/docs/static/img/ollama-gv.png differ diff --git a/docs/static/img/prompt-with-template.png b/docs/static/img/prompt-with-template.png new file mode 100644 index 000000000..0312b899f Binary files /dev/null and b/docs/static/img/prompt-with-template.png differ diff --git a/docs/static/img/prompt.png b/docs/static/img/prompt.png new file mode 100644 index 000000000..6d4260bed Binary files /dev/null and b/docs/static/img/prompt.png differ diff --git a/docs/static/img/text-input-expanded.png b/docs/static/img/text-input-expanded.png new file mode 100644 index 000000000..fe73e496c Binary files /dev/null and b/docs/static/img/text-input-expanded.png differ diff --git a/docs/static/img/text-input.png b/docs/static/img/text-input.png new file mode 100644 index 000000000..e8f8da248 Binary files /dev/null and b/docs/static/img/text-input.png differ diff --git a/lcserve.Dockerfile b/lcserve.Dockerfile deleted file mode 100644 index 883a2c040..000000000 --- a/lcserve.Dockerfile +++ /dev/null @@ -1,16 +0,0 @@ -# This file is used by `lc-serve` to build the image. -# Don't change the name of this file. - -FROM jinawolf/serving-gateway:${version} - -RUN apt-get update \ - && apt-get install --no-install-recommends -y build-essential libpq-dev - -COPY . /appdir/ - -RUN pip install poetry==1.4.0 && cd /appdir && pip install . && \ - pip uninstall -y poetry && \ - apt-get remove --auto-remove -y build-essential libpq-dev && \ - apt-get autoremove && apt-get clean && rm -rf /var/lib/apt/lists/* && rm -rf /tmp/* - -ENTRYPOINT [ "jina", "gateway", "--uses", "config.yml" ] \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index f27242d09..dc6cb7649 100644 --- a/poetry.lock +++ b/poetry.lock @@ -455,17 +455,17 @@ files = [ [[package]] name = "boto3" -version = "1.34.75" +version = "1.34.77" description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" files = [ - {file = "boto3-1.34.75-py3-none-any.whl", hash = "sha256:ba5d2104bba4370766036d64ad9021eb6289d154265852a2a821ec6a5e816faa"}, - {file = "boto3-1.34.75.tar.gz", hash = "sha256:eaec72fda124084105a31bcd67eafa1355b34df6da70cadae0c0f262d8a4294f"}, + {file = "boto3-1.34.77-py3-none-any.whl", hash = "sha256:7abd327980258ec2ae980d2ff7fc32ede7448146b14d34c56bf0be074e2a149b"}, + {file = "boto3-1.34.77.tar.gz", hash = "sha256:8ebed4fa5a3b84dd4037f28226985af00e00fb860d739fc8b1ed6381caa4b330"}, ] [package.dependencies] -botocore = ">=1.34.75,<1.35.0" +botocore = ">=1.34.77,<1.35.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -474,13 +474,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.34.75" +version = "1.34.77" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.8" files = [ - {file = "botocore-1.34.75-py3-none-any.whl", hash = "sha256:1d7f683d99eba65076dfb9af3b42fa967c64f11111d9699b65757420902aa002"}, - {file = "botocore-1.34.75.tar.gz", hash = "sha256:06113ee2587e6160211a6bd797e135efa6aa21b5bde97bf455c02f7dff40203c"}, + {file = "botocore-1.34.77-py3-none-any.whl", hash = "sha256:6d6a402032ca0b89525212356a865397f8f2839683dd53d41b8cee1aa84b2b4b"}, + {file = "botocore-1.34.77.tar.gz", hash = "sha256:6dab60261cdbfb7d0059488ea39408d5522fad419c004ba5db3484e6df854ea8"}, ] [package.dependencies] @@ -1129,13 +1129,13 @@ testing = ["pytest (>=7.2.1)", "pytest-cov (>=4.0.0)", "tox (>=4.4.3)"] [[package]] name = "cohere" -version = "5.1.7" +version = "5.2.1" description = "" optional = false python-versions = "<4.0,>=3.8" files = [ - {file = "cohere-5.1.7-py3-none-any.whl", hash = "sha256:66e149425ba10d9d6ed2980ad869afae2ed79b1f4c375f215ff4953f389cf5f9"}, - {file = "cohere-5.1.7.tar.gz", hash = "sha256:5b5ba38e614313d96f4eb362046a3470305e57119e39538afa3220a27614ba15"}, + {file = "cohere-5.2.1-py3-none-any.whl", hash = "sha256:c694f9d2cdafd87443f54ea5238b51a0fb807f119673e00b814c2a2993368e38"}, + {file = "cohere-5.2.1.tar.gz", hash = "sha256:7cd5522bb162c05c67b2db0b7aba2a103622e17ece9e885f5ef2de66bb67a324"}, ] [package.dependencies] @@ -1143,6 +1143,7 @@ fastavro = ">=1.9.4,<2.0.0" httpx = ">=0.21.2" pydantic = ">=1.9.2" requests = ">=2.31.0,<3.0.0" +tokenizers = ">=0.15.2,<0.16.0" types-requests = ">=2.31.0.20240311,<3.0.0.0" typing_extensions = ">=4.0.0" @@ -2560,13 +2561,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] [[package]] name = "google-api-python-client" -version = "2.124.0" +version = "2.125.0" description = "Google API Client Library for Python" optional = false python-versions = ">=3.7" files = [ - {file = "google-api-python-client-2.124.0.tar.gz", hash = "sha256:f6d3258420f7c76b0f5266b5e402e6f804e30351b018a10083f4a46c3ec33773"}, - {file = "google_api_python_client-2.124.0-py2.py3-none-any.whl", hash = "sha256:07dc674449ed353704b1169fdee792f74438d024261dad71b6ce7bb9c683d51f"}, + {file = "google-api-python-client-2.125.0.tar.gz", hash = "sha256:51a0385cff65ec135106e8be60ee7112557396dde5f44113ae23912baddda143"}, + {file = "google_api_python_client-2.125.0-py2.py3-none-any.whl", hash = "sha256:0a62b60fbd61b61a455f15d925264b3301099b67cafd2d33cf8bf151f1fca4f4"}, ] [package.dependencies] @@ -3772,13 +3773,13 @@ numpy = ">=1,<2" [[package]] name = "langchain-cohere" -version = "0.1.0rc1" +version = "0.1.0" description = "An integration package connecting Cohere and LangChain" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_cohere-0.1.0rc1-py3-none-any.whl", hash = "sha256:698ee4e889169c1115bc2b0992c152aafd574030e6ea18238dd6b5d034733c64"}, - {file = "langchain_cohere-0.1.0rc1.tar.gz", hash = "sha256:cc91b33cc5c6cb8d04c12034366c52b94798313d4503b776de9345e7261e8d15"}, + {file = "langchain_cohere-0.1.0-py3-none-any.whl", hash = "sha256:f60e9eb41f7d4ead9659bddb3fae7aa18ddc3fdf2b2867be4bd8a565229f488d"}, + {file = "langchain_cohere-0.1.0.tar.gz", hash = "sha256:960551293ea58d170fad37d44657d3ae4587f6b2e8f3f58922c53c59b9e9d85c"}, ] [package.dependencies] @@ -3813,13 +3814,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15. [[package]] name = "langchain-core" -version = "0.1.38" +version = "0.1.40" description = "Building applications with LLMs through composability" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_core-0.1.38-py3-none-any.whl", hash = "sha256:d881b2754254cb4bdb0d5bb56e5c138d032b6e75e5cb21f151b01224b322e02b"}, - {file = "langchain_core-0.1.38.tar.gz", hash = "sha256:ee8da6d061c06cce7dc22fec224b6ecbc3a8de106d6dd9f409c7fe448ea41861"}, + {file = "langchain_core-0.1.40-py3-none-any.whl", hash = "sha256:618dbb7ab44d8b263b91e384db1ff07d0db256ae5bdafa0123a115b6a75a13f1"}, + {file = "langchain_core-0.1.40.tar.gz", hash = "sha256:34c06fc0e6d3534b738c63f85403446b4be71161665b7e091f9bb19c914ec100"}, ] [package.dependencies] @@ -3828,7 +3829,6 @@ langsmith = ">=0.1.0,<0.2.0" packaging = ">=23.2,<24.0" pydantic = ">=1,<3" PyYAML = ">=5.3" -requests = ">=2,<3" tenacity = ">=8.1.0,<9.0.0" [package.extras] @@ -3920,7 +3920,7 @@ six = "*" [[package]] name = "langflow-base" -version = "0.0.16" +version = "0.0.17" description = "A Python package with a built-in web application" optional = false python-versions = ">=3.10,<3.12" @@ -3973,13 +3973,13 @@ url = "src/backend/base" [[package]] name = "langfuse" -version = "2.21.1" +version = "2.21.2" description = "A client library for accessing langfuse" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langfuse-2.21.1-py3-none-any.whl", hash = "sha256:5ef286823a4c9903e2120ad2bf0169a929d41789702535abc713e66a0d270f05"}, - {file = "langfuse-2.21.1.tar.gz", hash = "sha256:36494ea016784ac339a1a5375b88c33484e81668433956ead442d7a93c217078"}, + {file = "langfuse-2.21.2-py3-none-any.whl", hash = "sha256:bd65858e6326776f65c9b2e414e64fdea0f14402f5c784952af93346dfd489bb"}, + {file = "langfuse-2.21.2.tar.gz", hash = "sha256:eb7911aa640f020f097cb56eaa7d67f01d39f9e2bdd6226e0c5d642a87f3663c"}, ] [package.dependencies] @@ -3992,18 +3992,18 @@ wrapt = ">=1.14,<2.0" [package.extras] langchain = ["langchain (>=0.0.309)"] -llama-index = ["llama-index (>=0.10.12,<0.11.0)"] +llama-index = ["llama-index (>=0.10.12,<2.0.0)"] openai = ["openai (>=0.27.8)"] [[package]] name = "langsmith" -version = "0.1.38" +version = "0.1.39" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langsmith-0.1.38-py3-none-any.whl", hash = "sha256:f36479f82cf537cf40d129ac2e485e72a3981360c7b6cf2549dad77d98eafd8f"}, - {file = "langsmith-0.1.38.tar.gz", hash = "sha256:2c1f98ac0a8c02e43b625650a6e13c65b09523551bfc21a59d20963f46f7d265"}, + {file = "langsmith-0.1.39-py3-none-any.whl", hash = "sha256:85c19177162585728001cb7ae91ab48ca4abe39b7bc1ff783212ac426ded222b"}, + {file = "langsmith-0.1.39.tar.gz", hash = "sha256:2aec9d2f9cc664042d2121b13da569b0902aff842c86b17b440245d57da84ec5"}, ] [package.dependencies] @@ -4028,14 +4028,40 @@ interegular = ["interegular (>=0.3.1,<0.4.0)"] nearley = ["js2py"] regex = ["regex"] +[[package]] +name = "litellm" +version = "1.34.22" +description = "Library to easily interface with LLM API providers" +optional = false +python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8" +files = [ + {file = "litellm-1.34.22-py3-none-any.whl", hash = "sha256:0e573d56d762f4060c53493da4a08c48034b5bb5ba22e34517065739adfd9154"}, + {file = "litellm-1.34.22.tar.gz", hash = "sha256:ca50ede3ca8d3f9dc2765ca13cf2ff5c4e4b9afb4db222f9d7cb9ee838b6180f"}, +] + +[package.dependencies] +aiohttp = "*" +click = "*" +importlib-metadata = ">=6.8.0" +jinja2 = ">=3.1.2,<4.0.0" +openai = ">=1.0.0" +python-dotenv = ">=0.2.0" +requests = ">=2.31.0,<3.0.0" +tiktoken = ">=0.4.0" +tokenizers = "*" + +[package.extras] +extra-proxy = ["azure-identity (>=1.15.0,<2.0.0)", "azure-keyvault-secrets (>=4.8.0,<5.0.0)", "google-cloud-kms (>=2.21.3,<3.0.0)", "prisma (==0.11.0)", "resend (>=0.8.0,<0.9.0)"] +proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "cryptography (>=42.0.5,<43.0.0)", "fastapi (>=0.109.1,<0.110.0)", "fastapi-sso (>=0.10.0,<0.11.0)", "gunicorn (>=21.2.0,<22.0.0)", "orjson (>=3.9.7,<4.0.0)", "python-multipart (>=0.0.9,<0.0.10)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.22.0,<0.23.0)"] + [[package]] name = "llama-cpp-python" -version = "0.2.58" +version = "0.2.59" description = "Python bindings for the llama.cpp library" optional = true python-versions = ">=3.8" files = [ - {file = "llama_cpp_python-0.2.58.tar.gz", hash = "sha256:50d4d16835326b15f5c4ed20dbf2f24508bf29b34531d50612ce215a596dde3f"}, + {file = "llama_cpp_python-0.2.59.tar.gz", hash = "sha256:4b19283226ab91c74c6d811d88724a6f32d9dd7d07caf9d8b897dd3372d5d4d2"}, ] [package.dependencies] @@ -4077,18 +4103,19 @@ llama-index-readers-llama-parse = ">=0.1.2,<0.2.0" [[package]] name = "llama-index-agent-openai" -version = "0.2.1" +version = "0.2.2" description = "llama-index agent openai integration" optional = false 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"types-requests-2.31.0.20240403.tar.gz", hash = "sha256:e1e0cd0b655334f39d9f872b68a1310f0e343647688bf2cee932ec4c2b04de59"}, + {file = "types_requests-2.31.0.20240403-py3-none-any.whl", hash = "sha256:06abf6a68f5c4f2a62f6bb006672dfb26ed50ccbfddb281e1ee6f09a65707d5d"}, ] [package.dependencies] @@ -10249,4 +10317,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "3eb1181a83884c7ba52a7d1c98dcff13a307452eaf8f4a148fc0778f97499dfd" +content-hash = "ed8605b2934fceb591d03d5be7461ed05a8f427512b693ce1baefeaa4fa21500" diff --git a/pyproject.toml b/pyproject.toml index 346767203..5e34c2a0f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langflow" -version = "1.0.0a0" +version = "1.0.0a4" description = "A Python package with a built-in web application" authors = ["Logspace "] maintainers = [ @@ -23,8 +23,6 @@ documentation = "https://docs.langflow.org" [tool.poetry.scripts] langflow = "langflow.__main__:main" -[tool.poetry-monorepo-dependency-plugin] -enable = true [tool.poetry.dependencies] python = ">=3.10,<3.12" @@ -80,6 +78,7 @@ dspy-ai = "^2.4.0" crewai = "^0.22.5" html2text = "^2024.2.26" assemblyai = "^0.23.1" +litellm = "^1.34.22" [tool.poetry.group.dev.dependencies] types-redis = "^4.6.0.5" diff --git a/scripts/setup/update_poetry.sh b/scripts/setup/update_poetry.sh index ecaf21356..94c51fd56 100644 --- a/scripts/setup/update_poetry.sh +++ b/scripts/setup/update_poetry.sh @@ -146,11 +146,3 @@ else echo "Poetry version is $1 or higher. No need to update." fi -# Check if poetry-monorepo-dependency-plugin is installed -if poetry self show | grep -q "poetry-monorepo-dependency-plugin"; then - echo "poetry-monorepo-dependency-plugin is already installed." -else - echo "Installing poetry-monorepo-dependency-plugin..." - poetry run pip install poetry-monorepo-dependency-plugin - echo "poetry-monorepo-dependency-plugin installed successfully." -fi \ No newline at end of file diff --git a/scripts/update_dependencies.py b/scripts/update_dependencies.py index a46a95f8a..c70cf8f8f 100644 --- a/scripts/update_dependencies.py +++ b/scripts/update_dependencies.py @@ -11,6 +11,15 @@ def read_version_from_pyproject(file_path): return None +def get_version_from_pypi(package_name): + import requests + + response = requests.get(f"https://pypi.org/pypi/{package_name}/json") + if response.ok: + return response.json()["info"]["version"] + return None + + def update_pyproject_dependency(pyproject_path, version): pattern = re.compile(r'langflow-base = \{ path = "\./src/backend/base", develop = true \}') replacement = f'langflow-base = "^{version}"' @@ -35,7 +44,7 @@ if __name__ == "__main__": # Reading version and updating pyproject.toml langflow_base_path = Path(__file__).resolve().parent / "../src/backend/base/pyproject.toml" - version = read_version_from_pyproject(langflow_base_path) + version = get_version_from_pypi("langflow-base") if version: update_pyproject_dependency(pyproject_path, version) else: diff --git a/src/backend/base/langflow/__main__.py b/src/backend/base/langflow/__main__.py index 34ed75f9b..96f4f1575 100644 --- a/src/backend/base/langflow/__main__.py +++ b/src/backend/base/langflow/__main__.py @@ -246,9 +246,17 @@ def get_free_port(port): def print_banner(host, port): - from langflow.version import __version__ + try: + from langflow.version import __version__ + + version = __version__ + word = "Langflow" + except ImportError: + from importlib import metadata + + version = metadata.version("langflow-base") + word = "Langflow Base" - word = "Langflow" colors = ["#6e42f5"] styled_word = "" @@ -259,7 +267,7 @@ def print_banner(host, port): # Title with emojis and gradient text title = ( - f"[bold]Welcome to :chains: {styled_word} v{__version__}[/bold]\n" + f"[bold]Welcome to :chains: {styled_word} v{version}[/bold]\n" f"Access [link=http://{host}:{port}]http://{host}:{port}[/link]" ) info_text = ( diff --git a/src/backend/base/langflow/api/v1/endpoints.py b/src/backend/base/langflow/api/v1/endpoints.py index cdcf40443..4b032f6cb 100644 --- a/src/backend/base/langflow/api/v1/endpoints.py +++ b/src/backend/base/langflow/api/v1/endpoints.py @@ -372,9 +372,17 @@ async def create_upload_file( # get endpoint to return version of langflow @router.get("/version") def get_version(): - from langflow.version import __version__ # type: ignore + try: + from langflow.version import __version__ - return {"version": __version__} + version = __version__ + package = "Langflow" + except ImportError: + from importlib import metadata + + version = metadata.version("langflow-base") + package = "Langflow Base" + return {"version": version, "package": package} @router.post("/custom_component", status_code=HTTPStatus.OK) diff --git a/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py b/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py index cdeeac0e1..f177fa1f1 100644 --- a/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py +++ b/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py @@ -94,10 +94,10 @@ class OpenAIEmbeddingsComponent(CustomComponent): disallowed_special: List[str] = ["all"], chunk_size: int = 1000, client: Optional[Any] = None, - deployment: str = "text-embedding-3-small", + deployment: str = "text-embedding-ada-002", embedding_ctx_length: int = 8191, max_retries: int = 6, - model: str = "text-embedding-3-small", + model: str = "text-embedding-ada-002", model_kwargs: NestedDict = {}, openai_api_base: Optional[str] = None, openai_api_type: Optional[str] = None, diff --git a/src/backend/base/langflow/components/helpers/SplitText.py b/src/backend/base/langflow/components/helpers/SplitText.py index 3c823c4aa..a141bbddc 100644 --- a/src/backend/base/langflow/components/helpers/SplitText.py +++ b/src/backend/base/langflow/components/helpers/SplitText.py @@ -1,14 +1,11 @@ from typing import Optional -from langchain.text_splitter import ( - RecursiveCharacterTextSplitter, - CharacterTextSplitter, -) +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.field_typing import Text from langflow.utils.util import unescape_string @@ -18,10 +15,10 @@ class SplitTextComponent(CustomComponent): def build_config(self): return { - "texts": { - "display_name": "Texts", + "inputs": { + "display_name": "Inputs", "info": "Texts to split.", - "input_types": ["Text"], + "input_types": ["Record", "Text"], }, "separators": { "display_name": "Separators", @@ -48,7 +45,7 @@ class SplitTextComponent(CustomComponent): def build( self, - texts: list[Text], + inputs: list[Text], separators: Optional[list[str]] = [" "], chunk_size: Optional[int] = 1000, chunk_overlap: Optional[int] = 200, @@ -77,9 +74,11 @@ class SplitTextComponent(CustomComponent): ) documents = [] - for _text in texts: - # documents.append(_input.to_lc_document()) - documents.append(Document(page_content=_text)) + 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 diff --git a/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py b/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py index 5c1463b71..439266fbb 100644 --- a/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py +++ b/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py @@ -118,7 +118,7 @@ class ChatLiteLLMComponent(CustomComponent): max_tokens: int = 256, max_retries: int = 6, verbose: bool = False, - ) -> Union[BaseLanguageModel, Callable]: + ) -> BaseLanguageModel: try: import litellm # type: ignore diff --git a/src/backend/base/langflow/components/models/ChatLiteLLMModel.py b/src/backend/base/langflow/components/models/ChatLiteLLMModel.py new file mode 100644 index 000000000..5266c6935 --- /dev/null +++ b/src/backend/base/langflow/components/models/ChatLiteLLMModel.py @@ -0,0 +1,191 @@ +from typing import Any, Dict, Optional + +from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException + +from langflow.base.constants import STREAM_INFO_TEXT +from langflow.base.models.model import LCModelComponent +from langflow.field_typing import BaseLanguageModel, Text + + +class ChatLiteLLMModelComponent(LCModelComponent): + display_name = "LiteLLM" + description = "`LiteLLM` collection of large language models." + documentation = "https://python.langchain.com/docs/integrations/chat/litellm" + field_order = [ + "model", + "api_key", + "provider", + "temperature", + "model_kwargs", + "top_p", + "top_k", + "n", + "max_tokens", + "max_retries", + "verbose", + "stream", + "input_value", + "system_message", + ] + + def build_config(self): + return { + "model": { + "display_name": "Model name", + "field_type": "str", + "advanced": False, + "required": True, + "info": "The name of the model to use. For example, `gpt-3.5-turbo`.", + }, + "api_key": { + "display_name": "API key", + "field_type": "str", + "advanced": False, + "required": False, + "password": True, + }, + "provider": { + "display_name": "Provider", + "info": "The provider of the API key.", + "options": [ + "OpenAI", + "Azure", + "Anthropic", + "Replicate", + "Cohere", + "OpenRouter", + ], + }, + "temperature": { + "display_name": "Temperature", + "field_type": "float", + "advanced": False, + "required": False, + "default": 0.7, + }, + "model_kwargs": { + "display_name": "Model kwargs", + "field_type": "dict", + "advanced": True, + "required": False, + "default": {}, + }, + "top_p": { + "display_name": "Top p", + "field_type": "float", + "advanced": True, + "required": False, + }, + "top_k": { + "display_name": "Top k", + "field_type": "int", + "advanced": True, + "required": False, + }, + "n": { + "display_name": "N", + "field_type": "int", + "advanced": True, + "required": False, + "info": "Number of chat completions to generate for each prompt. " + "Note that the API may not return the full n completions if duplicates are generated.", + "default": 1, + }, + "max_tokens": { + "display_name": "Max tokens", + "field_type": "int", + "advanced": False, + "required": False, + "default": 256, + "info": "The maximum number of tokens to generate for each chat completion.", + }, + "max_retries": { + "display_name": "Max retries", + "field_type": "int", + "advanced": True, + "required": False, + "default": 6, + }, + "verbose": { + "display_name": "Verbose", + "field_type": "bool", + "advanced": True, + "required": False, + "default": False, + }, + "input_value": {"display_name": "Input"}, + "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, + }, + } + + def build( + self, + input_value: Text, + model: str, + provider: str, + api_key: Optional[str] = None, + stream: bool = False, + temperature: Optional[float] = 0.7, + model_kwargs: Optional[Dict[str, Any]] = {}, + top_p: Optional[float] = None, + top_k: Optional[int] = None, + n: int = 1, + max_tokens: int = 256, + max_retries: int = 6, + verbose: bool = False, + system_message: Optional[str] = None, + ) -> BaseLanguageModel: + try: + import litellm # type: ignore + + litellm.drop_params = True + litellm.set_verbose = verbose + except ImportError: + raise ChatLiteLLMException( + "Could not import litellm python package. " "Please install it with `pip install litellm`" + ) + provider_map = { + "OpenAI": "openai_api_key", + "Azure": "azure_api_key", + "Anthropic": "anthropic_api_key", + "Replicate": "replicate_api_key", + "Cohere": "cohere_api_key", + "OpenRouter": "openrouter_api_key", + } + # Set the API key based on the provider + api_keys: dict[str, Optional[str]] = {v: None for v in provider_map.values()} + + if variable_name := provider_map.get(provider): + api_keys[variable_name] = api_key + else: + raise ChatLiteLLMException( + f"Provider {provider} is not supported. Supported providers are: {', '.join(provider_map.keys())}" + ) + + output = ChatLiteLLM( + model=model, + client=None, + streaming=stream, + temperature=temperature, + model_kwargs=model_kwargs if model_kwargs is not None else {}, + top_p=top_p, + top_k=top_k, + n=n, + max_tokens=max_tokens, + max_retries=max_retries, + openai_api_key=api_keys["openai_api_key"], + azure_api_key=api_keys["azure_api_key"], + anthropic_api_key=api_keys["anthropic_api_key"], + replicate_api_key=api_keys["replicate_api_key"], + cohere_api_key=api_keys["cohere_api_key"], + openrouter_api_key=api_keys["openrouter_api_key"], + ) + return self.get_chat_result(output, stream, input_value, system_message) diff --git a/src/backend/base/langflow/components/models/__init__.py b/src/backend/base/langflow/components/models/__init__.py index 3efbe4b6f..9db6caa26 100644 --- a/src/backend/base/langflow/components/models/__init__.py +++ b/src/backend/base/langflow/components/models/__init__.py @@ -2,6 +2,7 @@ from .AmazonBedrockModel import AmazonBedrockComponent from .AnthropicModel import AnthropicLLM from .AzureOpenAIModel import AzureChatOpenAIComponent from .BaiduQianfanChatModel import QianfanChatEndpointComponent +from .ChatLiteLLMModel import ChatLiteLLMModelComponent from .CohereModel import CohereComponent from .GoogleGenerativeAIModel import GoogleGenerativeAIComponent from .HuggingFaceModel import HuggingFaceEndpointsComponent @@ -10,6 +11,7 @@ from .OpenAIModel import OpenAIModelComponent from .VertexAiModel import ChatVertexAIComponent __all__ = [ + "ChatLiteLLMModelComponent", "AmazonBedrockComponent", "AnthropicLLM", "AzureChatOpenAIComponent", diff --git a/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py b/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py index e2d8e61e9..83ed42daf 100644 --- a/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py @@ -7,8 +7,8 @@ from langflow.schema import Record class AstraDBSearchComponent(LCVectorStoreComponent): - display_name = "AstraDB Search" - description = "Searches an existing AstraDB Vector Store." + display_name = "Astra DB Search" + description = "Searches an existing Astra DB Vector Store." icon = "AstraDB" field_order = ["token", "api_endpoint", "collection_name", "input_value", "embedding"] @@ -25,20 +25,20 @@ class AstraDBSearchComponent(LCVectorStoreComponent): "embedding": {"display_name": "Embedding", "info": "Embedding to use"}, "collection_name": { "display_name": "Collection Name", - "info": "The name of the collection within AstraDB where the vectors will be stored.", + "info": "The name of the collection within Astra DB where the vectors will be stored.", }, "token": { "display_name": "Token", - "info": "Authentication token for accessing AstraDB.", + "info": "Authentication token for accessing Astra DB.", "password": True, }, "api_endpoint": { "display_name": "API Endpoint", - "info": "API endpoint URL for the AstraDB service.", + "info": "API endpoint URL for the Astra DB service.", }, "namespace": { "display_name": "Namespace", - "info": "Optional namespace within AstraDB to use for the collection.", + "info": "Optional namespace within Astra DB to use for the collection.", "advanced": True, }, "metric": { diff --git a/src/backend/base/langflow/components/vectorstores/AstraDB.py b/src/backend/base/langflow/components/vectorstores/AstraDB.py index 6dcbe3d57..460d1f6db 100644 --- a/src/backend/base/langflow/components/vectorstores/AstraDB.py +++ b/src/backend/base/langflow/components/vectorstores/AstraDB.py @@ -9,8 +9,8 @@ from langflow.schema import Record class AstraDBVectorStoreComponent(CustomComponent): - display_name = "AstraDB" - description = "Builds or loads an AstraDB Vector Store." + display_name = "Astra DB" + description = "Builds or loads an Astra DB Vector Store." icon = "AstraDB" field_order = ["token", "api_endpoint", "collection_name", "inputs", "embedding"] @@ -23,20 +23,20 @@ class AstraDBVectorStoreComponent(CustomComponent): "embedding": {"display_name": "Embedding", "info": "Embedding to use"}, "collection_name": { "display_name": "Collection Name", - "info": "The name of the collection within AstraDB where the vectors will be stored.", + "info": "The name of the collection within Astra DB where the vectors will be stored.", }, "token": { "display_name": "Token", - "info": "Authentication token for accessing AstraDB.", + "info": "Authentication token for accessing Astra DB.", "password": True, }, "api_endpoint": { "display_name": "API Endpoint", - "info": "API endpoint URL for the AstraDB service.", + "info": "API endpoint URL for the Astra DB service.", }, "namespace": { "display_name": "Namespace", - "info": "Optional namespace within AstraDB to use for the collection.", + "info": "Optional namespace within Astra DB to use for the collection.", "advanced": True, }, "metric": { diff --git a/src/backend/base/langflow/initial_setup/setup.py b/src/backend/base/langflow/initial_setup/setup.py index cf958d0c2..f2181f85e 100644 --- a/src/backend/base/langflow/initial_setup/setup.py +++ b/src/backend/base/langflow/initial_setup/setup.py @@ -88,7 +88,7 @@ def load_starter_projects(): starter_projects = [] folder = Path(__file__).parent / "starter_projects" for file in folder.glob("*.json"): - project = orjson.loads(file.read_text()) + project = orjson.loads(file.read_text(encoding="utf-8")) starter_projects.append((file, project)) logger.info(f"Loaded starter project {file}") return starter_projects @@ -124,7 +124,7 @@ def get_project_data(project): def update_project_file(project_path, project, updated_project_data): project["data"] = updated_project_data - with open(project_path, "w") as f: + with open(project_path, "w", encoding="utf-8") as f: f.write(orjson.dumps(project, option=orjson.OPT_INDENT_2).decode()) logger.info(f"Updated starter project {project['name']} file") @@ -197,7 +197,11 @@ def delete_start_projects(session): def create_or_update_starter_projects(): components_paths = get_settings_service().settings.COMPONENTS_PATH - all_types_dict = get_all_components(components_paths, as_dict=True) + try: + all_types_dict = get_all_components(components_paths, as_dict=True) + except Exception as e: + logger.exception(f"Error loading components: {e}") + raise e with session_scope() as session: starter_projects = load_starter_projects() delete_start_projects(session) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Ahoy World!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Ahoy World!).json new file mode 100644 index 000000000..4ef43f501 --- /dev/null +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Ahoy World!).json @@ -0,0 +1,888 @@ +{ + "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": "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\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\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\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\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 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=openai_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-preview", + "gpt-3.5-turbo", + "gpt-4-0125-preview", + "gpt-4-1106-preview", + "gpt-4-vision-preview", + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-1106" + ], + "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 Interaction Panel.\"\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(\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,\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 Interaction Panel.", + "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 Interaction Panel.\"\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(\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 Interaction Panel.", + "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": "{ล“baseClassesล“:[ล“objectล“,ล“Textล“,ล“strล“],ล“dataTypeล“:ล“OpenAIModelล“,ล“idล“:ล“OpenAIModel-k39HSล“}", + "target": "ChatOutput-njtka", + "targetHandle": "{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“ChatOutput-njtkaล“,ล“inputTypesล“:[ล“Textล“],ล“typeล“:ล“strล“}", + "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{ล“baseClassesล“:[ล“objectล“,ล“Textล“,ล“strล“],ล“dataTypeล“:ล“OpenAIModelล“,ล“idล“:ล“OpenAIModel-k39HSล“}-ChatOutput-njtka{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“ChatOutput-njtkaล“,ล“inputTypesล“:[ล“Textล“],ล“typeล“:ล“strล“}" + }, + { + "source": "Prompt-uxBqP", + "sourceHandle": "{ล“baseClassesล“:[ล“objectล“,ล“strล“,ล“Textล“],ล“dataTypeล“:ล“Promptล“,ล“idล“:ล“Prompt-uxBqPล“}", + "target": "OpenAIModel-k39HS", + "targetHandle": "{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“OpenAIModel-k39HSล“,ล“inputTypesล“:[ล“Textล“],ล“typeล“:ล“strล“}", + "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{ล“baseClassesล“:[ล“objectล“,ล“strล“,ล“Textล“],ล“dataTypeล“:ล“Promptล“,ล“idล“:ล“Prompt-uxBqPล“}-OpenAIModel-k39HS{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“OpenAIModel-k39HSล“,ล“inputTypesล“:[ล“Textล“],ล“typeล“:ล“strล“}" + }, + { + "source": "ChatInput-P3fgL", + "sourceHandle": "{ล“baseClassesล“:[ล“objectล“,ล“Recordล“,ล“strล“,ล“Textล“],ล“dataTypeล“:ล“ChatInputล“,ล“idล“:ล“ChatInput-P3fgLล“}", + "target": "Prompt-uxBqP", + "targetHandle": "{ล“fieldNameล“:ล“user_inputล“,ล“idล“:ล“Prompt-uxBqPล“,ล“inputTypesล“:[ล“Documentล“,ล“BaseOutputParserล“,ล“Recordล“,ล“Textล“],ล“typeล“:ล“strล“}", + "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{ล“baseClassesล“:[ล“objectล“,ล“Recordล“,ล“strล“,ล“Textล“],ล“dataTypeล“:ล“ChatInputล“,ล“idล“:ล“ChatInput-P3fgLล“}-Prompt-uxBqP{ล“fieldNameล“:ล“user_inputล“,ล“idล“:ล“Prompt-uxBqPล“,ล“inputTypesล“:[ล“Documentล“,ล“BaseOutputParserล“,ล“Recordล“,ล“Textล“],ล“typeล“:ล“strล“}" + } + ], + "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 (Ahoy World!)", + "last_tested_version": "1.0.0a4", + "is_component": false +} \ No newline at end of file diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Basic Prompting.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Basic Prompting.json deleted file mode 100644 index 9903a3ea0..000000000 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Basic Prompting.json +++ /dev/null @@ -1,1134 +0,0 @@ -{ - "id": "4cd8b920-a12d-4d98-b4e5-75b537f03316", - "icon": "๐Ÿ“", - "icon_bg_color": "#FFD700", - "data": { - "nodes": [ - { - "id": "Prompt-nzvwK", - "type": "genericNode", - "position": { - "x": 53.588791333410654, - "y": -105.1077989256227 - }, - "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": "Extract key phrases from the customer reviews provided below. Focus on {sentiment} phrases that seem to capture the essence of the customers' opinions about their experience. Output these phrases in a list format.\n\nCustomer Reviews:\n{reviews}\n\nKey Phrases:\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", - "reviews": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "reviews", - "display_name": "reviews", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "sentiment": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sentiment", - "display_name": "sentiment", - "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": [ - "str", - "Text", - "object" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "sentiment", - "reviews" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-nzvwK", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 477, - "dragging": false, - "positionAbsolute": { - "x": 53.588791333410654, - "y": -105.1077989256227 - } - }, - { - "id": "OpenAIModel-u4NL8", - "type": "genericNode", - "position": { - "x": 642.6764379749295, - "y": 1.4849847595449148 - }, - "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\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\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\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\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 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=openai_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-1106", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-4-0125-preview", - "gpt-4-1106-preview", - "gpt-4-vision-preview", - "gpt-3.5-turbo-0125", - "gpt-3.5-turbo-1106" - ], - "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": [ - "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-u4NL8", - "description": "Generates text using OpenAI LLMs.", - "display_name": "OpenAI" - }, - "selected": true, - "width": 384, - "height": 561, - "positionAbsolute": { - "x": 642.6764379749295, - "y": 1.4849847595449148 - }, - "dragging": false - }, - { - "id": "ChatOutput-stoNg", - "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 Interaction Panel.\"\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(\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,\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 Interaction Panel.", - "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-stoNg" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 1193.250417197867, - "y": 71.88476890163852 - }, - "dragging": false - }, - { - "id": "TextOutput-prj03", - "type": "genericNode", - "position": { - "x": 640.7986738161987, - "y": 724.7095283934595 - }, - "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 Interaction Panel.\"\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 Interaction Panel.", - "icon": "type", - "base_classes": [ - "Text", - "object", - "str" - ], - "display_name": "Inspect Prompt", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-prj03", - "description": "Display a text output in the Interaction Panel.", - "display_name": "Inspect Prompt" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 640.7986738161987, - "y": 724.7095283934595 - }, - "dragging": false - }, - { - "id": "TextInput-ARFMe", - "type": "genericNode", - "position": { - "x": -496.0627119809512, - "y": 212.27354495043377 - }, - "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 Interaction Panel.\"\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": "negative", - "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": "{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": "Get text inputs from the Interaction Panel.", - "icon": "type", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "Focus Sentiment", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextInput-ARFMe", - "description": "Get text inputs from the Interaction Panel.", - "display_name": "Text Input" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": -496.0627119809512, - "y": 212.27354495043377 - }, - "dragging": false - }, - { - "id": "TextInput-oKkcQ", - "type": "genericNode", - "position": { - "x": -501.8114356733282, - "y": -152.0266822138945 - }, - "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 Interaction Panel.\"\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": "\"Absolutely love this new coffee machine! It brews quickly and the coffee tastes amazing every time. However, the setup process was a bit complicated.\" \"Iโ€™m not happy with the battery life of the smartphone I purchased. It barely lasts half a day. On the plus side, the camera quality is exceptional.\"", - "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": "{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": "Get text inputs from the Interaction Panel.", - "icon": "type", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "Reviews", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextInput-oKkcQ", - "description": "Get text inputs from the Interaction Panel.", - "display_name": "Text Input" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": -501.8114356733282, - "y": -152.0266822138945 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "Prompt-nzvwK", - "sourceHandle": "{ล“baseClassesล“:[ล“strล“,ล“Textล“,ล“objectล“],ล“dataTypeล“:ล“Promptล“,ล“idล“:ล“Prompt-nzvwKล“}", - "target": "OpenAIModel-u4NL8", - "targetHandle": "{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“OpenAIModel-u4NL8ล“,ล“inputTypesล“:[ล“Textล“],ล“typeล“:ล“strล“}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-u4NL8", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "Prompt", - "id": "Prompt-nzvwK" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-nzvwK{ล“baseClassesล“:[ล“strล“,ล“Textล“,ล“objectล“],ล“dataTypeล“:ล“Promptล“,ล“idล“:ล“Prompt-nzvwKล“}-OpenAIModel-u4NL8{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“OpenAIModel-u4NL8ล“,ล“inputTypesล“:[ล“Textล“],ล“typeล“:ล“strล“}" - }, - { - "source": "TextInput-ARFMe", - "sourceHandle": "{ล“baseClassesล“:[ล“objectล“,ล“Textล“,ล“strล“],ล“dataTypeล“:ล“TextInputล“,ล“idล“:ล“TextInput-ARFMeล“}", - "target": "Prompt-nzvwK", - "targetHandle": "{ล“fieldNameล“:ล“sentimentล“,ล“idล“:ล“Prompt-nzvwKล“,ล“inputTypesล“:[ล“Documentล“,ล“BaseOutputParserล“,ล“Recordล“,ล“Textล“],ล“typeล“:ล“strล“}", - 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"data": { - "targetHandle": { - "fieldName": "reviews", - "id": "Prompt-nzvwK", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "TextInput", - "id": "TextInput-oKkcQ" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-TextInput-oKkcQ{ล“baseClassesล“:[ล“objectล“,ล“Textล“,ล“strล“],ล“dataTypeล“:ล“TextInputล“,ล“idล“:ล“TextInput-oKkcQล“}-Prompt-nzvwK{ล“fieldNameล“:ล“reviewsล“,ล“idล“:ล“Prompt-nzvwKล“,ล“inputTypesล“:[ล“Documentล“,ล“BaseOutputParserล“,ล“Recordล“,ล“Textล“],ล“typeล“:ล“strล“}" - }, - { - "source": "Prompt-nzvwK", - "sourceHandle": "{ล“baseClassesล“:[ล“strล“,ล“Textล“,ล“objectล“],ล“dataTypeล“:ล“Promptล“,ล“idล“:ล“Prompt-nzvwKล“}", - "target": "TextOutput-prj03", - "targetHandle": "{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“TextOutput-prj03ล“,ล“inputTypesล“:[ล“Recordล“,ล“Textล“],ล“typeล“:ล“strล“}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-prj03", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "Prompt", - "id": "Prompt-nzvwK" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-nzvwK{ล“baseClassesล“:[ล“strล“,ล“Textล“,ล“objectล“],ล“dataTypeล“:ล“Promptล“,ล“idล“:ล“Prompt-nzvwKล“}-TextOutput-prj03{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“TextOutput-prj03ล“,ล“inputTypesล“:[ล“Recordล“,ล“Textล“],ล“typeล“:ล“strล“}" - }, - { - "source": "OpenAIModel-u4NL8", - "sourceHandle": "{ล“baseClassesล“:[ล“objectล“,ล“Textล“,ล“strล“],ล“dataTypeล“:ล“OpenAIModelล“,ล“idล“:ล“OpenAIModel-u4NL8ล“}", - "target": "ChatOutput-stoNg", - "targetHandle": "{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“ChatOutput-stoNgล“,ล“inputTypesล“:[ล“Textล“],ล“typeล“:ล“strล“}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-stoNg", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-u4NL8" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-u4NL8{ล“baseClassesล“:[ล“objectล“,ล“Textล“,ล“strล“],ล“dataTypeล“:ล“OpenAIModelล“,ล“idล“:ล“OpenAIModel-u4NL8ล“}-ChatOutput-stoNg{ล“fieldNameล“:ล“input_valueล“,ล“idล“:ล“ChatOutput-stoNgล“,ล“inputTypesล“:[ล“Textล“],ล“typeล“:ล“strล“}" - } - ], - "viewport": { - "x": 319.9710437562943, - "y": 143.9251365088694, - "zoom": 0.5282102678500032 - } - }, - "description": "Use a language model to generate text based on a prompt. \n\nIn this project, you'll be able to generate text based on a request and some topics.\n\nThe Topic 1 and Topic 2 components are actually Text Input, while the Prompt Output component is a Text Output. Changing the name of the component makes them easier to identify when interacting with them.", - "name": "Basic Prompting", - "last_tested_version": "1.0.0a0", - "is_component": false -} \ No newline at end of file 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 new file mode 100644 index 000000000..5706a0fbf --- /dev/null +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -0,0 +1,3403 @@ +{ + "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 Interaction Panel.\"\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(\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 Interaction Panel.", + "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": "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 Interaction Panel.\"\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 Interaction Panel.", + "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 + }, + { + "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\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\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=openai_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 + }, + { + "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\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\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\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\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 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=openai_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-preview", + "gpt-3.5-turbo", + "gpt-4-0125-preview", + "gpt-4-1106-preview", + "gpt-4-vision-preview", + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-1106" + ], + "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 + }, + { + "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" + } + }, + "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 + }, + { + "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 Interaction Panel.\"\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(\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,\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 Interaction Panel.", + "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 + }, + { + "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" + ], + "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", + "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 + }, + { + "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 + }, + { + "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 โ€œSyncโ€, โ€œAsyncโ€, or โ€œOffโ€.\",\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 โ€œSyncโ€, โ€œAsyncโ€, or โ€œOffโ€.", + "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 + } + }, + { + "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\n\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 โ€œSyncโ€, โ€œAsyncโ€, or โ€œOffโ€.\",\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 = \"Async\",\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 ) -> VectorStore:\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": "Async", + "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 โ€œSyncโ€, โ€œAsyncโ€, or โ€œOffโ€.", + "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\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\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=openai_api_key,\n 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You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", + "name": "Vector Store RAG", + "last_tested_version": "1.0.0a0", + "is_component": false +} \ No newline at end of file diff --git a/src/backend/base/langflow/interface/custom/directory_reader/directory_reader.py b/src/backend/base/langflow/interface/custom/directory_reader/directory_reader.py index 9ffb9825d..e9f3f6ceb 100644 --- a/src/backend/base/langflow/interface/custom/directory_reader/directory_reader.py +++ b/src/backend/base/langflow/interface/custom/directory_reader/directory_reader.py @@ -3,9 +3,10 @@ import os import zlib from pathlib import Path -from langflow.interface.custom.custom_component import CustomComponent from loguru import logger +from langflow.interface.custom.custom_component import CustomComponent + class CustomComponentPathValueError(ValueError): pass @@ -106,8 +107,15 @@ class DirectoryReader: """ if not os.path.isfile(file_path): return None - with open(file_path, "r") as file: - return file.read() + with open(file_path, "r", encoding="utf-8") as file: + # UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 3069: character maps to + try: + return file.read() + except UnicodeDecodeError: + # This is happening in Windows, so we need to open the file in binary mode + # The file is always just a python file, so we can safely read it as utf-8 + with open(file_path, "rb") as file: + return file.read().decode("utf-8") def get_files(self): """ @@ -198,7 +206,12 @@ class DirectoryReader: Process a file by validating its content and returning the result and content/error message. """ - file_content = self.read_file_content(file_path) + try: + file_content = self.read_file_content(file_path) + except Exception as exc: + logger.exception(exc) + logger.error(f"Error while reading file {file_path}: {str(exc)}") + return False, f"Could not read {file_path}" if file_content is None: return False, f"Could not read {file_path}" @@ -233,7 +246,7 @@ class DirectoryReader: filename = os.path.basename(file_path) validation_result, result_content = self.process_file(file_path) if not validation_result: - logger.error(f"Error while processing file {file_path}: {result_content}") + logger.error(f"Error while processing file {file_path}") menu_result = self.find_menu(response, menu_name) or { "name": menu_name, diff --git a/src/backend/base/langflow/utils/validate.py b/src/backend/base/langflow/utils/validate.py index b5363dc8e..0871dbd82 100644 --- a/src/backend/base/langflow/utils/validate.py +++ b/src/backend/base/langflow/utils/validate.py @@ -203,10 +203,8 @@ def prepare_global_scope(code, module): imported_module = importlib.import_module(node.module) for alias in node.names: exec_globals[alias.name] = getattr(imported_module, alias.name) - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - f"Module {node.module} not found. Please install it and try again. Error: {repr(e)}" - ) + except ModuleNotFoundError: + raise ModuleNotFoundError(f"Module {node.module} not found. Please install it and try again") return exec_globals diff --git a/src/backend/base/poetry.lock b/src/backend/base/poetry.lock index 2d5c03cba..a0d213615 100644 --- a/src/backend/base/poetry.lock +++ b/src/backend/base/poetry.lock @@ -1186,6 +1186,20 @@ six = ">=1.9.0" gmpy = ["gmpy"] gmpy2 = ["gmpy2"] +[[package]] +name = "emoji" +version = "2.11.0" +description = "Emoji for Python" +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7" +files = [ + {file = "emoji-2.11.0-py2.py3-none-any.whl", hash = "sha256:63fc9107f06c6c2e48e5078ce9575cef98518f5ac09474f6148a43e989989582"}, + {file = "emoji-2.11.0.tar.gz", hash = "sha256:772eaa30f4e0b1ce95148a092df4c7dc97644532c03225326b0fd05e8a9f72a3"}, +] + +[package.extras] +dev = ["coverage", "coveralls", "pytest"] + [[package]] name = "exceptiongroup" version = "1.2.0" @@ -2487,13 +2501,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15. 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+++ b/src/frontend/src/components/cardComponent/index.tsx @@ -130,7 +130,7 @@ export default function CollectionCardComponent({ return ( diff --git a/src/frontend/src/modals/NewFlowModal/components/undrawCards/index.tsx b/src/frontend/src/modals/NewFlowModal/components/undrawCards/index.tsx index 010a1815d..be2c35321 100644 --- a/src/frontend/src/modals/NewFlowModal/components/undrawCards/index.tsx +++ b/src/frontend/src/modals/NewFlowModal/components/undrawCards/index.tsx @@ -43,7 +43,7 @@ export default function UndrawCardComponent({ }} /> ); - case "Basic Prompting": + case "Basic Prompting (Ahoy World!)": return ( ); - case "Prompt Chaining": + case "Vector Store RAG": return ( { return ; })} */} - {examples.find((e) => e.name == "Basic Prompting") && ( + {examples.find((e) => e.name == "Basic Prompting (Ahoy World!)") && ( e.name == "Basic Prompting")!} + flow={ + examples.find((e) => e.name == "Basic Prompting (Ahoy World!)")! + } /> )} {examples.find((e) => e.name == "Memory Chatbot") && ( @@ -52,18 +54,18 @@ export default function NewFlowModal({ flow={examples.find((e) => e.name == "Document QA")!} /> )} - {examples.find((e) => e.name == "Prompt Chaining") && ( - e.name == "Prompt Chaining")!} - /> - )} {examples.find((e) => e.name == "Blog Writer") && ( e.name == "Blog Writer")!} /> )} + {examples.find((e) => e.name == "Vector Store RAG") && ( + e.name == "Vector Store RAG")!} + /> + )}
diff --git a/tests/conftest.py b/tests/conftest.py index b890f9b6c..66ca01df2 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -10,10 +10,6 @@ import orjson import pytest from fastapi.testclient import TestClient from httpx import AsyncClient -from sqlmodel import Session, SQLModel, create_engine, select -from sqlmodel.pool import StaticPool -from typer.testing import CliRunner - from langflow.graph.graph.base import Graph from langflow.initial_setup.setup import STARTER_FOLDER_NAME from langflow.services.auth.utils import get_password_hash @@ -22,6 +18,9 @@ from langflow.services.database.models.flow.model import Flow, FlowCreate from langflow.services.database.models.user.model import User, UserCreate from langflow.services.database.utils import session_getter from langflow.services.deps import get_db_service +from sqlmodel import Session, SQLModel, create_engine, select +from sqlmodel.pool import StaticPool +from typer.testing import CliRunner if TYPE_CHECKING: from langflow.services.database.service import DatabaseService @@ -381,7 +380,7 @@ def get_starter_project(active_user): # once the client is created, we can get the starter project with session_getter(get_db_service()) as session: flow = session.exec( - select(Flow).where(Flow.folder == STARTER_FOLDER_NAME).where(Flow.name == "Basic Prompting") + select(Flow).where(Flow.folder == STARTER_FOLDER_NAME).where(Flow.name == "Basic Prompting (Ahoy World!)") ).first() if not flow: raise ValueError("No starter project found")