diff --git a/.env.example b/.env.example index b3e592bdf..0da27dd1b 100644 --- a/.env.example +++ b/.env.example @@ -63,3 +63,15 @@ LANGFLOW_SUPERUSER= # Superuser password # Example: LANGFLOW_SUPERUSER_PASSWORD=123456 LANGFLOW_SUPERUSER_PASSWORD= + +# STORE_URL +# Example: LANGFLOW_STORE_URL=https://api.langflow.store +LANGFLOW_STORE_URL= + +# DOWNLOAD_WEBHOOK_URL +# +LANGFLOW_DOWNLOAD_WEBHOOK_URL= + +# LIKE_WEBHOOK_URL +# +LANGFLOW_LIKE_WEBHOOK_URL= \ No newline at end of file diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index f32cbfc6b..58d953e9e 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -6,7 +6,7 @@ on: pull_request: env: - POETRY_VERSION: "1.4.0" + POETRY_VERSION: "1.7.0" jobs: lint: diff --git a/.gitignore b/.gitignore index 9b8ec1cc1..9f5f7314d 100644 --- a/.gitignore +++ b/.gitignore @@ -166,6 +166,7 @@ coverage.xml *.py,cover .hypothesis/ .pytest_cache/ +.testmondata* # Translations *.mo diff --git a/.vscode/launch.json b/.vscode/launch.json index bb61b0b9e..a8229b155 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -16,7 +16,8 @@ "debug" ], "jinja": true, - "justMyCode": true + "justMyCode": true, + "envFile": "${workspaceFolder}/.env" }, { "name": "Python: Remote Attach", diff --git a/Dockerfile b/Dockerfile index 520c407de..346348c0a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -11,5 +11,5 @@ WORKDIR $HOME/app COPY --chown=user . $HOME/app -RUN pip install langflow>==0.0.86 -U --user +RUN pip install langflow>==0.5.0 -U --user CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"] diff --git a/Makefile b/Makefile index 685dae466..0ba2d5442 100644 --- a/Makefile +++ b/Makefile @@ -20,17 +20,16 @@ coverage: tests: @make install_backend - poetry run pytest tests + poetry run pytest tests --instafail format: - poetry run black . poetry run ruff . --fix + poetry run ruff format . cd src/frontend && npm run format lint: make install_backend poetry run mypy src/backend/langflow - poetry run black . --check poetry run ruff . --fix install_frontend: @@ -40,6 +39,7 @@ install_frontendc: cd src/frontend && rm -rf node_modules package-lock.json && npm install run_frontend: + @-kill -9 `lsof -t -i:3000` cd src/frontend && npm start run_cli: @@ -66,7 +66,14 @@ install_backend: backend: make install_backend - poetry run uvicorn --factory src.backend.langflow.main:create_app --port 7860 --reload --log-level debug + @-kill -9 `lsof -t -i:7860` +ifeq ($(login),1) + @echo "Running backend without autologin"; + poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser --env-file .env +else + @echo "Running backend with autologin"; + LANGFLOW_AUTO_LOGIN=True poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser --env-file .env +endif build_and_run: echo 'Removing dist folder' diff --git a/README.md b/README.md index d3421ecc6..068a9bd47 100644 --- a/README.md +++ b/README.md @@ -114,6 +114,10 @@ Each option is detailed below: - `--remove-api-keys/--no-remove-api-keys`: Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`. - `--install-completion [bash|zsh|fish|powershell|pwsh]`: Installs completion for the specified shell. - `--show-completion [bash|zsh|fish|powershell|pwsh]`: Shows completion for the specified shell, allowing you to copy it or customize the installation. +- `--backend-only`: This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable. +- `store`: This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable. + +These parameters are important for users who need to customize the behavior of Langflow, especially in development or specialized deployment scenarios. You may want to update the documentation to include these parameters for completeness and clarity. ### Environment Variables @@ -143,7 +147,7 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google # 🎨 Creating Flows -Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://langchain.readthedocs.io/en/latest/reference.html) to choose from, including LLMs, prompt serializers, agents, and chains. +Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://python.langchain.com/docs/integrations/components) to choose from, including LLMs, prompt serializers, agents, and chains. Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow. diff --git a/base.Dockerfile b/base.Dockerfile index c76bbbcda..2293c35dd 100644 --- a/base.Dockerfile +++ b/base.Dockerfile @@ -23,7 +23,7 @@ ENV PYTHONUNBUFFERED=1 \ \ # poetry # https://python-poetry.org/docs/configuration/#using-environment-variables - POETRY_VERSION=1.5.1 \ + POETRY_VERSION=1.7.1 \ # make poetry install to this location POETRY_HOME="/opt/poetry" \ # make poetry create the virtual environment in the project's root diff --git a/deploy/docker-compose.with_tests.yml b/deploy/docker-compose.with_tests.yml index c16c14197..82da7eb49 100644 --- a/deploy/docker-compose.with_tests.yml +++ b/deploy/docker-compose.with_tests.yml @@ -146,7 +146,7 @@ services: build: context: ../ dockerfile: base.Dockerfile - command: celery -A langflow.worker.celery_app worker --loglevel=INFO --concurrency=1 -n lf-worker@%h + command: celery -A langflow.worker.celery_app worker --loglevel=DEBUG --concurrency=1 -n lf-worker@%h healthcheck: test: "exit 0" deploy: diff --git a/deploy/docker-compose.yml b/deploy/docker-compose.yml index 4eda0dc37..201f9bc97 100644 --- a/deploy/docker-compose.yml +++ b/deploy/docker-compose.yml @@ -134,8 +134,8 @@ services: image: redis:6.2.5 env_file: - .env - # ports: - # - 6379:6379 + ports: + - 6379:6379 healthcheck: test: "exit 0" @@ -146,7 +146,7 @@ services: build: context: ../ dockerfile: base.Dockerfile - command: celery -A langflow.worker.celery_app worker --loglevel=INFO --concurrency=1 -n lf-worker@%h + command: celery -A langflow.worker.celery_app worker --loglevel=INFO --concurrency=1 -n lf-worker@%h -P eventlet healthcheck: test: "exit 0" deploy: diff --git a/docker_example/Dockerfile b/docker_example/Dockerfile index 1b713c3a0..346348c0a 100644 --- a/docker_example/Dockerfile +++ b/docker_example/Dockerfile @@ -1,14 +1,15 @@ FROM python:3.10-slim -RUN apt-get update && apt-get install gcc g++ git make -y +RUN apt-get update && apt-get install gcc g++ git make -y && apt-get clean \ + && rm -rf /var/lib/apt/lists/* RUN useradd -m -u 1000 user USER user ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH + PATH=/home/user/.local/bin:$PATH WORKDIR $HOME/app COPY --chown=user . $HOME/app -RUN pip install langflow>==0.0.71 -U --user -CMD ["langflow", "--host", "0.0.0.0", "--port", "7860"] +RUN pip install langflow>==0.5.0 -U --user +CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"] diff --git a/docker_example/docker-compose.yml b/docker_example/docker-compose.yml index ffb033104..da68b4471 100644 --- a/docker_example/docker-compose.yml +++ b/docker_example/docker-compose.yml @@ -7,4 +7,4 @@ services: dockerfile: Dockerfile ports: - "7860:7860" - command: langflow --host 0.0.0.0 + command: langflow run --host 0.0.0.0 diff --git a/docs/docs/components/custom.mdx b/docs/docs/components/custom.mdx index 90282a73e..382c40cc4 100644 --- a/docs/docs/components/custom.mdx +++ b/docs/docs/components/custom.mdx @@ -56,7 +56,7 @@ The CustomComponent class serves as the foundation for creating custom component - **build_config**: Used to define the configuration fields of the component (if applicable). It should always return a dictionary with specific keys representing the field names and corresponding configurations. This method is called when the code is processed (i.e., when you click _Check and Save_ in the code editor). It must follow the format described below: - Top-level keys are field names. - - Their values are also of type _`dict`_. They specify the behavior of the generated fields. + - Their values are can be of type _`langflow.field_typing.TemplateField`_ or _`dict`_. They specify the behavior of the generated fields. Below are the available keys used to configure component fields: @@ -73,7 +73,7 @@ The CustomComponent class serves as the foundation for creating custom component | _`required: bool`_ | Makes the field required. | | _`info: str`_ | Adds a tooltip to the field. | | _`file_types: List[str]`_ | This is a requirement if the _`field_type`_ is _file_. Defines which file types will be accepted. For example, _json_, _yaml_ or _yml_. | - + | _`range_spec: langflow.field_typing.RangeSpec`_ | This is a requirement if the _`field_type`_ is _`float`_. Defines the range of values accepted and the step size. If none is defined, the default is _`[-1, 1, 0.1]`_. | - The CustomComponent class also provides helpful methods for specific tasks (e.g., to load and use other flows from the Langflow platform): | Method Name | Description | diff --git a/docs/docs/components/embeddings.mdx b/docs/docs/components/embeddings.mdx index 5b134f08d..1b7ca1dbe 100644 --- a/docs/docs/components/embeddings.mdx +++ b/docs/docs/components/embeddings.mdx @@ -12,6 +12,22 @@ Embeddings are vector representations of text that capture the semantic meaning --- +### BedrockEmbeddings + +Used to load [Amazon Bedrocks’s](https://aws.amazon.com/bedrock/) embedding models. + +**Params** + +- **credentials_profile_name:** The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which has either access keys or role information specified. If not specified, the default credential profile or, if on an EC2 instance, credentials from IMDS will be used. See [the AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html) for more details. + +- **model_id:** Id of the model to call, e.g., amazon.titan-embed-text-v1, this is equivalent to the modelId property in the list-foundation-models api. + +- **endpoint_url:** Needed if you don’t want to default to us-east-1 endpoint. + +- **region_name:** The aws region e.g., us-west-2. Fallsback to AWS_DEFAULT_REGION env variable or region specified in ~/.aws/config in case it is not provided here. + +--- + ### CohereEmbeddings Used to load [Cohere’s](https://cohere.com/) embedding models. diff --git a/docs/docs/guidelines/components.mdx b/docs/docs/guidelines/components.mdx index b7dadcfce..7188bf1e0 100644 --- a/docs/docs/guidelines/components.mdx +++ b/docs/docs/guidelines/components.mdx @@ -5,7 +5,7 @@ import ReactPlayer from "react-player"; # Component -Components are the building blocks of the flows. They are made of inputs, outputs, and parameters that define their functionality, providing a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work in the LangChain [documentation](https://docs.langchain.com/docs/category/components) section. +Components are the building blocks of the flows. They are made of inputs, outputs, and parameters that define their functionality, providing a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work in the LangChain [documentation](https://python.langchain.com/docs/integrations/components) section. ### Component's Features diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index 617aec3d0..538180ccd 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -31,7 +31,7 @@ module.exports = { [ remarkCodeHike, { - theme: "github-light", + theme: "github-dark", showCopyButton: true, lineNumbers: true, }, @@ -112,8 +112,10 @@ module.exports = { }, colorMode: { defaultMode: "light", - disableSwitch: true, - respectPrefersColorScheme: false, + /* Allow users to chose light or dark mode. */ + disableSwitch: false, + /* Respect user preferences, such as low light mode in the evening */ + respectPrefersColorScheme: true, }, announcementBar: { content: diff --git a/docs/package-lock.json b/docs/package-lock.json index 2cc1ec39b..fabe5941f 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -10,11 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Mastered.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-m2yFu\",\"type\":\"genericNode\",\"position\":{\"x\":462.6456058272081,\"y\":1033.9314297130313},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"transcription\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"create 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[\\\"str\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"TextLoader\\\",\\n \\\"name\\\": \\\"Text (.txt)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.TextLoader\\\",\\n \\\"defaultFor\\\": [\\\"txt\\\"],\\n \\\"allowdTypes\\\": [\\\"txt\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredEmailLoader\\\",\\n \\\"name\\\": \\\"Unstructured Email (.eml)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredEmailLoader\\\",\\n \\\"defaultFor\\\": [\\\"eml\\\"],\\n \\\"allowdTypes\\\": [\\\"eml\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredHTMLLoader\\\",\\n \\\"name\\\": \\\"Unstructured HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredHTMLLoader\\\",\\n \\\"defaultFor\\\": [\\\"html\\\", \\\"htm\\\"],\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredMarkdownLoader\\\",\\n \\\"name\\\": \\\"Unstructured Markdown (.md)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredMarkdownLoader\\\",\\n \\\"defaultFor\\\": [\\\"md\\\"],\\n \\\"allowdTypes\\\": [\\\"md\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredPowerPointLoader\\\",\\n \\\"name\\\": \\\"Unstructured PowerPoint (.pptx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredPowerPointLoader\\\",\\n \\\"defaultFor\\\": [\\\"pptx\\\"],\\n \\\"allowdTypes\\\": [\\\"pptx\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredWordLoader\\\",\\n \\\"name\\\": \\\"Unstructured Word (.docx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredWordLoader\\\",\\n \\\"defaultFor\\\": [\\\"docx\\\"],\\n \\\"allowdTypes\\\": [\\\"docx\\\"],\\n },\\n]\\n\\n\\n def build_config(self):\\n loader_options = [\\\"Automatic\\\"] + [\\n loader_info[\\\"name\\\"] for loader_info in self.loaders_info\\n ]\\n\\n file_types = []\\n suffixes = []\\n\\n for loader_info in self.loaders_info:\\n if \\\"allowedTypes\\\" in loader_info:\\n file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n return {\\n \\\"directory_path\\\": {\\n \\\"display_name\\\": \\\"Directory Path\\\",\\n \\\"required\\\": True,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": loader_options,\\n \\\"value\\\": \\\"Automatic\\\",\\n },\\n }\\n\\n def build(self, directory_path: str, loader: str) -> Document:\\n # Verifique se o diretório existe\\n if not os.path.exists(directory_path):\\n raise ValueError(f\\\"Directory not found: {directory_path}\\\")\\n\\n files = glob.glob(directory_path + \\\"/*.*\\\")\\n\\n\\n loader_info = None\\n if loader == \\\"Automatic\\\":\\n for file in files:\\n file_type = file.split(\\\".\\\")[-1]\\n\\n\\n for info in self.loaders_info:\\n if \\\"defaultFor\\\" in info:\\n if file_type in info[\\\"defaultFor\\\"]:\\n loader_info = info\\n break\\n if loader_info:\\n break\\n\\n if not loader_info:\\n raise ValueError(\\n \\\"No default loader found for any file in the directory\\\"\\n )\\n\\n else:\\n for info in self.loaders_info:\\n if info[\\\"name\\\"] == loader:\\n loader_info = info\\n break\\n\\n if not loader_info:\\n raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n loader_import = loader_info[\\\"import\\\"]\\n module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n try:\\n # Importe o loader dinamicamente\\n loader_module = __import__(module_name, fromlist=[class_name])\\n loader_instance = getattr(loader_module, class_name)\\n except ImportError as e:\\n raise ValueError(\\n f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{loader_info}\\\"\\n ) from e\\n\\n results = []\\n for file in files:\\n file_path = os.path.join(directory_path, file)\\n kwargs = loader_info.get(\\\"kwargs\\\", {})\\n result = loader_instance(file_path=file_path, **kwargs).load()\\n results.append(result)\\n self.status = results\\n return results\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"directory_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"directory_path\",\"display_name\":\"Directory Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"/Users/ogabrielluiz/Projects/langflow2/docs\"},\"loader\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true}},\"description\":\"Generic File Loader\",\"base_classes\":[\"Document\"],\"display_name\":\"Directory Loader\",\"custom_fields\":{\"directory_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Ip6tG\"},\"id\":\"CustomComponent-Ip6tG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:38.570303\",\"folder\":null,\"id\":\"5fe4debc-b6a7-45d4-a694-c02d8aa93b08\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Whisper Transcriber\",\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, List, Dict, Union\\nfrom langflow.field_typing import (\\n AgentExecutor,\\n BaseChatMemory,\\n BaseLanguageModel,\\n BaseLLM,\\n BaseLoader,\\n BaseMemory,\\n BaseOutputParser,\\n BasePromptTemplate,\\n BaseRetriever,\\n Callable,\\n Chain,\\n ChatPromptTemplate,\\n Data,\\n Document,\\n Embeddings,\\n NestedDict,\\n Object,\\n PromptTemplate,\\n TextSplitter,\\n Tool,\\n VectorStore,\\n)\\n\\nfrom openai import OpenAI\\nclass Component(CustomComponent):\\n display_name: str = \\\"Whisper Transcriber\\\"\\n description: str = \\\"Converts audio to text using OpenAI's Whisper.\\\"\\n\\n def build_config(self):\\n return {\\\"audio\\\":{\\\"field_type\\\":\\\"file\\\",\\\"suffixes\\\":[\\\".mp3\\\", \\\".mp4\\\", \\\".m4a\\\"]},\\\"OpenAIKey\\\":{\\\"field_type\\\":\\\"str\\\",\\\"password\\\":True}}\\n\\n def build(self, audio:str, OpenAIKey:str) -> str:\\n \\n # TODO: if output path, persist & load it instead\\n \\n client = OpenAI(api_key=OpenAIKey)\\n \\n audio_file= open(audio, \\\"rb\\\")\\n transcript = client.audio.transcriptions.create(\\n model=\\\"whisper-1\\\", \\n file=audio_file,\\n response_format=\\\"text\\\"\\n )\\n \\n \\n self.status = transcript\\n return transcript\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"OpenAIKey\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"OpenAIKey\",\"display_name\":\"OpenAIKey\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"audio\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"suffixes\":[\".mp3\",\".mp4\",\".m4a\"],\"password\":false,\"name\":\"audio\",\"display_name\":\"audio\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"file_path\":\"/Users/rodrigonader/Library/Caches/langflow/19b3e1c9-90bf-405f-898a-e982f47adf76/a3308ce7e9b10088fcd985aabb6d17b012c6b6e81ff8806356574474c9d10229.m4a\",\"value\":\"Audio Recording 2023-11-29 at 12.12.04.m4a\"}},\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"base_classes\":[\"str\"],\"display_name\":\"Whisper Transcriber\",\"custom_fields\":{\"OpenAIKey\":null,\"audio\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-GJRrs\"},\"id\":\"CustomComponent-GJRrs\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:39.710502\",\"folder\":null,\"id\":\"bd9e911d-46bd-429f-aa75-dd740430695e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Youtube Conversation\",\"description\":\"Craft Meaningful Interactions, Generate Value.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":589,\"id\":\"CustomComponent-h0NSI\",\"type\":\"genericNode\",\"position\":{\"x\":714.9531293402755,\"y\":0.4994374160582993},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import requests\\nfrom langflow import CustomComponent\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.schema import Document\\nfrom langchain.document_loaders import YoutubeLoader\\n\\n# Example usage:\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"YouTube Transcript\\\"\\n description: str = \\\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\\\"\\n\\n def build_config(self):\\n return {\\\"url\\\": {\\\"multiline\\\": True, \\\"required\\\": True}}\\n\\n def build(self, url: str, llm: BaseLLM, dependencies: Document, language: str = \\\"en\\\") -> Document:\\n dependencies\\n response = requests.get(url)\\n loader = YoutubeLoader.from_youtube_url(url, add_video_info=True, language=[language])\\n result = loader.load()\\n self.repr_value = str(result)\\n return result\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"dependencies\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"dependencies\",\"display_name\":\"dependencies\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"language\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"language\",\"display_name\":\"language\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\",\"base_classes\":[\"Document\"],\"display_name\":\"YouTube Transcript\",\"custom_fields\":{\"dependencies\":null,\"language\":null,\"llm\":null,\"url\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-h0NSI\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":714.9531293402755,\"y\":0.4994374160582993}},{\"width\":384,\"height\":629,\"id\":\"ChatOpenAI-q61Y2\",\"type\":\"genericNode\",\"position\":{\"x\":18,\"y\":625.5521045590924},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-16k\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k-0613\",\"gpt-3.5-turbo-16k\",\"gpt-4-0613\",\"gpt-4-32k-0613\",\"gpt-4\",\"gpt-4-32k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-q61Y2\"},\"selected\":false,\"positionAbsolute\":{\"x\":18,\"y\":625.5521045590924},\"dragging\":false},{\"width\":384,\"height\":539,\"id\":\"Chroma-gVhy7\",\"type\":\"genericNode\",\"position\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"data\":{\"type\":\"Chroma\",\"node\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.Client\",\"list\":false},\"client_settings\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client_settings\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.config.Setting\",\"list\":true},\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"chroma_server_cors_allow_origins\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Chroma Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"chroma_server_grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Chroma Server GRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Chroma Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_http_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_http_port\",\"display_name\":\"Chroma Server HTTP Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_ssl_enabled\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Chroma Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"collection_metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"collection_metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"collection_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"persist\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"persist\",\"display_name\":\"Persist\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"persist_directory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"persist_directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"_type\":\"Chroma\"},\"description\":\"Create a Chroma vectorstore from a raw documents.\",\"base_classes\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Chroma\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma\",\"beta\":false,\"error\":null},\"id\":\"Chroma-gVhy7\"},\"selected\":false,\"positionAbsolute\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"dragging\":false},{\"width\":384,\"height\":501,\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"type\":\"genericNode\",\"position\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"data\":{\"type\":\"RecursiveCharacterTextSplitter\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.utils.util import build_loader_repr_from_documents\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chunk_overlap\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"50\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"400\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"type\":\"int\",\"list\":false},\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"type\":\"Document\",\"list\":true},\"separators\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"type\":\"str\",\"list\":true,\"value\":[\" \"]}},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separators\":null},\"output_types\":[\"RecursiveCharacterTextSplitter\"],\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"beta\":true,\"error\":null},\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"selected\":false,\"positionAbsolute\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"dragging\":false},{\"width\":384,\"height\":367,\"id\":\"OpenAIEmbeddings-cduOO\",\"type\":\"genericNode\",\"position\":{\"x\":1405.1176670984544,\"y\":1029.459061269321},\"data\":{\"type\":\"OpenAIEmbeddings\",\"node\":{\"template\":{\"allowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[],\"password\":false,\"name\":\"allowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"disallowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"all\",\"password\":false,\"name\":\"disallowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"deployment\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"embedding_ctx_length\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":8191,\"password\":false,\"name\":\"embedding_ctx_length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"{'Authorization': 'Bearer '}\",\"password\":false,\"name\":\"headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"model\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_api_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_version\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"show_progress_bar\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"show_progress_bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"skip_empty\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"skip_empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"_type\":\"OpenAIEmbeddings\"},\"description\":\"OpenAI embedding models.\",\"base_classes\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"display_name\":\"OpenAIEmbeddings\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"OpenAIEmbeddings-cduOO\"},\"selected\":false,\"positionAbsolute\":{\"x\":1405.1176670984544,\"y\":1029.459061269321}},{\"width\":384,\"height\":339,\"id\":\"RetrievalQA-4PmTB\",\"type\":\"genericNode\",\"position\":{\"x\":2711.7786966415715,\"y\":709.4450828605463},\"data\":{\"type\":\"RetrievalQA\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"combine_documents_chain\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"combine_documents_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseCombineDocumentsChain\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"list\":false},\"input_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"query\",\"password\":false,\"name\":\"input_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"output_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"result\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"return_source_documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_source_documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"RetrievalQA\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"RetrievalQA\",\"Chain\",\"BaseRetrievalQA\",\"function\"],\"display_name\":\"RetrievalQA\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/vector_db_qa\",\"beta\":false,\"error\":null},\"id\":\"RetrievalQA-4PmTB\"},\"selected\":false,\"positionAbsolute\":{\"x\":2711.7786966415715,\"y\":709.4450828605463}},{\"width\":384,\"height\":333,\"id\":\"CombineDocsChain-yk0JF\",\"type\":\"genericNode\",\"position\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"data\":{\"type\":\"CombineDocsChain\",\"node\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"chain_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"BaseCombineDocumentsChain\",\"function\"],\"display_name\":\"CombineDocsChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"id\":\"CombineDocsChain-yk0JF\"},\"selected\":false,\"positionAbsolute\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"dragging\":false},{\"width\":384,\"height\":417,\"id\":\"CustomComponent-y6Wg0\",\"type\":\"genericNode\",\"position\":{\"x\":39.400034102832365,\"y\":-499.03551482195707},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nimport subprocess\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\nfrom typing import List\\n\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"PIP Install\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n\\n def build(self, libs: List[str]) -> Document:\\n def install_package(package_name):\\n subprocess.check_call([\\\"pip\\\", \\\"install\\\", package_name])\\n for lib in libs:\\n install_package(lib)\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"libs\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"libs\",\"display_name\":\"libs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"requests\",\"pytube\"]}},\"description\":\"Create any custom component you want!\",\"base_classes\":[\"Document\"],\"display_name\":\"PIP Install\",\"custom_fields\":{\"libs\":null},\"output_types\":[],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-y6Wg0\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":39.400034102832365,\"y\":-499.03551482195707}}],\"edges\":[{\"source\":\"ChatOpenAI-q61Y2\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CustomComponent-h0NSI{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"BaseLLM\"}}},{\"source\":\"CustomComponent-y6Wg0\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CustomComponent-y6Wg0{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}-CustomComponent-h0NSI{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-y6Wg0\"},\"targetHandle\":{\"fieldName\":\"dependencies\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"Document\"}}},{\"source\":\"CustomComponent-h0NSI\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-h0NSIœ}\",\"target\":\"RecursiveCharacterTextSplitter-1eaOX\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CustomComponent-h0NSI{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-h0NSIœ}-RecursiveCharacterTextSplitter-1eaOX{œfieldNameœ:œdocumentsœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-h0NSI\"},\"targetHandle\":{\"fieldName\":\"documents\",\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"inputTypes\":null,\"type\":\"Document\"}},\"selected\":false},{\"source\":\"OpenAIEmbeddings-cduOO\",\"sourceHandle\":\"{œbaseClassesœ:[œEmbeddingsœ,œOpenAIEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-cduOOœ}\",\"target\":\"Chroma-gVhy7\",\"targetHandle\":\"{œfieldNameœ:œembeddingœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-OpenAIEmbeddings-cduOO{œbaseClassesœ:[œEmbeddingsœ,œOpenAIEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-cduOOœ}-Chroma-gVhy7{œfieldNameœ:œembeddingœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"dataType\":\"OpenAIEmbeddings\",\"id\":\"OpenAIEmbeddings-cduOO\"},\"targetHandle\":{\"fieldName\":\"embedding\",\"id\":\"Chroma-gVhy7\",\"inputTypes\":null,\"type\":\"Embeddings\"}}},{\"source\":\"RecursiveCharacterTextSplitter-1eaOX\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ}\",\"target\":\"Chroma-gVhy7\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-RecursiveCharacterTextSplitter-1eaOX{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ}-Chroma-gVhy7{œfieldNameœ:œdocumentsœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"RecursiveCharacterTextSplitter\",\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"targetHandle\":{\"fieldName\":\"documents\",\"id\":\"Chroma-gVhy7\",\"inputTypes\":null,\"type\":\"Document\"}}},{\"source\":\"ChatOpenAI-q61Y2\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}\",\"target\":\"CombineDocsChain-yk0JF\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œCombineDocsChain-yk0JFœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CombineDocsChain-yk0JF{œfieldNameœ:œllmœ,œidœ:œCombineDocsChain-yk0JFœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CombineDocsChain-yk0JF\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"}}},{\"source\":\"CombineDocsChain-yk0JF\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CombineDocsChain-yk0JF{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}-RetrievalQA-4PmTB{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"BaseCombineDocumentsChain\",\"function\"],\"dataType\":\"CombineDocsChain\",\"id\":\"CombineDocsChain-yk0JF\"},\"targetHandle\":{\"fieldName\":\"combine_documents_chain\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseCombineDocumentsChain\"}}},{\"source\":\"Chroma-gVhy7\",\"sourceHandle\":\"{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-Chroma-gVhy7{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}-RetrievalQA-4PmTB{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"dataType\":\"Chroma\",\"id\":\"Chroma-gVhy7\"},\"targetHandle\":{\"fieldName\":\"retriever\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseRetriever\"}}}],\"viewport\":{\"x\":189.54413265004274,\"y\":259.89949657174975,\"zoom\":0.291027508374689}},\"is_component\":false,\"updated_at\":\"2023-12-04T22:59:08.830269\",\"folder\":null,\"id\":\"bc7eb94b-6fc6-49cb-9b19-35347ba51afa\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Web Scraper - Content & Links\",\"description\":\"Fetch and parse text and links from a given URL.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":338,\"id\":\"LLMChain-H7PBy\",\"type\":\"genericNode\",\"position\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-H7PBy\"},\"selected\":false,\"positionAbsolute\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"dragging\":false},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-VSAdc\",\"type\":\"genericNode\",\"position\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-VSAdc\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859}},{\"width\":384,\"height\":374,\"data\":{\"id\":\"GroupNode-WXPMk\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"give me links\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown 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{\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch 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is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser 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Conversation.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":338,\"id\":\"LLMChain-RQsU1\",\"type\":\"genericNode\",\"position\":{\"x\":514.4440482813261,\"y\":528.164086188516},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-RQsU1\"},\"selected\":false,\"positionAbsolute\":{\"x\":514.4440482813261,\"y\":528.164086188516}},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-NTTcv\",\"type\":\"genericNode\",\"position\":{\"x\":-221.33853765781578,\"y\":-35.48749923706055},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-NTTcv\"},\"selected\":false,\"positionAbsolute\":{\"x\":-221.33853765781578,\"y\":-35.48749923706055}},{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-VELMV\",\"type\":\"genericNode\",\"position\":{\"x\":-222,\"y\":682.560723386973},\"data\":{\"id\":\"PromptTemplate-VELMV\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-f6nOg\"},\"selected\":true,\"positionAbsolute\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"dragging\":false},{\"width\":384,\"height\":375,\"id\":\"CustomComponent-FGzJJ\",\"type\":\"genericNode\",\"position\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse 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LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, 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LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-cHel8\"},\"selected\":false,\"positionAbsolute\":{\"x\":547.3876889720291,\"y\":299.2057833881307},\"dragging\":false},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-LA6y0\",\"type\":\"genericNode\",\"position\":{\"x\":-272.94405331278074,\"y\":-603.148171441675},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-LA6y0\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":-272.94405331278074,\"y\":-603.148171441675}},{\"width\":384,\"height\":404,\"id\":\"CustomComponent-ZNoRM\",\"type\":\"genericNode\",\"position\":{\"x\":-103.65741264289085,\"y\":134.62332404764658},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-ZNoRM\"},\"selected\":false,\"positionAbsolute\":{\"x\":-103.65741264289085,\"y\":134.62332404764658},\"dragging\":false},{\"width\":384,\"height\":374,\"id\":\"CustomComponent-zNSTv\",\"type\":\"genericNode\",\"position\":{\"x\":-1233.3963469933499,\"y\":280.77850809220325},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-zNSTv\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":-1233.3963469933499,\"y\":280.77850809220325}},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-Ihm2o\",\"type\":\"genericNode\",\"position\":{\"x\":-554.9404152016002,\"y\":683.6763775860567},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse HTML\",\"custom_fields\":{\"html_content\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-Ihm2o\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":-554.9404152016002,\"y\":683.6763775860567}},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-6ST9l\",\"type\":\"genericNode\",\"position\":{\"x\":76.10921262566495,\"y\":872.9491114949525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract Hyperlinks\",\"custom_fields\":{\"soup\":null},\"output_types\":[\"list\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-6ST9l\"},\"selected\":false,\"positionAbsolute\":{\"x\":76.10921262566495,\"y\":872.9491114949525},\"dragging\":false},{\"width\":384,\"height\":654,\"id\":\"PromptTemplate-l35W1\",\"type\":\"genericNode\",\"position\":{\"x\":1461.4487148097069,\"y\":1090.896175351284},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"display_name\":\"output_parser\"},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"display_name\":\"input_types\"},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"display_name\":\"input_variables\"},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"display_name\":\"partial_variables\"},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"display_name\":\"template\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"display_name\":\"template_format\"},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"display_name\":\"validate_template\"},\"_type\":\"PromptTemplate\",\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_markdown\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_markdown\",\"display_name\":\"html_markdown\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_links\",\"display_name\":\"html_links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"query\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"name\":\"\",\"display_name\":\"PromptTemplate\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"custom_fields\":{\"\":[\"url\",\"html_markdown\",\"html_links\",\"query\"]},\"output_types\":[],\"full_path\":null,\"field_formatters\":{},\"beta\":false,\"error\":null},\"id\":\"PromptTemplate-l35W1\"},\"selected\":false,\"positionAbsolute\":{\"x\":1461.4487148097069,\"y\":1090.896175351284},\"dragging\":false},{\"width\":384,\"height\":346,\"id\":\"CustomComponent-iTLf4\",\"type\":\"genericNode\",\"position\":{\"x\":-1924.8908014123388,\"y\":704.5414990162741},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"https://paperswithcode.com/\"}},\"description\":null,\"base_classes\":[\"str\"],\"display_name\":\"URL Input\",\"custom_fields\":{\"url\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-iTLf4\"},\"selected\":false,\"positionAbsolute\":{\"x\":-1924.8908014123388,\"y\":704.5414990162741},\"dragging\":false},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-jWLUz\",\"type\":\"genericNode\",\"position\":{\"x\":627.6164007410564,\"y\":1201.2365384322047},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"items\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"items\",\"display_name\":\"items\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"list\",\"list\":true}},\"description\":\"Converts a Python list into Markdown bullet points.\",\"base_classes\":[\"str\"],\"display_name\":\"List to Bullets\",\"custom_fields\":{\"items\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-jWLUz\"},\"selected\":false,\"positionAbsolute\":{\"x\":627.6164007410564,\"y\":1201.2365384322047},\"dragging\":false}],\"edges\":[{\"source\":\"ChatOpenAI-LA6y0\",\"target\":\"LLMChain-cHel8\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseLanguageModelœ,œChatOpenAIœ,œBaseChatModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-LA6y0œ}\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œLLMChain-cHel8œ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"id\":\"reactflow__edge-ChatOpenAI-LA6y0{œbaseClassesœ:[œBaseLanguageModelœ,œChatOpenAIœ,œBaseChatModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-LA6y0œ}-LLMChain-cHel8{œfieldNameœ:œllmœ,œidœ:œLLMChain-cHel8œ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"LLMChain-cHel8\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"},\"sourceHandle\":{\"baseClasses\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-LA6y0\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 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local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-DXfcM\"},\"selected\":false,\"positionAbsolute\":{\"x\":-1110.8267574563533,\"y\":569.1107380883907},\"dragging\":false},{\"width\":384,\"height\":376,\"id\":\"PromptRunner-ckWMH\",\"type\":\"genericNode\",\"position\":{\"x\":-1149.4746387825978,\"y\":992.3970573758324},\"data\":{\"type\":\"PromptRunner\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\n\\nclass PromptRunner(CustomComponent):\\n display_name: str = \\\"Prompt Runner\\\"\\n description: str = \\\"Run a Chain with the given PromptTemplate\\\"\\n beta: bool = True\\n field_config = {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt Template\\\",\\n \\\"info\\\": \\\"Make sure the prompt has all variables filled.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}) -> Document:\\n chain = prompt | llm\\n # The input is an empty dict because the prompt is already filled\\n result = chain.invoke(input=inputs)\\n if hasattr(result, \\\"content\\\"):\\n result = result.content\\n self.repr_value = result\\n return Document(page_content=str(result))\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"inputs\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"inputs\",\"display_name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt Template\",\"advanced\":false,\"dynamic\":false,\"info\":\"Make sure the prompt has all variables filled.\",\"type\":\"PromptTemplate\",\"list\":false}},\"description\":\"Run a Chain with the given PromptTemplate\",\"base_classes\":[\"Document\"],\"display_name\":\"Prompt Runner\",\"custom_fields\":{\"inputs\":null,\"llm\":null,\"prompt\":null},\"output_types\":[\"PromptRunner\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"PromptRunner-ckWMH\"},\"selected\":false,\"positionAbsolute\":{\"x\":-1149.4746387825978,\"y\":992.3970573758324},\"dragging\":false}],\"edges\":[{\"source\":\"PromptRunner-ckWMH\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œPromptRunnerœ,œidœ:œPromptRunner-ckWMHœ}\",\"target\":\"PromptTemplate-K7xiS\",\"targetHandle\":\"{œfieldNameœ:œdasdasdœ,œidœ:œPromptTemplate-K7xiSœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ],œtypeœ:œstrœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"dasdasd\",\"id\":\"PromptTemplate-K7xiS\",\"inputTypes\":[\"Document\",\"BaseOutputParser\"],\"type\":\"str\"},\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"PromptRunner\",\"id\":\"PromptRunner-ckWMH\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-PromptRunner-ckWMH{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œPromptRunnerœ,œidœ:œPromptRunner-ckWMHœ}-PromptTemplate-K7xiS{œfieldNameœ:œdasdasdœ,œidœ:œPromptTemplate-K7xiSœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ],œtypeœ:œstrœ}\"},{\"source\":\"AirbyteJSONLoader-DXfcM\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œAirbyteJSONLoaderœ,œidœ:œAirbyteJSONLoader-DXfcMœ}\",\"target\":\"PromptTemplate-K7xiS\",\"targetHandle\":\"{œfieldNameœ:œdasdasœ,œidœ:œPromptTemplate-K7xiSœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ],œtypeœ:œstrœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"dasdas\",\"id\":\"PromptTemplate-K7xiS\",\"inputTypes\":[\"Document\",\"BaseOutputParser\"],\"type\":\"str\"},\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"AirbyteJSONLoader\",\"id\":\"AirbyteJSONLoader-DXfcM\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 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(1)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-dMB5d\"},\"id\":\"CustomComponent-dMB5d\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:43.668665\",\"folder\":null,\"id\":\"912265df-9b87-4b30-a585-1ca59b944391\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search (2)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-ipifC\"},\"id\":\"CustomComponent-ipifC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:49.799612\",\"folder\":null,\"id\":\"ca83ee08-2f93-427d-9897-80fef6dd5447\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Y4qL7\"},\"id\":\"CustomComponent-Y4qL7\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:53.719960\",\"folder\":null,\"id\":\"5847602b-769a-4a82-82e8-a70f54a59929\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Lively Williams\",\"description\":\"Conversational Cartography Unlocked.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T13:27:45.691254\",\"folder\":null,\"id\":\"0e3bdba9-127a-4399-81a4-2865b70a4a47\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Shared Component\",\"description\":\"Conversational Cartography 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Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-VEjyx\"},\"id\":\"CSVLoader-VEjyx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:57:37.560784\",\"folder\":null,\"id\":\"d80635ee-966c-41f2-981c-afa2c388ac6e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (3)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-PIhOc\"},\"id\":\"CSVLoader-PIhOc\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:00:27.991966\",\"folder\":null,\"id\":\"c5cc4c32-77c8-4889-a9f8-2632466b7366\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (4)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-deB43\"},\"id\":\"CSVLoader-deB43\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:00:42.509243\",\"folder\":null,\"id\":\"0ab59938-ccf4-4bb9-b285-1f5da38648f0\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-mdkLm\"},\"id\":\"CSVLoader-mdkLm\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:02:17.458354\",\"folder\":null,\"id\":\"f127b7e7-4149-492e-8998-6b1b35ec4153\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (5)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"cddscz23\":\"aaqd\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-FRc6Y\"},\"id\":\"CSVLoader-FRc6Y\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:02:26.958867\",\"folder\":null,\"id\":\"a870a096-725c-4914-add0-8d57d710b7e5\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (2)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-Z0uol\"},\"id\":\"AirbyteJSONLoader-Z0uol\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:33.764117\",\"folder\":null,\"id\":\"2a37c76c-65c8-4c01-a72d-eb46f3d41a56\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-28ASv\"},\"id\":\"AmazonBedrockEmbeddings-28ASv\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:41.066618\",\"folder\":null,\"id\":\"850ba4e6-96ca-49a7-9b0c-4b7048fd52c8\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"ConversationBufferMemory\",\"description\":\"Buffer for storing conversation memory.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"ConversationBufferMemory\",\"node\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"memory_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\"],\"display_name\":\"ConversationBufferMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"ConversationBufferMemory-WaoLx\"},\"id\":\"ConversationBufferMemory-WaoLx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:04:46.058568\",\"folder\":null,\"id\":\"be650940-0449-4eb0-a708-056310f924fd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (1)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\"},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:05:50.570800\",\"folder\":null,\"id\":\"53ad1fe2-f3af-4354-86e0-eb141ce12859\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (2)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\"},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:06:11.415088\",\"folder\":null,\"id\":\"e9ed1c90-146e-452d-8ccd-ebf2e0da4331\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (3)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\"},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:07:06.411108\",\"folder\":null,\"id\":\"83783c57-64e3-41a6-aa7e-ed82f80f1e53\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (6)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"z2b\":\"z9\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-2pn2o\"},\"id\":\"CSVLoader-2pn2o\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:38:30.706258\",\"folder\":null,\"id\":\"c4ae9de6-9df3-4d3c-afc9-1752af4fecd7\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-MJrAC\"},\"id\":\"AgentInitializer-MJrAC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:42:54.715163\",\"folder\":null,\"id\":\"ff84b5f9-5680-4084-a9f1-7d94fe675486\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer (1)\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-3lcZ4\"},\"id\":\"AgentInitializer-3lcZ4\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:43:20.819934\",\"folder\":null,\"id\":\"97f5db9f-d6cc-4555-88fb-3be102c67814\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Exuberant Banach\",\"description\":\"Unfolding Linguistic Possibilities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T14:52:49.951416\",\"folder\":null,\"id\":\"a600acd1-213a-4ce7-8648-ab2ee59f5918\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (3)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-JhQtx\"},\"id\":\"AirbyteJSONLoader-JhQtx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:44:49.587337\",\"folder\":null,\"id\":\"07c52ad7-ca52-4cdd-ab40-74a91dbad0dd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (4)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-bIvDc\"},\"id\":\"AirbyteJSONLoader-bIvDc\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:45:13.458500\",\"folder\":null,\"id\":\"53e4d256-3653-444b-b8e0-fb97b3ae5c7e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (5)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-2BLS8\"},\"id\":\"AirbyteJSONLoader-2BLS8\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:45:44.461699\",\"folder\":null,\"id\":\"b5158cc1-c6b8-4e25-907a-bc7e7637aa38\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (4)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile 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If it is false, explain why.\\n\\n\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"check_assertions_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"create_draft_answer_prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"template\":\"{question}\\n\\n\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"create_draft_answer_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"list_assertions_prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"statement\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"template\":\"Here is a statement:\\n{statement}\\nMake a bullet point list of the assumptions you made when producing the above statement.\\n\\n\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"list_assertions_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"revised_answer_prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"checked_assertions\",\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"template\":\"{checked_assertions}\\n\\nQuestion: In light of the above assertions and checks, how would you answer the question '{question}'?\\n\\nAnswer:\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"revised_answer_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"LLMCheckerChain\"},\"description\":\"\",\"base_classes\":[\"Chain\",\"LLMCheckerChain\",\"Callable\"],\"display_name\":\"LLMCheckerChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"LLMMathChain\":{\"template\":{\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"llm_chain\":{\"type\":\"LLMChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"template\":\"Translate a math problem into a expression that can be executed using Python's numexpr library. Use the output of running this code to answer the question.\\n\\nQuestion: ${{Question with math problem.}}\\n```text\\n${{single line mathematical expression that solves the problem}}\\n```\\n...numexpr.evaluate(text)...\\n```output\\n${{Output of running the code}}\\n```\\nAnswer: ${{Answer}}\\n\\nBegin.\\n\\nQuestion: What is 37593 * 67?\\n```text\\n37593 * 67\\n```\\n...numexpr.evaluate(\\\"37593 * 67\\\")...\\n```output\\n2518731\\n```\\nAnswer: 2518731\\n\\nQuestion: 37593^(1/5)\\n```text\\n37593**(1/5)\\n```\\n...numexpr.evaluate(\\\"37593**(1/5)\\\")...\\n```output\\n8.222831614237718\\n```\\nAnswer: 8.222831614237718\\n\\nQuestion: {question}\\n\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"question\",\"fileTypes\":[],\"password\":false,\"name\":\"input_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"answer\",\"fileTypes\":[],\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"_type\":\"LLMMathChain\"},\"description\":\"Chain that interprets a prompt and executes python code to do math.\",\"base_classes\":[\"Chain\",\"LLMMathChain\",\"Callable\"],\"display_name\":\"LLMMathChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_math\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"RetrievalQA\":{\"template\":{\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"combine_documents_chain\":{\"type\":\"BaseCombineDocumentsChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"combine_documents_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"query\",\"fileTypes\":[],\"password\":false,\"name\":\"input_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"result\",\"fileTypes\":[],\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"password\":false,\"name\":\"return_source_documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"_type\":\"RetrievalQA\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"RetrievalQA\",\"BaseRetrievalQA\",\"Chain\",\"Callable\"],\"display_name\":\"RetrievalQA\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/vector_db_qa\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"RetrievalQAWithSourcesChain\":{\"template\":{\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"combine_documents_chain\":{\"type\":\"BaseCombineDocumentsChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"combine_documents_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"answer_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"answer\",\"fileTypes\":[],\"password\":false,\"name\":\"answer_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"input_docs_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"docs\",\"fileTypes\":[],\"password\":false,\"name\":\"input_docs_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"max_tokens_limit\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":3375,\"fileTypes\":[],\"password\":false,\"name\":\"max_tokens_limit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"question_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"question\",\"fileTypes\":[],\"password\":false,\"name\":\"question_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"reduce_k_below_max_tokens\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"reduce_k_below_max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"password\":false,\"name\":\"return_source_documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"sources_answer_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"sources\",\"fileTypes\":[],\"password\":false,\"name\":\"sources_answer_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"_type\":\"RetrievalQAWithSourcesChain\"},\"description\":\"Question-answering with sources over an index.\",\"base_classes\":[\"RetrievalQAWithSourcesChain\",\"BaseQAWithSourcesChain\",\"Chain\",\"Callable\"],\"display_name\":\"RetrievalQAWithSourcesChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"SQLDatabaseChain\":{\"template\":{\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"SQLDatabaseChain\"},\"description\":\"Create a SQLDatabaseChain from an LLM and a database connection.\",\"base_classes\":[\"SQLDatabaseChain\",\"Chain\",\"Callable\"],\"display_name\":\"SQLDatabaseChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"CombineDocsChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"BaseCombineDocumentsChain\",\"Callable\"],\"display_name\":\"CombineDocsChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"SeriesCharacterChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"character\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"character\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"series\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"series\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"SeriesCharacterChain\"},\"description\":\"SeriesCharacterChain is a chain you can use to have a conversation with a character from a series.\",\"base_classes\":[\"LLMChain\",\"BaseCustomChain\",\"Chain\",\"ConversationChain\",\"SeriesCharacterChain\",\"Callable\"],\"display_name\":\"SeriesCharacterChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"MidJourneyPromptChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"MidJourneyPromptChain\"},\"description\":\"MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.\",\"base_classes\":[\"LLMChain\",\"BaseCustomChain\",\"Chain\",\"ConversationChain\",\"MidJourneyPromptChain\"],\"display_name\":\"MidJourneyPromptChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"TimeTravelGuideChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"TimeTravelGuideChain\"},\"description\":\"Time travel guide chain.\",\"base_classes\":[\"LLMChain\",\"BaseCustomChain\",\"TimeTravelGuideChain\",\"Chain\",\"ConversationChain\"],\"display_name\":\"TimeTravelGuideChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"LLMChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"field_formatters\":{},\"beta\":true},\"ConversationChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.chains import ConversationChain\\nfrom typing import Optional, Union, Callable\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, Chain\\n\\n\\nclass ConversationChainComponent(CustomComponent):\\n display_name = \\\"ConversationChain\\\"\\n description = \\\"Chain to have a conversation and load context from memory.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\n \\\"display_name\\\": \\\"Memory\\\",\\n \\\"info\\\": \\\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n if memory is None:\\n return ConversationChain(llm=llm)\\n return ConversationChain(llm=llm, memory=memory)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Chain to have a conversation and load context from memory.\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"ConversationChain\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"memory\":null},\"output_types\":[\"ConversationChain\"],\"field_formatters\":{},\"beta\":true},\"PromptRunner\":{\"template\":{\"llm\":{\"type\":\"BaseLLM\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt Template\",\"advanced\":false,\"dynamic\":false,\"info\":\"Make sure the prompt has all variables filled.\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\n\\nclass PromptRunner(CustomComponent):\\n display_name: str = \\\"Prompt Runner\\\"\\n description: str = \\\"Run a Chain with the given PromptTemplate\\\"\\n beta: bool = True\\n field_config = {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt Template\\\",\\n \\\"info\\\": \\\"Make sure the prompt has all variables filled.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}) -> Document:\\n chain = prompt | llm\\n # The input is an empty dict because the prompt is already filled\\n result = chain.invoke(input=inputs)\\n if hasattr(result, \\\"content\\\"):\\n result = result.content\\n self.repr_value = result\\n return Document(page_content=str(result))\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"inputs\":{\"type\":\"dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Run a Chain with the given PromptTemplate\",\"base_classes\":[\"Document\"],\"display_name\":\"Prompt Runner\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"llm\":null,\"prompt\":null},\"output_types\":[\"PromptRunner\"],\"field_formatters\":{},\"beta\":true}},\"agents\":{\"ZeroShotAgent\":{\"template\":{\"callback_manager\":{\"type\":\"BaseCallbackManager\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callback_manager\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"output_parser\":{\"type\":\"AgentOutputParser\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tools\":{\"type\":\"BaseTool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"format_instructions\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":true,\"value\":\"Use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [{tool_names}]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\",\"fileTypes\":[],\"password\":false,\"name\":\"format_instructions\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"input_variables\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Answer the following questions as best you can. You have access to the following tools:\",\"fileTypes\":[],\"password\":false,\"name\":\"prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Begin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\"fileTypes\":[],\"password\":false,\"name\":\"suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"ZeroShotAgent\"},\"description\":\"Construct an agent from an LLM and tools.\",\"base_classes\":[\"ZeroShotAgent\",\"BaseSingleActionAgent\",\"Agent\",\"Callable\"],\"display_name\":\"ZeroShotAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"JsonAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"toolkit\":{\"type\":\"BaseToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"json_agent\"},\"description\":\"Construct a json agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"JsonAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/openapi\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"CSVAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"csv_agent\"},\"description\":\"Construct a CSV agent from a CSV and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"CSVAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/csv\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"VectorStoreAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"vectorstoreinfo\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstoreinfo\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"vectorstore_agent\"},\"description\":\"Construct an agent from a Vector Store.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"VectorStoreAgent\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"VectorStoreRouterAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"vectorstoreroutertoolkit\":{\"type\":\"VectorStoreRouterToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstoreroutertoolkit\",\"display_name\":\"Vector Store Router Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"vectorstorerouter_agent\"},\"description\":\"Construct an agent from a Vector Store Router.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"VectorStoreRouterAgent\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"SQLAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"database_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database_uri\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"sql_agent\"},\"description\":\"Construct an SQL agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"SQLAgent\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"AgentInitializer\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"agent\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"max_iterations\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"AgentExecutor\",\"Chain\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"field_formatters\":{},\"beta\":true},\"OpenAIConversationalAgent\":{\"template\":{\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"system_message\":{\"type\":\"SystemMessagePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system_message\",\"display_name\":\"System Message\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain.agents.agent import AgentExecutor\\nfrom langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message\\nfrom langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\\nfrom langchain.chat_models import ChatOpenAI\\nfrom langchain.memory.token_buffer import ConversationTokenBufferMemory\\nfrom langchain.prompts import SystemMessagePromptTemplate\\nfrom langchain.prompts.chat import MessagesPlaceholder\\nfrom langchain.schema.memory import BaseMemory\\nfrom langchain.tools import Tool\\nfrom langflow import CustomComponent\\n\\n\\nclass ConversationalAgent(CustomComponent):\\n display_name: str = \\\"OpenAI Conversational Agent\\\"\\n description: str = \\\"Conversational Agent that can use OpenAI's function calling API\\\"\\n\\n def build_config(self):\\n openai_function_models = [\\n \\\"gpt-4-1106-preview\\\",\\n \\\"gpt-3.5-turbo\\\",\\n \\\"gpt-3.5-turbo-16k\\\",\\n \\\"gpt-4\\\",\\n \\\"gpt-4-32k\\\",\\n ]\\n return {\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"system_message\\\": {\\\"display_name\\\": \\\"System Message\\\"},\\n \\\"max_token_limit\\\": {\\\"display_name\\\": \\\"Max Token Limit\\\"},\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": openai_function_models,\\n \\\"value\\\": openai_function_models[0],\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_name: str,\\n openai_api_key: str,\\n tools: List[Tool],\\n openai_api_base: Optional[str] = None,\\n memory: Optional[BaseMemory] = None,\\n system_message: Optional[SystemMessagePromptTemplate] = None,\\n max_token_limit: int = 2000,\\n ) -> AgentExecutor:\\n llm = ChatOpenAI(\\n model=model_name,\\n api_key=openai_api_key,\\n base_url=openai_api_base,\\n )\\n if not memory:\\n memory_key = \\\"chat_history\\\"\\n memory = ConversationTokenBufferMemory(\\n memory_key=memory_key,\\n return_messages=True,\\n output_key=\\\"output\\\",\\n llm=llm,\\n max_token_limit=max_token_limit,\\n )\\n else:\\n memory_key = memory.memory_key # type: ignore\\n\\n _system_message = system_message or _get_default_system_message()\\n prompt = OpenAIFunctionsAgent.create_prompt(\\n system_message=_system_message, # type: ignore\\n extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],\\n )\\n agent = OpenAIFunctionsAgent(\\n llm=llm,\\n tools=tools,\\n prompt=prompt, # type: ignore\\n )\\n return AgentExecutor(\\n agent=agent,\\n tools=tools, # type: ignore\\n memory=memory,\\n verbose=True,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"max_token_limit\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":2000,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"max_token_limit\",\"display_name\":\"Max Token Limit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\",\"gpt-4\",\"gpt-4-32k\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"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\":false,\"dynamic\":false,\"info\":\"\"},\"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\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Conversational Agent that can use OpenAI's function calling API\",\"base_classes\":[\"AgentExecutor\",\"Chain\"],\"display_name\":\"OpenAI Conversational Agent\",\"documentation\":\"\",\"custom_fields\":{\"max_token_limit\":null,\"memory\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"system_message\":null,\"tools\":null},\"output_types\":[\"OpenAIConversationalAgent\"],\"field_formatters\":{},\"beta\":true}},\"prompts\":{\"ChatMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":true,\"dynamic\":true,\"info\":\"\"},\"prompt\":{\"type\":\"prompt\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\\nYou are a helpful assistant that talks casually about life in general.\\nYou are a good listener and you can talk about anything.\\n\",\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"role\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"role\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"_type\":\"ChatMessagePromptTemplate\"},\"description\":\"Chat message prompt template.\",\"base_classes\":[\"BaseStringMessagePromptTemplate\",\"ChatMessagePromptTemplate\",\"BaseMessagePromptTemplate\"],\"display_name\":\"ChatMessagePromptTemplate\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"ChatPromptTemplate\":{\"template\":{\"messages\":{\"type\":\"BaseMessagePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"messages\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"output_parser\":{\"type\":\"BaseOutputParser\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"input_types\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"input_variables\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"partial_variables\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"validate_template\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"_type\":\"ChatPromptTemplate\"},\"description\":\"A prompt template for chat models.\",\"base_classes\":[\"BaseChatPromptTemplate\",\"BasePromptTemplate\",\"ChatPromptTemplate\"],\"display_name\":\"ChatPromptTemplate\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/how_to/prompts\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"HumanMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":true,\"dynamic\":true,\"info\":\"\"},\"prompt\":{\"type\":\"prompt\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\\nYou are a helpful assistant that talks casually about life in general.\\nYou are a good listener and you can talk about anything.\\n\",\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"_type\":\"HumanMessagePromptTemplate\"},\"description\":\"Human message prompt template. This is a message sent from the 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Vertex AI large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"_VertexAIBase\",\"_VertexAICommon\",\"BaseLLM\",\"VertexAI\"],\"display_name\":\"VertexAI\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/google_vertex_ai_palm\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"ChatAnthropic\":{\"template\":{\"anthropic_api_key\":{\"type\":\"SecretStr\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"anthropic_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"count_tokens\":{\"type\":\"Callable[[str], int]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"count_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"AI_PROMPT\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"AI_PROMPT\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"HUMAN_PROMPT\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"HUMAN_PROMPT\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"anthropic_api_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"anthropic_api_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"async_client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"default_request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"default_request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"max_tokens_to_sample\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"password\":false,\"name\":\"max_tokens_to_sample\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"model\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"claude-2\",\"fileTypes\":[],\"password\":false,\"name\":\"model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"top_k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"top_p\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"ChatAnthropic\"},\"description\":\"`Anthropic` chat large language 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API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\"},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"openai_organization\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"openai_proxy\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"tiktoken_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models 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AI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"BaseChatModel\",\"_VertexAIBase\",\"_VertexAICommon\",\"ChatVertexAI\",\"BaseLLM\"],\"display_name\":\"ChatVertexAI\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"AmazonBedrock\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.llms.bedrock import Bedrock\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass AmazonBedrockComponent(CustomComponent):\\n display_name: str = \\\"Amazon Bedrock\\\"\\n description: str = \\\"LLM model from Amazon Bedrock.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\n \\\"ai21.j2-grande-instruct\\\",\\n \\\"ai21.j2-jumbo-instruct\\\",\\n \\\"ai21.j2-mid\\\",\\n \\\"ai21.j2-mid-v1\\\",\\n \\\"ai21.j2-ultra\\\",\\n \\\"ai21.j2-ultra-v1\\\",\\n \\\"anthropic.claude-instant-v1\\\",\\n \\\"anthropic.claude-v1\\\",\\n \\\"anthropic.claude-v2\\\",\\n \\\"cohere.command-text-v14\\\",\\n ],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"field_type\\\": \\\"bool\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"anthropic.claude-instant-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n ) -> BaseLLM:\\n try:\\n output = Bedrock(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"anthropic.claude-instant-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ai21.j2-grande-instruct\",\"ai21.j2-jumbo-instruct\",\"ai21.j2-mid\",\"ai21.j2-mid-v1\",\"ai21.j2-ultra\",\"ai21.j2-ultra-v1\",\"anthropic.claude-instant-v1\",\"anthropic.claude-v1\",\"anthropic.claude-v2\",\"cohere.command-text-v14\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Amazon Bedrock.\",\"base_classes\":[\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"Amazon Bedrock\",\"documentation\":\"\",\"custom_fields\":{\"credentials_profile_name\":null,\"model_id\":null},\"output_types\":[\"AmazonBedrock\"],\"field_formatters\":{},\"beta\":true},\"AnthropicLLM\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Anthropic API key.\"},\"api_endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_endpoint\",\"display_name\":\"API Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chat_models.anthropic import ChatAnthropic\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AnthropicLLM(CustomComponent):\\n display_name: str = \\\"AnthropicLLM\\\"\\n description: str = \\\"Anthropic Chat&Completion large language models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"claude-2.1\\\",\\n \\\"claude-2.0\\\",\\n \\\"claude-instant-1.2\\\",\\n \\\"claude-instant-1\\\",\\n # Add more models as needed\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/anthropic\\\",\\n \\\"required\\\": True,\\n \\\"value\\\": \\\"claude-2.1\\\",\\n },\\n \\\"anthropic_api_key\\\": {\\n \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"Your Anthropic API key.\\\",\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 256,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.7,\\n },\\n \\\"api_endpoint\\\": {\\n \\\"display_name\\\": \\\"API Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str,\\n anthropic_api_key: Optional[str] = None,\\n max_tokens: Optional[int] = None,\\n temperature: Optional[float] = None,\\n api_endpoint: Optional[str] = None,\\n ) -> BaseLanguageModel:\\n # Set default API endpoint if not provided\\n if not api_endpoint:\\n api_endpoint = \\\"https://api.anthropic.com\\\"\\n\\n try:\\n output = ChatAnthropic(\\n model_name=model,\\n anthropic_api_key=SecretStr(anthropic_api_key) if anthropic_api_key else None,\\n max_tokens_to_sample=max_tokens, # type: ignore\\n temperature=temperature,\\n anthropic_api_url=api_endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Anthropic API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"claude-2.1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"claude-2.1\",\"claude-2.0\",\"claude-instant-1.2\",\"claude-instant-1\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/anthropic\"},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"_type\":\"CustomComponent\"},\"description\":\"Anthropic Chat&Completion large language models.\",\"base_classes\":[\"BaseLanguageModel\"],\"display_name\":\"AnthropicLLM\",\"documentation\":\"\",\"custom_fields\":{\"anthropic_api_key\":null,\"api_endpoint\":null,\"max_tokens\":null,\"model\":null,\"temperature\":null},\"output_types\":[\"AnthropicLLM\"],\"field_formatters\":{},\"beta\":true},\"HuggingFaceEndpoints\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.llms.huggingface_endpoint import HuggingFaceEndpoint\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n display_name: str = \\\"Hugging Face Inference API\\\"\\n description: str = \\\"LLM model from Hugging Face Inference API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n \\\"task\\\": {\\n \\\"display_name\\\": \\\"Task\\\",\\n \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n },\\n \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n endpoint_url: str,\\n task: str = \\\"text2text-generation\\\",\\n huggingfacehub_api_token: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n ) -> BaseLLM:\\n try:\\n output = HuggingFaceEndpoint(\\n endpoint_url=endpoint_url,\\n task=task,\\n huggingfacehub_api_token=huggingfacehub_api_token,\\n model_kwargs=model_kwargs,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"endpoint_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"huggingfacehub_api_token\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"model_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"task\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Hugging Face Inference API.\",\"base_classes\":[\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"Hugging Face Inference API\",\"documentation\":\"\",\"custom_fields\":{\"endpoint_url\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null,\"task\":null},\"output_types\":[\"HuggingFaceEndpoints\"],\"field_formatters\":{},\"beta\":true},\"BaiduQianfanChatEndpoints\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom langchain.llms.base import BaseLLM\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanChatEndpoint\\\"\\n description: str = (\\n \\\"Baidu Qianfan chat models. Get more detail from \\\"\\n \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> BaseLLM:\\n try:\\n output = QianfanChatEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,\\n qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n return output # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\"},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\"},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\"},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\"},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"QianfanChatEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"endpoint\":null,\"model\":null,\"penalty_score\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"temperature\":null,\"top_p\":null},\"output_types\":[\"BaiduQianfanChatEndpoints\"],\"field_formatters\":{},\"beta\":true},\"BaiduQianfanLLMEndpoints\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass QianfanLLMEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanLLMEndpoint\\\"\\n description: str = (\\n \\\"Baidu Qianfan hosted open source or customized models. \\\"\\n \\\"Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> BaseLLM:\\n try:\\n output = QianfanLLMEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=qianfan_ak,\\n qianfan_sk=qianfan_sk,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n return output # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\"},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\"},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\"},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\"},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan hosted open source or customized models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"base_classes\":[\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"QianfanLLMEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"endpoint\":null,\"model\":null,\"penalty_score\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"temperature\":null,\"top_p\":null},\"output_types\":[\"BaiduQianfanLLMEndpoints\"],\"field_formatters\":{},\"beta\":true}},\"memories\":{\"ConversationBufferMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\"},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\"},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseChatMemory\",\"BaseMemory\",\"ConversationBufferMemory\"],\"display_name\":\"ConversationBufferMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"ConversationBufferWindowMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\"},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\"},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"ConversationBufferWindowMemory\"},\"description\":\"Buffer for storing conversation memory inside a limited size window.\",\"base_classes\":[\"BaseChatMemory\",\"ConversationBufferWindowMemory\",\"BaseMemory\"],\"display_name\":\"ConversationBufferWindowMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer_window\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"ConversationEntityMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"entity_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"entity_store\":{\"type\":\"BaseEntityStore\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_store\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"entity_summarization_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_summarization_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"chat_history_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"history\",\"fileTypes\":[],\"password\":false,\"name\":\"chat_history_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"entity_cache\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\"},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\"},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"ConversationEntityMemory\"},\"description\":\"Entity extractor & summarizer memory.\",\"base_classes\":[\"BaseChatMemory\",\"BaseMemory\",\"ConversationEntityMemory\"],\"display_name\":\"ConversationEntityMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/entity_memory_with_sqlite\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"ConversationKGMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"entity_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"kg\":{\"type\":\"NetworkxEntityGraph\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"kg\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"knowledge_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"knowledge_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"summary_message_cls\":{\"type\":\"Type[langchain_core.messages.base.BaseMessage]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"summary_message_cls\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\"},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\"},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"ConversationKGMemory\"},\"description\":\"Knowledge graph conversation 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"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"requests_wrapper\":{\"type\":\"TextRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"RequestsPostTool\"},\"description\":\"\",\"base_classes\":[\"Tool\",\"BaseTool\",\"RequestsPostTool\",\"BaseRequestsTool\",\"BaseTool\"],\"display_name\":\"RequestsPostTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"RequestsPutTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"requests_wrapper\":{\"type\":\"TextRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"RequestsPutTool\"},\"description\":\"\",\"base_classes\":[\"Tool\",\"BaseTool\",\"BaseRequestsTool\",\"BaseTool\",\"RequestsPutTool\"],\"display_name\":\"RequestsPutTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"WikipediaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WikipediaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"WikipediaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Tool\",\"BaseTool\",\"WikipediaQueryRun\",\"BaseTool\"],\"display_name\":\"WikipediaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"WolframAlphaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WolframAlphaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"WolframAlphaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Tool\",\"BaseTool\",\"BaseTool\",\"WolframAlphaQueryRun\"],\"display_name\":\"WolframAlphaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false}},\"toolkits\":{\"JsonToolkit\":{\"template\":{\"spec\":{\"type\":\"JsonSpec\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"spec\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"JsonToolkit\"},\"description\":\"Toolkit for interacting with a JSON spec.\",\"base_classes\":[\"JsonToolkit\",\"BaseToolkit\",\"Tool\"],\"display_name\":\"JsonToolkit\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"OpenAPIToolkit\":{\"template\":{\"json_agent\":{\"type\":\"AgentExecutor\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"json_agent\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"requests_wrapper\":{\"type\":\"TextRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"OpenAPIToolkit\"},\"description\":\"Toolkit for interacting with an OpenAPI API.\",\"base_classes\":[\"OpenAPIToolkit\",\"BaseToolkit\",\"Tool\"],\"display_name\":\"OpenAPIToolkit\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"VectorStoreInfo\":{\"template\":{\"vectorstore\":{\"type\":\"VectorStore\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"vectorstore\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"VectorStoreInfo\"},\"description\":\"Information about a VectorStore.\",\"base_classes\":[\"VectorStoreInfo\"],\"display_name\":\"VectorStoreInfo\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"VectorStoreRouterToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"vectorstores\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"vectorstores\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"VectorStoreRouterToolkit\"},\"description\":\"Toolkit for routing between Vector Stores.\",\"base_classes\":[\"VectorStoreRouterToolkit\",\"BaseToolkit\",\"Tool\"],\"display_name\":\"VectorStoreRouterToolkit\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"VectorStoreToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"vectorstore_info\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"vectorstore_info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"VectorStoreToolkit\"},\"description\":\"Toolkit for interacting with a Vector Store.\",\"base_classes\":[\"VectorStoreToolkit\",\"BaseToolkit\",\"Tool\"],\"display_name\":\"VectorStoreToolkit\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"Metaphor\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.agents import tool\\nfrom langchain.agents.agent_toolkits.base import BaseToolkit\\nfrom langchain.tools import Tool\\nfrom metaphor_python import Metaphor # type: ignore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass MetaphorToolkit(CustomComponent):\\n display_name: str = \\\"Metaphor\\\"\\n description: str = \\\"Metaphor Toolkit\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/tools/metaphor_search\\\"\\n beta: bool = True\\n # api key should be password = True\\n field_config = {\\n \\\"metaphor_api_key\\\": {\\\"display_name\\\": \\\"Metaphor API Key\\\", \\\"password\\\": True},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n metaphor_api_key: str,\\n use_autoprompt: bool = True,\\n search_num_results: int = 5,\\n similar_num_results: int = 5,\\n ) -> Union[Tool, BaseToolkit]:\\n # If documents, then we need to create a Vectara instance using .from_documents\\n client = Metaphor(api_key=metaphor_api_key)\\n\\n @tool\\n def search(query: str):\\n \\\"\\\"\\\"Call search engine with a query.\\\"\\\"\\\"\\n return client.search(query, use_autoprompt=use_autoprompt, num_results=search_num_results)\\n\\n @tool\\n def get_contents(ids: List[str]):\\n \\\"\\\"\\\"Get contents of a webpage.\\n\\n The ids passed in should be a list of ids as fetched from `search`.\\n \\\"\\\"\\\"\\n return client.get_contents(ids)\\n\\n @tool\\n def find_similar(url: str):\\n \\\"\\\"\\\"Get search results similar to a given URL.\\n\\n The url passed in should be a URL returned from `search`\\n \\\"\\\"\\\"\\n return client.find_similar(url, num_results=similar_num_results)\\n\\n return [search, get_contents, find_similar] # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\"},\"metaphor_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"metaphor_api_key\",\"display_name\":\"Metaphor API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"search_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"similar_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"similar_num_results\",\"display_name\":\"similar_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"use_autoprompt\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"use_autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Metaphor Toolkit\",\"base_classes\":[\"Tool\",\"BaseTool\",\"BaseToolkit\"],\"display_name\":\"Metaphor\",\"documentation\":\"https://python.langchain.com/docs/integrations/tools/metaphor_search\",\"custom_fields\":{\"metaphor_api_key\":null,\"search_num_results\":null,\"similar_num_results\":null,\"use_autoprompt\":null},\"output_types\":[\"Metaphor\"],\"field_formatters\":{},\"beta\":true}},\"wrappers\":{\"TextRequestsWrapper\":{\"template\":{\"aiosession\":{\"type\":\"ClientSession\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"auth\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"auth\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"password\":false,\"name\":\"headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"TextRequestsWrapper\"},\"description\":\"Lightweight wrapper around requests library.\",\"base_classes\":[\"TextRequestsWrapper\"],\"display_name\":\"TextRequestsWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"SQLDatabase\":{\"template\":{\"database_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"database_uri\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"engine_args\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"engine_args\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"SQLDatabase\"},\"description\":\"Construct a SQLAlchemy engine from URI.\",\"base_classes\":[\"SQLDatabase\",\"Callable\"],\"display_name\":\"SQLDatabase\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false}},\"embeddings\":{\"OpenAIEmbeddings\":{\"template\":{\"allowed_special\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"password\":false,\"name\":\"allowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"async_client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"async_client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"password\":false,\"name\":\"chunk_size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"default_headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"default_headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"default_query\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"default_query\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"deployment\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"fileTypes\":[],\"password\":false,\"name\":\"deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"disallowed_special\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"all\",\"fileTypes\":[],\"password\":false,\"name\":\"disallowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"embedding_ctx_length\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8191,\"fileTypes\":[],\"password\":false,\"name\":\"embedding_ctx_length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"headers\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"password\":false,\"name\":\"headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"http_client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"http_client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":2,\"fileTypes\":[],\"password\":false,\"name\":\"max_retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"model\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"fileTypes\":[],\"password\":false,\"name\":\"model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"openai_api_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"openai_api_version\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"openai_organization\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"openai_proxy\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"request_timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"retry_max_seconds\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":20,\"fileTypes\":[],\"password\":false,\"name\":\"retry_max_seconds\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"retry_min_seconds\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":4,\"fileTypes\":[],\"password\":false,\"name\":\"retry_min_seconds\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"show_progress_bar\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"show_progress_bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"skip_empty\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"skip_empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"tiktoken_enabled\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"password\":true,\"name\":\"tiktoken_enabled\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tiktoken_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"OpenAIEmbeddings\"},\"description\":\"OpenAI embedding models.\",\"base_classes\":[\"OpenAIEmbeddings\",\"Embeddings\"],\"display_name\":\"OpenAIEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"CohereEmbeddings\":{\"template\":{\"async_client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"async_client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"cohere_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"cohere_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"max_retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"model\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"embed-english-v2.0\",\"fileTypes\":[],\"password\":false,\"name\":\"model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"request_timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"truncate\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"truncate\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"user_agent\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"fileTypes\":[],\"password\":false,\"name\":\"user_agent\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CohereEmbeddings\"},\"description\":\"Cohere embedding models.\",\"base_classes\":[\"Embeddings\",\"CohereEmbeddings\"],\"display_name\":\"CohereEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/cohere\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"HuggingFaceEmbeddings\":{\"template\":{\"cache_folder\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"cache_folder\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"encode_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"encode_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"sentence-transformers/all-mpnet-base-v2\",\"fileTypes\":[],\"password\":false,\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"multi_process\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"multi_process\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"_type\":\"HuggingFaceEmbeddings\"},\"description\":\"HuggingFace sentence_transformers embedding models.\",\"base_classes\":[\"Embeddings\",\"HuggingFaceEmbeddings\"],\"display_name\":\"HuggingFaceEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"VertexAIEmbeddings\":{\"template\":{\"client\":{\"type\":\"ForwardRef(\\\"'_LanguageModel'\\\")\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"client_preview\":{\"type\":\"ForwardRef(\\\"'_LanguageModel'\\\")\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client_preview\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"password\":false,\"name\":\"location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"password\":true,\"name\":\"max_output_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"password\":false,\"name\":\"max_retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"textembedding-gecko\",\"fileTypes\":[],\"password\":false,\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"password\":false,\"name\":\"n\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"project\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"project\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"request_parallelism\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"password\":false,\"name\":\"request_parallelism\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"password\":false,\"name\":\"top_k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"password\":false,\"name\":\"top_p\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"_type\":\"VertexAIEmbeddings\"},\"description\":\"Google Cloud VertexAI embedding models.\",\"base_classes\":[\"_VertexAIBase\",\"_VertexAICommon\",\"Embeddings\",\"VertexAIEmbeddings\"],\"display_name\":\"VertexAIEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/google_vertex_ai_palm\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"AmazonBedrockEmbeddings\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"field_formatters\":{},\"beta\":true}},\"vectorstores\":{\"FAISS\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"folder_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"folder_path\",\"display_name\":\"Local Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"ids\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadatas\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"_type\":\"FAISS\"},\"description\":\"Construct FAISS wrapper from raw documents.\",\"base_classes\":[\"VectorStore\",\"FAISS\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"FAISS\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"MongoDBAtlasVectorSearch\":{\"template\":{\"collection\":{\"type\":\"Collection[MongoDBDocumentType]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"collection\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"collection_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"db_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db_name\",\"display_name\":\"Database Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadatas\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"mongodb_atlas_cluster_uri\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mongodb_atlas_cluster_uri\",\"display_name\":\"MongoDB Atlas Cluster URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"_type\":\"MongoDBAtlasVectorSearch\"},\"description\":\"Construct a `MongoDB Atlas Vector Search` vector store from raw documents.\",\"base_classes\":[\"VectorStore\",\"MongoDBAtlasVectorSearch\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"MongoDB Atlas\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/mongodb_atlas\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"Pinecone\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"batch_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":32,\"fileTypes\":[],\"password\":false,\"name\":\"batch_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"ids\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"index_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadatas\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"namespace\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"namespace\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"pinecone_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pinecone_api_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"pinecone_env\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pinecone_env\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"pool_threads\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":4,\"fileTypes\":[],\"password\":false,\"name\":\"pool_threads\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"text_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"password\":true,\"name\":\"text_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"upsert_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"upsert_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"_type\":\"Pinecone\"},\"description\":\"Construct Pinecone wrapper from raw documents.\",\"base_classes\":[\"VectorStore\",\"Pinecone\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Pinecone\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"Qdrant\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"hnsw_config\":{\"type\":\"common_types.HnswConfigDiff\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"hnsw_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"init_from\":{\"type\":\"common_types.InitFrom\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"init_from\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"optimizers_config\":{\"type\":\"common_types.OptimizersConfigDiff\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"optimizers_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"quantization_config\":{\"type\":\"common_types.QuantizationConfig\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"quantization_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"wal_config\":{\"type\":\"common_types.WalConfigDiff\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"wal_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"batch_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":64,\"fileTypes\":[],\"password\":false,\"name\":\"batch_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"collection_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"content_payload_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"page_content\",\"fileTypes\":[],\"password\":false,\"name\":\"content_payload_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"distance_func\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"Cosine\",\"fileTypes\":[],\"password\":false,\"name\":\"distance_func\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"force_recreate\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"force_recreate\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6334,\"fileTypes\":[],\"password\":false,\"name\":\"grpc_port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"https\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"https\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"ids\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\":memory:\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\":memory:\",\"fileTypes\":[],\"password\":false,\"name\":\"location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata_payload_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"metadata\",\"fileTypes\":[],\"password\":false,\"name\":\"metadata_payload_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"metadatas\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"on_disk\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"on_disk\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"on_disk_payload\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"on_disk_payload\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6333,\"fileTypes\":[],\"password\":false,\"name\":\"port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"prefer_grpc\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"prefer_grpc\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"password\":false,\"name\":\"prefix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"replication_factor\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"replication_factor\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"shard_number\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"shard_number\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1}},\"url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"url\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"vector_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"vector_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"write_consistency_factor\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"write_consistency_factor\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"Qdrant\"},\"description\":\"Construct Qdrant wrapper from a list of texts.\",\"base_classes\":[\"VectorStore\",\"Qdrant\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Qdrant\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"SupabaseVectorStore\":{\"template\":{\"client\":{\"type\":\"supabase.client.Client\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":500,\"fileTypes\":[],\"password\":false,\"name\":\"chunk_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"ids\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadatas\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"query_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"query_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"supabase_service_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"supabase_service_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"supabase_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"supabase_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"table_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"table_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"SupabaseVectorStore\"},\"description\":\"Return VectorStore initialized from texts and embeddings.\",\"base_classes\":[\"VectorStore\",\"SupabaseVectorStore\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Supabase\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"Weaviate\":{\"template\":{\"client\":{\"type\":\"weaviate.Client\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"relevance_score_fn\":{\"type\":\"Callable[[float], float]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"password\":false,\"name\":\"relevance_score_fn\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"batch_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"batch_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"by_text\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"by_text\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"client_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"index_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadatas\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"text_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"password\":true,\"name\":\"text_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"weaviate_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"weaviate_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"weaviate_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"http://localhost:8080\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"weaviate_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"Weaviate\"},\"description\":\"Construct Weaviate wrapper from raw documents.\",\"base_classes\":[\"VectorStore\",\"Weaviate\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Weaviate\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/weaviate\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"Redis\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"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\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\n\\nfrom langchain.vectorstores.redis import Redis\\nfrom langchain.schema import Document\\nfrom langchain.vectorstores.base import VectorStore\\nfrom langchain.embeddings.base import Embeddings\\n\\n\\nclass RedisComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using Redis.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Redis\\\"\\n description: str = \\\"Implementation of Vector Store using Redis\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/redis\\\"\\n beta = True\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\", \\\"value\\\": \\\"your_index\\\"},\\n \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"redis_server_url\\\": {\\n \\\"display_name\\\": \\\"Redis Server Connection String\\\",\\n \\\"advanced\\\": False,\\n },\\n \\\"redis_index_name\\\": {\\\"display_name\\\": \\\"Redis Index\\\", \\\"advanced\\\": False},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n redis_server_url: str,\\n redis_index_name: str,\\n documents: Optional[Document] = None,\\n ) -> VectorStore:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - embedding (Embeddings): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - redis_index_name (str): The name of the Redis index.\\n - redis_server_url (str): The URL for the Redis server.\\n\\n Returns:\\n - VectorStore: The Vector Store object.\\n \\\"\\\"\\\"\\n\\n return Redis.from_documents(\\n documents=documents, # type: ignore\\n embedding=embedding,\\n redis_url=redis_server_url,\\n index_name=redis_index_name,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"redis_index_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_index_name\",\"display_name\":\"Redis Index\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"redis_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_server_url\",\"display_name\":\"Redis Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Redis\",\"base_classes\":[\"VectorStore\"],\"display_name\":\"Redis\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/redis\",\"custom_fields\":{\"documents\":null,\"embedding\":null,\"redis_index_name\":null,\"redis_server_url\":null},\"output_types\":[\"Redis\"],\"field_formatters\":{},\"beta\":true},\"Chroma\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"embedding\":{\"type\":\"Embeddings\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"chroma_server_cors_allow_origins\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"chroma_server_grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"chroma_server_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"chroma_server_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"chroma_server_ssl_enabled\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\nfrom langflow import CustomComponent\\n\\nfrom langchain.vectorstores import Chroma\\nfrom langchain.schema import Document\\nfrom langchain.vectorstores.base import VectorStore\\nfrom langchain.schema import BaseRetriever\\nfrom langchain.embeddings.base import Embeddings\\nimport chromadb # type: ignore\\n\\n\\nclass ChromaComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using Chroma.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Chroma\\\"\\n description: str = \\\"Implementation of Vector Store using Chroma\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/chroma\\\"\\n beta: bool = True\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n \\\"persist\\\": {\\\"display_name\\\": \\\"Persist\\\"},\\n \\\"persist_directory\\\": {\\\"display_name\\\": \\\"Persist Directory\\\"},\\n \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"chroma_server_cors_allow_origins\\\": {\\n \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_grpc_port\\\": {\\n \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_ssl_enabled\\\": {\\n \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n collection_name: str,\\n persist: bool,\\n chroma_server_ssl_enabled: bool,\\n persist_directory: Optional[str] = None,\\n embedding: Optional[Embeddings] = None,\\n documents: Optional[Document] = None,\\n chroma_server_cors_allow_origins: Optional[str] = None,\\n chroma_server_host: Optional[str] = None,\\n chroma_server_port: Optional[int] = None,\\n chroma_server_grpc_port: Optional[int] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - collection_name (str): The name of the collection.\\n - persist_directory (Optional[str]): The directory to persist the Vector Store to.\\n - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n - persist (bool): Whether to persist the Vector Store or not.\\n - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n - chroma_server_host (Optional[str]): The host for the Chroma server.\\n - chroma_server_port (Optional[int]): The port for the Chroma server.\\n - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n Returns:\\n - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n \\\"\\\"\\\"\\n\\n # Chroma settings\\n chroma_settings = None\\n\\n if chroma_server_host is not None:\\n chroma_settings = chromadb.config.Settings(\\n chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or None,\\n chroma_server_host=chroma_server_host,\\n chroma_server_port=chroma_server_port or None,\\n chroma_server_grpc_port=chroma_server_grpc_port or None,\\n chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n )\\n\\n # If documents, then we need to create a Chroma instance using .from_documents\\n if documents is not None and embedding is not None:\\n return Chroma.from_documents(\\n documents=documents, # type: ignore\\n persist_directory=persist_directory if persist else None,\\n collection_name=collection_name,\\n embedding=embedding,\\n client_settings=chroma_settings,\\n )\\n\\n return Chroma(persist_directory=persist_directory, client_settings=chroma_settings)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"persist\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"persist\",\"display_name\":\"Persist\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"persist_directory\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"persist_directory\",\"display_name\":\"Persist Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Chroma\",\"base_classes\":[\"VectorStore\",\"BaseRetriever\"],\"display_name\":\"Chroma\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/chroma\",\"custom_fields\":{\"chroma_server_cors_allow_origins\":null,\"chroma_server_grpc_port\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_ssl_enabled\":null,\"collection_name\":null,\"documents\":null,\"embedding\":null,\"persist\":null,\"persist_directory\":null},\"output_types\":[\"Chroma\"],\"field_formatters\":{},\"beta\":true},\"pgvector\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"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\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, List\\nfrom langflow import CustomComponent\\n\\nfrom langchain.vectorstores.pgvector import PGVector\\nfrom langchain.schema import Document\\nfrom langchain.vectorstores.base import VectorStore\\nfrom langchain.embeddings.base import Embeddings\\n\\n\\nclass PostgresqlVectorComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using PostgreSQL.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"PGVector\\\"\\n description: str = \\\"Implementation of Vector Store using PostgreSQL\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\\\"\\n beta = True\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\", \\\"value\\\": \\\"your_index\\\"},\\n \\\"code\\\": {\\\"show\\\": True, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"pg_server_url\\\": {\\n \\\"display_name\\\": \\\"PostgreSQL Server Connection String\\\",\\n \\\"advanced\\\": False,\\n },\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Table\\\", \\\"advanced\\\": False},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n pg_server_url: str,\\n collection_name: str,\\n documents: Optional[List[Document]] = None,\\n ) -> VectorStore:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - embedding (Embeddings): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - collection_name (str): The name of the PG table.\\n - pg_server_url (str): The URL for the PG server.\\n\\n Returns:\\n - VectorStore: The Vector Store object.\\n \\\"\\\"\\\"\\n\\n try:\\n if documents is None:\\n return PGVector.from_existing_index(\\n embedding=embedding,\\n collection_name=collection_name,\\n connection_string=pg_server_url,\\n )\\n\\n return PGVector.from_documents(\\n embedding=embedding,\\n documents=documents,\\n collection_name=collection_name,\\n connection_string=pg_server_url,\\n )\\n except Exception as e:\\n raise RuntimeError(f\\\"Failed to build PGVector: {e}\\\")\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Table\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"pg_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pg_server_url\",\"display_name\":\"PostgreSQL Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using PostgreSQL\",\"base_classes\":[],\"display_name\":\"PGVector\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\",\"custom_fields\":{\"collection_name\":null,\"documents\":null,\"embedding\":null,\"pg_server_url\":null},\"output_types\":[\"pgvector\"],\"field_formatters\":{},\"beta\":true},\"Vectara\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.schema import BaseRetriever, Document\\nfrom langchain.vectorstores import Vectara\\nfrom langchain.vectorstores.base import VectorStore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass VectaraComponent(CustomComponent):\\n display_name: str = \\\"Vectara\\\"\\n description: str = \\\"Implementation of Vector Store using Vectara\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/vectara\\\"\\n beta = True\\n # api key should be password = True\\n field_config = {\\n \\\"vectara_customer_id\\\": {\\\"display_name\\\": \\\"Vectara Customer ID\\\"},\\n \\\"vectara_corpus_id\\\": {\\\"display_name\\\": \\\"Vectara Corpus ID\\\"},\\n \\\"vectara_api_key\\\": {\\\"display_name\\\": \\\"Vectara API Key\\\", \\\"password\\\": True},\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n }\\n\\n def build(\\n self,\\n vectara_customer_id: str,\\n vectara_corpus_id: str,\\n vectara_api_key: str,\\n documents: Optional[Document] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n # If documents, then we need to create a Vectara instance using .from_documents\\n if documents is not None:\\n return Vectara.from_documents(\\n documents=documents, # type: ignore\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=\\\"langflow\\\",\\n )\\n\\n return Vectara(\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=\\\"langflow\\\",\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"vectara_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"vectara_api_key\",\"display_name\":\"Vectara API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"vectara_corpus_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_corpus_id\",\"display_name\":\"Vectara Corpus ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"vectara_customer_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_customer_id\",\"display_name\":\"Vectara Customer ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Vectara\",\"base_classes\":[\"VectorStore\",\"BaseRetriever\"],\"display_name\":\"Vectara\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/vectara\",\"custom_fields\":{\"documents\":null,\"vectara_api_key\":null,\"vectara_corpus_id\":null,\"vectara_customer_id\":null},\"output_types\":[\"Vectara\"],\"field_formatters\":{},\"beta\":true}},\"documentloaders\":{\"AZLyricsLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"AZLyricsLoader\"},\"description\":\"Load `AZLyrics` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"AZLyricsLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/azlyrics\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"beta\":false},\"AirbyteJSONLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"beta\":false},\"BSHTMLLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".html\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"BSHTMLLoader\"},\"description\":\"Load `HTML` files and parse them with `beautiful soup`.\",\"base_classes\":[\"Document\"],\"display_name\":\"BSHTMLLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"beta\":false},\"CSVLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"beta\":false},\"CoNLLULoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CoNLLULoader\"},\"description\":\"Load `CoNLL-U` 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`Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredMarkdownLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/markdown\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"beta\":false},\"UnstructuredPowerPointLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".pptx\",\".ppt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"UnstructuredPowerPointLoader\"},\"description\":\"Load `Microsoft PowerPoint` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredPowerPointLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_powerpoint\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"beta\":false},\"UnstructuredWordDocumentLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".docx\",\".doc\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"UnstructuredWordDocumentLoader\"},\"description\":\"Load `Microsoft Word` file using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredWordDocumentLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_word\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"beta\":false},\"WebBaseLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"WebBaseLoader\"},\"description\":\"Load HTML pages using `urllib` and parse them with `BeautifulSoup'.\",\"base_classes\":[\"Document\"],\"display_name\":\"WebBaseLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"beta\":false},\"FileLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\"json\",\"txt\",\"csv\",\"jsonl\",\"html\",\"htm\",\"conllu\",\"enex\",\"msg\",\"pdf\",\"srt\",\"eml\",\"md\",\"pptx\",\"docx\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"display_name\":\"File Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain.schema import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.utils.constants import LOADERS_INFO\\n\\n\\nclass FileLoaderComponent(CustomComponent):\\n display_name: str = \\\"File Loader\\\"\\n description: str = \\\"Generic File Loader\\\"\\n beta = True\\n\\n def build_config(self):\\n loader_options = [\\\"Automatic\\\"] + [loader_info[\\\"name\\\"] for loader_info in LOADERS_INFO]\\n\\n file_types = []\\n suffixes = []\\n\\n for loader_info in LOADERS_INFO:\\n if \\\"allowedTypes\\\" in loader_info:\\n file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n return {\\n \\\"file_path\\\": {\\n \\\"display_name\\\": \\\"File Path\\\",\\n \\\"required\\\": True,\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\n \\\"json\\\",\\n \\\"txt\\\",\\n \\\"csv\\\",\\n \\\"jsonl\\\",\\n \\\"html\\\",\\n \\\"htm\\\",\\n \\\"conllu\\\",\\n \\\"enex\\\",\\n \\\"msg\\\",\\n \\\"pdf\\\",\\n \\\"srt\\\",\\n \\\"eml\\\",\\n \\\"md\\\",\\n \\\"pptx\\\",\\n \\\"docx\\\",\\n ],\\n \\\"suffixes\\\": [\\n \\\".json\\\",\\n \\\".txt\\\",\\n \\\".csv\\\",\\n \\\".jsonl\\\",\\n \\\".html\\\",\\n \\\".htm\\\",\\n \\\".conllu\\\",\\n \\\".enex\\\",\\n \\\".msg\\\",\\n \\\".pdf\\\",\\n \\\".srt\\\",\\n \\\".eml\\\",\\n \\\".md\\\",\\n \\\".pptx\\\",\\n \\\".docx\\\",\\n ],\\n # \\\"file_types\\\" : file_types,\\n # \\\"suffixes\\\": suffixes,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": loader_options,\\n \\\"value\\\": \\\"Automatic\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, file_path: str, loader: str) -> Document:\\n file_type = file_path.split(\\\".\\\")[-1]\\n\\n # Mapeie o nome do loader selecionado para suas informações\\n selected_loader_info = None\\n for loader_info in LOADERS_INFO:\\n if loader_info[\\\"name\\\"] == loader:\\n selected_loader_info = loader_info\\n break\\n\\n if selected_loader_info is None and loader != \\\"Automatic\\\":\\n raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n if loader == \\\"Automatic\\\":\\n # Determine o loader automaticamente com base na extensão do arquivo\\n default_loader_info = None\\n for info in LOADERS_INFO:\\n if \\\"defaultFor\\\" in info and file_type in info[\\\"defaultFor\\\"]:\\n default_loader_info = info\\n break\\n\\n if default_loader_info is None:\\n raise ValueError(f\\\"No default loader found for file type: {file_type}\\\")\\n\\n selected_loader_info = default_loader_info\\n if isinstance(selected_loader_info, dict):\\n loader_import: str = selected_loader_info[\\\"import\\\"]\\n else:\\n raise ValueError(f\\\"Loader info for {loader} is not a dict\\\\nLoader info:\\\\n{selected_loader_info}\\\")\\n module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n try:\\n # Importe o loader dinamicamente\\n loader_module = __import__(module_name, fromlist=[class_name])\\n loader_instance = getattr(loader_module, class_name)\\n except ImportError as e:\\n raise ValueError(f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{selected_loader_info}\\\") from e\\n\\n result = loader_instance(file_path=file_path)\\n return result.load()\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Generic File Loader\",\"base_classes\":[\"Document\"],\"display_name\":\"File Loader\",\"documentation\":\"\",\"custom_fields\":{\"file_path\":null,\"loader\":null},\"output_types\":[\"FileLoader\"],\"field_formatters\":{},\"beta\":true},\"UrlLoader\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain import document_loaders\\nfrom langchain.schema import Document\\nfrom langflow import CustomComponent\\n\\n\\nclass UrlLoaderComponent(CustomComponent):\\n display_name: str = \\\"Url Loader\\\"\\n description: str = \\\"Generic Url Loader Component\\\"\\n\\n def build_config(self):\\n return {\\n \\\"web_path\\\": {\\n \\\"display_name\\\": \\\"Url\\\",\\n \\\"required\\\": True,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": [\\n \\\"AZLyricsLoader\\\",\\n \\\"CollegeConfidentialLoader\\\",\\n \\\"GitbookLoader\\\",\\n \\\"HNLoader\\\",\\n \\\"IFixitLoader\\\",\\n \\\"IMSDbLoader\\\",\\n \\\"WebBaseLoader\\\",\\n ],\\n \\\"value\\\": \\\"WebBaseLoader\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, web_path: str, loader: str) -> List[Document]:\\n try:\\n loader_instance = getattr(document_loaders, loader)(web_path=web_path)\\n except Exception as e:\\n raise ValueError(f\\\"No loader found for: {web_path}\\\") from e\\n docs = loader_instance.load()\\n avg_length = sum(len(doc.page_content) for doc in docs if hasattr(doc, \\\"page_content\\\")) / len(docs)\\n self.status = f\\\"\\\"\\\"{len(docs)} documents)\\n \\\\nAvg. Document Length (characters): {int(avg_length)}\\n Documents: {docs[:3]}...\\\"\\\"\\\"\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"WebBaseLoader\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"AZLyricsLoader\",\"CollegeConfidentialLoader\",\"GitbookLoader\",\"HNLoader\",\"IFixitLoader\",\"IMSDbLoader\",\"WebBaseLoader\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Generic Url Loader Component\",\"base_classes\":[\"Document\"],\"display_name\":\"Url Loader\",\"documentation\":\"\",\"custom_fields\":{\"loader\":null,\"web_path\":null},\"output_types\":[\"UrlLoader\"],\"field_formatters\":{},\"beta\":true}},\"textsplitters\":{\"CharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"chunk_overlap\":{\"type\":\"int\",\"required\":true,\"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\":\"\"},\"chunk_size\":{\"type\":\"int\",\"required\":true,\"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\":\"\"},\"separator\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\\\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"separator\",\"display_name\":\"Separator\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CharacterTextSplitter\"},\"description\":\"Splitting text that looks at characters.\",\"base_classes\":[\"Document\"],\"display_name\":\"CharacterTextSplitter\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"beta\":false},\"LanguageRecursiveTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\"},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"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.\"},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"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.\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.text_splitter import Language\\nfrom langchain.schema import Document\\n\\n\\nclass LanguageRecursiveTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Language Recursive Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length based on language.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\\\"\\n\\n def build_config(self):\\n options = [x.value for x in Language]\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separator_type\\\": {\\n \\\"display_name\\\": \\\"Separator Type\\\",\\n \\\"info\\\": \\\"The type of separator to use.\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"options\\\": options,\\n \\\"value\\\": \\\"Python\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": \\\"The characters to split on.\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n separator_type: Optional[str] = \\\"Python\\\",\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n # 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\\n splitter = RecursiveCharacterTextSplitter.from_language(\\n language=Language(separator_type),\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"separator_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"value\":\"Python\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"cpp\",\"go\",\"java\",\"kotlin\",\"js\",\"ts\",\"php\",\"proto\",\"python\",\"rst\",\"ruby\",\"rust\",\"scala\",\"swift\",\"markdown\",\"latex\",\"html\",\"sol\",\"csharp\",\"cobol\"],\"name\":\"separator_type\",\"display_name\":\"Separator Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of separator to use.\"},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length based on language.\",\"base_classes\":[\"Document\"],\"display_name\":\"Language Recursive Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separator_type\":null},\"output_types\":[\"LanguageRecursiveTextSplitter\"],\"field_formatters\":{},\"beta\":true},\"RecursiveCharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\"},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"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.\"},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"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.\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.utils.util import build_loader_repr_from_documents\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"separators\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"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\\\", \\\" \\\", \\\"\\\"].\"},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separators\":null},\"output_types\":[\"RecursiveCharacterTextSplitter\"],\"field_formatters\":{},\"beta\":true}},\"utilities\":{\"BingSearchAPIWrapper\":{\"template\":{\"bing_search_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"bing_search_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"bing_subscription_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"bing_subscription_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\"},\"_type\":\"BingSearchAPIWrapper\"},\"description\":\"Wrapper for Bing Search API.\",\"base_classes\":[\"BingSearchAPIWrapper\"],\"display_name\":\"BingSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"GoogleSearchAPIWrapper\":{\"template\":{\"google_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"google_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"google_cse_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"google_cse_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_engine\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"search_engine\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"siterestrict\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"siterestrict\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"GoogleSearchAPIWrapper\"},\"description\":\"Wrapper for Google Search API.\",\"base_classes\":[\"GoogleSearchAPIWrapper\"],\"display_name\":\"GoogleSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"GoogleSerperAPIWrapper\":{\"template\":{\"aiosession\":{\"type\":\"ClientSession\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"gl\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"us\",\"fileTypes\":[],\"password\":false,\"name\":\"gl\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"hl\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"en\",\"fileTypes\":[],\"password\":false,\"name\":\"hl\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"result_key_for_type\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{\\n \\\"news\\\": \\\"news\\\",\\n \\\"places\\\": \\\"places\\\",\\n \\\"images\\\": \\\"images\\\",\\n \\\"search\\\": \\\"organic\\\"\\n}\",\"fileTypes\":[],\"password\":true,\"name\":\"result_key_for_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"serper_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"serper_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"tbs\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tbs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"search\",\"fileTypes\":[],\"password\":false,\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"GoogleSerperAPIWrapper\"},\"description\":\"Wrapper around the Serper.dev Google Search API.\",\"base_classes\":[\"GoogleSerperAPIWrapper\"],\"display_name\":\"GoogleSerperAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"SearxSearchWrapper\":{\"template\":{\"aiosession\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"categories\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"password\":false,\"name\":\"categories\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"engines\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"password\":false,\"name\":\"engines\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"password\":false,\"name\":\"headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"params\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"params\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"query_suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"password\":false,\"name\":\"query_suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"searx_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"password\":false,\"name\":\"searx_host\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"unsecure\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"unsecure\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"SearxSearchWrapper\"},\"description\":\"Wrapper for Searx API.\",\"base_classes\":[\"SearxSearchWrapper\"],\"display_name\":\"SearxSearchWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"SerpAPIWrapper\":{\"template\":{\"aiosession\":{\"type\":\"ClientSession\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"params\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"{\\n \\\"engine\\\": \\\"google\\\",\\n \\\"google_domain\\\": \\\"google.com\\\",\\n \\\"gl\\\": \\\"us\\\",\\n \\\"hl\\\": \\\"en\\\"\\n}\",\"fileTypes\":[],\"password\":false,\"name\":\"params\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"search_engine\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"search_engine\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"serpapi_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":true,\"name\":\"serpapi_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"SerpAPIWrapper\"},\"description\":\"Wrapper around SerpAPI.\",\"base_classes\":[\"SerpAPIWrapper\"],\"display_name\":\"SerpAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"WikipediaAPIWrapper\":{\"template\":{\"doc_content_chars_max\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":4000,\"fileTypes\":[],\"password\":false,\"name\":\"doc_content_chars_max\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"lang\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"en\",\"fileTypes\":[],\"password\":false,\"name\":\"lang\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"load_all_available_meta\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"load_all_available_meta\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"top_k_results\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"password\":false,\"name\":\"top_k_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"wiki_client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"wiki_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"WikipediaAPIWrapper\"},\"description\":\"Wrapper around WikipediaAPI.\",\"base_classes\":[\"WikipediaAPIWrapper\"],\"display_name\":\"WikipediaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"WolframAlphaAPIWrapper\":{\"template\":{\"wolfram_alpha_appid\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"wolfram_alpha_appid\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"wolfram_client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"wolfram_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"WolframAlphaAPIWrapper\"},\"description\":\"Wrapper for Wolfram Alpha.\",\"base_classes\":[\"WolframAlphaAPIWrapper\"],\"display_name\":\"WolframAlphaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"GetRequest\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.services.database.models.base import orjson_dumps\\nimport requests\\nfrom typing import Optional\\n\\n\\nclass GetRequest(CustomComponent):\\n display_name: str = \\\"GET Request\\\"\\n description: str = \\\"Make a GET request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#get-request\\\"\\n beta: bool = True\\n field_config = {\\n \\\"url\\\": {\\n \\\"display_name\\\": \\\"URL\\\",\\n \\\"info\\\": \\\"The URL to make the request to\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"timeout\\\": {\\n \\\"display_name\\\": \\\"Timeout\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"The timeout to use for the request.\\\",\\n \\\"value\\\": 5,\\n },\\n }\\n\\n def get_document(self, session: requests.Session, url: str, headers: Optional[dict], timeout: int) -> Document:\\n try:\\n response = session.get(url, headers=headers, timeout=int(timeout))\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response.status_code,\\n },\\n )\\n except requests.Timeout:\\n return Document(\\n page_content=\\\"Request Timed Out\\\",\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 408},\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n )\\n\\n def build(\\n self,\\n url: str,\\n headers: Optional[dict] = None,\\n timeout: int = 5,\\n ) -> list[Document]:\\n if headers is None:\\n headers = {}\\n urls = url if isinstance(url, list) else [url]\\n with requests.Session() as session:\\n documents = [self.get_document(session, u, headers, timeout) for u in urls]\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\"},\"timeout\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"The timeout to use for the request.\"},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to\"},\"_type\":\"CustomComponent\"},\"description\":\"Make a GET request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"GET Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#get-request\",\"custom_fields\":{\"headers\":null,\"timeout\":null,\"url\":null},\"output_types\":[\"GetRequest\"],\"field_formatters\":{},\"beta\":true},\"JSONDocumentBuilder\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"### JSON Document Builder\\n\\n# Build a Document containing a JSON object using a key and another Document page content.\\n\\n# **Params**\\n\\n# - **Key:** The key to use for the JSON object.\\n# - **Document:** The Document page to use for the JSON object.\\n\\n# **Output**\\n\\n# - **Document:** The Document containing the JSON object.\\n\\nfrom langchain.schema import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass JSONDocumentBuilder(CustomComponent):\\n display_name: str = \\\"JSON Document Builder\\\"\\n description: str = \\\"Build a Document containing a JSON object using a key and another Document page content.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n beta = True\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#json-document-builder\\\"\\n\\n field_config = {\\n \\\"key\\\": {\\\"display_name\\\": \\\"Key\\\"},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n }\\n\\n def build(\\n self,\\n key: str,\\n document: Document,\\n ) -> Document:\\n documents = None\\n if isinstance(document, list):\\n documents = [\\n Document(page_content=orjson_dumps({key: doc.page_content}, indent_2=False)) for doc in document\\n ]\\n elif isinstance(document, Document):\\n documents = Document(page_content=orjson_dumps({key: document.page_content}, indent_2=False))\\n else:\\n raise TypeError(f\\\"Expected Document or list of Documents, got {type(document)}\\\")\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"key\",\"display_name\":\"Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Build a Document containing a JSON object using a key and another Document page content.\",\"base_classes\":[\"Document\"],\"display_name\":\"JSON Document Builder\",\"documentation\":\"https://docs.langflow.org/components/utilities#json-document-builder\",\"custom_fields\":{\"document\":null,\"key\":null},\"output_types\":[\"JSONDocumentBuilder\"],\"field_formatters\":{},\"beta\":true},\"UpdateRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\nimport requests\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass UpdateRequest(CustomComponent):\\n display_name: str = \\\"Update Request\\\"\\n description: str = \\\"Make a PATCH request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#update-request\\\"\\n beta: bool = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"field_type\\\": \\\"NestedDict\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n \\\"method\\\": {\\n \\\"display_name\\\": \\\"Method\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"The HTTP method to use.\\\",\\n \\\"options\\\": [\\\"PATCH\\\", \\\"PUT\\\"],\\n \\\"value\\\": \\\"PATCH\\\",\\n },\\n }\\n\\n def update_document(\\n self,\\n session: requests.Session,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n method: str = \\\"PATCH\\\",\\n ) -> Document:\\n try:\\n if method == \\\"PATCH\\\":\\n response = session.patch(url, headers=headers, data=document.page_content)\\n elif method == \\\"PUT\\\":\\n response = session.put(url, headers=headers, data=document.page_content)\\n else:\\n raise ValueError(f\\\"Unsupported method: {method}\\\")\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response.status_code,\\n },\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n )\\n\\n def build(\\n self,\\n method: str,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> List[Document]:\\n if headers is None:\\n headers = {}\\n\\n if not isinstance(document, list) and isinstance(document, Document):\\n documents: list[Document] = [document]\\n elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n documents = document\\n else:\\n raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n with requests.Session() as session:\\n documents = [self.update_document(session, doc, url, headers, method) for doc in documents]\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"headers\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\"},\"method\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"PATCH\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"PATCH\",\"PUT\"],\"name\":\"method\",\"display_name\":\"Method\",\"advanced\":false,\"dynamic\":false,\"info\":\"The HTTP method to use.\"},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\"},\"_type\":\"CustomComponent\"},\"description\":\"Make a PATCH request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"Update Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#update-request\",\"custom_fields\":{\"document\":null,\"headers\":null,\"method\":null,\"url\":null},\"output_types\":[\"UpdateRequest\"],\"field_formatters\":{},\"beta\":true},\"PostRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.services.database.models.base import orjson_dumps\\nimport requests\\nfrom typing import Optional\\n\\n\\nclass PostRequest(CustomComponent):\\n display_name: str = \\\"POST Request\\\"\\n description: str = \\\"Make a POST request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#post-request\\\"\\n beta: bool = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n }\\n\\n def post_document(\\n self,\\n session: requests.Session,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> Document:\\n try:\\n response = session.post(url, headers=headers, data=document.page_content)\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response,\\n },\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": 500,\\n },\\n )\\n\\n def build(\\n self,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> list[Document]:\\n if headers is None:\\n headers = {}\\n\\n if not isinstance(document, list) and isinstance(document, Document):\\n documents: list[Document] = [document]\\n elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n documents = document\\n else:\\n raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n with requests.Session() as session:\\n documents = [self.post_document(session, doc, url, headers) for doc in documents]\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\"},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\"},\"_type\":\"CustomComponent\"},\"description\":\"Make a POST request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"POST Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#post-request\",\"custom_fields\":{\"document\":null,\"headers\":null,\"url\":null},\"output_types\":[\"PostRequest\"],\"field_formatters\":{},\"beta\":true}},\"output_parsers\":{\"ResponseSchema\":{\"template\":{\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"string\",\"fileTypes\":[],\"password\":false,\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"ResponseSchema\"},\"description\":\"A schema for a response from a structured output parser.\",\"base_classes\":[\"ResponseSchema\"],\"display_name\":\"ResponseSchema\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"StructuredOutputParser\":{\"template\":{\"response_schemas\":{\"type\":\"ResponseSchema\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"response_schemas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"StructuredOutputParser\"},\"description\":\"\",\"base_classes\":[\"BaseLLMOutputParser\",\"BaseOutputParser\",\"StructuredOutputParser\"],\"display_name\":\"StructuredOutputParser\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false}},\"retrievers\":{\"MultiQueryRetriever\":{\"template\":{\"llm\":{\"type\":\"BaseLLM\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{\"input_variables\":[\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"template\":\"You are an AI language model assistant. Your task is \\n to generate 3 different versions of the given user \\n question to retrieve relevant documents from a vector database. \\n By generating multiple perspectives on the user question, \\n your goal is to help the user overcome some of the limitations \\n of distance-based similarity search. Provide these alternative \\n questions separated by newlines. Original question: {question}\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"include_original\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"include_original\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"parser_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"lines\",\"fileTypes\":[],\"password\":false,\"name\":\"parser_key\",\"display_name\":\"Parser Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"MultiQueryRetriever\"},\"description\":\"Initialize from llm using default template.\",\"base_classes\":[\"MultiQueryRetriever\",\"BaseRetriever\"],\"display_name\":\"MultiQueryRetriever\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"beta\":false},\"AmazonKendra\":{\"template\":{\"attribute_filter\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"attribute_filter\",\"display_name\":\"Attribute Filter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.retrievers import AmazonKendraRetriever\\nfrom langchain.schema import BaseRetriever\\n\\n\\nclass AmazonKendraRetrieverComponent(CustomComponent):\\n display_name: str = \\\"Amazon Kendra Retriever\\\"\\n description: str = \\\"Retriever that uses the Amazon Kendra API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"attribute_filter\\\": {\\n \\\"display_name\\\": \\\"Attribute Filter\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"field_type\\\": \\\"int\\\"},\\n \\\"user_context\\\": {\\n \\\"display_name\\\": \\\"User Context\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n index_id: str,\\n top_k: int = 3,\\n region_name: Optional[str] = None,\\n credentials_profile_name: Optional[str] = None,\\n attribute_filter: Optional[dict] = None,\\n user_context: Optional[dict] = None,\\n ) -> BaseRetriever:\\n try:\\n output = AmazonKendraRetriever(\\n index_id=index_id,\\n top_k=top_k,\\n region_name=region_name,\\n credentials_profile_name=credentials_profile_name,\\n attribute_filter=attribute_filter,\\n user_context=user_context,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonKendra API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"user_context\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"user_context\",\"display_name\":\"User Context\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Amazon Kendra API.\",\"base_classes\":[\"BaseRetriever\"],\"display_name\":\"Amazon Kendra Retriever\",\"documentation\":\"\",\"custom_fields\":{\"attribute_filter\":null,\"credentials_profile_name\":null,\"index_id\":null,\"region_name\":null,\"top_k\":null,\"user_context\":null},\"output_types\":[\"AmazonKendra\"],\"field_formatters\":{},\"beta\":true},\"MetalRetriever\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"client_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"client_id\",\"display_name\":\"Client ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.retrievers import MetalRetriever\\nfrom langchain.schema import BaseRetriever\\nfrom metal_sdk.metal import Metal # type: ignore\\n\\n\\nclass MetalRetrieverComponent(CustomComponent):\\n display_name: str = \\\"Metal Retriever\\\"\\n description: str = \\\"Retriever that uses the Metal API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True},\\n \\\"client_id\\\": {\\\"display_name\\\": \\\"Client ID\\\", \\\"password\\\": True},\\n \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n \\\"params\\\": {\\\"display_name\\\": \\\"Parameters\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None) -> BaseRetriever:\\n try:\\n metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Metal API.\\\") from e\\n return MetalRetriever(client=metal, params=params or {})\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\"},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"params\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Metal API.\",\"base_classes\":[\"BaseRetriever\"],\"display_name\":\"Metal Retriever\",\"documentation\":\"\",\"custom_fields\":{\"api_key\":null,\"client_id\":null,\"index_id\":null,\"params\":null},\"output_types\":[\"MetalRetriever\"],\"field_formatters\":{},\"beta\":true}},\"custom_components\":{\"CustomComponent\":{\"template\":{\"param\":{\"type\":\"Data\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"param\",\"display_name\":\"Parameter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\"},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n def build(self, param: Data) -> 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Mastered.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-m2yFu\",\"type\":\"genericNode\",\"position\":{\"x\":462.6456058272081,\"y\":1033.9314297130313},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"transcription\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"create an image prompt based on the song's lyrics to be used as the album cover of this song:\\n\\nlyrics:\\n{transcription}\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"transcription\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"transcription\",\"display_name\":\"transcription\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt 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}\\n\\n def build(self, prompt:str,api_key:str,model:str,file_name:str,output_format:str,width:int,height:int):\\n client = OpenAI(api_key=api_key)\\n cache_dir = Path(user_cache_dir(\\\"langflow\\\"))\\n images_dir = cache_dir / \\\"images\\\"\\n images_dir.mkdir(parents=True, exist_ok=True)\\n image_path = images_dir / f\\\"{file_name}.{output_format}\\\"\\n response = client.images.generate(\\n model=model,\\n prompt=prompt,\\n size=f\\\"{height}x{width}\\\",\\n response_format=\\\"b64_json\\\",\\n n=1,\\n )\\n # Decode base64-encoded image string\\n binary_data = base64.b64decode(response.data[0].b64_json)\\n # Create PIL Image object from binary image data\\n image_pil = Image.open(BytesIO(binary_data))\\n image_pil.save(image_path, format=output_format.upper())\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"api_key\",\"display_name\":\"OpenAI API key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"file_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"file_name\",\"display_name\":\"File 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LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"display_name\":\"code\"},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-KlJb3\"},\"selected\":false,\"positionAbsolute\":{\"x\":1242.9851164540805,\"y\":-139.74634696823108},\"dragging\":false},{\"width\":384,\"height\":328,\"id\":\"CustomComponent-bCuc0\",\"type\":\"genericNode\",\"position\":{\"x\":1747.8107777342625,\"y\":319.13729017446667},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Custom Component\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n documentation: str = 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LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-NgFyo\"},\"selected\":false,\"positionAbsolute\":{\"x\":225.3113389084088,\"y\":-193.3520019494289},\"dragging\":false},{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-oAFjh\",\"type\":\"genericNode\",\"position\":{\"x\":-342.62522294052764,\"y\":391.20629510686103},\"data\":{\"type\":\"PromptTemplate\",\"node\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"links\"]},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Answer everything with links\\n{links}\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"links\",\"display_name\":\"links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"BasePromptTemplate\",\"StringPromptTemplate\",\"PromptTemplate\"],\"name\":\"\",\"display_name\":\"PromptTemplate\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"custom_fields\":{\"\":[\"links\"]},\"output_types\":[],\"full_path\":null,\"field_formatters\":{},\"beta\":false,\"error\":null},\"id\":\"PromptTemplate-oAFjh\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-342.62522294052764,\"y\":391.20629510686103}}],\"edges\":[{\"source\":\"ChatOpenAI-VPh47\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseChatModelœ,œChatOpenAIœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-VPh47œ}\",\"target\":\"LLMChain-NgFyo\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œLLMChain-NgFyoœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"LLMChain-NgFyo\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"},\"sourceHandle\":{\"baseClasses\":[\"BaseChatModel\",\"ChatOpenAI\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-VPh47\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-foreground stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-VPh47{œbaseClassesœ:[œBaseChatModelœ,œChatOpenAIœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-VPh47œ}-LLMChain-NgFyo{œfieldNameœ:œllmœ,œidœ:œLLMChain-NgFyoœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\"},{\"source\":\"PromptTemplate-oAFjh\",\"sourceHandle\":\"{œbaseClassesœ:[œBasePromptTemplateœ,œStringPromptTemplateœ,œPromptTemplateœ],œdataTypeœ:œPromptTemplateœ,œidœ:œPromptTemplate-oAFjhœ}\",\"target\":\"LLMChain-NgFyo\",\"targetHandle\":\"{œfieldNameœ:œpromptœ,œidœ:œLLMChain-NgFyoœ,œinputTypesœ:null,œtypeœ:œBasePromptTemplateœ}\",\"data\":{\"targetHandle\":{\"fieldName\":\"prompt\",\"id\":\"LLMChain-NgFyo\",\"inputTypes\":null,\"type\":\"BasePromptTemplate\"},\"sourceHandle\":{\"baseClasses\":[\"BasePromptTemplate\",\"StringPromptTemplate\",\"PromptTemplate\"],\"dataType\":\"PromptTemplate\",\"id\":\"PromptTemplate-oAFjh\"}},\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-foreground 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API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false,\"value\":60},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseChatModel\",\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\"},\"id\":\"ChatOpenAI-fBcfh\",\"value\":null},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":228.87326389541306,\"y\":465.8628482073749}},{\"width\":384,\"height\":310,\"id\":\"ConversationChain-bVNex\",\"type\":\"genericNode\",\"position\":{\"x\":806,\"y\":554},\"data\":{\"type\":\"ConversationChain\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"_type\":\"default\"},\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLMOutputParser\",\"list\":false},\"prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"history\",\"input\"],\"output_parser\":null,\"partial_variables\":{},\"template\":\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\\n\\nCurrent conversation:\\n{history}\\nHuman: 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to have a conversation and load context from memory.\",\"base_classes\":[\"ConversationChain\",\"Chain\",\"LLMChain\",\"function\"],\"display_name\":\"ConversationChain\",\"documentation\":\"\"},\"id\":\"ConversationChain-bVNex\",\"value\":null},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":806,\"y\":554}}],\"edges\":[{\"source\":\"ChatOpenAI-fBcfh\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseChatModelœ,œBaseLanguageModelœ,œChatOpenAIœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-fBcfhœ}\",\"target\":\"ConversationChain-bVNex\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œConversationChain-bVNexœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"className\":\"stroke-gray-900 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File Loader\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import os\\n\\nfrom langchain.schema import Document\\nfrom langflow import CustomComponent\\nimport glob\\n\\nclass DirectoryLoaderComponent(CustomComponent):\\n display_name: str = \\\"Directory Loader\\\"\\n description: str = \\\"Generic File Loader\\\"\\n beta = True\\n loaders_info = [\\n {\\n \\\"loader\\\": \\\"AirbyteJSONLoader\\\",\\n \\\"name\\\": \\\"Airbyte JSON (.jsonl)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.AirbyteJSONLoader\\\",\\n \\\"defaultFor\\\": [\\\"jsonl\\\"],\\n \\\"allowdTypes\\\": [\\\"jsonl\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"JSONLoader\\\",\\n \\\"name\\\": \\\"JSON (.json)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.JSONLoader\\\",\\n \\\"defaultFor\\\": [\\\"json\\\"],\\n \\\"allowdTypes\\\": [\\\"json\\\"],\\n \\\"kwargs\\\": {\\\"jq_schema\\\": \\\".\\\", \\\"text_content\\\": False}\\n },\\n {\\n \\\"loader\\\": \\\"BSHTMLLoader\\\",\\n \\\"name\\\": \\\"BeautifulSoup4 HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.BSHTMLLoader\\\",\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"CSVLoader\\\",\\n \\\"name\\\": \\\"CSV (.csv)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.CSVLoader\\\",\\n \\\"defaultFor\\\": [\\\"csv\\\"],\\n \\\"allowdTypes\\\": [\\\"csv\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"CoNLLULoader\\\",\\n \\\"name\\\": \\\"CoNLL-U (.conllu)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.CoNLLULoader\\\",\\n \\\"defaultFor\\\": [\\\"conllu\\\"],\\n \\\"allowdTypes\\\": [\\\"conllu\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"EverNoteLoader\\\",\\n \\\"name\\\": \\\"EverNote (.enex)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.EverNoteLoader\\\",\\n \\\"defaultFor\\\": [\\\"enex\\\"],\\n \\\"allowdTypes\\\": [\\\"enex\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"FacebookChatLoader\\\",\\n \\\"name\\\": \\\"Facebook Chat (.json)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.FacebookChatLoader\\\",\\n \\\"allowdTypes\\\": [\\\"json\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"OutlookMessageLoader\\\",\\n \\\"name\\\": \\\"Outlook Message (.msg)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.OutlookMessageLoader\\\",\\n \\\"defaultFor\\\": [\\\"msg\\\"],\\n \\\"allowdTypes\\\": [\\\"msg\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"PyPDFLoader\\\",\\n \\\"name\\\": \\\"PyPDF (.pdf)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.PyPDFLoader\\\",\\n \\\"defaultFor\\\": [\\\"pdf\\\"],\\n \\\"allowdTypes\\\": [\\\"pdf\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"STRLoader\\\",\\n \\\"name\\\": \\\"Subtitle (.str)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.STRLoader\\\",\\n \\\"defaultFor\\\": [\\\"str\\\"],\\n \\\"allowdTypes\\\": [\\\"str\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"TextLoader\\\",\\n \\\"name\\\": \\\"Text (.txt)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.TextLoader\\\",\\n \\\"defaultFor\\\": [\\\"txt\\\"],\\n \\\"allowdTypes\\\": [\\\"txt\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredEmailLoader\\\",\\n \\\"name\\\": \\\"Unstructured Email (.eml)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredEmailLoader\\\",\\n \\\"defaultFor\\\": [\\\"eml\\\"],\\n \\\"allowdTypes\\\": [\\\"eml\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredHTMLLoader\\\",\\n \\\"name\\\": \\\"Unstructured HTML (.html, .htm)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredHTMLLoader\\\",\\n \\\"defaultFor\\\": [\\\"html\\\", \\\"htm\\\"],\\n \\\"allowdTypes\\\": [\\\"html\\\", \\\"htm\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredMarkdownLoader\\\",\\n \\\"name\\\": \\\"Unstructured Markdown (.md)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredMarkdownLoader\\\",\\n \\\"defaultFor\\\": [\\\"md\\\"],\\n \\\"allowdTypes\\\": [\\\"md\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredPowerPointLoader\\\",\\n \\\"name\\\": \\\"Unstructured PowerPoint (.pptx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredPowerPointLoader\\\",\\n \\\"defaultFor\\\": [\\\"pptx\\\"],\\n \\\"allowdTypes\\\": [\\\"pptx\\\"],\\n },\\n {\\n \\\"loader\\\": \\\"UnstructuredWordLoader\\\",\\n \\\"name\\\": \\\"Unstructured Word (.docx)\\\",\\n \\\"import\\\": \\\"langchain.document_loaders.UnstructuredWordLoader\\\",\\n \\\"defaultFor\\\": [\\\"docx\\\"],\\n \\\"allowdTypes\\\": [\\\"docx\\\"],\\n },\\n]\\n\\n\\n def build_config(self):\\n loader_options = [\\\"Automatic\\\"] + [\\n loader_info[\\\"name\\\"] for loader_info in self.loaders_info\\n ]\\n\\n file_types = []\\n suffixes = []\\n\\n for loader_info in self.loaders_info:\\n if \\\"allowedTypes\\\" in loader_info:\\n file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n return {\\n \\\"directory_path\\\": {\\n \\\"display_name\\\": \\\"Directory Path\\\",\\n \\\"required\\\": True,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": loader_options,\\n \\\"value\\\": \\\"Automatic\\\",\\n },\\n }\\n\\n def build(self, directory_path: str, loader: str) -> Document:\\n # Verifique se o diretório existe\\n if not os.path.exists(directory_path):\\n raise ValueError(f\\\"Directory not found: {directory_path}\\\")\\n\\n files = glob.glob(directory_path + \\\"/*.*\\\")\\n\\n\\n loader_info = None\\n if loader == \\\"Automatic\\\":\\n for file in files:\\n file_type = file.split(\\\".\\\")[-1]\\n\\n\\n for info in self.loaders_info:\\n if \\\"defaultFor\\\" in info:\\n if file_type in info[\\\"defaultFor\\\"]:\\n loader_info = info\\n break\\n if loader_info:\\n break\\n\\n if not loader_info:\\n raise ValueError(\\n \\\"No default loader found for any file in the directory\\\"\\n )\\n\\n else:\\n for info in self.loaders_info:\\n if info[\\\"name\\\"] == loader:\\n loader_info = info\\n break\\n\\n if not loader_info:\\n raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n loader_import = loader_info[\\\"import\\\"]\\n module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n try:\\n # Importe o loader dinamicamente\\n loader_module = __import__(module_name, fromlist=[class_name])\\n loader_instance = getattr(loader_module, class_name)\\n except ImportError as e:\\n raise ValueError(\\n f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{loader_info}\\\"\\n ) from e\\n\\n results = []\\n for file in files:\\n file_path = os.path.join(directory_path, file)\\n kwargs = loader_info.get(\\\"kwargs\\\", {})\\n result = loader_instance(file_path=file_path, **kwargs).load()\\n results.append(result)\\n self.status = results\\n return results\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"directory_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"directory_path\",\"display_name\":\"Directory Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"/Users/ogabrielluiz/Projects/langflow2/docs\"},\"loader\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true}},\"description\":\"Generic File Loader\",\"base_classes\":[\"Document\"],\"display_name\":\"Directory Loader\",\"custom_fields\":{\"directory_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Ip6tG\"},\"id\":\"CustomComponent-Ip6tG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:38.570303\",\"folder\":null,\"id\":\"5fe4debc-b6a7-45d4-a694-c02d8aa93b08\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Whisper Transcriber\",\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, List, Dict, Union\\nfrom langflow.field_typing import (\\n AgentExecutor,\\n BaseChatMemory,\\n BaseLanguageModel,\\n BaseLLM,\\n BaseLoader,\\n BaseMemory,\\n BaseOutputParser,\\n BasePromptTemplate,\\n BaseRetriever,\\n Callable,\\n Chain,\\n ChatPromptTemplate,\\n Data,\\n Document,\\n Embeddings,\\n NestedDict,\\n Object,\\n PromptTemplate,\\n TextSplitter,\\n Tool,\\n VectorStore,\\n)\\n\\nfrom openai import OpenAI\\nclass Component(CustomComponent):\\n display_name: str = \\\"Whisper Transcriber\\\"\\n description: str = \\\"Converts audio to text using OpenAI's Whisper.\\\"\\n\\n def build_config(self):\\n return {\\\"audio\\\":{\\\"field_type\\\":\\\"file\\\",\\\"suffixes\\\":[\\\".mp3\\\", \\\".mp4\\\", \\\".m4a\\\"]},\\\"OpenAIKey\\\":{\\\"field_type\\\":\\\"str\\\",\\\"password\\\":True}}\\n\\n def build(self, audio:str, OpenAIKey:str) -> str:\\n \\n # TODO: if output path, persist & load it instead\\n \\n client = OpenAI(api_key=OpenAIKey)\\n \\n audio_file= open(audio, \\\"rb\\\")\\n transcript = client.audio.transcriptions.create(\\n model=\\\"whisper-1\\\", \\n file=audio_file,\\n response_format=\\\"text\\\"\\n )\\n \\n \\n self.status = transcript\\n return transcript\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"OpenAIKey\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"OpenAIKey\",\"display_name\":\"OpenAIKey\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"audio\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"suffixes\":[\".mp3\",\".mp4\",\".m4a\"],\"password\":false,\"name\":\"audio\",\"display_name\":\"audio\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"file_path\":\"/Users/rodrigonader/Library/Caches/langflow/19b3e1c9-90bf-405f-898a-e982f47adf76/a3308ce7e9b10088fcd985aabb6d17b012c6b6e81ff8806356574474c9d10229.m4a\",\"value\":\"Audio Recording 2023-11-29 at 12.12.04.m4a\"}},\"description\":\"Converts audio to text using OpenAI's Whisper.\",\"base_classes\":[\"str\"],\"display_name\":\"Whisper Transcriber\",\"custom_fields\":{\"OpenAIKey\":null,\"audio\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-GJRrs\"},\"id\":\"CustomComponent-GJRrs\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-04T22:58:39.710502\",\"folder\":null,\"id\":\"bd9e911d-46bd-429f-aa75-dd740430695e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Youtube Conversation\",\"description\":\"Craft Meaningful Interactions, Generate Value.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":589,\"id\":\"CustomComponent-h0NSI\",\"type\":\"genericNode\",\"position\":{\"x\":714.9531293402755,\"y\":0.4994374160582993},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"import requests\\nfrom langflow import CustomComponent\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.schema import Document\\nfrom langchain.document_loaders import YoutubeLoader\\n\\n# Example usage:\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"YouTube Transcript\\\"\\n description: str = \\\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\\\"\\n\\n def build_config(self):\\n return {\\\"url\\\": {\\\"multiline\\\": True, \\\"required\\\": True}}\\n\\n def build(self, url: str, llm: BaseLLM, dependencies: Document, language: str = \\\"en\\\") -> Document:\\n dependencies\\n response = requests.get(url)\\n loader = YoutubeLoader.from_youtube_url(url, add_video_info=True, language=[language])\\n result = loader.load()\\n self.repr_value = str(result)\\n return result\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"dependencies\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"dependencies\",\"display_name\":\"dependencies\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"language\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"language\",\"display_name\":\"language\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"YouTube transcripts refer to the written text versions of the spoken content in a video uploaded to the YouTube platform.\",\"base_classes\":[\"Document\"],\"display_name\":\"YouTube Transcript\",\"custom_fields\":{\"dependencies\":null,\"language\":null,\"llm\":null,\"url\":null},\"output_types\":[\"Document\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-h0NSI\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":714.9531293402755,\"y\":0.4994374160582993}},{\"width\":384,\"height\":629,\"id\":\"ChatOpenAI-q61Y2\",\"type\":\"genericNode\",\"position\":{\"x\":18,\"y\":625.5521045590924},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-16k\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k-0613\",\"gpt-3.5-turbo-16k\",\"gpt-4-0613\",\"gpt-4-32k-0613\",\"gpt-4\",\"gpt-4-32k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-q61Y2\"},\"selected\":false,\"positionAbsolute\":{\"x\":18,\"y\":625.5521045590924},\"dragging\":false},{\"width\":384,\"height\":539,\"id\":\"Chroma-gVhy7\",\"type\":\"genericNode\",\"position\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"data\":{\"type\":\"Chroma\",\"node\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.Client\",\"list\":false},\"client_settings\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client_settings\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"chromadb.config.Setting\",\"list\":true},\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"chroma_server_cors_allow_origins\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Chroma Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"chroma_server_grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Chroma Server GRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Chroma Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_http_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_http_port\",\"display_name\":\"Chroma Server HTTP Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_ssl_enabled\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Chroma Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"collection_metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"collection_metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"collection_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"persist\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"persist\",\"display_name\":\"Persist\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"persist_directory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"persist_directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"_type\":\"Chroma\"},\"description\":\"Create a Chroma vectorstore from a raw documents.\",\"base_classes\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Chroma\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma\",\"beta\":false,\"error\":null},\"id\":\"Chroma-gVhy7\"},\"selected\":false,\"positionAbsolute\":{\"x\":2110.365967257855,\"y\":805.0149521868784},\"dragging\":false},{\"width\":384,\"height\":501,\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"type\":\"genericNode\",\"position\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"data\":{\"type\":\"RecursiveCharacterTextSplitter\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.utils.util import build_loader_repr_from_documents\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chunk_overlap\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"50\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"400\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"type\":\"int\",\"list\":false},\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"type\":\"Document\",\"list\":true},\"separators\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"type\":\"str\",\"list\":true,\"value\":[\" \"]}},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separators\":null},\"output_types\":[\"RecursiveCharacterTextSplitter\"],\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"beta\":true,\"error\":null},\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"selected\":false,\"positionAbsolute\":{\"x\":1409.2872773444874,\"y\":19.45456141103284},\"dragging\":false},{\"width\":384,\"height\":367,\"id\":\"OpenAIEmbeddings-cduOO\",\"type\":\"genericNode\",\"position\":{\"x\":1405.1176670984544,\"y\":1029.459061269321},\"data\":{\"type\":\"OpenAIEmbeddings\",\"node\":{\"template\":{\"allowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[],\"password\":false,\"name\":\"allowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"disallowed_special\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"all\",\"password\":false,\"name\":\"disallowed_special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Literal'all'\",\"list\":true},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"deployment\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"embedding_ctx_length\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":8191,\"password\":false,\"name\":\"embedding_ctx_length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"{'Authorization': 'Bearer '}\",\"password\":false,\"name\":\"headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"model\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text-embedding-ada-002\",\"password\":false,\"name\":\"model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_api_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_api_version\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"show_progress_bar\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"show_progress_bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"skip_empty\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"skip_empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"},\"_type\":\"OpenAIEmbeddings\"},\"description\":\"OpenAI embedding models.\",\"base_classes\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"display_name\":\"OpenAIEmbeddings\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"OpenAIEmbeddings-cduOO\"},\"selected\":false,\"positionAbsolute\":{\"x\":1405.1176670984544,\"y\":1029.459061269321}},{\"width\":384,\"height\":339,\"id\":\"RetrievalQA-4PmTB\",\"type\":\"genericNode\",\"position\":{\"x\":2711.7786966415715,\"y\":709.4450828605463},\"data\":{\"type\":\"RetrievalQA\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"combine_documents_chain\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"combine_documents_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseCombineDocumentsChain\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"list\":false},\"input_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"query\",\"password\":false,\"name\":\"input_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"output_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"result\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"return_source_documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_source_documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"RetrievalQA\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"RetrievalQA\",\"Chain\",\"BaseRetrievalQA\",\"function\"],\"display_name\":\"RetrievalQA\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/vector_db_qa\",\"beta\":false,\"error\":null},\"id\":\"RetrievalQA-4PmTB\"},\"selected\":false,\"positionAbsolute\":{\"x\":2711.7786966415715,\"y\":709.4450828605463}},{\"width\":384,\"height\":333,\"id\":\"CombineDocsChain-yk0JF\",\"type\":\"genericNode\",\"position\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"data\":{\"type\":\"CombineDocsChain\",\"node\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"chain_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"BaseCombineDocumentsChain\",\"function\"],\"display_name\":\"CombineDocsChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"id\":\"CombineDocsChain-yk0JF\"},\"selected\":false,\"positionAbsolute\":{\"x\":2125.6202053356537,\"y\":199.07708924409633},\"dragging\":false},{\"width\":384,\"height\":417,\"id\":\"CustomComponent-y6Wg0\",\"type\":\"genericNode\",\"position\":{\"x\":39.400034102832365,\"y\":-499.03551482195707},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nimport subprocess\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\nfrom typing import List\\n\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"PIP Install\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n\\n def build(self, libs: List[str]) -> Document:\\n def install_package(package_name):\\n subprocess.check_call([\\\"pip\\\", \\\"install\\\", package_name])\\n for lib in libs:\\n install_package(lib)\\n return \\\"\\\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"libs\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"libs\",\"display_name\":\"libs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"requests\",\"pytube\"]}},\"description\":\"Create any custom component you want!\",\"base_classes\":[\"Document\"],\"display_name\":\"PIP Install\",\"custom_fields\":{\"libs\":null},\"output_types\":[],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-y6Wg0\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":39.400034102832365,\"y\":-499.03551482195707}}],\"edges\":[{\"source\":\"ChatOpenAI-q61Y2\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CustomComponent-h0NSI{œfieldNameœ:œllmœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œBaseLLMœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"BaseLLM\"}}},{\"source\":\"CustomComponent-y6Wg0\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}\",\"target\":\"CustomComponent-h0NSI\",\"targetHandle\":\"{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CustomComponent-y6Wg0{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-y6Wg0œ}-CustomComponent-h0NSI{œfieldNameœ:œdependenciesœ,œidœ:œCustomComponent-h0NSIœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-y6Wg0\"},\"targetHandle\":{\"fieldName\":\"dependencies\",\"id\":\"CustomComponent-h0NSI\",\"inputTypes\":null,\"type\":\"Document\"}}},{\"source\":\"CustomComponent-h0NSI\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-h0NSIœ}\",\"target\":\"RecursiveCharacterTextSplitter-1eaOX\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CustomComponent-h0NSI{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-h0NSIœ}-RecursiveCharacterTextSplitter-1eaOX{œfieldNameœ:œdocumentsœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"CustomComponent\",\"id\":\"CustomComponent-h0NSI\"},\"targetHandle\":{\"fieldName\":\"documents\",\"id\":\"RecursiveCharacterTextSplitter-1eaOX\",\"inputTypes\":null,\"type\":\"Document\"}},\"selected\":false},{\"source\":\"OpenAIEmbeddings-cduOO\",\"sourceHandle\":\"{œbaseClassesœ:[œEmbeddingsœ,œOpenAIEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-cduOOœ}\",\"target\":\"Chroma-gVhy7\",\"targetHandle\":\"{œfieldNameœ:œembeddingœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-OpenAIEmbeddings-cduOO{œbaseClassesœ:[œEmbeddingsœ,œOpenAIEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-cduOOœ}-Chroma-gVhy7{œfieldNameœ:œembeddingœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Embeddings\",\"OpenAIEmbeddings\"],\"dataType\":\"OpenAIEmbeddings\",\"id\":\"OpenAIEmbeddings-cduOO\"},\"targetHandle\":{\"fieldName\":\"embedding\",\"id\":\"Chroma-gVhy7\",\"inputTypes\":null,\"type\":\"Embeddings\"}}},{\"source\":\"RecursiveCharacterTextSplitter-1eaOX\",\"sourceHandle\":\"{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ}\",\"target\":\"Chroma-gVhy7\",\"targetHandle\":\"{œfieldNameœ:œdocumentsœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-RecursiveCharacterTextSplitter-1eaOX{œbaseClassesœ:[œDocumentœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-1eaOXœ}-Chroma-gVhy7{œfieldNameœ:œdocumentsœ,œidœ:œChroma-gVhy7œ,œinputTypesœ:null,œtypeœ:œDocumentœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"Document\"],\"dataType\":\"RecursiveCharacterTextSplitter\",\"id\":\"RecursiveCharacterTextSplitter-1eaOX\"},\"targetHandle\":{\"fieldName\":\"documents\",\"id\":\"Chroma-gVhy7\",\"inputTypes\":null,\"type\":\"Document\"}}},{\"source\":\"ChatOpenAI-q61Y2\",\"sourceHandle\":\"{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}\",\"target\":\"CombineDocsChain-yk0JF\",\"targetHandle\":\"{œfieldNameœ:œllmœ,œidœ:œCombineDocsChain-yk0JFœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-ChatOpenAI-q61Y2{œbaseClassesœ:[œChatOpenAIœ,œBaseChatModelœ,œBaseLanguageModelœ,œBaseLLMœ],œdataTypeœ:œChatOpenAIœ,œidœ:œChatOpenAI-q61Y2œ}-CombineDocsChain-yk0JF{œfieldNameœ:œllmœ,œidœ:œCombineDocsChain-yk0JFœ,œinputTypesœ:null,œtypeœ:œBaseLanguageModelœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"ChatOpenAI\",\"BaseChatModel\",\"BaseLanguageModel\",\"BaseLLM\"],\"dataType\":\"ChatOpenAI\",\"id\":\"ChatOpenAI-q61Y2\"},\"targetHandle\":{\"fieldName\":\"llm\",\"id\":\"CombineDocsChain-yk0JF\",\"inputTypes\":null,\"type\":\"BaseLanguageModel\"}}},{\"source\":\"CombineDocsChain-yk0JF\",\"sourceHandle\":\"{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-CombineDocsChain-yk0JF{œbaseClassesœ:[œBaseCombineDocumentsChainœ,œfunctionœ],œdataTypeœ:œCombineDocsChainœ,œidœ:œCombineDocsChain-yk0JFœ}-RetrievalQA-4PmTB{œfieldNameœ:œcombine_documents_chainœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseCombineDocumentsChainœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"BaseCombineDocumentsChain\",\"function\"],\"dataType\":\"CombineDocsChain\",\"id\":\"CombineDocsChain-yk0JF\"},\"targetHandle\":{\"fieldName\":\"combine_documents_chain\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseCombineDocumentsChain\"}}},{\"source\":\"Chroma-gVhy7\",\"sourceHandle\":\"{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}\",\"target\":\"RetrievalQA-4PmTB\",\"targetHandle\":\"{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"style\":{\"stroke\":\"#555\"},\"className\":\"stroke-gray-900 stroke-connection\",\"animated\":false,\"id\":\"reactflow__edge-Chroma-gVhy7{œbaseClassesœ:[œVectorStoreœ,œChromaœ,œBaseRetrieverœ,œVectorStoreRetrieverœ],œdataTypeœ:œChromaœ,œidœ:œChroma-gVhy7œ}-RetrievalQA-4PmTB{œfieldNameœ:œretrieverœ,œidœ:œRetrievalQA-4PmTBœ,œinputTypesœ:null,œtypeœ:œBaseRetrieverœ}\",\"data\":{\"sourceHandle\":{\"baseClasses\":[\"VectorStore\",\"Chroma\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"dataType\":\"Chroma\",\"id\":\"Chroma-gVhy7\"},\"targetHandle\":{\"fieldName\":\"retriever\",\"id\":\"RetrievalQA-4PmTB\",\"inputTypes\":null,\"type\":\"BaseRetriever\"}}}],\"viewport\":{\"x\":189.54413265004274,\"y\":259.89949657174975,\"zoom\":0.291027508374689}},\"is_component\":false,\"updated_at\":\"2023-12-04T22:59:08.830269\",\"folder\":null,\"id\":\"bc7eb94b-6fc6-49cb-9b19-35347ba51afa\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Web Scraper - Content & Links\",\"description\":\"Fetch and parse text and links from a given URL.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":338,\"id\":\"LLMChain-H7PBy\",\"type\":\"genericNode\",\"position\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"data\":{\"type\":\"LLMChain\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Chain,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable]:\\n return LLMChain(prompt=prompt, llm=llm, memory=memory)\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false}},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Chain\",\"Callable\"],\"display_name\":\"LLMChain\",\"custom_fields\":{\"llm\":null,\"memory\":null,\"prompt\":null},\"output_types\":[\"LLMChain\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"LLMChain-H7PBy\"},\"selected\":false,\"positionAbsolute\":{\"x\":1682.494010974207,\"y\":275.5701585623092},\"dragging\":false},{\"width\":384,\"height\":626,\"id\":\"ChatOpenAI-VSAdc\",\"type\":\"genericNode\",\"position\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859},\"data\":{\"type\":\"ChatOpenAI\",\"node\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"list\":true},\"async_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"async_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"default_headers\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"default_query\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"http_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"http_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":2,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false,\"value\":\"\"},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"password\":false,\"options\":[\"gpt-4-1106-preview\",\"gpt-4\",\"gpt-4-32k\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"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\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-VSAdc\"},\"selected\":false,\"dragging\":false,\"positionAbsolute\":{\"x\":1002.4263147022411,\"y\":-198.2305006886859}},{\"width\":384,\"height\":374,\"data\":{\"id\":\"GroupNode-WXPMk\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input Types\"},\"input_variables_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true,\"value\":[\"url\",\"html_markdown\",\"html_links\",\"query\"],\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_variables\"},\"display_name\":\"Input Variables\"},\"partial_variables_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"partial_variables\"},\"display_name\":\"Partial Variables\"},\"template_PromptTemplate-H9Udy\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false,\"value\":\"Below is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\",\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template\"},\"display_name\":\"Template\"},\"template_format_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"template_format\"},\"display_name\":\"Template Format\"},\"validate_template_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"validate_template\"},\"display_name\":\"Validate Template\"},\"query_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"give me links\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"query\"}},\"code_CustomComponent-OACE0\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown format.\",\"base_classes\":[\"str\"],\"display_name\":\"HTML to Markdown\",\"custom_fields\":{\"html_content\":null,\"ignore_links\":null},\"output_types\":[\"str\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-f6nOg\"},\"selected\":true,\"positionAbsolute\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"dragging\":false},{\"width\":384,\"height\":375,\"id\":\"CustomComponent-FGzJJ\",\"type\":\"genericNode\",\"position\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse HTML\",\"custom_fields\":{\"html_content\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-0XtUN\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":214.00059169497604,\"y\":177.27071061129823}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-ODIcp\",\"type\":\"genericNode\",\"position\":{\"x\":845.0502195222412,\"y\":366.54344452019404},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract 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is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser 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Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"0.1\",\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"ChatOpenAI\",\"BaseChatModel\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"id\":\"ChatOpenAI-NTTcv\"},\"selected\":false,\"positionAbsolute\":{\"x\":-221.33853765781578,\"y\":-35.48749923706055}},{\"width\":384,\"height\":374,\"id\":\"PromptTemplate-VELMV\",\"type\":\"genericNode\",\"position\":{\"x\":-222,\"y\":682.560723386973},\"data\":{\"id\":\"PromptTemplate-VELMV\",\"type\":\"PromptTemplate\",\"node\":{\"output_types\":[],\"display_name\":\"Web Scraper\",\"documentation\":\"\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"description\":\"Fetch and parse text and links from a given URL.\",\"template\":{\"code_CustomComponent-f6nOg\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"code\"},\"display_name\":\"Code\"},\"ignore_links_CustomComponent-f6nOg\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-f6nOg\",\"field\":\"ignore_links\"}},\"code_CustomComponent-FGzJJ\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"Fetch HTML\\\"\\n description: str = \\\"Fetches HTML content from a specified URL.\\\"\\n \\n def build_config(self):\\n return {\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-FGzJJ\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-0XtUN\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-0XtUN\",\"field\":\"code\"},\"display_name\":\"Code\"},\"code_CustomComponent-ODIcp\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ExtractHyperlinks(CustomComponent):\\n display_name: str = \\\"Extract Hyperlinks\\\"\\n description: str = \\\"Extracts hyperlinks from parsed HTML content.\\\"\\n\\n def build(self, soup: Data) -> list:\\n links = [link.get('href') for link in soup.find_all('a')]\\n self.status = links\\n return links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-ODIcp\",\"field\":\"code\"},\"display_name\":\"Code\"},\"output_parser_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"output_parser\"},\"display_name\":\"Output Parser\"},\"input_types_PromptTemplate-H9Udy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false,\"proxy\":{\"id\":\"PromptTemplate-H9Udy\",\"field\":\"input_types\"},\"display_name\":\"Input 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requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"code\"},\"display_name\":\"Code\"},\"url_CustomComponent-OACE0\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-OACE0\",\"field\":\"url\"},\"value\":\"https://paperswithcode.com/\"},\"code_CustomComponent-whsQ5\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nclass ListToMarkdownBullets(CustomComponent):\\n display_name: str = \\\"List to Bullets\\\"\\n description: str = \\\"Converts a Python list into Markdown bullet points.\\\"\\n\\n def build(self, items: list) -> str:\\n markdown_bullets = '\\\\n'.join(f'* {item}' for item in items)\\n return markdown_bullets\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false,\"proxy\":{\"id\":\"CustomComponent-whsQ5\",\"field\":\"code\"},\"display_name\":\"Code\"}},\"flow\":{\"data\":{\"nodes\":[{\"width\":384,\"height\":405,\"id\":\"CustomComponent-f6nOg\",\"type\":\"genericNode\",\"position\":{\"x\":665.2835942536854,\"y\":-371.7823429271119},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\nimport html2text\\n\\nclass ConvertHTMLToMarkdown(CustomComponent):\\n display_name: str = \\\"HTML to Markdown\\\"\\n description: str = \\\"Converts HTML content to Markdown format.\\\"\\n\\n def build(self, html_content: Data, ignore_links: bool = False) -> str:\\n converter = html2text.HTML2Text()\\n converter.ignore_links = ignore_links\\n markdown_text = converter.handle(html_content)\\n self.status = markdown_text\\n return markdown_text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"ignore_links\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"ignore_links\",\"display_name\":\"ignore_links\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Converts HTML content to Markdown 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{\\\"url\\\": {\\\"input_types\\\": [\\\"Data\\\", \\\"str\\\"]}} \\n\\n def build(self, url: str) -> Data:\\n response = requests.get(url)\\n self.status = str(response) + \\\"\\\\n\\\\n\\\" + response.text\\n return response.text\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Data\",\"str\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"\"}},\"description\":\"Fetches HTML content from a specified URL.\",\"base_classes\":[\"Data\"],\"display_name\":\"Fetch HTML\",\"custom_fields\":{\"url\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"id\":\"CustomComponent-FGzJJ\"},\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":-464.4553400967736,\"y\":-225.62715888255525}},{\"width\":384,\"height\":329,\"id\":\"CustomComponent-0XtUN\",\"type\":\"genericNode\",\"position\":{\"x\":214.00059169497604,\"y\":177.27071061129823},\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from bs4 import BeautifulSoup\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\nclass ParseHTMLContent(CustomComponent):\\n display_name: str = \\\"Parse HTML\\\"\\n description: str = \\\"Parses HTML content using Beautiful Soup.\\\"\\n\\n def build(self, html_content: Data) -> Data:\\n soup = BeautifulSoup(html_content, 'html.parser')\\n self.status = soup\\n return soup\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"html_content\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"html_content\",\"display_name\":\"html_content\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Parses HTML content using Beautiful Soup.\",\"base_classes\":[\"Data\"],\"display_name\":\"Parse 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links\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"soup\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"soup\",\"display_name\":\"soup\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false}},\"description\":\"Extracts hyperlinks from parsed HTML content.\",\"base_classes\":[\"list\"],\"display_name\":\"Extract 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is the HTML content (as markdown) and hyperlinks extracted from {url}\\n\\n---\\n\\nContent:\\n\\n{html_markdown}\\n\\n---\\n\\nLinks:\\n\\n{html_links}\\n\\n---\\n\\nAnswer the user query as best as possible.\\n\\nUser query:\\n\\n{query}\\n\\nAnswer:\\n\"},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\",\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_markdown\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_markdown\",\"display_name\":\"html_markdown\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"html_links\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"html_links\",\"display_name\":\"html_links\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"query\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\",\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"input_types\":[\"Document\",\"BaseOutputParser\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"A 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Data\\nimport requests\\n\\nclass FetchHTMLContent(CustomComponent):\\n display_name: str = \\\"URL Input\\\"\\n\\n def build(self, url: str) -> str:\\n return url\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":null,\"base_classes\":[\"str\"],\"display_name\":\"URL 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PromptRunner(CustomComponent):\\n display_name: str = \\\"Prompt Runner\\\"\\n description: str = \\\"Run a Chain with the given PromptTemplate\\\"\\n beta: bool = True\\n field_config = {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt Template\\\",\\n \\\"info\\\": \\\"Make sure the prompt has all variables filled.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}) -> Document:\\n chain = prompt | llm\\n # The input is an empty dict because the prompt is already filled\\n result = chain.invoke(input=inputs)\\n if hasattr(result, \\\"content\\\"):\\n result = result.content\\n self.repr_value = result\\n return 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(1)\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per 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string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per domain)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"keyword\",\"password\":false,\"options\":[\"neural\",\"keyword\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"start_date\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"start_date\",\"display_name\":\"start_date\",\"advanced\":false,\"input_types\":[\"Data\"],\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"use_autoprompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"use_autoprompt\",\"display_name\":\"Use Autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false}},\"description\":\"Search in Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-ipifC\"},\"id\":\"CustomComponent-ipifC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:49.799612\",\"folder\":null,\"id\":\"ca83ee08-2f93-427d-9897-80fef6dd5447\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Metaphor Search\",\"description\":\"Search in Metaphor with a custom string.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CustomComponent\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\nfrom metaphor_python import Metaphor\\nimport json\\n\\nfrom typing import List\\nfrom langflow.field_typing import Data\\n\\nclass MetaphorSearch(CustomComponent):\\n display_name: str = \\\"Metaphor Search\\\"\\n description: str = \\\"Search in Metaphor with a custom string.\\\"\\n beta = True\\n \\n def build_config(self):\\n return {\\n \\\"metaphor_client\\\": {\\\"display_name\\\": \\\"Metaphor Wrapper\\\"}, \\n \\\"search_num_results\\\": {\\\"display_name\\\": \\\"Number of Results (per domain)\\\"},\\n \\\"include_domains\\\": {\\\"display_name\\\": \\\"Include Domains\\\", \\\"is_list\\\": True},\\n \\\"start_date\\\": {\\\"display_name\\\": \\\"Start Date\\\"},\\n \\\"use_autoprompt\\\": {\\\"display_name\\\": \\\"Use Autoprompt\\\", \\\"type\\\": \\\"boolean\\\"},\\n \\\"search_type\\\": {\\\"display_name\\\": \\\"Search Type\\\", \\\"options\\\": [\\\"neural\\\", \\\"keyword\\\"]},\\n \\\"start_date\\\": {\\\"input_types\\\": [\\\"Data\\\"]}\\n }\\n\\n def build(\\n self,\\n methaphor_client: Data,\\n query: str,\\n search_type: str='keyword',\\n search_num_results: int = 5,\\n include_domains: List[str]= [\\\"youtube.com\\\"],\\n use_autoprompt: bool = False,\\n start_date: str=\\\"2023-01-01\\\",\\n \\n ) -> Data:\\n \\n results = []\\n for domain in include_domains:\\n response = methaphor_client.search(\\n query,\\n num_results=int(search_num_results),\\n include_domains=[domain],\\n use_autoprompt=use_autoprompt,\\n type=search_type,\\n # start_crawl_date=start_date,\\n start_published_date=start_date\\n )\\n results.extend(response.results)\\n \\n self.repr_value = results\\n\\n return results\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"include_domains\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[\"yout\"],\"password\":false,\"name\":\"include_domains\",\"display_name\":\"Include Domains\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"methaphor_client\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"methaphor_client\",\"display_name\":\"methaphor_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Data\",\"list\":false},\"query\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"query\",\"display_name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false,\"value\":\"pasteldasdasdas\"},\"search_num_results\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"search_num_results\",\"display_name\":\"Number of Results (per 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Metaphor with a custom string.\",\"base_classes\":[\"Data\"],\"display_name\":\"Metaphor Search\",\"custom_fields\":{\"include_domains\":null,\"methaphor_client\":null,\"query\":null,\"search_num_results\":null,\"search_type\":null,\"start_date\":null,\"use_autoprompt\":null},\"output_types\":[\"Data\"],\"documentation\":\"\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"CustomComponent-Y4qL7\"},\"id\":\"CustomComponent-Y4qL7\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T13:26:53.719960\",\"folder\":null,\"id\":\"5847602b-769a-4a82-82e8-a70f54a59929\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Lively Williams\",\"description\":\"Conversational Cartography Unlocked.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T13:27:45.691254\",\"folder\":null,\"id\":\"0e3bdba9-127a-4399-81a4-2865b70a4a47\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Shared Component\",\"description\":\"Conversational Cartography Unlocked.\",\"data\":{\"nodes\":[{\"width\":384,\"height\":328,\"id\":\"CSVAgent-TK9Ea\",\"type\":\"genericNode\",\"position\":{\"x\":251.25514772667083,\"y\":160.7424529887874},\"data\":{\"type\":\"CSVAgent\",\"node\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"_type\":\"csv_agent\"},\"description\":\"Construct a CSV agent from a CSV and 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files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-Z0uol\"},\"id\":\"AirbyteJSONLoader-Z0uol\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:33.764117\",\"folder\":null,\"id\":\"2a37c76c-65c8-4c01-a72d-eb46f3d41a56\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-28ASv\"},\"id\":\"AmazonBedrockEmbeddings-28ASv\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:03:41.066618\",\"folder\":null,\"id\":\"850ba4e6-96ca-49a7-9b0c-4b7048fd52c8\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"ConversationBufferMemory\",\"description\":\"Buffer for storing conversation memory.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"ConversationBufferMemory\",\"node\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"memory_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\"],\"display_name\":\"ConversationBufferMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"ConversationBufferMemory-WaoLx\"},\"id\":\"ConversationBufferMemory-WaoLx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:04:46.058568\",\"folder\":null,\"id\":\"be650940-0449-4eb0-a708-056310f924fd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (1)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\"},\"id\":\"AmazonBedrockEmbeddings-Bs5PG\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:05:50.570800\",\"folder\":null,\"id\":\"53ad1fe2-f3af-4354-86e0-eb141ce12859\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (2)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\"},\"id\":\"AmazonBedrockEmbeddings-zSZ4t\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:06:11.415088\",\"folder\":null,\"id\":\"e9ed1c90-146e-452d-8ccd-ebf2e0da4331\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (3)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\"},\"id\":\"AmazonBedrockEmbeddings-Bxhlt\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:07:06.411108\",\"folder\":null,\"id\":\"83783c57-64e3-41a6-aa7e-ed82f80f1e53\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"CSVLoader (6)\",\"description\":\"Load a `CSV` file into a list of Documents.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"CSVLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"z2b\":\"z9\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"CSVLoader-2pn2o\"},\"id\":\"CSVLoader-2pn2o\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:38:30.706258\",\"folder\":null,\"id\":\"c4ae9de6-9df3-4d3c-afc9-1752af4fecd7\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-MJrAC\"},\"id\":\"AgentInitializer-MJrAC\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:42:54.715163\",\"folder\":null,\"id\":\"ff84b5f9-5680-4084-a9f1-7d94fe675486\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Agent Initializer (1)\",\"description\":\"Initialize a Langchain Agent.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AgentInitializer\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n }\\n\\n def build(\\n self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"AgentInitializer\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"max_iterations\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Chain\",\"AgentExecutor\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"max_iterations\":null,\"memory\":null,\"tools\":null},\"output_types\":[\"AgentInitializer\"],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AgentInitializer-3lcZ4\"},\"id\":\"AgentInitializer-3lcZ4\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:43:20.819934\",\"folder\":null,\"id\":\"97f5db9f-d6cc-4555-88fb-3be102c67814\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Exuberant Banach\",\"description\":\"Unfolding Linguistic Possibilities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-09T14:52:49.951416\",\"folder\":null,\"id\":\"a600acd1-213a-4ce7-8648-ab2ee59f5918\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (3)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-JhQtx\"},\"id\":\"AirbyteJSONLoader-JhQtx\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:44:49.587337\",\"folder\":null,\"id\":\"07c52ad7-ca52-4cdd-ab40-74a91dbad0dd\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (4)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-bIvDc\"},\"id\":\"AirbyteJSONLoader-bIvDc\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:45:13.458500\",\"folder\":null,\"id\":\"53e4d256-3653-444b-b8e0-fb97b3ae5c7e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"AirbyteJSONLoader (5)\",\"description\":\"Load local `Airbyte` json files.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AirbyteJSONLoader\",\"node\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\".json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":[{\"\":\"\"}],\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null,\"official\":false},\"id\":\"AirbyteJSONLoader-2BLS8\"},\"id\":\"AirbyteJSONLoader-2BLS8\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:45:44.461699\",\"folder\":null,\"id\":\"b5158cc1-c6b8-4e25-907a-bc7e7637aa38\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Amazon Bedrock Embeddings (4)\",\"description\":\"Embeddings model from Amazon Bedrock.\",\"data\":{\"edges\":[],\"nodes\":[{\"data\":{\"type\":\"AmazonBedrockEmbeddings\",\"node\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings import BedrockEmbeddings\\nfrom langchain.embeddings.base import Embeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"credentials_profile_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"endpoint_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"region_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"custom_fields\":{\"credentials_profile_name\":null,\"endpoint_url\":null,\"model_id\":null,\"region_name\":null},\"output_types\":[\"AmazonBedrockEmbeddings\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"beta\":true,\"error\":null,\"official\":false},\"id\":\"AmazonBedrockEmbeddings-NsBzN\"},\"id\":\"AmazonBedrockEmbeddings-NsBzN\",\"position\":{\"x\":0,\"y\":0},\"type\":\"genericNode\"}],\"viewport\":{\"x\":1,\"y\":1,\"zoom\":1}},\"is_component\":true,\"updated_at\":\"2023-12-09T14:50:29.791575\",\"folder\":null,\"id\":\"3fb77f72-1be7-4ce0-ae89-a82b0eee9acf\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Evil Golick\",\"description\":\"Where Language Meets Logic.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.707854\",\"folder\":null,\"id\":\"9c5d21c2-ea82-4cf4-a591-c3d3fd3f13e6\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Gleeful Davinci (1)\",\"description\":\"Unleashing Linguistic Creativity.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.761150\",\"folder\":null,\"id\":\"f8e2e16e-129c-4116-9626-2c6b524d97b7\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Effervescent Degrasse\",\"description\":\"Language Models, Mapped and Mastered.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.764440\",\"folder\":null,\"id\":\"d7f4f7b5-effe-4834-8cab-4f2fc4f6e9de\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Prickly Archimedes\",\"description\":\"Language Chainlink Master.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.767546\",\"folder\":null,\"id\":\"2265d813-4a87-410f-b06a-65e35db8219e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Boring Heyrovsky\",\"description\":\"Building Intelligent Interactions.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.765105\",\"folder\":null,\"id\":\"10bfd881-e67e-4c33-965d-ee041695edce\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Nostalgic Carroll\",\"description\":\"Unfolding Linguistic Possibilities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.811794\",\"folder\":null,\"id\":\"63fa5653-4513-47f2-8dfa-baeb1d981f93\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Pensive Perlman\",\"description\":\"Empowering Communication, Enabling Opportunities.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.814993\",\"folder\":null,\"id\":\"f4bc5945-20d9-4703-a5bb-6a4a3ef7fc8e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Hilarious Stallman\",\"description\":\"Connect the Dots, Craft Language.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:19:02.813144\",\"folder\":null,\"id\":\"8d8b80f9-4b74-4cd5-bc5c-08a32c4d2b95\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Awesome Einstein\",\"description\":\"The Power of Language at Your Fingertips.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:19.518262\",\"folder\":null,\"id\":\"21808fd7-a492-4e29-8863-301c2785f542\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Funky Swanson\",\"description\":\"Sculpting Language with Precision.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.185637\",\"folder\":null,\"id\":\"7752299a-4aa9-44db-8d9c-5c4a2ac1b071\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Romantic Pascal\",\"description\":\"Unlock the Power of AI in Your Business Conversations.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.223064\",\"folder\":null,\"id\":\"78f48a13-6a9f-42ce-9d58-ec9b63b381d2\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Twinkly Bartik\",\"description\":\"Sculpting Language with Precision.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.239758\",\"folder\":null,\"id\":\"38074091-3d7c-4d42-b61b-ae195c1b8a4e\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Condescending Joliot\",\"description\":\"Language Models, Unleashed.\",\"data\":{\"nodes\":[],\"edges\":[],\"viewport\":{\"zoom\":1,\"x\":0,\"y\":0}},\"is_component\":false,\"updated_at\":\"2023-12-11T18:20:20.271996\",\"folder\":null,\"id\":\"773020aa-5c2d-4632-ad9e-21276861ab93\",\"user_id\":\"d253bfba-6368-44dc-85f7-0d6da9e45968\"},{\"name\":\"Gleeful Kalam\",\"description\":\"The Power of Language at Your 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+extract-msg = "^0.45.0" +jq = "^1.6.0" +boto3 = "^1.28.63" +numexpr = "^2.8.6" +qianfan = "0.0.5" +pgvector = "^0.2.3" +pyautogen = "^0.2.0" [tool.poetry.group.dev.dependencies] +pytest-asyncio = "^0.23.1" types-redis = "^4.6.0.5" -black = "^23.1.0" -ipykernel = "^6.21.2" -mypy = "^1.1.1" -ruff = "^0.0.254" +ipykernel = "^6.27.0" +mypy = "^1.7.1" +ruff = "^0.1.5" httpx = "*" -pytest = "^7.2.2" -types-requests = "^2.28.11" -requests = "^2.28.0" -pytest-cov = "^4.0.0" +pytest = "^7.4.2" +types-requests = "^2.31.0" +requests = "^2.31.0" +pytest-cov = "^4.1.0" pandas-stubs = "^2.0.0.230412" types-pillow = "^9.5.0.2" -types-appdirs = "^1.4.3.5" types-pyyaml = "^6.0.12.8" types-python-jose = "^3.3.4.8" types-passlib = "^1.7.7.13" -locust = "^2.16.1" -pytest-mock = "^3.11.1" -pytest-xdist = "^3.3.1" +locust = "^2.19.1" +pytest-mock = "^3.12.0" +pytest-xdist = "^3.5.0" types-pywin32 = "^306.0.0.4" types-google-cloud-ndb = "^2.2.0.0" pytest-sugar = "^0.9.7" +pytest-instafail = "^0.5.0" [tool.poetry.extras] @@ -134,8 +145,12 @@ markers = ["async_test"] [tool.ruff] +exclude = ["src/backend/langflow/alembic/*"] line-length = 120 +[tool.mypy] +plugins = "pydantic.mypy" + [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/src/backend/langflow/__main__.py b/src/backend/langflow/__main__.py index dfc2e27a5..2b1d4bbe3 100644 --- a/src/backend/langflow/__main__.py +++ b/src/backend/langflow/__main__.py @@ -9,17 +9,19 @@ from typing import Optional import httpx import typer from dotenv import load_dotenv -from langflow.main import setup_app -from langflow.services.database.utils import session_getter -from langflow.services.getters import get_db_service, get_settings_service -from langflow.services.utils import initialize_services, initialize_settings_service -from langflow.utils.logger import configure, logger from multiprocess import Process, cpu_count # type: ignore from rich import box from rich import print as rprint from rich.console import Console from rich.panel import Panel from rich.table import Table +from sqlmodel import select + +from langflow.main import setup_app +from langflow.services.database.utils import session_getter +from langflow.services.deps import get_db_service, get_settings_service +from langflow.services.utils import initialize_services, initialize_settings_service +from langflow.utils.logger import configure, logger console = Console() @@ -70,6 +72,7 @@ def update_settings( dev: bool = False, remove_api_keys: bool = False, components_path: Optional[Path] = None, + store: bool = False, ): """Update the settings from a config file.""" @@ -88,16 +91,38 @@ def update_settings( if components_path: logger.debug(f"Adding component path {components_path}") settings_service.settings.update_settings(COMPONENTS_PATH=components_path) + if not store: + logger.debug("Setting store to False") + settings_service.settings.update_settings(STORE=False) + + +def version_callback(value: bool): + """ + Show the version and exit. + """ + from langflow import __version__ + + if value: + typer.echo(f"Langflow Version: {__version__}") + raise typer.Exit() + + +@app.callback() +def main_entry_point( + version: bool = typer.Option( + None, "--version", callback=version_callback, is_eager=True, help="Show the version and exit." + ), +): + """ + Main entry point for the Langflow CLI. + """ + pass @app.command() def run( - host: str = typer.Option( - "127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST" - ), - workers: int = typer.Option( - 1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS" - ), + host: str = typer.Option("127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"), + workers: int = typer.Option(1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"), timeout: int = typer.Option(300, help="Worker timeout in seconds."), port: int = typer.Option(7860, help="Port to listen on.", envvar="LANGFLOW_PORT"), components_path: Optional[Path] = typer.Option( @@ -105,32 +130,17 @@ def run( help="Path to the directory containing custom components.", envvar="LANGFLOW_COMPONENTS_PATH", ), - config: str = typer.Option( - Path(__file__).parent / "config.yaml", help="Path to the configuration file." - ), + config: str = typer.Option(Path(__file__).parent / "config.yaml", help="Path to the configuration file."), # .env file param - env_file: Path = typer.Option( - None, help="Path to the .env file containing environment variables." - ), - log_level: str = typer.Option( - "critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL" - ), - log_file: Path = typer.Option( - "logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE" - ), + env_file: Path = typer.Option(None, help="Path to the .env file containing environment variables."), + log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"), + log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"), cache: Optional[str] = typer.Option( envvar="LANGFLOW_LANGCHAIN_CACHE", help="Type of cache to use. (InMemoryCache, SQLiteCache)", default=None, ), dev: bool = typer.Option(False, help="Run in development mode (may contain bugs)"), - # This variable does not work but is set by the .env file - # and works with Pydantic - # database_url: str = typer.Option( - # None, - # help="Database URL to connect to. If not provided, a local SQLite database will be used.", - # envvar="LANGFLOW_DATABASE_URL", - # ), path: str = typer.Option( None, help="Path to the frontend directory containing build files. This is for development purposes only.", @@ -151,6 +161,11 @@ def run( help="Run only the backend server without the frontend.", envvar="LANGFLOW_BACKEND_ONLY", ), + store: bool = typer.Option( + True, + help="Enables the store features.", + envvar="LANGFLOW_STORE", + ), ): """ Run the Langflow. @@ -169,6 +184,7 @@ def run( remove_api_keys=remove_api_keys, cache=cache, components_path=components_path, + store=store, ) # create path object if path is provided static_files_dir: Optional[Path] = Path(path) if path else None @@ -198,9 +214,7 @@ def run( def run_on_mac_or_linux(host, port, log_level, options, app, open_browser=True): - webapp_process = Process( - target=run_langflow, args=(host, port, log_level, options, app) - ) + webapp_process = Process(target=run_langflow, args=(host, port, log_level, options, app)) webapp_process.start() status_code = 0 while status_code != 200: @@ -276,9 +290,7 @@ def print_banner(host, port): ) # Create a panel with the title and the info text, and a border around it - panel = Panel( - f"{title}\n{info_text}", box=box.ROUNDED, border_style="blue", expand=False - ) + panel = Panel(f"{title}\n{info_text}", box=box.ROUNDED, border_style="blue", expand=False) # Print the banner with a separator line before and after rprint(panel) @@ -310,12 +322,8 @@ def run_langflow(host, port, log_level, options, app): @app.command() def superuser( username: str = typer.Option(..., prompt=True, help="Username for the superuser."), - password: str = typer.Option( - ..., prompt=True, hide_input=True, help="Password for the superuser." - ), - log_level: str = typer.Option( - "critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL" - ), + password: str = typer.Option(..., prompt=True, hide_input=True, help="Password for the superuser."), + log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"), ): """ Create a superuser. @@ -328,9 +336,9 @@ def superuser( if create_super_user(db=session, username=username, password=password): # Verify that the superuser was created - from langflow.services.database.models.user.user import User + from langflow.services.database.models.user.model import User - user: User = session.query(User).filter(User.username == username).first() + user: User = session.exec(select(User).where(User.username == username)).first() if user is None or not user.is_superuser: typer.echo("Superuser creation failed.") return @@ -342,11 +350,23 @@ def superuser( @app.command() -def migration(test: bool = typer.Option(True, help="Run migrations in test mode.")): +def migration( + test: bool = typer.Option(True, help="Run migrations in test mode."), + fix: bool = typer.Option( + False, + help="Fix migrations. This is a destructive operation, and should only be used if you know what you are doing.", + ), +): """ Run or test migrations. """ - initialize_services() + if fix: + if not typer.confirm( + "This will delete all data necessary to fix migrations. Are you sure you want to continue?" + ): + raise typer.Abort() + + initialize_services(fix_migration=fix) db_service = get_db_service() if not test: db_service.run_migrations() diff --git a/src/backend/langflow/alembic/env.py b/src/backend/langflow/alembic/env.py index e606036f1..283b24a6f 100644 --- a/src/backend/langflow/alembic/env.py +++ b/src/backend/langflow/alembic/env.py @@ -5,7 +5,7 @@ from sqlalchemy import pool from alembic import context -from langflow.services.database.manager import SQLModel +from langflow.services.database.service import SQLModel # this is the Alembic Config object, which provides # access to the values within the .ini file in use. diff --git a/src/backend/langflow/alembic/versions/1ef9c4f3765d_.py b/src/backend/langflow/alembic/versions/1ef9c4f3765d_.py new file mode 100644 index 000000000..f2bc42917 --- /dev/null +++ b/src/backend/langflow/alembic/versions/1ef9c4f3765d_.py @@ -0,0 +1,43 @@ +""" + + +Revision ID: 1ef9c4f3765d +Revises: fd531f8868b1 +Create Date: 2023-12-04 15:00:27.968998 + +""" +from typing import Sequence, Union + +import sqlalchemy as sa +import sqlmodel +from alembic import op + +# revision identifiers, used by Alembic. +revision: str = '1ef9c4f3765d' +down_revision: Union[str, None] = 'fd531f8868b1' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table('apikey', schema=None) as batch_op: + batch_op.alter_column('name', + existing_type=sqlmodel.sql.sqltypes.AutoString(), + nullable=True) + except Exception as e: + pass + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table('apikey', schema=None) as batch_op: + batch_op.alter_column('name', + existing_type=sa.VARCHAR(), + nullable=False) + except Exception as e: + pass + # ### end Alembic commands ### diff --git a/src/backend/langflow/alembic/versions/260dbcc8b680_adds_tables.py b/src/backend/langflow/alembic/versions/260dbcc8b680_adds_tables.py index 53e049a05..729e1898f 100644 --- a/src/backend/langflow/alembic/versions/260dbcc8b680_adds_tables.py +++ b/src/backend/langflow/alembic/versions/260dbcc8b680_adds_tables.py @@ -7,9 +7,9 @@ Create Date: 2023-08-27 19:49:02.681355 """ from typing import Sequence, Union -from alembic import op import sqlalchemy as sa import sqlmodel +from alembic import op from sqlalchemy.engine.reflection import Inspector # revision identifiers, used by Alembic. @@ -23,7 +23,7 @@ def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### conn = op.get_bind() - inspector = Inspector.from_engine(conn) + inspector = Inspector.from_engine(conn) # type: ignore # List existing tables existing_tables = inspector.get_table_names() # Drop 'flowstyle' table if it exists @@ -145,8 +145,8 @@ def upgrade() -> None: def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### - conn = op.get_bind() - inspector = Inspector.from_engine(conn) + conn = op.get_bind() + inspector = Inspector.from_engine(conn) # type: ignore # List existing tables existing_tables = inspector.get_table_names() if "flow" in existing_tables: diff --git a/src/backend/langflow/alembic/versions/2ac71eb9c3ae_adds_credential_table.py b/src/backend/langflow/alembic/versions/2ac71eb9c3ae_adds_credential_table.py new file mode 100644 index 000000000..3f974dc04 --- /dev/null +++ b/src/backend/langflow/alembic/versions/2ac71eb9c3ae_adds_credential_table.py @@ -0,0 +1,45 @@ +"""Adds Credential table + +Revision ID: c1c8e217a069 +Revises: 7d2162acc8b2 +Create Date: 2023-11-24 10:45:38.465302 + +""" +from typing import Sequence, Union + +import sqlalchemy as sa +import sqlmodel +from alembic import op + +# revision identifiers, used by Alembic. +revision: str = '2ac71eb9c3ae' +down_revision: Union[str, None] = '7d2162acc8b2' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + op.create_table('credential', + sa.Column('name', sqlmodel.sql.sqltypes.AutoString(), nullable=True), + sa.Column('value', sqlmodel.sql.sqltypes.AutoString(), nullable=True), + sa.Column('provider', sqlmodel.sql.sqltypes.AutoString(), nullable=True), + sa.Column('user_id', sqlmodel.sql.sqltypes.GUID(), nullable=False), + sa.Column('id', sqlmodel.sql.sqltypes.GUID(), nullable=False), + sa.Column('created_at', sa.DateTime(), nullable=False), + sa.Column('updated_at', sa.DateTime(), nullable=True), + sa.PrimaryKeyConstraint('id') + ) + except Exception: + pass + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + op.drop_table('credential') + except Exception: + pass + # ### end Alembic commands ### diff --git a/src/backend/langflow/alembic/versions/67cc006d50bf_add_profile_image_column.py b/src/backend/langflow/alembic/versions/67cc006d50bf_add_profile_image_column.py index ca0aa0542..6ce9316ac 100644 --- a/src/backend/langflow/alembic/versions/67cc006d50bf_add_profile_image_column.py +++ b/src/backend/langflow/alembic/versions/67cc006d50bf_add_profile_image_column.py @@ -7,9 +7,9 @@ Create Date: 2023-09-08 07:36:13.387318 """ from typing import Sequence, Union -from alembic import op import sqlalchemy as sa import sqlmodel +from alembic import op from sqlalchemy.engine.reflection import Inspector # revision identifiers, used by Alembic. @@ -22,7 +22,7 @@ depends_on: Union[str, Sequence[str], None] = None def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### conn = op.get_bind() - inspector = Inspector.from_engine(conn) + inspector = Inspector.from_engine(conn) # type: ignore if "user" in inspector.get_table_names() and "profile_image" not in [ column["name"] for column in inspector.get_columns("user") ]: @@ -39,7 +39,7 @@ def upgrade() -> None: def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### conn = op.get_bind() - inspector = Inspector.from_engine(conn) + inspector = Inspector.from_engine(conn) # type: ignore if "user" in inspector.get_table_names() and "profile_image" in [ column["name"] for column in inspector.get_columns("user") ]: diff --git a/src/backend/langflow/alembic/versions/7843803a87b5_store_updates.py b/src/backend/langflow/alembic/versions/7843803a87b5_store_updates.py new file mode 100644 index 000000000..54c418943 --- /dev/null +++ b/src/backend/langflow/alembic/versions/7843803a87b5_store_updates.py @@ -0,0 +1,50 @@ +"""Store updates + +Revision ID: 7843803a87b5 +Revises: eb5866d51fd2 +Create Date: 2023-10-18 23:08:57.744906 + +""" +from typing import Sequence, Union + +import sqlalchemy as sa +import sqlmodel +from alembic import op +from loguru import logger + +# revision identifiers, used by Alembic. +revision: str = "7843803a87b5" +down_revision: Union[str, None] = "eb5866d51fd2" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table("flow", schema=None) as batch_op: + batch_op.add_column(sa.Column("is_component", sa.Boolean(), nullable=True)) + + with op.batch_alter_table("user", schema=None) as batch_op: + batch_op.add_column( + sa.Column( + "store_api_key", sqlmodel.AutoString(), nullable=True + ) + ) + except Exception as e: + logger.exception(e) + + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table("user", schema=None) as batch_op: + batch_op.drop_column("store_api_key") + + with op.batch_alter_table("flow", schema=None) as batch_op: + batch_op.drop_column("is_component") + except Exception: + pass + # ### end Alembic commands ### diff --git a/src/backend/langflow/alembic/versions/7d2162acc8b2_adds_updated_at_and_folder_cols.py b/src/backend/langflow/alembic/versions/7d2162acc8b2_adds_updated_at_and_folder_cols.py new file mode 100644 index 000000000..f8280053b --- /dev/null +++ b/src/backend/langflow/alembic/versions/7d2162acc8b2_adds_updated_at_and_folder_cols.py @@ -0,0 +1,93 @@ +"""Adds updated_at and folder cols + +Revision ID: 7d2162acc8b2 +Revises: f5ee9749d1a6 +Create Date: 2023-11-21 20:56:53.998781 + +""" +from typing import Sequence, Union + +import sqlalchemy as sa +import sqlmodel +from alembic import op + +# revision identifiers, used by Alembic. +revision: str = '7d2162acc8b2' +down_revision: Union[str, None] = 'f5ee9749d1a6' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table('component', schema=None) as batch_op: + batch_op.drop_index('ix_component_frontend_node_id') + batch_op.drop_index('ix_component_name') + op.drop_table('component') + op.drop_table('flowstyle') + except Exception as e: + print(e) + pass + with op.batch_alter_table('apikey', schema=None) as batch_op: + batch_op.alter_column('name', + existing_type=sa.VARCHAR(), + nullable=False) + + with op.batch_alter_table('flow', schema=None) as batch_op: + batch_op.add_column(sa.Column('updated_at', sa.DateTime(), nullable=True)) + batch_op.add_column(sa.Column('folder', sqlmodel.sql.sqltypes.AutoString(), nullable=True)) + + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table('flow', schema=None) as batch_op: + batch_op.drop_column('folder') + batch_op.drop_column('updated_at') + except Exception as e: + print(e) + pass + + try: + + with op.batch_alter_table('apikey', schema=None) as batch_op: + batch_op.alter_column('name', + existing_type=sa.VARCHAR(), + nullable=True) + except Exception as e: + print(e) + pass + try: + op.create_table('flowstyle', + sa.Column('color', sa.VARCHAR(), nullable=False), + sa.Column('emoji', sa.VARCHAR(), nullable=False), + sa.Column('flow_id', sa.CHAR(length=32), nullable=True), + sa.Column('id', sa.CHAR(length=32), nullable=False), + sa.ForeignKeyConstraint(['flow_id'], ['flow.id'], ), + sa.PrimaryKeyConstraint('id'), + sa.UniqueConstraint('id') + ) + op.create_table('component', + sa.Column('id', sa.CHAR(length=32), nullable=False), + sa.Column('frontend_node_id', sa.CHAR(length=32), nullable=False), + sa.Column('name', sa.VARCHAR(), nullable=False), + sa.Column('description', sa.VARCHAR(), nullable=True), + sa.Column('python_code', sa.VARCHAR(), nullable=True), + sa.Column('return_type', sa.VARCHAR(), nullable=True), + sa.Column('is_disabled', sa.BOOLEAN(), nullable=False), + sa.Column('is_read_only', sa.BOOLEAN(), nullable=False), + sa.Column('create_at', sa.DATETIME(), nullable=False), + sa.Column('update_at', sa.DATETIME(), nullable=False), + sa.PrimaryKeyConstraint('id') + ) + + with op.batch_alter_table('component', schema=None) as batch_op: + batch_op.create_index('ix_component_name', ['name'], unique=False) + batch_op.create_index('ix_component_frontend_node_id', ['frontend_node_id'], unique=False) + except Exception as e: + print(e) + pass + # ### end Alembic commands ### diff --git a/src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py b/src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py index cb126c926..080602358 100644 --- a/src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py +++ b/src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py @@ -7,10 +7,9 @@ Create Date: 2023-10-04 10:18:25.640458 """ from typing import Sequence, Union -from alembic import op import sqlalchemy as sa +from alembic import op from sqlalchemy import exc -import sqlmodel # noqa: F401 # revision identifiers, used by Alembic. revision: str = "eb5866d51fd2" @@ -28,14 +27,16 @@ def upgrade() -> None: batch_op.drop_index("ix_component_frontend_node_id") batch_op.drop_index("ix_component_name") except exc.SQLAlchemyError: - connection.execute("ROLLBACK") + # connection.execute(text("ROLLBACK")) + pass except Exception: pass try: op.drop_table("component") except exc.SQLAlchemyError: - connection.execute("ROLLBACK") + # connection.execute(text("ROLLBACK")) + pass except Exception: pass # ### end Alembic commands ### diff --git a/src/backend/langflow/alembic/versions/f5ee9749d1a6_user_id_can_be_null_in_flow.py b/src/backend/langflow/alembic/versions/f5ee9749d1a6_user_id_can_be_null_in_flow.py new file mode 100644 index 000000000..60e19e69e --- /dev/null +++ b/src/backend/langflow/alembic/versions/f5ee9749d1a6_user_id_can_be_null_in_flow.py @@ -0,0 +1,45 @@ +"""User id can be null in Flow + +Revision ID: f5ee9749d1a6 +Revises: 7843803a87b5 +Create Date: 2023-10-18 23:12:27.297016 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +import sqlmodel + + +# revision identifiers, used by Alembic. +revision: str = "f5ee9749d1a6" +down_revision: Union[str, None] = "7843803a87b5" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table("flow", schema=None) as batch_op: + batch_op.alter_column( + "user_id", existing_type=sa.CHAR(length=32), nullable=True + ) + except Exception: + pass + + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table("flow", schema=None) as batch_op: + batch_op.alter_column( + "user_id", existing_type=sa.CHAR(length=32), nullable=False + ) + except Exception: + pass + + # ### end Alembic commands ### diff --git a/src/backend/langflow/alembic/versions/fd531f8868b1_fix_credential_table.py b/src/backend/langflow/alembic/versions/fd531f8868b1_fix_credential_table.py new file mode 100644 index 000000000..db20f928b --- /dev/null +++ b/src/backend/langflow/alembic/versions/fd531f8868b1_fix_credential_table.py @@ -0,0 +1,38 @@ +"""Fix Credential table + +Revision ID: fd531f8868b1 +Revises: 2ac71eb9c3ae +Create Date: 2023-11-24 15:07:37.566516 + +""" +from typing import Sequence, Union + +from alembic import op + +# revision identifiers, used by Alembic. +revision: str = 'fd531f8868b1' +down_revision: Union[str, None] = '2ac71eb9c3ae' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table('credential', schema=None) as batch_op: + batch_op.create_foreign_key("fk_credential_user_id", 'user', ['user_id'], ['id']) + except Exception: + pass + + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + with op.batch_alter_table('credential', schema=None) as batch_op: + batch_op.drop_constraint("fk_credential_user_id", type_='foreignkey') + except Exception: + pass + + # ### end Alembic commands ### diff --git a/src/backend/langflow/api/router.py b/src/backend/langflow/api/router.py index dbaf20e75..24d64b401 100644 --- a/src/backend/langflow/api/router.py +++ b/src/backend/langflow/api/router.py @@ -1,14 +1,16 @@ # Router for base api from fastapi import APIRouter + from langflow.api.v1 import ( - chat_router, - endpoints_router, - validate_router, - flows_router, - component_router, - users_router, api_key_router, + chat_router, + credentials_router, + endpoints_router, + flows_router, login_router, + store_router, + users_router, + validate_router, ) router = APIRouter( @@ -17,8 +19,9 @@ router = APIRouter( router.include_router(chat_router) router.include_router(endpoints_router) router.include_router(validate_router) -router.include_router(component_router) +router.include_router(store_router) router.include_router(flows_router) router.include_router(users_router) router.include_router(api_key_router) router.include_router(login_router) +router.include_router(credentials_router) diff --git a/src/backend/langflow/api/utils.py b/src/backend/langflow/api/utils.py index c519fffed..141c79229 100644 --- a/src/backend/langflow/api/utils.py +++ b/src/backend/langflow/api/utils.py @@ -1,10 +1,22 @@ +import warnings +from pathlib import Path +from typing import TYPE_CHECKING, List + +from fastapi import HTTPException +from platformdirs import user_cache_dir + +from langflow.services.store.schema import StoreComponentCreate +from langflow.services.store.utils import get_lf_version_from_pypi + +if TYPE_CHECKING: + from langflow.services.database.models.flow.model import Flow + + API_WORDS = ["api", "key", "token"] def has_api_terms(word: str): - return "api" in word and ( - "key" in word or ("token" in word and "tokens" not in word) - ) + return "api" in word and ("key" in word or ("token" in word and "tokens" not in word)) def remove_api_keys(flow: dict): @@ -14,11 +26,7 @@ def remove_api_keys(flow: dict): node_data = node.get("data").get("node") template = node_data.get("template") for value in template.values(): - if ( - isinstance(value, dict) - and has_api_terms(value["name"]) - and value.get("password") - ): + if isinstance(value, dict) and has_api_terms(value["name"]) and value.get("password"): value["value"] = None return flow @@ -39,9 +47,7 @@ def build_input_keys_response(langchain_object, artifacts): input_keys_response["input_keys"][key] = value # If the object has memory, that memory will have a memory_variables attribute # memory variables should be removed from the input keys - if hasattr(langchain_object, "memory") and hasattr( - langchain_object.memory, "memory_variables" - ): + if hasattr(langchain_object, "memory") and hasattr(langchain_object.memory, "memory_variables"): # Remove memory variables from input keys input_keys_response["input_keys"] = { key: value @@ -51,18 +57,133 @@ def build_input_keys_response(langchain_object, artifacts): # Add memory variables to memory_keys input_keys_response["memory_keys"] = langchain_object.memory.memory_variables - if hasattr(langchain_object, "prompt") and hasattr( - langchain_object.prompt, "template" - ): + if hasattr(langchain_object, "prompt") and hasattr(langchain_object.prompt, "template"): input_keys_response["template"] = langchain_object.prompt.template return input_keys_response -def get_new_key(dictionary, original_key): - counter = 1 - new_key = original_key + " (" + str(counter) + ")" - while new_key in dictionary: - counter += 1 - new_key = original_key + " (" + str(counter) + ")" - return new_key +def update_frontend_node_with_template_values(frontend_node, raw_frontend_node): + """ + Updates the given frontend node with values from the raw template data. + + :param frontend_node: A dict representing a built frontend node. + :param raw_template_data: A dict representing raw template data. + :return: Updated frontend node. + """ + if not is_valid_data(frontend_node, raw_frontend_node): + return frontend_node + + # Check if the display_name is different than "CustomComponent" + # if so, update the display_name in the frontend_node + if raw_frontend_node["display_name"] != "CustomComponent": + frontend_node["display_name"] = raw_frontend_node["display_name"] + + update_template_values(frontend_node["template"], raw_frontend_node["template"]) + + return frontend_node + + +def raw_frontend_data_is_valid(raw_frontend_data): + """Check if the raw frontend data is valid for processing.""" + return "template" in raw_frontend_data and "display_name" in raw_frontend_data + + +def is_valid_data(frontend_node, raw_frontend_data): + """Check if the data is valid for processing.""" + + return frontend_node and "template" in frontend_node and raw_frontend_data_is_valid(raw_frontend_data) + + +def update_template_values(frontend_template, raw_template): + """Updates the frontend template with values from the raw template.""" + for key, value_dict in raw_template.items(): + if key == "code" or not isinstance(value_dict, dict): + continue + + update_template_field(frontend_template, key, value_dict) + + +def update_template_field(frontend_template, key, value_dict): + """Updates a specific field in the frontend template.""" + template_field = frontend_template.get(key) + if not template_field or template_field.get("type") != value_dict.get("type"): + return + + if "value" in value_dict and value_dict["value"]: + template_field["value"] = value_dict["value"] + + if "file_path" in value_dict and value_dict["file_path"]: + file_path_value = get_file_path_value(value_dict["file_path"]) + if not file_path_value: + # If the file does not exist, remove the value from the template_field["value"] + template_field["value"] = "" + template_field["file_path"] = file_path_value + + +def get_file_path_value(file_path): + """Get the file path value if the file exists, else return empty string.""" + try: + path = Path(file_path) + except TypeError: + return "" + + # Check for safety + # If the path is not in the cache dir, return empty string + # This is to prevent access to files outside the cache dir + # If the path is not a file, return empty string + if not path.exists() or not str(path).startswith(user_cache_dir("langflow", "langflow")): + return "" + return file_path + + +def validate_is_component(flows: List["Flow"]): + for flow in flows: + if not flow.data or flow.is_component is not None: + continue + + is_component = get_is_component_from_data(flow.data) + if is_component is not None: + flow.is_component = is_component + else: + flow.is_component = len(flow.data.get("nodes", [])) == 1 + return flows + + +def get_is_component_from_data(data: dict): + """Returns True if the data is a component.""" + return data.get("is_component") + + +async def check_langflow_version(component: StoreComponentCreate): + from langflow import __version__ as current_version + + if not component.last_tested_version: + component.last_tested_version = current_version + + langflow_version = get_lf_version_from_pypi() + if langflow_version is None: + raise HTTPException(status_code=500, detail="Unable to verify the latest version of Langflow") + elif langflow_version != component.last_tested_version: + warnings.warn( + f"Your version of Langflow ({component.last_tested_version}) is outdated. " + f"Please update to the latest version ({langflow_version}) and try again." + ) + + +def format_elapsed_time(elapsed_time) -> str: + # Format elapsed time to human readable format coming from + # perf_counter() + # If the elapsed time is less than 1 second, return ms + # If the elapsed time is less than 1 minute, return seconds rounded to 2 decimals + time_str = "" + if elapsed_time < 1: + elapsed_time = int(round(elapsed_time * 1000)) + time_str = f"{elapsed_time} ms" + elif elapsed_time < 60: + elapsed_time = round(elapsed_time, 2) + time_str = f"{elapsed_time} seconds" + else: + elapsed_time = round(elapsed_time / 60, 2) + time_str = f"{elapsed_time} minutes" + return time_str diff --git a/src/backend/langflow/api/v1/__init__.py b/src/backend/langflow/api/v1/__init__.py index 9335a4607..6368a0cc8 100644 --- a/src/backend/langflow/api/v1/__init__.py +++ b/src/backend/langflow/api/v1/__init__.py @@ -1,19 +1,21 @@ -from langflow.api.v1.endpoints import router as endpoints_router -from langflow.api.v1.validate import router as validate_router -from langflow.api.v1.chat import router as chat_router -from langflow.api.v1.flows import router as flows_router -from langflow.api.v1.components import router as component_router -from langflow.api.v1.users import router as users_router from langflow.api.v1.api_key import router as api_key_router +from langflow.api.v1.chat import router as chat_router +from langflow.api.v1.credential import router as credentials_router +from langflow.api.v1.endpoints import router as endpoints_router +from langflow.api.v1.flows import router as flows_router from langflow.api.v1.login import router as login_router +from langflow.api.v1.store import router as store_router +from langflow.api.v1.users import router as users_router +from langflow.api.v1.validate import router as validate_router __all__ = [ "chat_router", "endpoints_router", - "component_router", + "store_router", "validate_router", "flows_router", "users_router", "api_key_router", "login_router", + "credentials_router", ] diff --git a/src/backend/langflow/api/v1/api_key.py b/src/backend/langflow/api/v1/api_key.py index 7f5916d06..48cf7d1ca 100644 --- a/src/backend/langflow/api/v1/api_key.py +++ b/src/backend/langflow/api/v1/api_key.py @@ -1,22 +1,30 @@ +from typing import TYPE_CHECKING from uuid import UUID -from fastapi import APIRouter, HTTPException, Depends -from langflow.api.v1.schemas import ApiKeysResponse -from langflow.services.auth.utils import get_current_active_user -from langflow.services.database.models.api_key.api_key import ( - ApiKeyCreate, - UnmaskedApiKeyRead, -) + +from fastapi import APIRouter, Depends, HTTPException +from sqlmodel import Session + +from langflow.api.v1.schemas import ApiKeyCreateRequest, ApiKeysResponse +from langflow.services.auth import utils as auth_utils # Assuming you have these methods in your service layer from langflow.services.database.models.api_key.crud import ( - get_api_keys, create_api_key, delete_api_key, + get_api_keys, +) +from langflow.services.database.models.api_key.model import ( + ApiKeyCreate, + UnmaskedApiKeyRead, +) +from langflow.services.database.models.user.model import User +from langflow.services.deps import ( + get_session, + get_settings_service, ) -from langflow.services.database.models.user.user import User -from langflow.services.getters import get_session -from sqlmodel import Session +if TYPE_CHECKING: + pass router = APIRouter(tags=["APIKey"], prefix="/api_key") @@ -24,7 +32,7 @@ router = APIRouter(tags=["APIKey"], prefix="/api_key") @router.get("/", response_model=ApiKeysResponse) def get_api_keys_route( db: Session = Depends(get_session), - current_user: User = Depends(get_current_active_user), + current_user: User = Depends(auth_utils.get_current_active_user), ): try: user_id = current_user.id @@ -38,7 +46,7 @@ def get_api_keys_route( @router.post("/", response_model=UnmaskedApiKeyRead) def create_api_key_route( req: ApiKeyCreate, - current_user: User = Depends(get_current_active_user), + current_user: User = Depends(auth_utils.get_current_active_user), db: Session = Depends(get_session), ): try: @@ -51,7 +59,7 @@ def create_api_key_route( @router.delete("/{api_key_id}") def delete_api_key_route( api_key_id: UUID, - current_user=Depends(get_current_active_user), + current_user=Depends(auth_utils.get_current_active_user), db: Session = Depends(get_session), ): try: @@ -59,3 +67,34 @@ def delete_api_key_route( return {"detail": "API Key deleted"} except Exception as e: raise HTTPException(status_code=400, detail=str(e)) from e + + +@router.post("/store") +def save_store_api_key( + api_key_request: ApiKeyCreateRequest, + current_user: User = Depends(auth_utils.get_current_active_user), + db: Session = Depends(get_session), + settings_service=Depends(get_settings_service), +): + try: + api_key = api_key_request.api_key + # Encrypt the API key + encrypted = auth_utils.encrypt_api_key(api_key, settings_service=settings_service) + current_user.store_api_key = encrypted + db.commit() + return {"detail": "API Key saved"} + except Exception as e: + raise HTTPException(status_code=400, detail=str(e)) from e + + +@router.delete("/store") +def delete_store_api_key( + current_user: User = Depends(auth_utils.get_current_active_user), + db: Session = Depends(get_session), +): + try: + current_user.store_api_key = None + db.commit() + return {"detail": "API Key deleted"} + except Exception as e: + raise HTTPException(status_code=400, detail=str(e)) from e diff --git a/src/backend/langflow/api/v1/base.py b/src/backend/langflow/api/v1/base.py index f2c2f3f59..01e4e9c79 100644 --- a/src/backend/langflow/api/v1/base.py +++ b/src/backend/langflow/api/v1/base.py @@ -1,9 +1,10 @@ from typing import Optional -from langflow.template.frontend_node.base import FrontendNode -from pydantic import BaseModel, validator + +from langchain.prompts import PromptTemplate +from pydantic import BaseModel, field_validator, model_serializer from langflow.interface.utils import extract_input_variables_from_prompt -from langchain.prompts import PromptTemplate +from langflow.template.frontend_node.base import FrontendNode class CacheResponse(BaseModel): @@ -17,6 +18,12 @@ class Code(BaseModel): class FrontendNodeRequest(FrontendNode): template: dict # type: ignore + @model_serializer(mode="wrap") + def serialize_model(self, handler): + # Override the default serialization method in FrontendNode + # because we don't need the name in the response (i.e. {name: {}}) + return handler(self) + class ValidatePromptRequest(BaseModel): name: str @@ -30,11 +37,13 @@ class CodeValidationResponse(BaseModel): imports: dict function: dict - @validator("imports") + @field_validator("imports") + @classmethod def validate_imports(cls, v): return v or {"errors": []} - @validator("function") + @field_validator("function") + @classmethod def validate_function(cls, v): return v or {"errors": []} @@ -79,9 +88,7 @@ def validate_prompt(template: str): # Check if there are invalid characters in the input_variables input_variables = check_input_variables(input_variables) if any(var in INVALID_NAMES for var in input_variables): - raise ValueError( - f"Invalid input variables. None of the variables can be named {', '.join(input_variables)}. " - ) + raise ValueError(f"Invalid input variables. None of the variables can be named {', '.join(input_variables)}. ") try: PromptTemplate(template=template, input_variables=input_variables) @@ -132,9 +139,7 @@ def check_input_variables(input_variables: list): return input_variables -def build_error_message( - input_variables, invalid_chars, wrong_variables, fixed_variables, empty_variables -): +def build_error_message(input_variables, invalid_chars, wrong_variables, fixed_variables, empty_variables): input_variables_str = ", ".join([f"'{var}'" for var in input_variables]) error_string = f"Invalid input variables: {input_variables_str}. " diff --git a/src/backend/langflow/api/v1/callback.py b/src/backend/langflow/api/v1/callback.py index 787ca9680..a838a8750 100644 --- a/src/backend/langflow/api/v1/callback.py +++ b/src/backend/langflow/api/v1/callback.py @@ -1,19 +1,15 @@ import asyncio +from typing import Any, Dict, List, Optional from uuid import UUID from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler - -from langflow.api.v1.schemas import ChatResponse, PromptResponse - - -from typing import Any, Dict, List, Optional -from langflow.services.getters import get_chat_service - - -from langflow.utils.util import remove_ansi_escape_codes from langchain.schema import AgentAction, AgentFinish from loguru import logger +from langflow.api.v1.schemas import ChatResponse, PromptResponse +from langflow.services.deps import get_chat_service +from langflow.utils.util import remove_ansi_escape_codes + # https://github.com/hwchase17/chat-langchain/blob/master/callback.py class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): @@ -26,18 +22,16 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): async def on_llm_new_token(self, token: str, **kwargs: Any) -> None: resp = ChatResponse(message=token, type="stream", intermediate_steps="") - await self.websocket.send_json(resp.dict()) + await self.websocket.send_json(resp.model_dump()) - async def on_tool_start( - self, serialized: Dict[str, Any], input_str: str, **kwargs: Any - ) -> Any: + async def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> Any: """Run when tool starts running.""" resp = ChatResponse( message="", type="stream", intermediate_steps=f"Tool input: {input_str}", ) - await self.websocket.send_json(resp.dict()) + await self.websocket.send_json(resp.model_dump()) async def on_tool_end(self, output: str, **kwargs: Any) -> Any: """Run when tool ends running.""" @@ -68,7 +62,7 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): try: # This is to emulate the stream of tokens for resp in resps: - await self.websocket.send_json(resp.dict()) + await self.websocket.send_json(resp.model_dump()) except Exception as exc: logger.error(f"Error sending response: {exc}") @@ -94,7 +88,7 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): resp = PromptResponse( prompt=text, ) - await self.websocket.send_json(resp.dict()) + await self.websocket.send_json(resp.model_dump()) self.chat_service.chat_history.add_message(self.client_id, resp) async def on_agent_action(self, action: AgentAction, **kwargs: Any): @@ -105,10 +99,10 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): logs = log.split("\n") for log in logs: resp = ChatResponse(message="", type="stream", intermediate_steps=log) - await self.websocket.send_json(resp.dict()) + await self.websocket.send_json(resp.model_dump()) else: resp = ChatResponse(message="", type="stream", intermediate_steps=log) - await self.websocket.send_json(resp.dict()) + await self.websocket.send_json(resp.model_dump()) async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any: """Run on agent end.""" @@ -117,7 +111,7 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): type="stream", intermediate_steps=finish.log, ) - await self.websocket.send_json(resp.dict()) + await self.websocket.send_json(resp.model_dump()) class StreamingLLMCallbackHandler(BaseCallbackHandler): @@ -132,5 +126,5 @@ class StreamingLLMCallbackHandler(BaseCallbackHandler): resp = ChatResponse(message=token, type="stream", intermediate_steps="") loop = asyncio.get_event_loop() - coroutine = self.websocket.send_json(resp.dict()) + coroutine = self.websocket.send_json(resp.model_dump()) asyncio.run_coroutine_threadsafe(coroutine, loop) diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py index 6fdfb8897..512f64341 100644 --- a/src/backend/langflow/api/v1/chat.py +++ b/src/backend/langflow/api/v1/chat.py @@ -1,25 +1,22 @@ -from fastapi import ( - APIRouter, - Depends, - HTTPException, - Query, - WebSocket, - WebSocketException, - status, -) +import time + +from fastapi import (APIRouter, Depends, HTTPException, Query, WebSocket, + WebSocketException, status) from fastapi.responses import StreamingResponse -from langflow.api.utils import build_input_keys_response -from langflow.api.v1.schemas import BuildStatus, BuiltResponse, InitResponse, StreamData - -from langflow.graph.graph.base import Graph -from langflow.services.auth.utils import get_current_active_user, get_current_user -from langflow.services.cache.utils import update_build_status from loguru import logger -from langflow.services.getters import get_chat_service, get_session, get_cache_service from sqlmodel import Session -from langflow.services.chat.manager import ChatService -from langflow.services.cache.manager import BaseCacheService +from langflow.api.utils import build_input_keys_response, format_elapsed_time +from langflow.api.v1.schemas import (BuildStatus, BuiltResponse, InitResponse, + StreamData) +from langflow.graph.graph.base import Graph +from langflow.services.auth.utils import (get_current_active_user, + get_current_user_by_jwt) +from langflow.services.cache.service import BaseCacheService +from langflow.services.cache.utils import update_build_status +from langflow.services.chat.service import ChatService +from langflow.services.deps import (get_cache_service, get_chat_service, + get_session) router = APIRouter(tags=["Chat"]) @@ -34,16 +31,12 @@ async def chat( ): """Websocket endpoint for chat.""" try: + user = await get_current_user_by_jwt(token, db) await websocket.accept() - user = await get_current_user(token, db) if not user: - await websocket.close( - code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized" - ) + await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized") if not user.is_active: - await websocket.close( - code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized" - ) + await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized") if client_id in chat_service.cache_service: await chat_service.handle_websocket(client_id, websocket) @@ -59,9 +52,7 @@ async def chat( logger.error(f"Error in chat websocket: {exc}") messsage = exc.detail if isinstance(exc, HTTPException) else str(exc) if "Could not validate credentials" in str(exc): - await websocket.close( - code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized" - ) + await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized") else: await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=messsage) @@ -103,15 +94,10 @@ async def init_build( @router.get("/build/{flow_id}/status", response_model=BuiltResponse) -async def build_status( - flow_id: str, cache_service: "BaseCacheService" = Depends(get_cache_service) -): +async def build_status(flow_id: str, cache_service: "BaseCacheService" = Depends(get_cache_service)): """Check the flow_id is in the cache_service.""" try: - built = ( - flow_id in cache_service - and cache_service[flow_id]["status"] == BuildStatus.SUCCESS - ) + built = flow_id in cache_service and cache_service[flow_id]["status"] == BuildStatus.SUCCESS return BuiltResponse( built=built, @@ -133,19 +119,20 @@ async def stream_build( async def event_stream(flow_id): final_response = {"end_of_stream": True} artifacts = {} + flow_cache = cache_service[flow_id] + flow_cache = flow_cache if isinstance(flow_cache, dict) else {} try: if flow_id not in cache_service: error_message = "Invalid session ID" yield str(StreamData(event="error", data={"error": error_message})) return - if cache_service[flow_id].get("status") == BuildStatus.IN_PROGRESS: + if flow_cache.get("status") == BuildStatus.IN_PROGRESS: error_message = "Already building" yield str(StreamData(event="error", data={"error": error_message})) return - graph_data = cache_service[flow_id].get("graph_data") - cache_service[flow_id]["user_id"] + graph_data = flow_cache.get("graph_data") if not graph_data: error_message = "No data provided" @@ -157,25 +144,32 @@ async def stream_build( # Some error could happen when building the graph graph = Graph.from_payload(graph_data) - number_of_nodes = len(graph.nodes) + number_of_nodes = len(graph.vertices) update_build_status(cache_service, flow_id, BuildStatus.IN_PROGRESS) + try: + user_id = flow_cache["user_id"] + except KeyError: + logger.debug("No user_id found in cache_service") + user_id = None for i, vertex in enumerate(graph.generator_build(), 1): try: log_dict = { "log": f"Building node {vertex.vertex_type}", } yield str(StreamData(event="log", data=log_dict)) + # time this + start_time = time.perf_counter() if vertex.is_task: - vertex = try_running_celery_task(vertex) + vertex = await try_running_celery_task(vertex, user_id) else: - vertex.build() + await vertex.build(user_id=user_id) + time_elapsed = format_elapsed_time(time.perf_counter() - start_time) params = vertex._built_object_repr() valid = True + logger.debug(f"Building node {str(vertex.vertex_type)}") - logger.debug( - f"Output: {params[:100]}{'...' if len(params) > 100 else ''}" - ) + logger.debug(f"Output: {params[:100]}{'...' if len(params) > 100 else ''}") if vertex.artifacts: # The artifacts will be prompt variables # passed to build_input_keys_response @@ -187,21 +181,22 @@ async def stream_build( valid = False update_build_status(cache_service, flow_id, BuildStatus.FAILURE) - response = { - "valid": valid, - "params": params, - "id": vertex.id, - "progress": round(i / number_of_nodes, 2), - } + vertex_id = vertex.parent_node_id if vertex.parent_is_top_level else vertex.id + if vertex_id in graph.top_level_vertices: + response = { + "valid": valid, + "params": params, + "id": vertex_id, + "progress": round(i / number_of_nodes, 2), + "duration": time_elapsed, + } yield str(StreamData(event="message", data=response)) - langchain_object = graph.build() + langchain_object = await graph.build() # Now we need to check the input_keys to send them to the client if hasattr(langchain_object, "input_keys"): - input_keys_response = build_input_keys_response( - langchain_object, artifacts - ) + input_keys_response = build_input_keys_response(langchain_object, artifacts) else: input_keys_response = { "input_keys": None, @@ -229,7 +224,7 @@ async def stream_build( raise HTTPException(status_code=500, detail=str(exc)) -def try_running_celery_task(vertex): +async def try_running_celery_task(vertex, user_id): # Try running the task in celery # and set the task_id to the local vertex # if it fails, run the task locally @@ -241,5 +236,5 @@ def try_running_celery_task(vertex): except Exception as exc: logger.debug(f"Error running task in celery: {exc}") vertex.task_id = None - vertex.build() + await vertex.build(user_id=user_id) return vertex diff --git a/src/backend/langflow/api/v1/components.py b/src/backend/langflow/api/v1/components.py deleted file mode 100644 index d2b39dfd2..000000000 --- a/src/backend/langflow/api/v1/components.py +++ /dev/null @@ -1,77 +0,0 @@ -from datetime import timezone -from typing import List -from uuid import UUID -from langflow.services.database.models.component import Component, ComponentModel -from langflow.services.getters import get_session -from sqlmodel import Session, select -from fastapi import APIRouter, Depends, HTTPException -from sqlalchemy.exc import IntegrityError -from datetime import datetime - - -COMPONENT_NOT_FOUND = "Component not found" -COMPONENT_ALREADY_EXISTS = "A component with the same id already exists." -COMPONENT_DELETED = "Component deleted" - - -router = APIRouter(prefix="/components", tags=["Components"]) - - -@router.post("/", response_model=Component) -def create_component(component: ComponentModel, db: Session = Depends(get_session)): - db_component = Component(**component.dict()) - try: - db.add(db_component) - db.commit() - db.refresh(db_component) - except IntegrityError as e: - db.rollback() - raise HTTPException( - status_code=400, - detail=COMPONENT_ALREADY_EXISTS, - ) from e - return db_component - - -@router.get("/{component_id}", response_model=Component) -def read_component(component_id: UUID, db: Session = Depends(get_session)): - if component := db.get(Component, component_id): - return component - else: - raise HTTPException(status_code=404, detail=COMPONENT_NOT_FOUND) - - -@router.get("/", response_model=List[Component]) -def read_components(skip: int = 0, limit: int = 50, db: Session = Depends(get_session)): - query = select(Component) - query = query.offset(skip).limit(limit) - - return db.execute(query).fetchall() - - -@router.patch("/{component_id}", response_model=Component) -def update_component( - component_id: UUID, component: ComponentModel, db: Session = Depends(get_session) -): - db_component = db.get(Component, component_id) - if not db_component: - raise HTTPException(status_code=404, detail=COMPONENT_NOT_FOUND) - component_data = component.dict(exclude_unset=True) - - for key, value in component_data.items(): - setattr(db_component, key, value) - - db_component.update_at = datetime.now(timezone.utc) - db.commit() - db.refresh(db_component) - return db_component - - -@router.delete("/{component_id}") -def delete_component(component_id: UUID, db: Session = Depends(get_session)): - component = db.get(Component, component_id) - if not component: - raise HTTPException(status_code=404, detail=COMPONENT_NOT_FOUND) - db.delete(component) - db.commit() - return {"detail": COMPONENT_DELETED} diff --git a/src/backend/langflow/api/v1/credential.py b/src/backend/langflow/api/v1/credential.py new file mode 100644 index 000000000..1026319c4 --- /dev/null +++ b/src/backend/langflow/api/v1/credential.py @@ -0,0 +1,86 @@ +from datetime import datetime +from uuid import UUID + +from fastapi import APIRouter, Depends, HTTPException +from langflow.services.auth import utils as auth_utils +from langflow.services.auth.utils import get_current_active_user +from langflow.services.database.models.credential import Credential, CredentialCreate, CredentialRead, CredentialUpdate +from langflow.services.database.models.user.model import User +from langflow.services.deps import get_session, get_settings_service +from sqlmodel import Session, select + +router = APIRouter(prefix="/credentials", tags=["Credentials"]) + + +@router.post("/", response_model=CredentialRead, status_code=201) +def create_credential( + *, + session: Session = Depends(get_session), + credential: CredentialCreate, + current_user: User = Depends(get_current_active_user), + settings_service=Depends(get_settings_service), +): + """Create a new credential.""" + try: + # check if credential name already exists + credential_exists = session.exec( + select(Credential).where(Credential.name == credential.name, Credential.user_id == current_user.id) + ).first() + if credential_exists: + raise HTTPException(status_code=400, detail="Credential name already exists") + + db_credential = Credential.model_validate(credential, from_attributes=True) + if not db_credential.value: + raise HTTPException(status_code=400, detail="Credential value cannot be empty") + encrypted = auth_utils.encrypt_api_key(db_credential.value, settings_service=settings_service) + db_credential.value = encrypted + db_credential.user_id = current_user.id + session.add(db_credential) + session.commit() + session.refresh(db_credential) + return db_credential + except Exception as e: + if isinstance(e, HTTPException): + raise e + raise HTTPException(status_code=500, detail=str(e)) from e + + +@router.get("/", response_model=list[CredentialRead], status_code=200) +def read_credentials( + *, + session: Session = Depends(get_session), + current_user: User = Depends(get_current_active_user), +): + """Read all credentials.""" + try: + credentials = session.exec(select(Credential).where(Credential.user_id == current_user.id)).all() + return credentials + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) from e + + +@router.patch("/{credential_id}", response_model=CredentialRead, status_code=200) +def update_credential( + *, + session: Session = Depends(get_session), + credential_id: UUID, + credential: CredentialUpdate, + current_user: User = Depends(get_current_active_user), +): + """Update a credential.""" + try: + db_credential = session.exec( + select(Credential).where(Credential.id == credential_id, Credential.user_id == current_user.id) + ).first() + if not db_credential: + raise HTTPException(status_code=404, detail="Credential not found") + + credential_data = credential.model_dump(exclude_unset=True) + for key, value in credential_data.items(): + setattr(db_credential, key, value) + db_credential.updated_at = datetime.utcnow() + session.commit() + session.refresh(db_credential) + return db_credential + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) from e diff --git a/src/backend/langflow/api/v1/endpoints.py b/src/backend/langflow/api/v1/endpoints.py index 5c76539ab..e07b00614 100644 --- a/src/backend/langflow/api/v1/endpoints.py +++ b/src/backend/langflow/api/v1/endpoints.py @@ -1,33 +1,28 @@ from http import HTTPStatus from typing import Annotated, Optional, Union -from langflow.services.auth.utils import api_key_security, get_current_active_user - -from langflow.services.cache.utils import save_uploaded_file -from langflow.services.database.models.flow import Flow -from langflow.processing.process import process_graph_cached, process_tweaks -from langflow.services.database.models.user.user import User -from langflow.services.getters import ( - get_session_service, - get_settings_service, - get_task_service, -) -from loguru import logger -from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body, status import sqlalchemy as sa -from langflow.interface.custom.custom_component import CustomComponent - +from fastapi import APIRouter, Body, Depends, HTTPException, UploadFile, status +from loguru import logger +from sqlmodel import select +from langflow.api.utils import update_frontend_node_with_template_values from langflow.api.v1.schemas import ( + CustomComponentCode, ProcessResponse, TaskResponse, TaskStatusResponse, UploadFileResponse, - CustomComponentCode, ) - - -from langflow.services.getters import get_session +from langflow.interface.custom.custom_component import CustomComponent +from langflow.interface.custom.directory_reader import DirectoryReader +from langflow.interface.types import build_custom_component_template, create_and_validate_component +from langflow.processing.process import process_graph_cached, process_tweaks +from langflow.services.auth.utils import api_key_security, get_current_active_user +from langflow.services.cache.utils import save_uploaded_file +from langflow.services.database.models.flow import Flow +from langflow.services.database.models.user.model import User +from langflow.services.deps import get_session, get_session_service, get_settings_service, get_task_service try: from langflow.worker import process_graph_cached_task @@ -39,8 +34,7 @@ except ImportError: from sqlmodel import Session - -from langflow.services.task.manager import TaskService +from langflow.services.task.service import TaskService # build router router = APIRouter(tags=["Base"]) @@ -93,18 +87,15 @@ async def process_flow( ) # Get the flow that matches the flow_id and belongs to the user - flow = ( - session.query(Flow) - .filter(Flow.id == flow_id) - .filter(Flow.user_id == api_key_user.id) - .first() - ) + # flow = session.query(Flow).filter(Flow.id == flow_id).filter(Flow.user_id == api_key_user.id).first() + flow = session.exec(select(Flow).where(Flow.id == flow_id).where(Flow.user_id == api_key_user.id)).first() if flow is None: raise ValueError(f"Flow {flow_id} not found") if flow.data is None: raise ValueError(f"Flow {flow_id} has no data") graph_data = flow.data + task_result = None if tweaks: try: graph_data = process_tweaks(graph_data, tweaks) @@ -112,9 +103,7 @@ async def process_flow( logger.error(f"Error processing tweaks: {exc}") if sync: task_id, result = await task_service.launch_and_await_task( - process_graph_cached_task - if task_service.use_celery - else process_graph_cached, + process_graph_cached_task if task_service.use_celery else process_graph_cached, graph_data, inputs, clear_cache, @@ -134,13 +123,9 @@ async def process_flow( ) if session_id is None: # Generate a session ID - session_id = get_session_service().generate_key( - session_id=session_id, data_graph=graph_data - ) + session_id = get_session_service().generate_key(session_id=session_id, data_graph=graph_data) task_id, task = await task_service.launch_task( - process_graph_cached_task - if task_service.use_celery - else process_graph_cached, + process_graph_cached_task if task_service.use_celery else process_graph_cached, graph_data, inputs, clear_cache, @@ -163,18 +148,12 @@ async def process_flow( # StatementError('(builtins.ValueError) badly formed hexadecimal UUID string') if "badly formed hexadecimal UUID string" in str(exc): # This means the Flow ID is not a valid UUID which means it can't find the flow - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, detail=str(exc) - ) from exc + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc except ValueError as exc: if f"Flow {flow_id} not found" in str(exc): - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, detail=str(exc) - ) from exc + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc else: - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc) - ) from exc + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc)) from exc except Exception as e: # Log stack trace logger.exception(e) @@ -188,6 +167,10 @@ async def get_task_status(task_id: str): result = None if task.ready(): result = task.result + # If result isinstance of Exception, can we get the traceback? + if isinstance(result, Exception): + logger.exception(task.traceback) + if isinstance(result, dict) and "result" in result: result = result["result"] elif hasattr(result, "result"): @@ -195,6 +178,10 @@ async def get_task_status(task_id: str): if task is None: raise HTTPException(status_code=404, detail="Task not found") + if task.status == "FAILURE": + result = str(task.result) + logger.error(f"Task {task_id} failed: {task.traceback}") + return TaskStatusResponse(status=task.status, result=result) @@ -228,12 +215,40 @@ def get_version(): @router.post("/custom_component", status_code=HTTPStatus.OK) async def custom_component( raw_code: CustomComponentCode, + user: User = Depends(get_current_active_user), ): - from langflow.interface.types import ( - build_langchain_template_custom_component, - ) + component = create_and_validate_component(raw_code.code) - extractor = CustomComponent(code=raw_code.code) - extractor.is_check_valid() + built_frontend_node = build_custom_component_template(component, user_id=user.id) - return build_langchain_template_custom_component(extractor) + built_frontend_node = update_frontend_node_with_template_values(built_frontend_node, raw_code.frontend_node) + return built_frontend_node + + +@router.post("/custom_component/reload", status_code=HTTPStatus.OK) +async def reload_custom_component(path: str, user: User = Depends(get_current_active_user)): + from langflow.interface.types import build_custom_component_template + + try: + reader = DirectoryReader("") + valid, content = reader.process_file(path) + if not valid: + raise ValueError(content) + + extractor = CustomComponent(code=content) + extractor.validate() + return build_custom_component_template(extractor, user_id=user.id) + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) + + +@router.post("/custom_component/update", status_code=HTTPStatus.OK) +async def custom_component_update( + raw_code: CustomComponentCode, + user: User = Depends(get_current_active_user), +): + component = create_and_validate_component(raw_code.code) + + component_node = build_custom_component_template(component, user_id=user.id, update_field=raw_code.field) + # Update the field + return component_node diff --git a/src/backend/langflow/api/v1/flows.py b/src/backend/langflow/api/v1/flows.py index 9953db0db..28c11dfb4 100644 --- a/src/backend/langflow/api/v1/flows.py +++ b/src/backend/langflow/api/v1/flows.py @@ -1,24 +1,17 @@ +from datetime import datetime from typing import List from uuid import UUID -from fastapi.encoders import jsonable_encoder -from langflow.api.utils import remove_api_keys +import orjson +from fastapi import APIRouter, Depends, File, HTTPException, UploadFile +from fastapi.encoders import jsonable_encoder +from langflow.api.utils import remove_api_keys, validate_is_component from langflow.api.v1.schemas import FlowListCreate, FlowListRead from langflow.services.auth.utils import get_current_active_user -from langflow.services.database.models.flow import ( - Flow, - FlowCreate, - FlowRead, - FlowUpdate, -) -from langflow.services.database.models.user.user import User -from langflow.services.getters import get_session -from langflow.services.getters import get_settings_service -import orjson -from sqlmodel import Session -from fastapi import APIRouter, Depends, HTTPException - -from fastapi import File, UploadFile +from langflow.services.database.models.flow import Flow, FlowCreate, FlowRead, FlowUpdate +from langflow.services.database.models.user.model import User +from langflow.services.deps import get_session, get_settings_service +from sqlmodel import Session, select # build router router = APIRouter(prefix="/flows", tags=["Flows"]) @@ -35,7 +28,8 @@ def create_flow( if flow.user_id is None: flow.user_id = current_user.id - db_flow = Flow.from_orm(flow) + db_flow = Flow.model_validate(flow, from_attributes=True) + db_flow.updated_at = datetime.utcnow() session.add(db_flow) session.commit() @@ -46,12 +40,12 @@ def create_flow( @router.get("/", response_model=list[FlowRead], status_code=200) def read_flows( *, - session: Session = Depends(get_session), current_user: User = Depends(get_current_active_user), ): """Read all flows.""" try: flows = current_user.flows + flows = validate_is_component(flows) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) from e return [jsonable_encoder(flow) for flow in flows] @@ -65,12 +59,7 @@ def read_flow( current_user: User = Depends(get_current_active_user), ): """Read a flow.""" - if user_flow := ( - session.query(Flow) - .filter(Flow.id == flow_id) - .filter(Flow.user_id == current_user.id) - .first() - ): + if user_flow := (session.exec(select(Flow).where(Flow.id == flow_id, Flow.user_id == current_user.id)).first()): return user_flow else: raise HTTPException(status_code=404, detail="Flow not found") @@ -90,12 +79,13 @@ def update_flow( db_flow = read_flow(session=session, flow_id=flow_id, current_user=current_user) if not db_flow: raise HTTPException(status_code=404, detail="Flow not found") - flow_data = flow.dict(exclude_unset=True) + flow_data = flow.model_dump(exclude_unset=True) if settings_service.settings.REMOVE_API_KEYS: flow_data = remove_api_keys(flow_data) for key, value in flow_data.items(): if value is not None: setattr(db_flow, key, value) + db_flow.updated_at = datetime.utcnow() session.add(db_flow) session.commit() session.refresh(db_flow) @@ -169,5 +159,5 @@ async def download_file( current_user: User = Depends(get_current_active_user), ): """Download all flows as a file.""" - flows = read_flows(session=session, current_user=current_user) + flows = read_flows(current_user=current_user) return FlowListRead(flows=flows) diff --git a/src/backend/langflow/api/v1/login.py b/src/backend/langflow/api/v1/login.py index 4dfc723b5..9021b40b6 100644 --- a/src/backend/langflow/api/v1/login.py +++ b/src/backend/langflow/api/v1/login.py @@ -1,18 +1,15 @@ -from sqlmodel import Session from fastapi import APIRouter, Depends, HTTPException, status from fastapi.security import OAuth2PasswordRequestForm +from sqlmodel import Session -from langflow.services.getters import get_session from langflow.api.v1.schemas import Token from langflow.services.auth.utils import ( authenticate_user, - create_user_tokens, create_refresh_token, create_user_longterm_token, - get_current_active_user, + create_user_tokens, ) - -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_session, get_settings_service router = APIRouter(tags=["Login"]) @@ -44,9 +41,7 @@ async def login_to_get_access_token( @router.get("/auto_login") -async def auto_login( - db: Session = Depends(get_session), settings_service=Depends(get_settings_service) -): +async def auto_login(db: Session = Depends(get_session), settings_service=Depends(get_settings_service)): if settings_service.auth_settings.AUTO_LOGIN: return create_user_longterm_token(db) @@ -60,9 +55,7 @@ async def auto_login( @router.post("/refresh") -async def refresh_token( - token: str, current_user: Session = Depends(get_current_active_user) -): +async def refresh_token(token: str): if token: return create_refresh_token(token) else: diff --git a/src/backend/langflow/api/v1/schemas.py b/src/backend/langflow/api/v1/schemas.py index 37e7d712d..8c8c4efd7 100644 --- a/src/backend/langflow/api/v1/schemas.py +++ b/src/backend/langflow/api/v1/schemas.py @@ -2,12 +2,13 @@ from enum import Enum from pathlib import Path from typing import Any, Dict, List, Optional, Union from uuid import UUID -from langflow.services.database.models.api_key.api_key import ApiKeyRead + +from pydantic import BaseModel, Field, field_validator + +from langflow.services.database.models.api_key.model import ApiKeyRead +from langflow.services.database.models.base import orjson_dumps from langflow.services.database.models.flow import FlowCreate, FlowRead from langflow.services.database.models.user import UserRead -from langflow.services.database.models.base import orjson_dumps - -from pydantic import BaseModel, Field, validator class BuildStatus(Enum): @@ -91,7 +92,8 @@ class ChatResponse(ChatMessage): is_bot: bool = True files: list = [] - @validator("type") + @field_validator("type") + @classmethod def validate_message_type(cls, v): if v not in ["start", "stream", "end", "error", "info", "file"]: raise ValueError("type must be start, stream, end, error, info, or file") @@ -109,12 +111,13 @@ class PromptResponse(ChatMessage): class FileResponse(ChatMessage): """File response schema.""" - data: Any + data: Any = None data_type: str type: str = "file" is_bot: bool = True - @validator("data_type") + @field_validator("data_type") + @classmethod def validate_data_type(cls, v): if v not in ["image", "csv"]: raise ValueError("data_type must be image or csv") @@ -149,13 +152,13 @@ class StreamData(BaseModel): data: dict def __str__(self) -> str: - return ( - f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n" - ) + return f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n" class CustomComponentCode(BaseModel): code: str + field: Optional[str] = None + frontend_node: Optional[dict] = None class CustomComponentResponseError(BaseModel): @@ -198,3 +201,7 @@ class Token(BaseModel): access_token: str refresh_token: str token_type: str + + +class ApiKeyCreateRequest(BaseModel): + api_key: str diff --git a/src/backend/langflow/api/v1/store.py b/src/backend/langflow/api/v1/store.py new file mode 100644 index 000000000..313b0638b --- /dev/null +++ b/src/backend/langflow/api/v1/store.py @@ -0,0 +1,193 @@ +from typing import Annotated, List, Optional, Union +from uuid import UUID + +from fastapi import APIRouter, Depends, HTTPException, Query + +from langflow.api.utils import check_langflow_version +from langflow.services.auth import utils as auth_utils +from langflow.services.database.models.user.model import User +from langflow.services.deps import get_settings_service, get_store_service +from langflow.services.store.exceptions import CustomException +from langflow.services.store.schema import ( + CreateComponentResponse, + DownloadComponentResponse, + ListComponentResponseModel, + StoreComponentCreate, + TagResponse, + UsersLikesResponse, +) +from langflow.services.store.service import StoreService + +router = APIRouter(prefix="/store", tags=["Components Store"]) + + +def get_user_store_api_key( + user: User = Depends(auth_utils.get_current_active_user), + settings_service=Depends(get_settings_service), +): + if not user.store_api_key: + raise HTTPException(status_code=400, detail="You must have a store API key set.") + decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service) + return decrypted + + +def get_optional_user_store_api_key( + user: User = Depends(auth_utils.get_current_active_user), + settings_service=Depends(get_settings_service), +): + if not user.store_api_key: + return None + decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service) + return decrypted + + +@router.get("/check/") +def check_if_store_is_enabled( + settings_service=Depends(get_settings_service), +): + return { + "enabled": settings_service.settings.STORE, + } + + +@router.get("/check/api_key") +async def check_if_store_has_api_key( + api_key: Optional[str] = Depends(get_optional_user_store_api_key), + store_service: StoreService = Depends(get_store_service), +): + if api_key is None: + return {"has_api_key": False, "is_valid": False} + + try: + is_valid = await store_service.check_api_key(api_key) + except Exception as e: + raise HTTPException(status_code=400, detail=str(e)) + + return {"has_api_key": api_key is not None, "is_valid": is_valid} + + +@router.post("/components/", response_model=CreateComponentResponse, status_code=201) +async def share_component( + component: StoreComponentCreate, + store_service: StoreService = Depends(get_store_service), + store_api_key: str = Depends(get_user_store_api_key), +): + try: + await check_langflow_version(component) + result = await store_service.upload(store_api_key, component) + return result + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) + + +@router.patch("/components/{component_id}", response_model=CreateComponentResponse, status_code=201) +async def update_shared_component( + component_id: UUID, + component: StoreComponentCreate, + store_service: StoreService = Depends(get_store_service), + store_api_key: str = Depends(get_user_store_api_key), +): + try: + await check_langflow_version(component) + result = await store_service.update(store_api_key, component_id, component) + return result + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) + + +@router.get("/components/", response_model=ListComponentResponseModel) +async def get_components( + component_id: Annotated[Optional[str], Query()] = None, + search: Annotated[Optional[str], Query()] = None, + private: Annotated[Optional[bool], Query()] = None, + is_component: Annotated[Optional[bool], Query()] = None, + tags: Annotated[Optional[list[str]], Query()] = None, + sort: Annotated[Union[list[str], None], Query()] = None, + liked: Annotated[bool, Query()] = False, + filter_by_user: Annotated[bool, Query()] = False, + fields: Annotated[Optional[list[str]], Query()] = None, + page: int = 1, + limit: int = 10, + store_service: StoreService = Depends(get_store_service), + store_api_key: Optional[str] = Depends(get_optional_user_store_api_key), +): + try: + return await store_service.get_list_component_response_model( + component_id=component_id, + search=search, + private=private, + is_component=is_component, + fields=fields, + tags=tags, + sort=sort, + liked=liked, + filter_by_user=filter_by_user, + page=page, + limit=limit, + store_api_key=store_api_key, + ) + except CustomException as exc: + raise HTTPException(status_code=exc.status_code, detail=str(exc)) from exc + except Exception as exc: + raise HTTPException(status_code=500, detail=str(exc)) from exc + + +@router.get("/components/{component_id}", response_model=DownloadComponentResponse) +async def download_component( + component_id: UUID, + store_service: StoreService = Depends(get_store_service), + store_api_key: str = Depends(get_user_store_api_key), +): + try: + component = await store_service.download(store_api_key, component_id) + except CustomException as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + except Exception as exc: + raise HTTPException(status_code=500, detail=str(exc)) from exc + + if component is None: + raise HTTPException(status_code=400, detail="Component not found") + + return component + + +@router.get("/tags", response_model=List[TagResponse]) +async def get_tags( + store_service: StoreService = Depends(get_store_service), +): + try: + return await store_service.get_tags() + except CustomException as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + except Exception as exc: + raise HTTPException(status_code=500, detail=str(exc)) + + +@router.get("/users/likes", response_model=List[UsersLikesResponse]) +async def get_list_of_components_liked_by_user( + store_service: StoreService = Depends(get_store_service), + store_api_key: str = Depends(get_user_store_api_key), +): + try: + return await store_service.get_user_likes(store_api_key) + except CustomException as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + except Exception as exc: + raise HTTPException(status_code=500, detail=str(exc)) + + +@router.post("/users/likes/{component_id}", response_model=UsersLikesResponse) +async def like_component( + component_id: UUID, + store_service: StoreService = Depends(get_store_service), + store_api_key: str = Depends(get_user_store_api_key), +): + try: + result = await store_service.like_component(store_api_key, str(component_id)) + likes_count = await store_service.get_component_likes_count(str(component_id), store_api_key) + + return UsersLikesResponse(likes_count=likes_count, liked_by_user=result) + except CustomException as exc: + raise HTTPException(status_code=exc.status_code, detail=str(exc)) from exc + except Exception as exc: + raise HTTPException(status_code=500, detail=str(exc)) diff --git a/src/backend/langflow/api/v1/users.py b/src/backend/langflow/api/v1/users.py index 73c7346d9..9d4c3f32e 100644 --- a/src/backend/langflow/api/v1/users.py +++ b/src/backend/langflow/api/v1/users.py @@ -1,29 +1,20 @@ from uuid import UUID -from langflow.api.v1.schemas import UsersResponse -from langflow.services.database.models.user import ( - User, - UserCreate, - UserRead, - UserUpdate, -) -from sqlalchemy import func -from sqlalchemy.exc import IntegrityError - -from sqlmodel import Session, select from fastapi import APIRouter, Depends, HTTPException - -from langflow.services.getters import get_session, get_settings_service +from langflow.api.v1.schemas import UsersResponse from langflow.services.auth.utils import ( get_current_active_superuser, get_current_active_user, get_password_hash, verify_password, ) -from langflow.services.database.models.user.crud import ( - get_user_by_id, - update_user, -) +from langflow.services.database.models.user import User, UserCreate, UserRead, UserUpdate +from langflow.services.database.models.user.crud import get_user_by_id, update_user +from langflow.services.deps import get_session, get_settings_service +from sqlalchemy import func +from sqlalchemy.exc import IntegrityError +from sqlmodel import Session, select +from sqlmodel.sql.expression import SelectOfScalar router = APIRouter(tags=["Users"], prefix="/users") @@ -46,9 +37,7 @@ def add_user( session.refresh(new_user) except IntegrityError as e: session.rollback() - raise HTTPException( - status_code=400, detail="This username is unavailable." - ) from e + raise HTTPException(status_code=400, detail="This username is unavailable.") from e return new_user @@ -73,15 +62,15 @@ def read_all_users( """ Retrieve a list of users from the database with pagination. """ - query = select(User).offset(skip).limit(limit) - users = session.execute(query).fetchall() + query: SelectOfScalar = select(User).offset(skip).limit(limit) + users = session.exec(query).fetchall() count_query = select(func.count()).select_from(User) # type: ignore - total_count = session.execute(count_query).scalar() + total_count = session.exec(count_query).first() return UsersResponse( total_count=total_count, # type: ignore - users=[UserRead(**dict(user.User)) for user in users], + users=[UserRead(**user.model_dump()) for user in users], ) @@ -96,14 +85,10 @@ def patch_user( Update an existing user's data. """ if not user.is_superuser and user.id != user_id: - raise HTTPException( - status_code=403, detail="You don't have the permission to update this user" - ) + raise HTTPException(status_code=403, detail="You don't have the permission to update this user") if user_update.password: if not user.is_superuser: - raise HTTPException( - status_code=400, detail="You can't change your password here" - ) + raise HTTPException(status_code=400, detail="You can't change your password here") user_update.password = get_password_hash(user_update.password) if user_db := get_user_by_id(session, user_id): @@ -123,16 +108,12 @@ def reset_password( Reset a user's password. """ if user_id != user.id: - raise HTTPException( - status_code=400, detail="You can't change another user's password" - ) + raise HTTPException(status_code=400, detail="You can't change another user's password") if not user: raise HTTPException(status_code=404, detail="User not found") if verify_password(user_update.password, user.password): - raise HTTPException( - status_code=400, detail="You can't use your current password" - ) + raise HTTPException(status_code=400, detail="You can't use your current password") new_password = get_password_hash(user_update.password) user.password = new_password session.commit() @@ -151,15 +132,11 @@ def delete_user( Delete a user from the database. """ if current_user.id == user_id: - raise HTTPException( - status_code=400, detail="You can't delete your own user account" - ) + raise HTTPException(status_code=400, detail="You can't delete your own user account") elif not current_user.is_superuser: - raise HTTPException( - status_code=403, detail="You don't have the permission to delete this user" - ) + raise HTTPException(status_code=403, detail="You don't have the permission to delete this user") - user_db = session.query(User).filter(User.id == user_id).first() + user_db = session.exec(select(User).where(User.id == user_id)).first() if not user_db: raise HTTPException(status_code=404, detail="User not found") diff --git a/src/backend/langflow/api/v1/validate.py b/src/backend/langflow/api/v1/validate.py index 65fb66bd2..51d0f0d76 100644 --- a/src/backend/langflow/api/v1/validate.py +++ b/src/backend/langflow/api/v1/validate.py @@ -1,15 +1,14 @@ from fastapi import APIRouter, HTTPException - from langflow.api.v1.base import ( Code, CodeValidationResponse, - ValidatePromptRequest, PromptValidationResponse, + ValidatePromptRequest, validate_prompt, ) from langflow.template.field.base import TemplateField -from loguru import logger from langflow.utils.validate import validate_code +from loguru import logger # build router router = APIRouter(prefix="/validate", tags=["Validate"]) @@ -41,9 +40,7 @@ def post_validate_prompt(prompt_request: ValidatePromptRequest): add_new_variables_to_template(input_variables, prompt_request) - remove_old_variables_from_template( - old_custom_fields, input_variables, prompt_request - ) + remove_old_variables_from_template(old_custom_fields, input_variables, prompt_request) update_input_variables_field(input_variables, prompt_request) @@ -58,19 +55,16 @@ def post_validate_prompt(prompt_request: ValidatePromptRequest): def get_old_custom_fields(prompt_request): try: - if ( - len(prompt_request.frontend_node.custom_fields) == 1 - and prompt_request.name == "" - ): + if len(prompt_request.frontend_node.custom_fields) == 1 and prompt_request.name == "": # If there is only one custom field and the name is empty string # then we are dealing with the first prompt request after the node was created - prompt_request.name = list( - prompt_request.frontend_node.custom_fields.keys() - )[0] + prompt_request.name = list(prompt_request.frontend_node.custom_fields.keys())[0] - old_custom_fields = prompt_request.frontend_node.custom_fields[ - prompt_request.name - ].copy() + old_custom_fields = prompt_request.frontend_node.custom_fields[prompt_request.name] + if old_custom_fields is None: + old_custom_fields = [] + + old_custom_fields = old_custom_fields.copy() except KeyError: old_custom_fields = [] prompt_request.frontend_node.custom_fields[prompt_request.name] = [] @@ -92,40 +86,26 @@ def add_new_variables_to_template(input_variables, prompt_request): ) if variable in prompt_request.frontend_node.template: # Set the new field with the old value - template_field.value = prompt_request.frontend_node.template[variable][ - "value" - ] + template_field.value = prompt_request.frontend_node.template[variable]["value"] prompt_request.frontend_node.template[variable] = template_field.to_dict() # Check if variable is not already in the list before appending - if ( - variable - not in prompt_request.frontend_node.custom_fields[prompt_request.name] - ): - prompt_request.frontend_node.custom_fields[prompt_request.name].append( - variable - ) + if variable not in prompt_request.frontend_node.custom_fields[prompt_request.name]: + prompt_request.frontend_node.custom_fields[prompt_request.name].append(variable) except Exception as exc: logger.exception(exc) raise HTTPException(status_code=500, detail=str(exc)) from exc -def remove_old_variables_from_template( - old_custom_fields, input_variables, prompt_request -): +def remove_old_variables_from_template(old_custom_fields, input_variables, prompt_request): for variable in old_custom_fields: if variable not in input_variables: try: # Remove the variable from custom_fields associated with the given name - if ( - variable - in prompt_request.frontend_node.custom_fields[prompt_request.name] - ): - prompt_request.frontend_node.custom_fields[ - prompt_request.name - ].remove(variable) + if variable in prompt_request.frontend_node.custom_fields[prompt_request.name]: + prompt_request.frontend_node.custom_fields[prompt_request.name].remove(variable) # Remove the variable from the template prompt_request.frontend_node.template.pop(variable, None) @@ -137,6 +117,4 @@ def remove_old_variables_from_template( def update_input_variables_field(input_variables, prompt_request): if "input_variables" in prompt_request.frontend_node.template: - prompt_request.frontend_node.template["input_variables"][ - "value" - ] = input_variables + prompt_request.frontend_node.template["input_variables"]["value"] = input_variables diff --git a/src/backend/langflow/components/agents/AgentInitializer.py b/src/backend/langflow/components/agents/AgentInitializer.py new file mode 100644 index 000000000..2e8a9de2f --- /dev/null +++ b/src/backend/langflow/components/agents/AgentInitializer.py @@ -0,0 +1,37 @@ +from typing import Callable, List, Union + +from langchain.agents import AgentExecutor, AgentType, initialize_agent, types + +from langflow import CustomComponent +from langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool + + +class AgentInitializerComponent(CustomComponent): + display_name: str = "Agent Initializer" + description: str = "Initialize a Langchain Agent." + documentation: str = "https://python.langchain.com/docs/modules/agents/agent_types/" + + def build_config(self): + agents = list(types.AGENT_TO_CLASS.keys()) + # field_type and required are optional + return { + "agent": {"options": agents, "value": agents[0], "display_name": "Agent Type"}, + "max_iterations": {"display_name": "Max Iterations", "value": 10}, + "memory": {"display_name": "Memory"}, + "tools": {"display_name": "Tools"}, + "llm": {"display_name": "Language Model"}, + } + + def build( + self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int + ) -> Union[AgentExecutor, Callable]: + agent = AgentType(agent) + return initialize_agent( + tools=tools, + llm=llm, + agent=agent, + memory=memory, + return_intermediate_steps=True, + handle_parsing_errors=True, + max_iterations=max_iterations, + ) diff --git a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py b/src/backend/langflow/components/agents/OpenAIConversationalAgent.py index ff1a993b1..f2df4c7d2 100644 --- a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py +++ b/src/backend/langflow/components/agents/OpenAIConversationalAgent.py @@ -1,17 +1,15 @@ -from langflow import CustomComponent -from typing import Optional -from langchain.prompts import SystemMessagePromptTemplate -from langchain.tools import Tool -from langchain.schema.memory import BaseMemory -from langchain.chat_models import ChatOpenAI +from typing import List, Optional from langchain.agents.agent import AgentExecutor +from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent +from langchain.chat_models import ChatOpenAI from langchain.memory.token_buffer import ConversationTokenBufferMemory +from langchain.prompts import SystemMessagePromptTemplate from langchain.prompts.chat import MessagesPlaceholder -from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import ( - _get_default_system_message, -) +from langchain.schema.memory import BaseMemory +from langchain.tools import Tool +from langflow import CustomComponent class ConversationalAgent(CustomComponent): @@ -20,13 +18,14 @@ class ConversationalAgent(CustomComponent): def build_config(self): openai_function_models = [ - "gpt-3.5-turbo-0613", - "gpt-3.5-turbo-16k-0613", - "gpt-4-0613", - "gpt-4-32k-0613", + "gpt-4-1106-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-16k", + "gpt-4", + "gpt-4-32k", ] return { - "tools": {"is_list": True, "display_name": "Tools"}, + "tools": {"display_name": "Tools"}, "memory": {"display_name": "Memory"}, "system_message": {"display_name": "System Message"}, "max_token_limit": {"display_name": "Max Token Limit"}, @@ -42,7 +41,7 @@ class ConversationalAgent(CustomComponent): self, model_name: str, openai_api_key: str, - tools: Tool, + tools: List[Tool], openai_api_base: Optional[str] = None, memory: Optional[BaseMemory] = None, system_message: Optional[SystemMessagePromptTemplate] = None, @@ -50,8 +49,8 @@ class ConversationalAgent(CustomComponent): ) -> AgentExecutor: llm = ChatOpenAI( model=model_name, - openai_api_key=openai_api_key, - openai_api_base=openai_api_base, + api_key=openai_api_key, + base_url=openai_api_base, ) if not memory: memory_key = "chat_history" @@ -71,7 +70,9 @@ class ConversationalAgent(CustomComponent): extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)], ) agent = OpenAIFunctionsAgent( - llm=llm, tools=tools, prompt=prompt # type: ignore + llm=llm, + tools=tools, + prompt=prompt, # type: ignore ) return AgentExecutor( agent=agent, diff --git a/src/backend/langflow/components/chains/ConversationChain.py b/src/backend/langflow/components/chains/ConversationChain.py new file mode 100644 index 000000000..76e6d8e25 --- /dev/null +++ b/src/backend/langflow/components/chains/ConversationChain.py @@ -0,0 +1,29 @@ +from langflow import CustomComponent +from langchain.chains import ConversationChain +from typing import Optional, Union, Callable +from langflow.field_typing import BaseLanguageModel, BaseMemory, Chain + + +class ConversationChainComponent(CustomComponent): + display_name = "ConversationChain" + description = "Chain to have a conversation and load context from memory." + + def build_config(self): + return { + "prompt": {"display_name": "Prompt"}, + "llm": {"display_name": "LLM"}, + "memory": { + "display_name": "Memory", + "info": "Memory to load context from. If none is provided, a ConversationBufferMemory will be used.", + }, + "code": {"show": False}, + } + + def build( + self, + llm: BaseLanguageModel, + memory: Optional[BaseMemory] = None, + ) -> Union[Chain, Callable]: + if memory is None: + return ConversationChain(llm=llm) + return ConversationChain(llm=llm, memory=memory) diff --git a/src/backend/langflow/components/chains/LLMChain.py b/src/backend/langflow/components/chains/LLMChain.py new file mode 100644 index 000000000..ec88e128a --- /dev/null +++ b/src/backend/langflow/components/chains/LLMChain.py @@ -0,0 +1,32 @@ +from typing import Callable, Optional, Union + +from langchain.chains import LLMChain + +from langflow import CustomComponent +from langflow.field_typing import ( + BaseLanguageModel, + BaseMemory, + BasePromptTemplate, + Chain, +) + + +class LLMChainComponent(CustomComponent): + display_name = "LLMChain" + description = "Chain to run queries against LLMs" + + def build_config(self): + return { + "prompt": {"display_name": "Prompt"}, + "llm": {"display_name": "LLM"}, + "memory": {"display_name": "Memory"}, + "code": {"show": False}, + } + + def build( + self, + prompt: BasePromptTemplate, + llm: BaseLanguageModel, + memory: Optional[BaseMemory] = None, + ) -> Union[Chain, Callable]: + return LLMChain(prompt=prompt, llm=llm, memory=memory) diff --git a/src/backend/langflow/components/chains/PromptRunner.py b/src/backend/langflow/components/chains/PromptRunner.py index 96a64f6fc..496be610e 100644 --- a/src/backend/langflow/components/chains/PromptRunner.py +++ b/src/backend/langflow/components/chains/PromptRunner.py @@ -8,7 +8,7 @@ from langchain.schema import Document class PromptRunner(CustomComponent): display_name: str = "Prompt Runner" description: str = "Run a Chain with the given PromptTemplate" - beta = True + beta: bool = True field_config = { "llm": {"display_name": "LLM"}, "prompt": { @@ -18,9 +18,7 @@ class PromptRunner(CustomComponent): "code": {"show": False}, } - def build( - self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {} - ) -> Document: + def build(self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}) -> Document: chain = prompt | llm # The input is an empty dict because the prompt is already filled result = chain.invoke(input=inputs) diff --git a/src/backend/langflow/components/custom_components/CustomComponent.py b/src/backend/langflow/components/custom_components/CustomComponent.py new file mode 100644 index 000000000..533ccb727 --- /dev/null +++ b/src/backend/langflow/components/custom_components/CustomComponent.py @@ -0,0 +1,12 @@ +from langflow import CustomComponent +from langflow.field_typing import Data + + +class Component(CustomComponent): + documentation: str = "http://docs.langflow.org/components/custom" + + def build_config(self): + return {"param": {"display_name": "Parameter"}} + + def build(self, param: Data) -> Data: + return param diff --git a/src/backend/langflow/components/custom_components/__init__.py b/src/backend/langflow/components/custom_components/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/components/documentloaders/FileLoader.py b/src/backend/langflow/components/documentloaders/FileLoader.py new file mode 100644 index 000000000..eea401b83 --- /dev/null +++ b/src/backend/langflow/components/documentloaders/FileLoader.py @@ -0,0 +1,114 @@ +from langchain.schema import Document + +from langflow import CustomComponent +from langflow.utils.constants import LOADERS_INFO + + +class FileLoaderComponent(CustomComponent): + display_name: str = "File Loader" + description: str = "Generic File Loader" + beta = True + + def build_config(self): + loader_options = ["Automatic"] + [loader_info["name"] for loader_info in LOADERS_INFO] + + file_types = [] + suffixes = [] + + for loader_info in LOADERS_INFO: + if "allowedTypes" in loader_info: + file_types.extend(loader_info["allowedTypes"]) + suffixes.extend([f".{ext}" for ext in loader_info["allowedTypes"]]) + + return { + "file_path": { + "display_name": "File Path", + "required": True, + "field_type": "file", + "file_types": [ + "json", + "txt", + "csv", + "jsonl", + "html", + "htm", + "conllu", + "enex", + "msg", + "pdf", + "srt", + "eml", + "md", + "pptx", + "docx", + ], + "suffixes": [ + ".json", + ".txt", + ".csv", + ".jsonl", + ".html", + ".htm", + ".conllu", + ".enex", + ".msg", + ".pdf", + ".srt", + ".eml", + ".md", + ".pptx", + ".docx", + ], + # "file_types" : file_types, + # "suffixes": suffixes, + }, + "loader": { + "display_name": "Loader", + "is_list": True, + "required": True, + "options": loader_options, + "value": "Automatic", + }, + "code": {"show": False}, + } + + def build(self, file_path: str, loader: str) -> Document: + file_type = file_path.split(".")[-1] + + # Mapeie o nome do loader selecionado para suas informações + selected_loader_info = None + for loader_info in LOADERS_INFO: + if loader_info["name"] == loader: + selected_loader_info = loader_info + break + + if selected_loader_info is None and loader != "Automatic": + raise ValueError(f"Loader {loader} not found in the loader info list") + + if loader == "Automatic": + # Determine o loader automaticamente com base na extensão do arquivo + default_loader_info = None + for info in LOADERS_INFO: + if "defaultFor" in info and file_type in info["defaultFor"]: + default_loader_info = info + break + + if default_loader_info is None: + raise ValueError(f"No default loader found for file type: {file_type}") + + selected_loader_info = default_loader_info + if isinstance(selected_loader_info, dict): + loader_import: str = selected_loader_info["import"] + else: + raise ValueError(f"Loader info for {loader} is not a dict\nLoader info:\n{selected_loader_info}") + module_name, class_name = loader_import.rsplit(".", 1) + + try: + # Importe o loader dinamicamente + loader_module = __import__(module_name, fromlist=[class_name]) + loader_instance = getattr(loader_module, class_name) + except ImportError as e: + raise ValueError(f"Loader {loader} could not be imported\nLoader info:\n{selected_loader_info}") from e + + result = loader_instance(file_path=file_path) + return result.load() diff --git a/src/backend/langflow/components/documentloaders/UrlLoader.py b/src/backend/langflow/components/documentloaders/UrlLoader.py new file mode 100644 index 000000000..f9c4363f0 --- /dev/null +++ b/src/backend/langflow/components/documentloaders/UrlLoader.py @@ -0,0 +1,46 @@ +from typing import List + +from langchain import document_loaders +from langchain.schema import Document +from langflow import CustomComponent + + +class UrlLoaderComponent(CustomComponent): + display_name: str = "Url Loader" + description: str = "Generic Url Loader Component" + + def build_config(self): + return { + "web_path": { + "display_name": "Url", + "required": True, + }, + "loader": { + "display_name": "Loader", + "is_list": True, + "required": True, + "options": [ + "AZLyricsLoader", + "CollegeConfidentialLoader", + "GitbookLoader", + "HNLoader", + "IFixitLoader", + "IMSDbLoader", + "WebBaseLoader", + ], + "value": "WebBaseLoader", + }, + "code": {"show": False}, + } + + def build(self, web_path: str, loader: str) -> List[Document]: + try: + loader_instance = getattr(document_loaders, loader)(web_path=web_path) + except Exception as e: + raise ValueError(f"No loader found for: {web_path}") from e + docs = loader_instance.load() + avg_length = sum(len(doc.page_content) for doc in docs if hasattr(doc, "page_content")) / len(docs) + self.status = f"""{len(docs)} documents) + \nAvg. Document Length (characters): {int(avg_length)} + Documents: {docs[:3]}...""" + return docs diff --git a/src/backend/langflow/components/embeddings/AmazonBedrockEmbeddings.py b/src/backend/langflow/components/embeddings/AmazonBedrockEmbeddings.py new file mode 100644 index 000000000..450e5ee3b --- /dev/null +++ b/src/backend/langflow/components/embeddings/AmazonBedrockEmbeddings.py @@ -0,0 +1,46 @@ +from typing import Optional + +from langchain.embeddings import BedrockEmbeddings +from langchain.embeddings.base import Embeddings +from langflow import CustomComponent + + +class AmazonBedrockEmeddingsComponent(CustomComponent): + """ + A custom component for implementing an Embeddings Model using Amazon Bedrock. + """ + + display_name: str = "Amazon Bedrock Embeddings" + description: str = "Embeddings model from Amazon Bedrock." + documentation = "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock" + beta = True + + def build_config(self): + return { + "model_id": { + "display_name": "Model Id", + "options": ["amazon.titan-embed-text-v1"], + }, + "credentials_profile_name": {"display_name": "Credentials Profile Name"}, + "endpoint_url": {"display_name": "Bedrock Endpoint URL"}, + "region_name": {"display_name": "AWS Region"}, + "code": {"show": False}, + } + + def build( + self, + model_id: str = "amazon.titan-embed-text-v1", + credentials_profile_name: Optional[str] = None, + endpoint_url: Optional[str] = None, + region_name: Optional[str] = None, + ) -> Embeddings: + try: + output = BedrockEmbeddings( + credentials_profile_name=credentials_profile_name, + model_id=model_id, + endpoint_url=endpoint_url, + region_name=region_name, + ) # type: ignore + except Exception as e: + raise ValueError("Could not connect to AmazonBedrock API.") from e + return output diff --git a/src/backend/langflow/components/embeddings/__init__.py b/src/backend/langflow/components/embeddings/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/components/llms/AnthropicLLM.py b/src/backend/langflow/components/llms/AnthropicLLM.py index 1f75ba39f..82fba24fb 100644 --- a/src/backend/langflow/components/llms/AnthropicLLM.py +++ b/src/backend/langflow/components/llms/AnthropicLLM.py @@ -1,7 +1,10 @@ from typing import Optional -from langflow import CustomComponent + from langchain.chat_models.anthropic import ChatAnthropic -from langchain.llms.base import BaseLLM +from langchain.llms.base import BaseLanguageModel +from pydantic.v1 import SecretStr + +from langflow import CustomComponent class AnthropicLLM(CustomComponent): @@ -53,16 +56,16 @@ class AnthropicLLM(CustomComponent): max_tokens: Optional[int] = None, temperature: Optional[float] = None, api_endpoint: Optional[str] = None, - ) -> BaseLLM: + ) -> BaseLanguageModel: # Set default API endpoint if not provided if not api_endpoint: api_endpoint = "https://api.anthropic.com" try: output = ChatAnthropic( - model=model, - anthropic_api_key=anthropic_api_key, - max_tokens_to_sample=max_tokens, + model_name=model, + anthropic_api_key=SecretStr(anthropic_api_key) if anthropic_api_key else None, + max_tokens_to_sample=max_tokens, # type: ignore temperature=temperature, anthropic_api_url=api_endpoint, ) diff --git a/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py b/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py new file mode 100644 index 000000000..8c828dc64 --- /dev/null +++ b/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py @@ -0,0 +1,95 @@ +from typing import Optional + +from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint +from langchain.llms.base import BaseLLM +from pydantic.v1 import SecretStr + +from langflow import CustomComponent + + +class QianfanChatEndpointComponent(CustomComponent): + display_name: str = "QianfanChatEndpoint" + description: str = ( + "Baidu Qianfan chat models. Get more detail from " + "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint." + ) + + def build_config(self): + return { + "model": { + "display_name": "Model Name", + "options": [ + "ERNIE-Bot", + "ERNIE-Bot-turbo", + "BLOOMZ-7B", + "Llama-2-7b-chat", + "Llama-2-13b-chat", + "Llama-2-70b-chat", + "Qianfan-BLOOMZ-7B-compressed", + "Qianfan-Chinese-Llama-2-7B", + "ChatGLM2-6B-32K", + "AquilaChat-7B", + ], + "info": "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint", + "required": True, + }, + "qianfan_ak": { + "display_name": "Qianfan Ak", + "required": True, + "password": True, + "info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop", + }, + "qianfan_sk": { + "display_name": "Qianfan Sk", + "required": True, + "password": True, + "info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop", + }, + "top_p": { + "display_name": "Top p", + "field_type": "float", + "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo", + "value": 0.8, + }, + "temperature": { + "display_name": "Temperature", + "field_type": "float", + "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo", + "value": 0.95, + }, + "penalty_score": { + "display_name": "Penalty Score", + "field_type": "float", + "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo", + "value": 1.0, + }, + "endpoint": { + "display_name": "Endpoint", + "info": "Endpoint of the Qianfan LLM, required if custom model used.", + }, + "code": {"show": False}, + } + + def build( + self, + model: str = "ERNIE-Bot-turbo", + qianfan_ak: Optional[str] = None, + qianfan_sk: Optional[str] = None, + top_p: Optional[float] = None, + temperature: Optional[float] = None, + penalty_score: Optional[float] = None, + endpoint: Optional[str] = None, + ) -> BaseLLM: + try: + output = QianfanChatEndpoint( # type: ignore + model=model, + qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None, + qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None, + top_p=top_p, + temperature=temperature, + penalty_score=penalty_score, + endpoint=endpoint, + ) + except Exception as e: + raise ValueError("Could not connect to Baidu Qianfan API.") from e + return output # type: ignore diff --git a/src/backend/langflow/components/retrievers/MetalRetriever.py b/src/backend/langflow/components/retrievers/MetalRetriever.py index b105cd24f..88393f26d 100644 --- a/src/backend/langflow/components/retrievers/MetalRetriever.py +++ b/src/backend/langflow/components/retrievers/MetalRetriever.py @@ -18,9 +18,7 @@ class MetalRetrieverComponent(CustomComponent): "code": {"show": False}, } - def build( - self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None - ) -> BaseRetriever: + def build(self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None) -> BaseRetriever: try: metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id) except Exception as e: diff --git a/src/backend/langflow/components/toolkits/Metaphor.py b/src/backend/langflow/components/toolkits/Metaphor.py index 6f43d24b4..0f9f23334 100644 --- a/src/backend/langflow/components/toolkits/Metaphor.py +++ b/src/backend/langflow/components/toolkits/Metaphor.py @@ -1,19 +1,18 @@ from typing import List, Union -from langflow import CustomComponent -from metaphor_python import Metaphor # type: ignore -from langchain.tools import Tool from langchain.agents import tool from langchain.agents.agent_toolkits.base import BaseToolkit +from langchain.tools import Tool +from metaphor_python import Metaphor # type: ignore + +from langflow import CustomComponent class MetaphorToolkit(CustomComponent): display_name: str = "Metaphor" description: str = "Metaphor Toolkit" - documentation = ( - "https://python.langchain.com/docs/integrations/tools/metaphor_search" - ) - beta = True + documentation = "https://python.langchain.com/docs/integrations/tools/metaphor_search" + beta: bool = True # api key should be password = True field_config = { "metaphor_api_key": {"display_name": "Metaphor API Key", "password": True}, @@ -33,9 +32,7 @@ class MetaphorToolkit(CustomComponent): @tool def search(query: str): """Call search engine with a query.""" - return client.search( - query, use_autoprompt=use_autoprompt, num_results=search_num_results - ) + return client.search(query, use_autoprompt=use_autoprompt, num_results=search_num_results) @tool def get_contents(ids: List[str]): diff --git a/src/backend/langflow/components/utilities/GetRequest.py b/src/backend/langflow/components/utilities/GetRequest.py index d5df32cca..627d0804a 100644 --- a/src/backend/langflow/components/utilities/GetRequest.py +++ b/src/backend/langflow/components/utilities/GetRequest.py @@ -10,7 +10,7 @@ class GetRequest(CustomComponent): description: str = "Make a GET request to the given URL." output_types: list[str] = ["Document"] documentation: str = "https://docs.langflow.org/components/utilities#get-request" - beta = True + beta: bool = True field_config = { "url": { "display_name": "URL", @@ -30,9 +30,7 @@ class GetRequest(CustomComponent): }, } - def get_document( - self, session: requests.Session, url: str, headers: Optional[dict], timeout: int - ) -> Document: + def get_document(self, session: requests.Session, url: str, headers: Optional[dict], timeout: int) -> Document: try: response = session.get(url, headers=headers, timeout=int(timeout)) try: diff --git a/src/backend/langflow/components/utilities/JSONDocumentBuilder.py b/src/backend/langflow/components/utilities/JSONDocumentBuilder.py index 26a2afd94..b5f3b1263 100644 --- a/src/backend/langflow/components/utilities/JSONDocumentBuilder.py +++ b/src/backend/langflow/components/utilities/JSONDocumentBuilder.py @@ -11,8 +11,8 @@ # - **Document:** The Document containing the JSON object. -from langflow import CustomComponent from langchain.schema import Document +from langflow import CustomComponent from langflow.services.database.models.base import orjson_dumps @@ -21,9 +21,7 @@ class JSONDocumentBuilder(CustomComponent): description: str = "Build a Document containing a JSON object using a key and another Document page content." output_types: list[str] = ["Document"] beta = True - documentation: str = ( - "https://docs.langflow.org/components/utilities#json-document-builder" - ) + documentation: str = "https://docs.langflow.org/components/utilities#json-document-builder" field_config = { "key": {"display_name": "Key"}, @@ -38,18 +36,11 @@ class JSONDocumentBuilder(CustomComponent): documents = None if isinstance(document, list): documents = [ - Document( - page_content=orjson_dumps({key: doc.page_content}, indent_2=False) - ) - for doc in document + Document(page_content=orjson_dumps({key: doc.page_content}, indent_2=False)) for doc in document ] elif isinstance(document, Document): - documents = Document( - page_content=orjson_dumps({key: document.page_content}, indent_2=False) - ) + documents = Document(page_content=orjson_dumps({key: document.page_content}, indent_2=False)) else: - raise TypeError( - f"Expected Document or list of Documents, got {type(document)}" - ) + raise TypeError(f"Expected Document or list of Documents, got {type(document)}") self.repr_value = documents return documents diff --git a/src/backend/langflow/components/utilities/PostRequest.py b/src/backend/langflow/components/utilities/PostRequest.py index 6857f4866..cbecac535 100644 --- a/src/backend/langflow/components/utilities/PostRequest.py +++ b/src/backend/langflow/components/utilities/PostRequest.py @@ -10,7 +10,7 @@ class PostRequest(CustomComponent): description: str = "Make a POST request to the given URL." output_types: list[str] = ["Document"] documentation: str = "https://docs.langflow.org/components/utilities#post-request" - beta = True + beta: bool = True field_config = { "url": {"display_name": "URL", "info": "The URL to make the request to."}, "headers": { @@ -65,16 +65,12 @@ class PostRequest(CustomComponent): if not isinstance(document, list) and isinstance(document, Document): documents: list[Document] = [document] - elif isinstance(document, list) and all( - isinstance(doc, Document) for doc in document - ): + elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document): documents = document else: raise ValueError("document must be a Document or a list of Documents") with requests.Session() as session: - documents = [ - self.post_document(session, doc, url, headers) for doc in documents - ] + documents = [self.post_document(session, doc, url, headers) for doc in documents] self.repr_value = documents return documents diff --git a/src/backend/langflow/components/utilities/UpdateRequest.py b/src/backend/langflow/components/utilities/UpdateRequest.py index d18c94a56..1e6355b2c 100644 --- a/src/backend/langflow/components/utilities/UpdateRequest.py +++ b/src/backend/langflow/components/utilities/UpdateRequest.py @@ -10,7 +10,7 @@ class UpdateRequest(CustomComponent): description: str = "Make a PATCH request to the given URL." output_types: list[str] = ["Document"] documentation: str = "https://docs.langflow.org/components/utilities#update-request" - beta = True + beta: bool = True field_config = { "url": {"display_name": "URL", "info": "The URL to make the request to."}, "headers": { @@ -39,9 +39,7 @@ class UpdateRequest(CustomComponent): ) -> Document: try: if method == "PATCH": - response = session.patch( - url, headers=headers, data=document.page_content - ) + response = session.patch(url, headers=headers, data=document.page_content) elif method == "PUT": response = session.put(url, headers=headers, data=document.page_content) else: @@ -78,17 +76,12 @@ class UpdateRequest(CustomComponent): if not isinstance(document, list) and isinstance(document, Document): documents: list[Document] = [document] - elif isinstance(document, list) and all( - isinstance(doc, Document) for doc in document - ): + elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document): documents = document else: raise ValueError("document must be a Document or a list of Documents") with requests.Session() as session: - documents = [ - self.update_document(session, doc, url, headers, method) - for doc in documents - ] + documents = [self.update_document(session, doc, url, headers, method) for doc in documents] self.repr_value = documents return documents diff --git a/src/backend/langflow/components/vectorstores/Chroma.py b/src/backend/langflow/components/vectorstores/Chroma.py index 3cfd4771e..4c877dc85 100644 --- a/src/backend/langflow/components/vectorstores/Chroma.py +++ b/src/backend/langflow/components/vectorstores/Chroma.py @@ -1,12 +1,12 @@ from typing import Optional, Union -from langflow import CustomComponent -from langchain.vectorstores import Chroma -from langchain.schema import Document -from langchain.vectorstores.base import VectorStore -from langchain.schema import BaseRetriever -from langchain.embeddings.base import Embeddings import chromadb # type: ignore +from langchain.embeddings.base import Embeddings +from langchain.schema import BaseRetriever, Document +from langchain.vectorstores import Chroma +from langchain.vectorstores.base import VectorStore + +from langflow import CustomComponent class ChromaComponent(CustomComponent): @@ -17,7 +17,7 @@ class ChromaComponent(CustomComponent): display_name: str = "Chroma (Custom Component)" description: str = "Implementation of Vector Store using Chroma" documentation = "https://python.langchain.com/docs/integrations/vectorstores/chroma" - beta = True + beta: bool = True def build_config(self): """ @@ -53,9 +53,9 @@ class ChromaComponent(CustomComponent): self, collection_name: str, persist: bool, + embedding: Embeddings, chroma_server_ssl_enabled: bool, persist_directory: Optional[str] = None, - embedding: Optional[Embeddings] = None, documents: Optional[Document] = None, chroma_server_cors_allow_origins: Optional[str] = None, chroma_server_host: Optional[str] = None, @@ -86,8 +86,7 @@ class ChromaComponent(CustomComponent): if chroma_server_host is not None: chroma_settings = chromadb.config.Settings( - chroma_server_cors_allow_origins=chroma_server_cors_allow_origins - or None, + chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or None, chroma_server_host=chroma_server_host, chroma_server_port=chroma_server_port or None, chroma_server_grpc_port=chroma_server_grpc_port or None, @@ -96,6 +95,8 @@ class ChromaComponent(CustomComponent): # If documents, then we need to create a Chroma instance using .from_documents if documents is not None and embedding is not None: + if len(documents) == 0: + raise ValueError("If documents are provided, there must be at least one document.") return Chroma.from_documents( documents=documents, # type: ignore persist_directory=persist_directory if persist else None, @@ -104,6 +105,4 @@ class ChromaComponent(CustomComponent): client_settings=chroma_settings, ) - return Chroma( - persist_directory=persist_directory, client_settings=chroma_settings - ) + return Chroma(persist_directory=persist_directory, client_settings=chroma_settings) diff --git a/src/backend/langflow/components/vectorstores/Redis.py b/src/backend/langflow/components/vectorstores/Redis.py new file mode 100644 index 000000000..f13428829 --- /dev/null +++ b/src/backend/langflow/components/vectorstores/Redis.py @@ -0,0 +1,64 @@ +from typing import Optional +from langflow import CustomComponent + +from langchain.vectorstores.redis import Redis +from langchain.schema import Document +from langchain.vectorstores.base import VectorStore +from langchain.embeddings.base import Embeddings + + +class RedisComponent(CustomComponent): + """ + A custom component for implementing a Vector Store using Redis. + """ + + display_name: str = "Redis" + description: str = "Implementation of Vector Store using Redis" + documentation = "https://python.langchain.com/docs/integrations/vectorstores/redis" + beta = True + + def build_config(self): + """ + Builds the configuration for the component. + + Returns: + - dict: A dictionary containing the configuration options for the component. + """ + return { + "index_name": {"display_name": "Index Name", "value": "your_index"}, + "code": {"show": False, "display_name": "Code"}, + "documents": {"display_name": "Documents", "is_list": True}, + "embedding": {"display_name": "Embedding"}, + "redis_server_url": { + "display_name": "Redis Server Connection String", + "advanced": False, + }, + "redis_index_name": {"display_name": "Redis Index", "advanced": False}, + } + + def build( + self, + embedding: Embeddings, + redis_server_url: str, + redis_index_name: str, + documents: Optional[Document] = None, + ) -> VectorStore: + """ + Builds the Vector Store or BaseRetriever object. + + Args: + - embedding (Embeddings): The embeddings to use for the Vector Store. + - documents (Optional[Document]): The documents to use for the Vector Store. + - redis_index_name (str): The name of the Redis index. + - redis_server_url (str): The URL for the Redis server. + + Returns: + - VectorStore: The Vector Store object. + """ + + return Redis.from_documents( + documents=documents, # type: ignore + embedding=embedding, + redis_url=redis_server_url, + index_name=redis_index_name, + ) diff --git a/src/backend/langflow/components/vectorstores/Vectara.py b/src/backend/langflow/components/vectorstores/Vectara.py index 6edc69822..09d8b500b 100644 --- a/src/backend/langflow/components/vectorstores/Vectara.py +++ b/src/backend/langflow/components/vectorstores/Vectara.py @@ -1,19 +1,17 @@ from typing import Optional, Union -from langflow import CustomComponent -from langchain.vectorstores import Vectara -from langchain.schema import Document -from langchain.vectorstores.base import VectorStore -from langchain.schema import BaseRetriever from langchain.embeddings.base import Embeddings +from langchain.schema import BaseRetriever, Document +from langchain.vectorstores import Vectara +from langchain.vectorstores.base import VectorStore + +from langflow import CustomComponent class VectaraComponent(CustomComponent): display_name: str = "Vectara" description: str = "Implementation of Vector Store using Vectara" - documentation = ( - "https://python.langchain.com/docs/integrations/vectorstores/vectara" - ) + documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara" beta = True # api key should be password = True field_config = { diff --git a/src/backend/langflow/components/vectorstores/pgvector.py b/src/backend/langflow/components/vectorstores/pgvector.py new file mode 100644 index 000000000..4e0c2eb4d --- /dev/null +++ b/src/backend/langflow/components/vectorstores/pgvector.py @@ -0,0 +1,74 @@ +from typing import Optional, List +from langflow import CustomComponent + +from langchain.vectorstores.pgvector import PGVector +from langchain.schema import Document +from langchain.vectorstores.base import VectorStore +from langchain.embeddings.base import Embeddings + + +class PostgresqlVectorComponent(CustomComponent): + """ + A custom component for implementing a Vector Store using PostgreSQL. + """ + + display_name: str = "PGVector" + description: str = "Implementation of Vector Store using PostgreSQL" + documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector" + beta = True + + def build_config(self): + """ + Builds the configuration for the component. + + Returns: + - dict: A dictionary containing the configuration options for the component. + """ + return { + "index_name": {"display_name": "Index Name", "value": "your_index"}, + "code": {"show": True, "display_name": "Code"}, + "documents": {"display_name": "Documents", "is_list": True}, + "embedding": {"display_name": "Embedding"}, + "pg_server_url": { + "display_name": "PostgreSQL Server Connection String", + "advanced": False, + }, + "collection_name": {"display_name": "Table", "advanced": False}, + } + + def build( + self, + embedding: Embeddings, + pg_server_url: str, + collection_name: str, + documents: Optional[List[Document]] = None, + ) -> VectorStore: + """ + Builds the Vector Store or BaseRetriever object. + + Args: + - embedding (Embeddings): The embeddings to use for the Vector Store. + - documents (Optional[Document]): The documents to use for the Vector Store. + - collection_name (str): The name of the PG table. + - pg_server_url (str): The URL for the PG server. + + Returns: + - VectorStore: The Vector Store object. + """ + + try: + if documents is None: + return PGVector.from_existing_index( + embedding=embedding, + collection_name=collection_name, + connection_string=pg_server_url, + ) + + return PGVector.from_documents( + embedding=embedding, + documents=documents, + collection_name=collection_name, + connection_string=pg_server_url, + ) + except Exception as e: + raise RuntimeError(f"Failed to build PGVector: {e}") diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml index 0f18d01a4..60af58d63 100644 --- a/src/backend/langflow/config.yaml +++ b/src/backend/langflow/config.yaml @@ -5,8 +5,6 @@ agents: documentation: "https://python.langchain.com/docs/modules/agents/toolkits/openapi" CSVAgent: documentation: "https://python.langchain.com/docs/modules/agents/toolkits/csv" - AgentInitializer: - documentation: "https://python.langchain.com/docs/modules/agents/agent_types/" VectorStoreAgent: documentation: "" VectorStoreRouterAgent: @@ -14,14 +12,14 @@ agents: SQLAgent: documentation: "" chains: - LLMChain: - documentation: "https://python.langchain.com/docs/modules/chains/foundational/llm_chain" + # LLMChain: + # documentation: "https://python.langchain.com/docs/modules/chains/foundational/llm_chain" LLMMathChain: documentation: "https://python.langchain.com/docs/modules/chains/additional/llm_math" LLMCheckerChain: documentation: "https://python.langchain.com/docs/modules/chains/additional/llm_checker" - ConversationChain: - documentation: "" + # ConversationChain: + # documentation: "" SeriesCharacterChain: documentation: "" MidJourneyPromptChain: @@ -106,6 +104,9 @@ embeddings: documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/cohere" VertexAIEmbeddings: documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/google_vertex_ai_palm" + AmazonBedrockEmbeddings: + documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock" + llms: OpenAI: documentation: "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai" @@ -294,4 +295,4 @@ output_parsers: documentation: "https://python.langchain.com/docs/modules/model_io/output_parsers/structured" custom_components: CustomComponent: - documentation: "" + documentation: "https://docs.langflow.org/guidelines/custom-component" diff --git a/src/backend/langflow/core/celeryconfig.py b/src/backend/langflow/core/celeryconfig.py index 35d51bba0..128139071 100644 --- a/src/backend/langflow/core/celeryconfig.py +++ b/src/backend/langflow/core/celeryconfig.py @@ -3,12 +3,16 @@ import os langflow_redis_host = os.environ.get("LANGFLOW_REDIS_HOST") langflow_redis_port = os.environ.get("LANGFLOW_REDIS_PORT") -if "BROKER_URL" in os.environ and "RESULT_BACKEND" in os.environ: - # RabbitMQ - broker_url = os.environ.get("BROKER_URL", "amqp://localhost") - result_backend = os.environ.get("RESULT_BACKEND", "redis://localhost:6379/0") -elif langflow_redis_host and langflow_redis_port: +# broker default user + +if langflow_redis_host and langflow_redis_port: broker_url = f"redis://{langflow_redis_host}:{langflow_redis_port}/0" result_backend = f"redis://{langflow_redis_host}:{langflow_redis_port}/0" +else: + # RabbitMQ + mq_user = os.environ.get("RABBITMQ_DEFAULT_USER", "langflow") + mq_password = os.environ.get("RABBITMQ_DEFAULT_PASS", "langflow") + broker_url = os.environ.get("BROKER_URL", f"amqp://{mq_user}:{mq_password}@localhost:5672//") + result_backend = os.environ.get("RESULT_BACKEND", "redis://localhost:6379/0") # tasks should be json or pickle accept_content = ["json", "pickle"] diff --git a/src/backend/langflow/field_typing/__init__.py b/src/backend/langflow/field_typing/__init__.py index 927716b11..d90087838 100644 --- a/src/backend/langflow/field_typing/__init__.py +++ b/src/backend/langflow/field_typing/__init__.py @@ -1,3 +1,56 @@ -from .base import NestedDict +from typing import Any -__all__ = ["NestedDict"] +from .constants import (AgentExecutor, BaseChatMemory, BaseLanguageModel, + BaseLLM, BaseLoader, BaseMemory, BaseOutputParser, + BasePromptTemplate, BaseRetriever, Callable, Chain, + ChatPromptTemplate, Data, Document, Embeddings, + NestedDict, Object, Prompt, PromptTemplate, + TextSplitter, Tool, VectorStore) +from .range_spec import RangeSpec + + +def _import_template_field(): + from langflow.template.field.base import TemplateField + + return TemplateField + + +def __getattr__(name: str) -> Any: + # This is to avoid circular imports + if name == "TemplateField": + return _import_template_field() + elif name == "RangeSpec": + return RangeSpec + # The other names should work as if they were imported from constants + # Import the constants module langflow.field_typing.constants + from . import constants + + return getattr(constants, name) + + +__all__ = [ + "NestedDict", + "Data", + "Tool", + "PromptTemplate", + "Chain", + "BaseChatMemory", + "BaseLLM", + "BaseLanguageModel", + "BaseLoader", + "BaseMemory", + "BaseOutputParser", + "BaseRetriever", + "VectorStore", + "Embeddings", + "TextSplitter", + "Document", + "AgentExecutor", + "Object", + "Callable", + "BasePromptTemplate", + "ChatPromptTemplate", + "Prompt", + "RangeSpec", + "TemplateField", +] diff --git a/src/backend/langflow/interface/custom/constants.py b/src/backend/langflow/field_typing/constants.py similarity index 51% rename from src/backend/langflow/interface/custom/constants.py rename to src/backend/langflow/field_typing/constants.py index 58ecef637..d3ca22baa 100644 --- a/src/backend/langflow/interface/custom/constants.py +++ b/src/backend/langflow/field_typing/constants.py @@ -1,23 +1,44 @@ -from langchain.prompts import PromptTemplate +from typing import Callable, Dict, Union + +from langchain.agents.agent import AgentExecutor from langchain.chains.base import Chain from langchain.document_loaders.base import BaseLoader -from langchain.schema.embeddings import Embeddings from langchain.llms.base import BaseLLM -from langchain.schema import BaseRetriever, Document +from langchain.memory.chat_memory import BaseChatMemory +from langchain.prompts import BasePromptTemplate, ChatPromptTemplate, PromptTemplate +from langchain.schema import BaseOutputParser, BaseRetriever, Document +from langchain.schema.embeddings import Embeddings +from langchain.schema.language_model import BaseLanguageModel +from langchain.schema.memory import BaseMemory from langchain.text_splitter import TextSplitter from langchain.tools import Tool from langchain.vectorstores.base import VectorStore -from langchain.schema import BaseOutputParser -from langchain.schema.memory import BaseMemory -from langchain.memory.chat_memory import BaseChatMemory -from langchain.agents.agent import AgentExecutor + +# Type alias for more complex dicts +NestedDict = Dict[str, Union[str, Dict]] + + +class Object: + pass + + +class Data: + pass + + +class Prompt: + pass + LANGCHAIN_BASE_TYPES = { "Chain": Chain, "AgentExecutor": AgentExecutor, "Tool": Tool, "BaseLLM": BaseLLM, + "BaseLanguageModel": BaseLanguageModel, "PromptTemplate": PromptTemplate, + "ChatPromptTemplate": ChatPromptTemplate, + "BasePromptTemplate": BasePromptTemplate, "BaseLoader": BaseLoader, "Document": Document, "TextSplitter": TextSplitter, @@ -28,38 +49,12 @@ LANGCHAIN_BASE_TYPES = { "BaseMemory": BaseMemory, "BaseChatMemory": BaseChatMemory, } - # Langchain base types plus Python base types CUSTOM_COMPONENT_SUPPORTED_TYPES = { **LANGCHAIN_BASE_TYPES, - "str": str, - "int": int, - "float": float, - "bool": bool, - "list": list, - "dict": dict, + "NestedDict": NestedDict, + "Data": Data, + "Object": Object, + "Callable": Callable, + "Prompt": Prompt, } - - -DEFAULT_CUSTOM_COMPONENT_CODE = """from langflow import CustomComponent - -from langchain.llms.base import BaseLLM -from langchain.chains import LLMChain -from langchain.prompts import PromptTemplate -from langchain.schema import Document - -import requests - -class YourComponent(CustomComponent): - display_name: str = "Custom Component" - description: str = "Create any custom component you want!" - - def build_config(self): - return { "url": { "multiline": True, "required": True } } - - def build(self, url: str, llm: BaseLLM, prompt: PromptTemplate) -> Document: - response = requests.get(url) - chain = LLMChain(llm=llm, prompt=prompt) - result = chain.run(response.text[:300]) - return Document(page_content=str(result)) -""" diff --git a/src/backend/langflow/field_typing/range_spec.py b/src/backend/langflow/field_typing/range_spec.py new file mode 100644 index 000000000..036d21fa9 --- /dev/null +++ b/src/backend/langflow/field_typing/range_spec.py @@ -0,0 +1,21 @@ +from pydantic import BaseModel, field_validator + + +class RangeSpec(BaseModel): + min: float = -1.0 + max: float = 1.0 + step: float = 0.1 + + @field_validator("max") + @classmethod + def max_must_be_greater_than_min(cls, v, values, **kwargs): + if "min" in values.data and v <= values.data["min"]: + raise ValueError("max must be greater than min") + return v + + @field_validator("step") + @classmethod + def step_must_be_positive(cls, v): + if v <= 0: + raise ValueError("step must be positive") + return v diff --git a/src/backend/langflow/graph/edge/base.py b/src/backend/langflow/graph/edge/base.py index 2c60b0288..fae994571 100644 --- a/src/backend/langflow/graph/edge/base.py +++ b/src/backend/langflow/graph/edge/base.py @@ -1,46 +1,77 @@ +from typing import TYPE_CHECKING, List, Optional + from loguru import logger -from typing import TYPE_CHECKING +from pydantic import BaseModel, Field if TYPE_CHECKING: from langflow.graph.vertex.base import Vertex +class SourceHandle(BaseModel): + baseClasses: List[str] = Field(..., description="List of base classes for the source handle.") + dataType: str = Field(..., description="Data type for the source handle.") + id: str = Field(..., description="Unique identifier for the source handle.") + + +class TargetHandle(BaseModel): + fieldName: str = Field(..., description="Field name for the target handle.") + id: str = Field(..., description="Unique identifier for the target handle.") + inputTypes: Optional[List[str]] = Field(None, description="List of input types for the target handle.") + type: str = Field(..., description="Type of the target handle.") + + class Edge: def __init__(self, source: "Vertex", target: "Vertex", edge: dict): - self.source: "Vertex" = source - self.target: "Vertex" = target - self.source_handle = edge.get("sourceHandle", "") - self.target_handle = edge.get("targetHandle", "") - # 'BaseLoader;BaseOutputParser|documents|PromptTemplate-zmTlD' - # target_param is documents - self.target_param = self.target_handle.split("|")[1] + self.source_id: str = source.id if source else "" + self.target_id: str = target.id if target else "" + if data := edge.get("data", {}): + self._source_handle = data.get("sourceHandle", {}) + self._target_handle = data.get("targetHandle", {}) + self.source_handle: SourceHandle = SourceHandle(**self._source_handle) + self.target_handle: TargetHandle = TargetHandle(**self._target_handle) + self.target_param = self.target_handle.fieldName + # validate handles + self.validate_handles(source, target) + else: + # Logging here because this is a breaking change + logger.error("Edge data is empty") + self._source_handle = edge.get("sourceHandle", "") + self._target_handle = edge.get("targetHandle", "") + # 'BaseLoader;BaseOutputParser|documents|PromptTemplate-zmTlD' + # target_param is documents + self.target_param = self._target_handle.split("|")[1] + # Validate in __init__ to fail fast + self.validate_edge(source, target) - self.validate_edge() + def validate_handles(self, source, target) -> None: + if self.target_handle.inputTypes is None: + self.valid_handles = self.target_handle.type in self.source_handle.baseClasses + else: + self.valid_handles = ( + any(baseClass in self.target_handle.inputTypes for baseClass in self.source_handle.baseClasses) + or self.target_handle.type in self.source_handle.baseClasses + ) + if not self.valid_handles: + logger.debug(self.source_handle) + logger.debug(self.target_handle) + raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has invalid handles") def __setstate__(self, state): - self.source = state["source"] - self.target = state["target"] + self.source_id = state["source_id"] + self.target_id = state["target_id"] self.target_param = state["target_param"] self.source_handle = state["source_handle"] self.target_handle = state["target_handle"] - def reset(self) -> None: - self.source._build_params() - self.target._build_params() - - def validate_edge(self) -> None: + def validate_edge(self, source, target) -> None: # Validate that the outputs of the source node are valid inputs # for the target node - self.source_types = self.source.output - self.target_reqs = self.target.required_inputs + self.target.optional_inputs + self.source_types = source.output + self.target_reqs = target.required_inputs + target.optional_inputs # Both lists contain strings and sometimes a string contains the value we are # looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"] # so we need to check if any of the strings in source_types is in target_reqs - self.valid = any( - output in target_req - for output in self.source_types - for target_req in self.target_reqs - ) + self.valid = any(output in target_req for output in self.source_types for target_req in self.target_reqs) # Get what type of input the target node is expecting self.matched_type = next( @@ -51,14 +82,11 @@ class Edge: if no_matched_type: logger.debug(self.source_types) logger.debug(self.target_reqs) - raise ValueError( - f"Edge between {self.source.vertex_type} and {self.target.vertex_type} " - f"has no matched type" - ) + raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has no matched type") def __repr__(self) -> str: return ( - f"Edge(source={self.source.id}, target={self.target.id}, target_param={self.target_param}" + f"Edge(source={self.source_id}, target={self.target_id}, target_param={self.target_param}" f", matched_type={self.matched_type})" ) @@ -66,8 +94,4 @@ class Edge: return hash(self.__repr__()) def __eq__(self, __value: object) -> bool: - return ( - self.__repr__() == __value.__repr__() - if isinstance(__value, Edge) - else False - ) + return self.__repr__() == __value.__repr__() if isinstance(__value, Edge) else False diff --git a/src/backend/langflow/graph/graph/base.py b/src/backend/langflow/graph/graph/base.py index 227b04bf9..ad8f80835 100644 --- a/src/backend/langflow/graph/graph/base.py +++ b/src/backend/langflow/graph/graph/base.py @@ -1,36 +1,45 @@ from typing import Dict, Generator, List, Type, Union +from langchain.chains.base import Chain +from loguru import logger + from langflow.graph.edge.base import Edge from langflow.graph.graph.constants import lazy_load_vertex_dict +from langflow.graph.graph.utils import process_flow from langflow.graph.vertex.base import Vertex -from langflow.graph.vertex.types import ( - FileToolVertex, - LLMVertex, - ToolkitVertex, -) +from langflow.graph.vertex.types import (FileToolVertex, LLMVertex, + ToolkitVertex) from langflow.interface.tools.constants import FILE_TOOLS from langflow.utils import payload -from loguru import logger -from langchain.chains.base import Chain class Graph: - """A class representing a graph of nodes and edges.""" + """A class representing a graph of vertices and edges.""" def __init__( self, nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]], edges: List[Dict[str, str]], ) -> None: - self._nodes = nodes + self._vertices = nodes self._edges = edges + self.raw_graph_data = {"nodes": nodes, "edges": edges} + + self.top_level_vertices = [] + for vertex in self._vertices: + if vertex_id := vertex.get("id"): + self.top_level_vertices.append(vertex_id) + self._graph_data = process_flow(self.raw_graph_data) + + self._vertices = self._graph_data["nodes"] + self._edges = self._graph_data["edges"] self._build_graph() + def __getstate__(self): + return self.raw_graph_data + def __setstate__(self, state): - self.__dict__.update(state) - for edge in self.edges: - edge.reset() - edge.validate_edge() + self.__init__(**state) @classmethod def from_payload(cls, payload: Dict) -> "Graph": @@ -46,9 +55,9 @@ class Graph: if "data" in payload: payload = payload["data"] try: - nodes = payload["nodes"] + vertices = payload["nodes"] edges = payload["edges"] - return cls(nodes, edges) + return cls(vertices, edges) except KeyError as exc: raise ValueError( f"Invalid payload. Expected keys 'nodes' and 'edges'. Found {list(payload.keys())}" @@ -60,65 +69,69 @@ class Graph: return self.__repr__() == other.__repr__() def _build_graph(self) -> None: - """Builds the graph from the nodes and edges.""" - self.nodes = self._build_vertices() + """Builds the graph from the vertices and edges.""" + self.vertices = self._build_vertices() + self.vertex_map = {vertex.id: vertex for vertex in self.vertices} self.edges = self._build_edges() - for edge in self.edges: - edge.source.add_edge(edge) - edge.target.add_edge(edge) - # This is a hack to make sure that the LLM node is sent to - # the toolkit node - self._build_node_params() - # remove invalid nodes - self._validate_nodes() + # This is a hack to make sure that the LLM vertex is sent to + # the toolkit vertex + self._build_vertex_params() + # remove invalid vertices + self._validate_vertices() - def _build_node_params(self) -> None: - """Identifies and handles the LLM node within the graph.""" - llm_node = None - for node in self.nodes: - node._build_params() - if isinstance(node, LLMVertex): - llm_node = node + def _build_vertex_params(self) -> None: + """Identifies and handles the LLM vertex within the graph.""" + llm_vertex = None + for vertex in self.vertices: + vertex._build_params() + if isinstance(vertex, LLMVertex): + llm_vertex = vertex - if llm_node: - for node in self.nodes: - if isinstance(node, ToolkitVertex): - node.params["llm"] = llm_node + if llm_vertex: + for vertex in self.vertices: + if isinstance(vertex, ToolkitVertex): + vertex.params["llm"] = llm_vertex - def _validate_nodes(self) -> None: - """Check that all nodes have edges""" - if len(self.nodes) == 1: + def _validate_vertices(self) -> None: + """Check that all vertices have edges""" + if len(self.vertices) == 1: return - for node in self.nodes: - if not self._validate_node(node): - raise ValueError( - f"{node.vertex_type} is not connected to any other components" - ) + for vertex in self.vertices: + if not self._validate_vertex(vertex): + raise ValueError(f"{vertex.vertex_type} is not connected to any other components") - def _validate_node(self, node: Vertex) -> bool: - """Validates a node.""" - # All nodes that do not have edges are invalid - return len(node.edges) > 0 + def _validate_vertex(self, vertex: Vertex) -> bool: + """Validates a vertex.""" + # All vertices that do not have edges are invalid + return len(self.get_vertex_edges(vertex.id)) > 0 - def get_node(self, node_id: str) -> Union[None, Vertex]: - """Returns a node by id.""" - return next((node for node in self.nodes if node.id == node_id), None) + def get_vertex(self, vertex_id: str) -> Union[None, Vertex]: + """Returns a vertex by id.""" + return self.vertex_map.get(vertex_id) - def get_nodes_with_target(self, node: Vertex) -> List[Vertex]: - """Returns the nodes connected to a node.""" - connected_nodes: List[Vertex] = [ - edge.source for edge in self.edges if edge.target == node - ] - return connected_nodes + def get_vertex_edges(self, vertex_id: str) -> List[Edge]: + """Returns a list of edges for a given vertex.""" + return [edge for edge in self.edges if edge.source_id == vertex_id or edge.target_id == vertex_id] - def build(self) -> Chain: + def get_vertices_with_target(self, vertex_id: str) -> List[Vertex]: + """Returns the vertices connected to a vertex.""" + vertices: List[Vertex] = [] + for edge in self.edges: + if edge.target_id == vertex_id: + vertex = self.get_vertex(edge.source_id) + if vertex is None: + continue + vertices.append(vertex) + return vertices + + async def build(self) -> Chain: """Builds the graph.""" - # Get root node - root_node = payload.get_root_node(self) - if root_node is None: - raise ValueError("No root node found") - return root_node.build() + # Get root vertex + root_vertex = payload.get_root_vertex(self) + if root_vertex is None: + raise ValueError("No root vertex found") + return await root_vertex.build() def topological_sort(self) -> List[Vertex]: """ @@ -131,27 +144,25 @@ class Graph: ValueError: If the graph contains a cycle. """ # States: 0 = unvisited, 1 = visiting, 2 = visited - state = {node: 0 for node in self.nodes} + state = {vertex: 0 for vertex in self.vertices} sorted_vertices = [] - def dfs(node): - if state[node] == 1: + def dfs(vertex): + if state[vertex] == 1: # We have a cycle - raise ValueError( - "Graph contains a cycle, cannot perform topological sort" - ) - if state[node] == 0: - state[node] = 1 - for edge in node.edges: - if edge.source == node: - dfs(edge.target) - state[node] = 2 - sorted_vertices.append(node) + raise ValueError("Graph contains a cycle, cannot perform topological sort") + if state[vertex] == 0: + state[vertex] = 1 + for edge in vertex.edges: + if edge.source_id == vertex.id: + dfs(self.get_vertex(edge.target_id)) + state[vertex] = 2 + sorted_vertices.append(vertex) - # Visit each node - for node in self.nodes: - if state[node] == 0: - dfs(node) + # Visit each vertex + for vertex in self.vertices: + if state[vertex] == 0: + dfs(vertex) return list(reversed(sorted_vertices)) @@ -161,17 +172,21 @@ class Graph: logger.debug("There are %s vertices in the graph", len(sorted_vertices)) yield from sorted_vertices - def get_node_neighbors(self, node: Vertex) -> Dict[Vertex, int]: - """Returns the neighbors of a node.""" + def get_vertex_neighbors(self, vertex: Vertex) -> Dict[Vertex, int]: + """Returns the neighbors of a vertex.""" neighbors: Dict[Vertex, int] = {} for edge in self.edges: - if edge.source == node: - neighbor = edge.target + if edge.source_id == vertex.id: + neighbor = self.get_vertex(edge.target_id) + if neighbor is None: + continue if neighbor not in neighbors: neighbors[neighbor] = 0 neighbors[neighbor] += 1 - elif edge.target == node: - neighbor = edge.source + elif edge.target_id == vertex.id: + neighbor = self.get_vertex(edge.source_id) + if neighbor is None: + continue if neighbor not in neighbors: neighbors[neighbor] = 0 neighbors[neighbor] += 1 @@ -179,59 +194,59 @@ class Graph: def _build_edges(self) -> List[Edge]: """Builds the edges of the graph.""" - # Edge takes two nodes as arguments, so we need to build the nodes first + # Edge takes two vertices as arguments, so we need to build the vertices first # and then build the edges - # if we can't find a node, we raise an error + # if we can't find a vertex, we raise an error edges: List[Edge] = [] for edge in self._edges: - source = self.get_node(edge["source"]) - target = self.get_node(edge["target"]) + source = self.get_vertex(edge["source"]) + target = self.get_vertex(edge["target"]) if source is None: - raise ValueError(f"Source node {edge['source']} not found") + raise ValueError(f"Source vertex {edge['source']} not found") if target is None: - raise ValueError(f"Target node {edge['target']} not found") + raise ValueError(f"Target vertex {edge['target']} not found") edges.append(Edge(source, target, edge)) return edges - def _get_vertex_class(self, node_type: str, node_lc_type: str) -> Type[Vertex]: - """Returns the node class based on the node type.""" - if node_type in FILE_TOOLS: + def _get_vertex_class(self, vertex_type: str, vertex_lc_type: str) -> Type[Vertex]: + """Returns the vertex class based on the vertex type.""" + if vertex_type in FILE_TOOLS: return FileToolVertex - if node_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP: - return lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_type] + if vertex_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP: + return lazy_load_vertex_dict.VERTEX_TYPE_MAP[vertex_type] return ( - lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_lc_type] - if node_lc_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP + lazy_load_vertex_dict.VERTEX_TYPE_MAP[vertex_lc_type] + if vertex_lc_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP else Vertex ) def _build_vertices(self) -> List[Vertex]: """Builds the vertices of the graph.""" - nodes: List[Vertex] = [] - for node in self._nodes: - node_data = node["data"] - node_type: str = node_data["type"] # type: ignore - node_lc_type: str = node_data["node"]["template"]["_type"] # type: ignore + vertices: List[Vertex] = [] + for vertex in self._vertices: + vertex_data = vertex["data"] + vertex_type: str = vertex_data["type"] # type: ignore + vertex_lc_type: str = vertex_data["node"]["template"]["_type"] # type: ignore - VertexClass = self._get_vertex_class(node_type, node_lc_type) - nodes.append(VertexClass(node)) + VertexClass = self._get_vertex_class(vertex_type, vertex_lc_type) + vertex_instance = VertexClass(vertex, graph=self) + vertex_instance.set_top_level(self.top_level_vertices) + vertices.append(vertex_instance) - return nodes + return vertices - def get_children_by_node_type(self, node: Vertex, node_type: str) -> List[Vertex]: - """Returns the children of a node based on the node type.""" + def get_children_by_vertex_type(self, vertex: Vertex, vertex_type: str) -> List[Vertex]: + """Returns the children of a vertex based on the vertex type.""" children = [] - node_types = [node.data["type"]] - if "node" in node.data: - node_types += node.data["node"]["base_classes"] - if node_type in node_types: - children.append(node) + vertex_types = [vertex.data["type"]] + if "node" in vertex.data: + vertex_types += vertex.data["node"]["base_classes"] + if vertex_type in vertex_types: + children.append(vertex) return children def __repr__(self): - node_ids = [node.id for node in self.nodes] - edges_repr = "\n".join( - [f"{edge.source.id} --> {edge.target.id}" for edge in self.edges] - ) - return f"Graph:\nNodes: {node_ids}\nConnections:\n{edges_repr}" + vertex_ids = [vertex.id for vertex in self.vertices] + edges_repr = "\n".join([f"{edge.source_id} --> {edge.target_id}" for edge in self.edges]) + return f"Graph:\nNodes: {vertex_ids}\nConnections:\n{edges_repr}" diff --git a/src/backend/langflow/graph/graph/constants.py b/src/backend/langflow/graph/graph/constants.py index c9fea48b5..abfc2970f 100644 --- a/src/backend/langflow/graph/graph/constants.py +++ b/src/backend/langflow/graph/graph/constants.py @@ -47,10 +47,7 @@ class VertexTypesDict(LazyLoadDictBase): **{t: types.DocumentLoaderVertex for t in documentloader_creator.to_list()}, **{t: types.TextSplitterVertex for t in textsplitter_creator.to_list()}, **{t: types.OutputParserVertex for t in output_parser_creator.to_list()}, - **{ - t: types.CustomComponentVertex - for t in custom_component_creator.to_list() - }, + **{t: types.CustomComponentVertex for t in custom_component_creator.to_list()}, **{t: types.RetrieverVertex for t in retriever_creator.to_list()}, } diff --git a/src/backend/langflow/graph/graph/utils.py b/src/backend/langflow/graph/graph/utils.py index e69de29bb..d43fc4d84 100644 --- a/src/backend/langflow/graph/graph/utils.py +++ b/src/backend/langflow/graph/graph/utils.py @@ -0,0 +1,247 @@ +import copy +from collections import deque +from typing import Dict, List + + +def find_last_node(nodes, edges): + """ + This function receives a flow and returns the last node. + """ + return next((n for n in nodes if all(e["source"] != n["id"] for e in edges)), None) + + +def add_parent_node_id(nodes, parent_node_id): + """ + This function receives a list of nodes and adds a parent_node_id to each node. + """ + for node in nodes: + node["parent_node_id"] = parent_node_id + + +def ungroup_node(group_node_data, base_flow): + template, flow = ( + group_node_data["node"]["template"], + group_node_data["node"]["flow"], + ) + parent_node_id = group_node_data["id"] + g_nodes = flow["data"]["nodes"] + add_parent_node_id(g_nodes, parent_node_id) + g_edges = flow["data"]["edges"] + + # Redirect edges to the correct proxy node + updated_edges = get_updated_edges(base_flow, g_nodes, g_edges, group_node_data["id"]) + + # Update template values + update_template(template, g_nodes) + + nodes = [n for n in base_flow["nodes"] if n["id"] != group_node_data["id"]] + g_nodes + edges = ( + [e for e in base_flow["edges"] if e["target"] != group_node_data["id"] and e["source"] != group_node_data["id"]] + + g_edges + + updated_edges + ) + + base_flow["nodes"] = nodes + base_flow["edges"] = edges + + return nodes + + +def raw_topological_sort(nodes, edges) -> List[Dict]: + # Redefine the above function but using the nodes and self._edges + # which are dicts instead of Vertex and Edge objects + # nodes have an id, edges have a source and target keys + # return a list of node ids in topological order + + # States: 0 = unvisited, 1 = visiting, 2 = visited + state = {node["id"]: 0 for node in nodes} + nodes_dict = {node["id"]: node for node in nodes} + sorted_vertices = [] + + def dfs(node): + if state[node] == 1: + # We have a cycle + raise ValueError("Graph contains a cycle, cannot perform topological sort") + if state[node] == 0: + state[node] = 1 + for edge in edges: + if edge["source"] == node: + dfs(edge["target"]) + state[node] = 2 + sorted_vertices.append(node) + + # Visit each node + for node in nodes: + if state[node["id"]] == 0: + dfs(node["id"]) + + reverse_sorted = list(reversed(sorted_vertices)) + return [nodes_dict[node_id] for node_id in reverse_sorted] + + +def process_flow(flow_object): + cloned_flow = copy.deepcopy(flow_object) + processed_nodes = set() # To keep track of processed nodes + + def process_node(node): + node_id = node.get("id") + + # If node already processed, skip + if node_id in processed_nodes: + return + + if node.get("data") and node["data"].get("node") and node["data"]["node"].get("flow"): + process_flow(node["data"]["node"]["flow"]["data"]) + new_nodes = ungroup_node(node["data"], cloned_flow) + # Add new nodes to the queue for future processing + nodes_to_process.extend(new_nodes) + + # Mark node as processed + processed_nodes.add(node_id) + + sorted_nodes_list = raw_topological_sort(cloned_flow["nodes"], cloned_flow["edges"]) + nodes_to_process = deque(sorted_nodes_list) + + while nodes_to_process: + node = nodes_to_process.popleft() + process_node(node) + + return cloned_flow + + +def update_template(template, g_nodes): + """ + Updates the template of a node in a graph with the given template. + + Args: + template (dict): The new template to update the node with. + g_nodes (list): The list of nodes in the graph. + + Returns: + None + """ + for _, value in template.items(): + if not value.get("proxy"): + continue + proxy_dict = value["proxy"] + field, id_ = proxy_dict["field"], proxy_dict["id"] + node_index = next((i for i, n in enumerate(g_nodes) if n["id"] == id_), -1) + if node_index != -1: + display_name = None + show = g_nodes[node_index]["data"]["node"]["template"][field]["show"] + advanced = g_nodes[node_index]["data"]["node"]["template"][field]["advanced"] + if "display_name" in g_nodes[node_index]["data"]["node"]["template"][field]: + display_name = g_nodes[node_index]["data"]["node"]["template"][field]["display_name"] + else: + display_name = g_nodes[node_index]["data"]["node"]["template"][field]["name"] + + g_nodes[node_index]["data"]["node"]["template"][field] = value + g_nodes[node_index]["data"]["node"]["template"][field]["show"] = show + g_nodes[node_index]["data"]["node"]["template"][field]["advanced"] = advanced + g_nodes[node_index]["data"]["node"]["template"][field]["display_name"] = display_name + + +def update_target_handle( + new_edge, + g_nodes, + group_node_id, +): + """ + Updates the target handle of a given edge if it is a proxy node. + + Args: + new_edge (dict): The edge to update. + g_nodes (list): The list of nodes in the graph. + group_node_id (str): The ID of the group node. + + Returns: + dict: The updated edge. + """ + target_handle = new_edge["data"]["targetHandle"] + if target_handle.get("proxy"): + proxy_id = target_handle["proxy"]["id"] + if node := next((n for n in g_nodes if n["id"] == proxy_id), None): + set_new_target_handle(proxy_id, new_edge, target_handle, node) + else: + raise ValueError(f"Group node {group_node_id} has an invalid target proxy node {proxy_id}") + return new_edge + + +def set_new_target_handle(proxy_id, new_edge, target_handle, node): + """ + Sets a new target handle for a given edge. + + Args: + proxy_id (str): The ID of the proxy. + new_edge (dict): The new edge to be created. + target_handle (dict): The target handle of the edge. + node (dict): The node containing the edge. + + Returns: + None + """ + new_edge["target"] = proxy_id + _type = target_handle.get("type") + if _type is None: + raise KeyError("The 'type' key must be present in target_handle.") + + field = target_handle["proxy"]["field"] + new_target_handle = { + "fieldName": field, + "type": _type, + "id": proxy_id, + } + if node["data"]["node"].get("flow"): + new_target_handle["proxy"] = { + "field": node["data"]["node"]["template"][field]["proxy"]["field"], + "id": node["data"]["node"]["template"][field]["proxy"]["id"], + } + if input_types := target_handle.get("inputTypes"): + new_target_handle["inputTypes"] = input_types + new_edge["data"]["targetHandle"] = new_target_handle + + +def update_source_handle(new_edge, g_nodes, g_edges): + """ + Updates the source handle of a given edge to the last node in the flow data. + + Args: + new_edge (dict): The edge to update. + flow_data (dict): The flow data containing the nodes and edges. + + Returns: + dict: The updated edge with the new source handle. + """ + last_node = copy.deepcopy(find_last_node(g_nodes, g_edges)) + new_edge["source"] = last_node["id"] + new_source_handle = new_edge["data"]["sourceHandle"] + new_source_handle["id"] = last_node["id"] + new_edge["data"]["sourceHandle"] = new_source_handle + return new_edge + + +def get_updated_edges(base_flow, g_nodes, g_edges, group_node_id): + """ + Given a base flow, a list of graph nodes and a group node id, returns a list of updated edges. + An updated edge is an edge that has its target or source handle updated based on the group node id. + + Args: + base_flow (dict): The base flow containing a list of edges. + g_nodes (list): A list of graph nodes. + group_node_id (str): The id of the group node. + + Returns: + list: A list of updated edges. + """ + updated_edges = [] + for edge in base_flow["edges"]: + new_edge = copy.deepcopy(edge) + if new_edge["target"] == group_node_id: + new_edge = update_target_handle(new_edge, g_nodes, group_node_id) + + if new_edge["source"] == group_node_id: + new_edge = update_source_handle(new_edge, g_nodes, g_edges) + + if edge["target"] == group_node_id or edge["source"] == group_node_id: + updated_edges.append(new_edge) + return updated_edges diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py index 51fa8d815..167acf8bf 100644 --- a/src/backend/langflow/graph/vertex/base.py +++ b/src/backend/langflow/graph/vertex/base.py @@ -1,35 +1,33 @@ import ast -import pickle +import inspect +import types +from typing import TYPE_CHECKING, Any, Coroutine, Dict, List, Optional + +from loguru import logger + from langflow.graph.utils import UnbuiltObject -from langflow.graph.vertex.utils import is_basic_type from langflow.interface.initialize import loading from langflow.interface.listing import lazy_load_dict from langflow.utils.constants import DIRECT_TYPES -from loguru import logger from langflow.utils.util import sync_to_async - -import inspect -import types -from typing import Any, Dict, List, Optional -from typing import TYPE_CHECKING - - if TYPE_CHECKING: from langflow.graph.edge.base import Edge + from langflow.graph.graph.base import Graph class Vertex: def __init__( self, data: Dict, + graph: "Graph", base_type: Optional[str] = None, is_task: bool = False, params: Optional[Dict] = None, ) -> None: + self.graph = graph self.id: str = data["id"] self._data = data - self.edges: List["Edge"] = [] self.base_type: Optional[str] = base_type self._parse_data() self._built_object = UnbuiltObject() @@ -39,45 +37,28 @@ class Vertex: self.is_task = is_task self.params = params or {} - def reset_params(self): - for edge in self.edges: - if edge.source != self: - target_param = edge.target_param - if target_param in ["document", "texts"]: - # this means they got data and have already ingested it - # so we continue after removing the param - self.params.pop(target_param, None) - continue - - if target_param in self.params and not is_basic_type( - self.params[target_param] - ): - # edge.source.params = {} - edge.source._build_params() - edge.source._built_object = UnbuiltObject() - edge.source._built = False - - self.params[target_param] = edge.source + @property + def edges(self) -> List["Edge"]: + return self.graph.get_vertex_edges(self.id) def __getstate__(self): - state_dict = self.__dict__.copy() - try: - # try pickling the built object - # if it fails, then we need to delete it - # and build it again - pickle.dumps(state_dict["_built_object"]) - except Exception: - self.reset_params() - del state_dict["_built_object"] - del state_dict["_built"] - return state_dict + return { + "_data": self._data, + "params": {}, + "base_type": self.base_type, + "is_task": self.is_task, + "id": self.id, + "_built_object": UnbuiltObject(), + "_built": False, + "parent_node_id": self.parent_node_id, + "parent_is_top_level": self.parent_is_top_level, + } def __setstate__(self, state): self._data = state["_data"] self.params = state["params"] self.base_type = state["base_type"] self.is_task = state["is_task"] - self.edges = state["edges"] self.id = state["id"] self._parse_data() if "_built_object" in state: @@ -88,33 +69,26 @@ class Vertex: self._built = False self.artifacts: Dict[str, Any] = {} self.task_id: Optional[str] = None + self.parent_node_id = state["parent_node_id"] + self.parent_is_top_level = state["parent_is_top_level"] + + def set_top_level(self, top_level_vertices: List[str]) -> None: + self.parent_is_top_level = self.parent_node_id in top_level_vertices def _parse_data(self) -> None: self.data = self._data["data"] self.output = self.data["node"]["base_classes"] - template_dicts = { - key: value - for key, value in self.data["node"]["template"].items() - if isinstance(value, dict) - } + template_dicts = {key: value for key, value in self.data["node"]["template"].items() if isinstance(value, dict)} self.required_inputs = [ - template_dicts[key]["type"] - for key, value in template_dicts.items() - if value["required"] + template_dicts[key]["type"] for key, value in template_dicts.items() if value["required"] ] self.optional_inputs = [ - template_dicts[key]["type"] - for key, value in template_dicts.items() - if not value["required"] + template_dicts[key]["type"] for key, value in template_dicts.items() if not value["required"] ] # Add the template_dicts[key]["input_types"] to the optional_inputs self.optional_inputs.extend( - [ - input_type - for value in template_dicts.values() - for input_type in value.get("input_types", []) - ] + [input_type for value in template_dicts.values() for input_type in value.get("input_types", [])] ) template_dict = self.data["node"]["template"] @@ -153,11 +127,11 @@ class Vertex: # and use that as the value for the param # If the type is "str", then we need to get the value of the "value" key # and use that as the value for the param - template_dict = { - key: value - for key, value in self.data["node"]["template"].items() - if isinstance(value, dict) - } + + if self.graph is None: + raise ValueError("Graph not found") + + template_dict = {key: value for key, value in self.data["node"]["template"].items() if isinstance(value, dict)} params = self.params.copy() if self.params else {} for edge in self.edges: @@ -168,9 +142,9 @@ class Vertex: if template_dict[param_key]["list"]: if param_key not in params: params[param_key] = [] - params[param_key].append(edge.source) - elif edge.target.id == self.id: - params[param_key] = edge.source + params[param_key].append(self.graph.get_vertex(edge.source_id)) + elif edge.target_id == self.id: + params[param_key] = self.graph.get_vertex(edge.source_id) for key, value in template_dict.items(): if key in params: @@ -182,7 +156,7 @@ class Vertex: # If the type is not transformable to a python base class # then we need to get the edge that connects to this node if value.get("type") == "file": - # Load the type in value.get('suffixes') using + # Load the type in value.get('fileTypes') using # what is inside value.get('content') # value.get('value') is the file name if file_path := value.get("file_path"): @@ -190,27 +164,33 @@ class Vertex: else: raise ValueError(f"File path not found for {self.vertex_type}") elif value.get("type") in DIRECT_TYPES and params.get(key) is None: + val = value.get("value") if value.get("type") == "code": try: - params[key] = ast.literal_eval(value.get("value")) + params[key] = ast.literal_eval(val) if val else None except Exception as exc: logger.debug(f"Error parsing code: {exc}") - params[key] = value.get("value") + params[key] = val elif value.get("type") in ["dict", "NestedDict"]: # When dict comes from the frontend it comes as a # list of dicts, so we need to convert it to a dict # before passing it to the build method - _value = value.get("value") - if isinstance(_value, list): - params[key] = { - k: v - for item in value.get("value", []) - for k, v in item.items() - } - elif isinstance(_value, dict): - params[key] = _value - else: - params[key] = value.get("value") + if isinstance(val, list): + params[key] = {k: v for item in value.get("value", []) for k, v in item.items()} + elif isinstance(val, dict): + params[key] = val + elif value.get("type") == "int" and val is not None: + try: + params[key] = int(val) + except ValueError: + params[key] = val + elif value.get("type") == "float" and val is not None: + try: + params[key] = float(val) + except ValueError: + params[key] = val + elif val is not None and val != "": + params[key] = val if not value.get("required") and params.get(key) is None: if value.get("default"): @@ -221,18 +201,18 @@ class Vertex: self._raw_params = params self.params = params - def _build(self, user_id=None): + async def _build(self, user_id=None): """ Initiate the build process. """ logger.debug(f"Building {self.vertex_type}") - self._build_each_node_in_params_dict(user_id) - self._get_and_instantiate_class(user_id) + await self._build_each_node_in_params_dict(user_id) + await self._get_and_instantiate_class(user_id) self._validate_built_object() self._built = True - def _build_each_node_in_params_dict(self, user_id=None): + async def _build_each_node_in_params_dict(self, user_id=None): """ Iterates over each node in the params dictionary and builds it. """ @@ -241,9 +221,9 @@ class Vertex: if value == self: del self.params[key] continue - self._build_node_and_update_params(key, value, user_id) + await self._build_node_and_update_params(key, value, user_id) elif isinstance(value, list) and self._is_list_of_nodes(value): - self._build_list_of_nodes_and_update_params(key, value, user_id) + await self._build_list_of_nodes_and_update_params(key, value, user_id) def _is_node(self, value): """ @@ -257,14 +237,17 @@ class Vertex: """ return all(self._is_node(node) for node in value) - def get_result(self, user_id=None, timeout=None) -> Any: + async def get_result(self, user_id=None, timeout=None) -> Any: # Check if the Vertex was built already if self._built: return self._built_object if self.is_task and self.task_id is not None: task = self.get_task() + result = task.get(timeout=timeout) + if isinstance(result, Coroutine): + result = await result if result is not None: # If result is ready self._update_built_object_and_artifacts(result) return self._built_object @@ -273,29 +256,27 @@ class Vertex: pass # If there's no task_id, build the vertex locally - self.build(user_id) + await self.build(user_id=user_id) return self._built_object - def _build_node_and_update_params(self, key, node, user_id=None): + async def _build_node_and_update_params(self, key, node, user_id=None): """ Builds a given node and updates the params dictionary accordingly. """ - result = node.get_result(user_id) + result = await node.get_result(user_id) self._handle_func(key, result) if isinstance(result, list): self._extend_params_list_with_result(key, result) self.params[key] = result - def _build_list_of_nodes_and_update_params( - self, key, nodes: List["Vertex"], user_id=None - ): + async def _build_list_of_nodes_and_update_params(self, key, nodes: List["Vertex"], user_id=None): """ Iterates over a list of nodes, builds each and updates the params dictionary. """ self.params[key] = [] for node in nodes: - built = node.get_result(user_id) + built = await node.get_result(user_id) if isinstance(built, list): if key not in self.params: self.params[key] = [] @@ -325,14 +306,14 @@ class Vertex: if isinstance(self.params[key], list): self.params[key].extend(result) - def _get_and_instantiate_class(self, user_id=None): + async def _get_and_instantiate_class(self, user_id=None): """ Gets the class from a dictionary and instantiates it with the params. """ if self.base_type is None: raise ValueError(f"Base type for node {self.vertex_type} not found") try: - result = loading.instantiate_class( + result = await loading.instantiate_class( node_type=self.vertex_type, base_type=self.base_type, params=self.params, @@ -341,9 +322,7 @@ class Vertex: self._update_built_object_and_artifacts(result) except Exception as exc: logger.exception(exc) - raise ValueError( - f"Error building node {self.vertex_type}: {str(exc)}" - ) from exc + raise ValueError(f"Error building node {self.vertex_type}(ID:{self.id}): {str(exc)}") from exc def _update_built_object_and_artifacts(self, result): """ @@ -365,11 +344,11 @@ class Vertex: if self.base_type == "custom_components": message += " Make sure your build method returns a component." - raise ValueError(message) + logger.warning(message) - def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any: + async def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any: if not self._built or force: - self._build(user_id, *args, **kwargs) + await self._build(user_id, *args, **kwargs) return self._built_object @@ -391,8 +370,4 @@ class Vertex: def _built_object_repr(self): # Add a message with an emoji, stars for sucess, - return ( - "Built sucessfully ✨" - if self._built_object is not None - else "Failed to build 😵‍💫" - ) + return "Built sucessfully ✨" if self._built_object is not None else "Failed to build 😵‍💫" diff --git a/src/backend/langflow/graph/vertex/types.py b/src/backend/langflow/graph/vertex/types.py index e8bcd91b3..42a1ee316 100644 --- a/src/backend/langflow/graph/vertex/types.py +++ b/src/backend/langflow/graph/vertex/types.py @@ -1,14 +1,14 @@ import ast from typing import Any, Dict, List, Optional, Union +from langflow.graph.utils import UnbuiltObject, flatten_list from langflow.graph.vertex.base import Vertex -from langflow.graph.utils import flatten_list from langflow.interface.utils import extract_input_variables_from_prompt class AgentVertex(Vertex): - def __init__(self, data: Dict, params: Optional[Dict] = None): - super().__init__(data, base_type="agents", params=params) + def __init__(self, data: Dict, graph, params: Optional[Dict] = None): + super().__init__(data, graph=graph, base_type="agents", params=params) self.tools: List[Union[ToolkitVertex, ToolVertex]] = [] self.chains: List[ChainVertex] = [] @@ -26,49 +26,54 @@ class AgentVertex(Vertex): def _set_tools_and_chains(self) -> None: for edge in self.edges: - if not hasattr(edge, "source"): + if not hasattr(edge, "source_id"): continue - source_node = edge.source + source_node = self.graph.get_vertex(edge.source_id) if isinstance(source_node, (ToolVertex, ToolkitVertex)): self.tools.append(source_node) elif isinstance(source_node, ChainVertex): self.chains.append(source_node) - def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any: + async def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any: if not self._built or force: self._set_tools_and_chains() # First, build the tools for tool_node in self.tools: - tool_node.build(user_id=user_id) + await tool_node.build(user_id=user_id) # Next, build the chains and the rest for chain_node in self.chains: - chain_node.build(tools=self.tools, user_id=user_id) + await chain_node.build(tools=self.tools, user_id=user_id) - self._build(user_id=user_id) + await self._build(user_id=user_id) return self._built_object class ToolVertex(Vertex): - def __init__(self, data: Dict, params: Optional[Dict] = None): - super().__init__(data, base_type="tools", params=params) + def __init__( + self, + data: Dict, + graph, + params: Optional[Dict] = None, + ): + super().__init__(data, graph=graph, base_type="tools", params=params) class LLMVertex(Vertex): built_node_type = None class_built_object = None - def __init__(self, data: Dict, params: Optional[Dict] = None): - super().__init__(data, base_type="llms", params=params) + def __init__(self, data: Dict, graph, params: Optional[Dict] = None): + super().__init__(data, graph=graph, base_type="llms", params=params) - def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any: + async def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any: # LLM is different because some models might take up too much memory # or time to load. So we only load them when we need them.ß if self.vertex_type == self.built_node_type: return self.class_built_object if not self._built or force: - self._build(user_id=user_id) + await self._build(user_id=user_id) self.built_node_type = self.vertex_type self.class_built_object = self._built_object # Avoid deepcopying the LLM @@ -77,41 +82,39 @@ class LLMVertex(Vertex): class ToolkitVertex(Vertex): - def __init__(self, data: Dict, params=None): - super().__init__(data, base_type="toolkits", params=params) + def __init__(self, data: Dict, graph, params=None): + super().__init__(data, graph=graph, base_type="toolkits", params=params) class FileToolVertex(ToolVertex): - def __init__(self, data: Dict, params=None): - super().__init__(data, params=params) + def __init__(self, data: Dict, graph, params=None): + super().__init__(data, graph=graph, params=params) class WrapperVertex(Vertex): - def __init__(self, data: Dict): - super().__init__(data, base_type="wrappers") + def __init__(self, data: Dict, graph): + super().__init__(data, graph=graph, base_type="wrappers") - def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any: + async def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any: if not self._built or force: if "headers" in self.params: self.params["headers"] = ast.literal_eval(self.params["headers"]) - self._build(user_id=user_id) + await self._build(user_id=user_id) return self._built_object class DocumentLoaderVertex(Vertex): - def __init__(self, data: Dict, params: Optional[Dict] = None): - super().__init__(data, base_type="documentloaders", params=params) + def __init__(self, data: Dict, graph, params: Optional[Dict] = None): + super().__init__(data, graph=graph, base_type="documentloaders", params=params) def _built_object_repr(self): # This built_object is a list of documents. Maybe we should # show how many documents are in the list? - if self._built_object: - avg_length = sum( - len(doc.page_content) - for doc in self._built_object - if hasattr(doc, "page_content") - ) / len(self._built_object) + if self._built_object and not isinstance(self._built_object, UnbuiltObject): + avg_length = sum(len(doc.page_content) for doc in self._built_object if hasattr(doc, "page_content")) / len( + self._built_object + ) return f"""{self.vertex_type}({len(self._built_object)} documents) \nAvg. Document Length (characters): {int(avg_length)} Documents: {self._built_object[:3]}...""" @@ -119,28 +122,19 @@ class DocumentLoaderVertex(Vertex): class EmbeddingVertex(Vertex): - def __init__(self, data: Dict, params: Optional[Dict] = None): - super().__init__(data, base_type="embeddings", params=params) + def __init__(self, data: Dict, graph, params: Optional[Dict] = None): + super().__init__(data, graph=graph, base_type="embeddings", params=params) class VectorStoreVertex(Vertex): - def __init__(self, data: Dict, params=None): - super().__init__(data, base_type="vectorstores") + def __init__(self, data: Dict, graph, params=None): + super().__init__(data, graph=graph, base_type="vectorstores") self.params = params or {} # VectorStores may contain databse connections # so we need to define the __reduce__ method and the __setstate__ method # to avoid pickling errors - def clean_edges_for_pickling(self): - # for each edge that has self as source - # we need to clear the _built_object of the target - # so that we don't try to pickle a database connection - for edge in self.edges: - if edge.source == self: - edge.target._built_object = None - edge.target._built = False - edge.target.params[edge.target_param] = self def remove_docs_and_texts_from_params(self): # remove documents and texts from params @@ -148,17 +142,16 @@ class VectorStoreVertex(Vertex): self.params.pop("documents", None) self.params.pop("texts", None) - def __getstate__(self): - # We want to save the params attribute - # and if "documents" or "texts" are in the params - # we want to remove them because they have already - # been processed. - params = self.params.copy() - params.pop("documents", None) - params.pop("texts", None) - self.clean_edges_for_pickling() + # def __getstate__(self): + # # We want to save the params attribute + # # and if "documents" or "texts" are in the params + # # we want to remove them because they have already + # # been processed. + # params = self.params.copy() + # params.pop("documents", None) + # params.pop("texts", None) - return super().__getstate__() + # return super().__getstate__() def __setstate__(self, state): super().__setstate__(state) @@ -166,27 +159,25 @@ class VectorStoreVertex(Vertex): class MemoryVertex(Vertex): - def __init__(self, data: Dict): - super().__init__(data, base_type="memory") + def __init__(self, data: Dict, graph): + super().__init__(data, graph=graph, base_type="memory") class RetrieverVertex(Vertex): - def __init__(self, data: Dict): - super().__init__(data, base_type="retrievers") + def __init__(self, data: Dict, graph): + super().__init__(data, graph=graph, base_type="retrievers") class TextSplitterVertex(Vertex): - def __init__(self, data: Dict, params: Optional[Dict] = None): - super().__init__(data, base_type="textsplitters", params=params) + def __init__(self, data: Dict, graph, params: Optional[Dict] = None): + super().__init__(data, graph=graph, base_type="textsplitters", params=params) def _built_object_repr(self): # This built_object is a list of documents. Maybe we should # show how many documents are in the list? - if self._built_object: - avg_length = sum(len(doc.page_content) for doc in self._built_object) / len( - self._built_object - ) + if self._built_object and not isinstance(self._built_object, UnbuiltObject): + avg_length = sum(len(doc.page_content) for doc in self._built_object) / len(self._built_object) return f"""{self.vertex_type}({len(self._built_object)} documents) \nAvg. Document Length (characters): {int(avg_length)} \nDocuments: {self._built_object[:3]}...""" @@ -194,10 +185,10 @@ class TextSplitterVertex(Vertex): class ChainVertex(Vertex): - def __init__(self, data: Dict): - super().__init__(data, base_type="chains") + def __init__(self, data: Dict, graph): + super().__init__(data, graph=graph, base_type="chains") - def build( + async def build( self, force: bool = False, user_id=None, @@ -205,6 +196,8 @@ class ChainVertex(Vertex): **kwargs, ) -> Any: if not self._built or force: + # Temporarily remove the code from the params + self.params.pop("code", None) # Check if the chain requires a PromptVertex # Temporarily remove the code from the params @@ -214,18 +207,18 @@ class ChainVertex(Vertex): if isinstance(value, PromptVertex): # Build the PromptVertex, passing the tools if available tools = kwargs.get("tools", None) - self.params[key] = value.build(tools=tools, force=force) + self.params[key] = await value.build(tools=tools, force=force) - self._build(user_id=user_id) + await self._build(user_id=user_id) return self._built_object class PromptVertex(Vertex): - def __init__(self, data: Dict): - super().__init__(data, base_type="prompts") + def __init__(self, data: Dict, graph): + super().__init__(data, graph=graph, base_type="prompts") - def build( + async def build( self, force: bool = False, user_id=None, @@ -234,27 +227,18 @@ class PromptVertex(Vertex): **kwargs, ) -> Any: if not self._built or force: - if ( - "input_variables" not in self.params - or self.params["input_variables"] is None - ): + if "input_variables" not in self.params or self.params["input_variables"] is None: self.params["input_variables"] = [] # Check if it is a ZeroShotPrompt and needs a tool if "ShotPrompt" in self.vertex_type: - tools = ( - [tool_node.build(user_id=user_id) for tool_node in tools] - if tools is not None - else [] - ) + tools = [await tool_node.build(user_id=user_id) for tool_node in tools] if tools is not None else [] # flatten the list of tools if it is a list of lists # first check if it is a list if tools and isinstance(tools, list) and isinstance(tools[0], list): tools = flatten_list(tools) self.params["tools"] = tools prompt_params = [ - key - for key, value in self.params.items() - if isinstance(value, str) and key != "format_instructions" + key for key, value in self.params.items() if isinstance(value, str) and key != "format_instructions" ] else: prompt_params = ["template"] @@ -264,21 +248,15 @@ class PromptVertex(Vertex): prompt_text = self.params[param] variables = extract_input_variables_from_prompt(prompt_text) self.params["input_variables"].extend(variables) - self.params["input_variables"] = list( - set(self.params["input_variables"]) - ) + self.params["input_variables"] = list(set(self.params["input_variables"])) elif isinstance(self.params, dict): self.params.pop("input_variables", None) - self._build(user_id=user_id) + await self._build(user_id=user_id) return self._built_object def _built_object_repr(self): - if ( - not self.artifacts - or self._built_object is None - or not hasattr(self._built_object, "format") - ): + if not self.artifacts or self._built_object is None or not hasattr(self._built_object, "format"): return super()._built_object_repr() # We'll build the prompt with the artifacts # to show the user what the prompt looks like @@ -288,33 +266,31 @@ class PromptVertex(Vertex): # so the prompt format doesn't break artifacts.pop("handle_keys", None) try: - if not hasattr(self._built_object, "template") and hasattr( - self._built_object, "prompt" + if ( + not hasattr(self._built_object, "template") + and hasattr(self._built_object, "prompt") + and not isinstance(self._built_object, UnbuiltObject) ): template = self._built_object.prompt.template - else: + elif not isinstance(self._built_object, UnbuiltObject) and hasattr(self._built_object, "template"): template = self._built_object.template for key, value in artifacts.items(): if value: replace_key = "{" + key + "}" template = template.replace(replace_key, value) - return ( - template - if isinstance(template, str) - else f"{self.vertex_type}({template})" - ) + return template if isinstance(template, str) else f"{self.vertex_type}({template})" except KeyError: return str(self._built_object) class OutputParserVertex(Vertex): - def __init__(self, data: Dict): - super().__init__(data, base_type="output_parsers") + def __init__(self, data: Dict, graph): + super().__init__(data, graph=graph, base_type="output_parsers") class CustomComponentVertex(Vertex): - def __init__(self, data: Dict): - super().__init__(data, base_type="custom_components", is_task=True) + def __init__(self, data: Dict, graph): + super().__init__(data, graph=graph, base_type="custom_components", is_task=False) def _built_object_repr(self): if self.task_id and self.is_task: diff --git a/src/backend/langflow/interface/agents/base.py b/src/backend/langflow/interface/agents/base.py index 696f5afd0..9c0210b27 100644 --- a/src/backend/langflow/interface/agents/base.py +++ b/src/backend/langflow/interface/agents/base.py @@ -1,11 +1,11 @@ -from typing import Dict, List, Optional +from typing import ClassVar, Dict, List, Optional from langchain.agents import types from langflow.custom.customs import get_custom_nodes from langflow.interface.agents.custom import CUSTOM_AGENTS from langflow.interface.base import LangChainTypeCreator -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.agents import AgentFrontendNode from loguru import logger @@ -15,7 +15,7 @@ from langflow.utils.util import build_template_from_class, build_template_from_m class AgentCreator(LangChainTypeCreator): type_name: str = "agents" - from_method_nodes = {"ZeroShotAgent": "from_llm_and_tools"} + from_method_nodes: ClassVar[Dict] = {"ZeroShotAgent": "from_llm_and_tools"} @property def frontend_node_class(self) -> type[AgentFrontendNode]: @@ -42,9 +42,7 @@ class AgentCreator(LangChainTypeCreator): add_function=True, method_name=self.from_method_nodes[name], ) - return build_template_from_class( - name, self.type_to_loader_dict, add_function=True - ) + return build_template_from_class(name, self.type_to_loader_dict, add_function=True) except ValueError as exc: raise ValueError("Agent not found") from exc except AttributeError as exc: @@ -56,15 +54,8 @@ class AgentCreator(LangChainTypeCreator): names = [] settings_service = get_settings_service() for _, agent in self.type_to_loader_dict.items(): - agent_name = ( - agent.function_name() - if hasattr(agent, "function_name") - else agent.__name__ - ) - if ( - agent_name in settings_service.settings.AGENTS - or settings_service.settings.DEV - ): + agent_name = agent.function_name() if hasattr(agent, "function_name") else agent.__name__ + if agent_name in settings_service.settings.AGENTS or settings_service.settings.DEV: names.append(agent_name) return names diff --git a/src/backend/langflow/interface/agents/custom.py b/src/backend/langflow/interface/agents/custom.py index 735b27917..58c4166f8 100644 --- a/src/backend/langflow/interface/agents/custom.py +++ b/src/backend/langflow/interface/agents/custom.py @@ -1,38 +1,31 @@ from typing import Any, List, Optional -from langchain.chains.llm import LLMChain -from langchain.agents import ( - AgentExecutor, - Tool, - ZeroShotAgent, - initialize_agent, - AgentType, -) -from langchain.agents.agent_toolkits import ( - SQLDatabaseToolkit, - VectorStoreInfo, - VectorStoreRouterToolkit, - VectorStoreToolkit, -) -from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX +from langchain.agents import (AgentExecutor, AgentType, Tool, ZeroShotAgent, + initialize_agent) +from langchain.agents.agent_toolkits import (SQLDatabaseToolkit, + VectorStoreInfo, + VectorStoreRouterToolkit, + VectorStoreToolkit) +from langchain.agents.agent_toolkits.json.prompt import (JSON_PREFIX, + JSON_SUFFIX) from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit -from langchain.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX -from langchain.agents.agent_toolkits.pandas.prompt import ( - SUFFIX_WITH_DF as PANDAS_SUFFIX, -) from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX -from langchain.agents.agent_toolkits.vectorstore.prompt import ( - PREFIX as VECTORSTORE_PREFIX, -) -from langchain.agents.agent_toolkits.vectorstore.prompt import ( - ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX, -) +from langchain.agents.agent_toolkits.vectorstore.prompt import \ + PREFIX as VECTORSTORE_PREFIX +from langchain.agents.agent_toolkits.vectorstore.prompt import \ + ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS from langchain.base_language import BaseLanguageModel +from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.sql_database import SQLDatabase -from langchain.tools.python.tool import PythonAstREPLTool from langchain.tools.sql_database.prompt import QUERY_CHECKER +from langchain_experimental.agents.agent_toolkits.pandas.prompt import \ + PREFIX as PANDAS_PREFIX +from langchain_experimental.agents.agent_toolkits.pandas.prompt import \ + SUFFIX_WITH_DF as PANDAS_SUFFIX +from langchain_experimental.tools.python.tool import PythonAstREPLTool + from langflow.interface.base import CustomAgentExecutor @@ -53,7 +46,7 @@ class JsonAgent(CustomAgentExecutor): @classmethod def from_toolkit_and_llm(cls, toolkit: JsonToolkit, llm: BaseLanguageModel): tools = toolkit if isinstance(toolkit, list) else toolkit.get_tools() - tool_names = {tool.name for tool in tools} + tool_names = list({tool.name for tool in tools}) prompt = ZeroShotAgent.create_prompt( tools, prefix=JSON_PREFIX, @@ -66,7 +59,8 @@ class JsonAgent(CustomAgentExecutor): prompt=prompt, ) agent = ZeroShotAgent( - llm_chain=llm_chain, allowed_tools=tool_names # type: ignore + llm_chain=llm_chain, + allowed_tools=tool_names, # type: ignore ) return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) @@ -90,11 +84,7 @@ class CSVAgent(CustomAgentExecutor): @classmethod def from_toolkit_and_llm( - cls, - path: str, - llm: BaseLanguageModel, - pandas_kwargs: Optional[dict] = None, - **kwargs: Any + cls, path: str, llm: BaseLanguageModel, pandas_kwargs: Optional[dict] = None, **kwargs: Any ): import pandas as pd # type: ignore @@ -106,16 +96,18 @@ class CSVAgent(CustomAgentExecutor): tools, prefix=PANDAS_PREFIX, suffix=PANDAS_SUFFIX, - input_variables=["df", "input", "agent_scratchpad"], + input_variables=["df_head", "input", "agent_scratchpad"], ) - partial_prompt = prompt.partial(df=str(df.head())) + partial_prompt = prompt.partial(df_head=str(df.head())) llm_chain = LLMChain( llm=llm, prompt=partial_prompt, ) - tool_names = {tool.name for tool in tools} + tool_names = list({tool.name for tool in tools}) agent = ZeroShotAgent( - llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore + llm_chain=llm_chain, + allowed_tools=tool_names, + **kwargs, # type: ignore ) return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) @@ -139,9 +131,7 @@ class VectorStoreAgent(CustomAgentExecutor): super().__init__(*args, **kwargs) @classmethod - def from_toolkit_and_llm( - cls, llm: BaseLanguageModel, vectorstoreinfo: VectorStoreInfo, **kwargs: Any - ): + def from_toolkit_and_llm(cls, llm: BaseLanguageModel, vectorstoreinfo: VectorStoreInfo, **kwargs: Any): """Construct a vectorstore agent from an LLM and tools.""" toolkit = VectorStoreToolkit(vectorstore_info=vectorstoreinfo, llm=llm) @@ -152,13 +142,13 @@ class VectorStoreAgent(CustomAgentExecutor): llm=llm, prompt=prompt, ) - tool_names = {tool.name for tool in tools} + tool_names = list({tool.name for tool in tools}) agent = ZeroShotAgent( - llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore - ) - return AgentExecutor.from_agent_and_tools( - agent=agent, tools=tools, verbose=True, handle_parsing_errors=True + llm_chain=llm_chain, + allowed_tools=tool_names, + **kwargs, # type: ignore ) + return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True) def run(self, *args, **kwargs): return super().run(*args, **kwargs) @@ -179,9 +169,7 @@ class SQLAgent(CustomAgentExecutor): super().__init__(*args, **kwargs) @classmethod - def from_toolkit_and_llm( - cls, llm: BaseLanguageModel, database_uri: str, **kwargs: Any - ): + def from_toolkit_and_llm(cls, llm: BaseLanguageModel, database_uri: str, **kwargs: Any): """Construct an SQL agent from an LLM and tools.""" db = SQLDatabase.from_uri(database_uri) toolkit = SQLDatabaseToolkit(db=db, llm=llm) @@ -190,18 +178,14 @@ class SQLAgent(CustomAgentExecutor): # related to `OPENAI_API_KEY` # return create_sql_agent(llm=llm, toolkit=toolkit, verbose=True) from langchain.prompts import PromptTemplate - from langchain.tools.sql_database.tool import ( - InfoSQLDatabaseTool, - ListSQLDatabaseTool, - QuerySQLCheckerTool, - QuerySQLDataBaseTool, - ) + from langchain.tools.sql_database.tool import (InfoSQLDatabaseTool, + ListSQLDatabaseTool, + QuerySQLCheckerTool, + QuerySQLDataBaseTool) llmchain = LLMChain( llm=llm, - prompt=PromptTemplate( - template=QUERY_CHECKER, input_variables=["query", "dialect"] - ), + prompt=PromptTemplate(template=QUERY_CHECKER, input_variables=["query", "dialect"]), ) tools = [ @@ -222,9 +206,11 @@ class SQLAgent(CustomAgentExecutor): llm=llm, prompt=prompt, ) - tool_names = {tool.name for tool in tools} # type: ignore + tool_names = list({tool.name for tool in tools}) # type: ignore agent = ZeroShotAgent( - llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore + llm_chain=llm_chain, + allowed_tools=tool_names, + **kwargs, # type: ignore ) return AgentExecutor.from_agent_and_tools( agent=agent, @@ -255,10 +241,7 @@ class VectorStoreRouterAgent(CustomAgentExecutor): @classmethod def from_toolkit_and_llm( - cls, - llm: BaseLanguageModel, - vectorstoreroutertoolkit: VectorStoreRouterToolkit, - **kwargs: Any + cls, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit, **kwargs: Any ): """Construct a vector store router agent from an LLM and tools.""" @@ -272,13 +255,13 @@ class VectorStoreRouterAgent(CustomAgentExecutor): llm=llm, prompt=prompt, ) - tool_names = {tool.name for tool in tools} + tool_names = list({tool.name for tool in tools}) agent = ZeroShotAgent( - llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore - ) - return AgentExecutor.from_agent_and_tools( - agent=agent, tools=tools, verbose=True, handle_parsing_errors=True + llm_chain=llm_chain, + allowed_tools=tool_names, + **kwargs, # type: ignore ) + return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True) def run(self, *args, **kwargs): return super().run(*args, **kwargs) diff --git a/src/backend/langflow/interface/base.py b/src/backend/langflow/interface/base.py index 13dd05619..d535838ac 100644 --- a/src/backend/langflow/interface/base.py +++ b/src/backend/langflow/interface/base.py @@ -2,7 +2,7 @@ from abc import ABC, abstractmethod from typing import Any, Dict, List, Optional, Type, Union from langchain.chains.base import Chain from langchain.agents import AgentExecutor -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from pydantic import BaseModel from langflow.template.field.base import TemplateField @@ -30,13 +30,8 @@ class LangChainTypeCreator(BaseModel, ABC): settings_service = get_settings_service() if self.name_docs_dict is None: try: - type_settings = getattr( - settings_service.settings, self.type_name.upper() - ) - self.name_docs_dict = { - name: value_dict["documentation"] - for name, value_dict in type_settings.items() - } + type_settings = getattr(settings_service.settings, self.type_name.upper()) + self.name_docs_dict = {name: value_dict["documentation"] for name, value_dict in type_settings.items()} except AttributeError as exc: logger.error(f"Error getting settings for {self.type_name}: {exc}") @@ -88,7 +83,6 @@ class LangChainTypeCreator(BaseModel, ABC): show=value.get("show", True), multiline=value.get("multiline", False), value=value.get("value", None), - suffixes=value.get("suffixes", []), file_types=value.get("fileTypes", []), file_path=value.get("file_path", None), ) diff --git a/src/backend/langflow/interface/chains/base.py b/src/backend/langflow/interface/chains/base.py index b2d07b7e4..8017902bd 100644 --- a/src/backend/langflow/interface/chains/base.py +++ b/src/backend/langflow/interface/chains/base.py @@ -1,15 +1,15 @@ -from typing import Any, Dict, List, Optional, Type +from typing import Any, ClassVar, Dict, List, Optional, Type from langflow.custom.customs import get_custom_nodes from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.chains import ChainFrontendNode from loguru import logger from langflow.utils.util import build_template_from_class, build_template_from_method from langchain import chains -from langchain_experimental.sql import SQLDatabaseChain # type: ignore +from langchain_experimental.sql import SQLDatabaseChain # Assuming necessary imports for Field, Template, and FrontendNode classes @@ -22,7 +22,7 @@ class ChainCreator(LangChainTypeCreator): return ChainFrontendNode #! We need to find a better solution for this - from_method_nodes = { + from_method_nodes: ClassVar[Dict] = { "ConversationalRetrievalChain": "from_llm", "LLMCheckerChain": "from_llm", "SQLDatabaseChain": "from_llm", @@ -33,8 +33,7 @@ class ChainCreator(LangChainTypeCreator): if self.type_dict is None: settings_service = get_settings_service() self.type_dict: dict[str, Any] = { - chain_name: import_class(f"langchain.chains.{chain_name}") - for chain_name in chains.__all__ + chain_name: import_class(f"langchain.chains.{chain_name}") for chain_name in chains.__all__ } from langflow.interface.chains.custom import CUSTOM_CHAINS @@ -45,8 +44,7 @@ class ChainCreator(LangChainTypeCreator): self.type_dict = { name: chain for name, chain in self.type_dict.items() - if name in settings_service.settings.CHAINS - or settings_service.settings.DEV + if name in settings_service.settings.CHAINS or settings_service.settings.DEV } return self.type_dict @@ -61,9 +59,7 @@ class ChainCreator(LangChainTypeCreator): method_name=self.from_method_nodes[name], add_function=True, ) - return build_template_from_class( - name, self.type_to_loader_dict, add_function=True - ) + return build_template_from_class(name, self.type_to_loader_dict, add_function=True) except ValueError as exc: raise ValueError(f"Chain {name} not found: {exc}") from exc except AttributeError as exc: @@ -73,11 +69,7 @@ class ChainCreator(LangChainTypeCreator): def to_list(self) -> List[str]: names = [] for _, chain in self.type_to_loader_dict.items(): - chain_name = ( - chain.function_name() - if hasattr(chain, "function_name") - else chain.__name__ - ) + chain_name = chain.function_name() if hasattr(chain, "function_name") else chain.__name__ names.append(chain_name) return names diff --git a/src/backend/langflow/interface/chains/custom.py b/src/backend/langflow/interface/chains/custom.py index 01dd9bab0..01aad73c3 100644 --- a/src/backend/langflow/interface/chains/custom.py +++ b/src/backend/langflow/interface/chains/custom.py @@ -4,7 +4,7 @@ from langchain.chains import ConversationChain from langchain.memory.buffer import ConversationBufferMemory from langchain.schema import BaseMemory from langflow.interface.base import CustomChain -from pydantic import Field, root_validator +from pydantic.v1 import Field, root_validator from langchain.chains.question_answering import load_qa_chain from langflow.interface.utils import extract_input_variables_from_prompt from langchain.base_language import BaseLanguageModel @@ -41,9 +41,7 @@ class BaseCustomConversationChain(ConversationChain): values["template"] = values["template"].format(**format_dict) values["template"] = values["template"] - values["input_variables"] = extract_input_variables_from_prompt( - values["template"] - ) + values["input_variables"] = extract_input_variables_from_prompt(values["template"]) values["prompt"].template = values["template"] values["prompt"].input_variables = values["input_variables"] return values @@ -54,9 +52,7 @@ class SeriesCharacterChain(BaseCustomConversationChain): character: str series: str - template: Optional[ - str - ] = """I want you to act like {character} from {series}. + template: Optional[str] = """I want you to act like {character} from {series}. I want you to respond and answer like {character}. do not write any explanations. only answer like {character}. You must know all of the knowledge of {character}. Current conversation: @@ -71,9 +67,7 @@ Human: {input} class MidJourneyPromptChain(BaseCustomConversationChain): """MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.""" - template: Optional[ - str - ] = """I want you to act as a prompt generator for Midjourney's artificial intelligence program. + template: Optional[str] = """I want you to act as a prompt generator for Midjourney's artificial intelligence program. Your job is to provide detailed and creative descriptions that will inspire unique and interesting images from the AI. Keep in mind that the AI is capable of understanding a wide range of language and can interpret abstract concepts, so feel free to be as imaginative and descriptive as possible. For example, you could describe a scene from a futuristic city, or a surreal landscape filled with strange creatures. @@ -87,9 +81,7 @@ class MidJourneyPromptChain(BaseCustomConversationChain): class TimeTravelGuideChain(BaseCustomConversationChain): - template: Optional[ - str - ] = """I want you to act as my time travel guide. You are helpful and creative. I will provide you with the historical period or future time I want to visit and you will suggest the best events, sights, or people to experience. Provide the suggestions and any necessary information. + template: Optional[str] = """I want you to act as my time travel guide. You are helpful and creative. I will provide you with the historical period or future time I want to visit and you will suggest the best events, sights, or people to experience. Provide the suggestions and any necessary information. Current conversation: {history} Human: {input} diff --git a/src/backend/langflow/interface/custom/code_parser.py b/src/backend/langflow/interface/custom/code_parser.py index d42f82635..a7bf8023c 100644 --- a/src/backend/langflow/interface/custom/code_parser.py +++ b/src/backend/langflow/interface/custom/code_parser.py @@ -1,9 +1,12 @@ import ast import inspect +import operator import traceback +from typing import Any, Dict, List, Type, Union -from typing import Dict, Any, List, Type, Union +from cachetools import TTLCache, cachedmethod, keys from fastapi import HTTPException + from langflow.interface.custom.schema import CallableCodeDetails, ClassCodeDetails @@ -11,6 +14,19 @@ class CodeSyntaxError(HTTPException): pass +def get_data_type(): + from langflow.field_typing import Data + + return Data + + +def imports_key(*args, **kwargs): + imports = kwargs.pop("imports") + key = keys.methodkey(*args, **kwargs) + key += tuple(imports) + return key + + class CodeParser: """ A parser for Python source code, extracting code details. @@ -20,6 +36,7 @@ class CodeParser: """ Initializes the parser with the provided code. """ + self.cache: TTLCache = TTLCache(maxsize=1024, ttl=60) if isinstance(code, type): if not inspect.isclass(code): raise ValueError("The provided code must be a class.") @@ -65,14 +82,20 @@ class CodeParser: def parse_imports(self, node: Union[ast.Import, ast.ImportFrom]) -> None: """ - Extracts "imports" from the code. + Extracts "imports" from the code, including aliases. """ if isinstance(node, ast.Import): for alias in node.names: - self.data["imports"].append(alias.name) + if alias.asname: + self.data["imports"].append(f"{alias.name} as {alias.asname}") + else: + self.data["imports"].append(alias.name) elif isinstance(node, ast.ImportFrom): for alias in node.names: - self.data["imports"].append((node.module, alias.name)) + if alias.asname: + self.data["imports"].append((node.module, f"{alias.name} as {alias.asname}")) + else: + self.data["imports"].append((node.module, alias.name)) def parse_functions(self, node: ast.FunctionDef) -> None: """ @@ -89,22 +112,54 @@ class CodeParser: arg_dict["type"] = ast.unparse(arg.annotation) return arg_dict + @cachedmethod(operator.attrgetter("cache")) + def construct_eval_env(self, return_type_str: str, imports) -> dict: + """ + Constructs an evaluation environment with the necessary imports for the return type, + taking into account module aliases. + """ + eval_env: dict = {} + for import_entry in imports: + if isinstance(import_entry, tuple): # from module import name + module, name = import_entry + if name in return_type_str: + exec(f"import {module}", eval_env) + exec(f"from {module} import {name}", eval_env) + else: # import module + module = import_entry + alias = None + if " as " in module: + module, alias = module.split(" as ") + if module in return_type_str or (alias and alias in return_type_str): + exec(f"import {module} as {alias if alias else module}", eval_env) + return eval_env + + @cachedmethod(cache=operator.attrgetter("cache")) def parse_callable_details(self, node: ast.FunctionDef) -> Dict[str, Any]: """ Extracts details from a single function or method node. """ + return_type = None + if node.returns: + return_type_str = ast.unparse(node.returns) + eval_env = self.construct_eval_env(return_type_str, tuple(self.data["imports"])) + + try: + return_type = eval(return_type_str, eval_env) + except NameError: + # Handle cases where the type is not found in the constructed environment + pass + func = CallableCodeDetails( name=node.name, doc=ast.get_docstring(node), - args=[], - body=[], - return_type=ast.unparse(node.returns) if node.returns else None, + args=self.parse_function_args(node), + body=self.parse_function_body(node), + return_type=return_type or get_data_type(), + has_return=self.parse_return_statement(node), ) - func.args = self.parse_function_args(node) - func.body = self.parse_function_body(node) - - return func.dict() + return func.model_dump() def parse_function_args(self, node: ast.FunctionDef) -> List[Dict[str, Any]]: """ @@ -115,7 +170,9 @@ class CodeParser: args += self.parse_positional_args(node) args += self.parse_varargs(node) args += self.parse_keyword_args(node) - args += self.parse_kwargs(node) + # Commented out because we don't want kwargs + # showing up as fields in the frontend + # args += self.parse_kwargs(node) return args @@ -127,22 +184,14 @@ class CodeParser: num_defaults = len(node.args.defaults) num_missing_defaults = num_args - num_defaults missing_defaults = [None] * num_missing_defaults - default_values = [ - ast.unparse(default).strip("'") if default else None - for default in node.args.defaults - ] + default_values = [ast.unparse(default).strip("'") if default else None for default in node.args.defaults] # Now check all default values to see if there # are any "None" values in the middle - default_values = [ - None if value == "None" else value for value in default_values - ] + default_values = [None if value == "None" else value for value in default_values] defaults = missing_defaults + default_values - args = [ - self.parse_arg(arg, default) - for arg, default in zip(node.args.args, defaults) - ] + args = [self.parse_arg(arg, default) for arg, default in zip(node.args.args, defaults)] return args def parse_varargs(self, node: ast.FunctionDef) -> List[Dict[str, Any]]: @@ -160,17 +209,11 @@ class CodeParser: """ Parses the keyword-only arguments of a function or method node. """ - kw_defaults = [None] * ( - len(node.args.kwonlyargs) - len(node.args.kw_defaults) - ) + [ - ast.unparse(default) if default else None - for default in node.args.kw_defaults + kw_defaults = [None] * (len(node.args.kwonlyargs) - len(node.args.kw_defaults)) + [ + ast.unparse(default) if default else None for default in node.args.kw_defaults ] - args = [ - self.parse_arg(arg, default) - for arg, default in zip(node.args.kwonlyargs, kw_defaults) - ] + args = [self.parse_arg(arg, default) for arg, default in zip(node.args.kwonlyargs, kw_defaults)] return args def parse_kwargs(self, node: ast.FunctionDef) -> List[Dict[str, Any]]: @@ -190,6 +233,13 @@ class CodeParser: """ return [ast.unparse(line) for line in node.body] + def parse_return_statement(self, node: ast.FunctionDef) -> bool: + """ + Parses the return statement of a function or method node. + """ + + return any(isinstance(n, ast.Return) for n in node.body) + def parse_assign(self, stmt): """ Parses an Assign statement and returns a dictionary @@ -240,23 +290,21 @@ class CodeParser: elif isinstance(stmt, ast.AnnAssign): if attr := self.parse_ann_assign(stmt): class_details.attributes.append(attr) - elif isinstance(stmt, ast.FunctionDef): + elif isinstance(stmt, (ast.FunctionDef, ast.AsyncFunctionDef)): method, is_init = self.parse_function_def(stmt) if is_init: class_details.init = method else: class_details.methods.append(method) - self.data["classes"].append(class_details.dict()) + self.data["classes"].append(class_details.model_dump()) def parse_global_vars(self, node: ast.Assign) -> None: """ Extracts global variables from the code. """ global_var = { - "targets": [ - t.id if hasattr(t, "id") else ast.dump(t) for t in node.targets - ], + "targets": [t.id if hasattr(t, "id") else ast.dump(t) for t in node.targets], "value": ast.unparse(node.value), } self.data["global_vars"].append(global_var) diff --git a/src/backend/langflow/interface/custom/component.py b/src/backend/langflow/interface/custom/component.py index 06db5bd46..594ec982f 100644 --- a/src/backend/langflow/interface/custom/component.py +++ b/src/backend/langflow/interface/custom/component.py @@ -1,10 +1,13 @@ import ast -from typing import Any, Optional -from pydantic import BaseModel +import operator +import warnings +from typing import Any, ClassVar, Optional + +from cachetools import TTLCache, cachedmethod from fastapi import HTTPException -from langflow.utils import validate from langflow.interface.custom.code_parser import CodeParser +from langflow.utils import validate class ComponentCodeNullError(HTTPException): @@ -15,19 +18,29 @@ class ComponentFunctionEntrypointNameNullError(HTTPException): pass -class Component(BaseModel): - ERROR_CODE_NULL = "Python code must be provided." - ERROR_FUNCTION_ENTRYPOINT_NAME_NULL = ( - "The name of the entrypoint function must be provided." - ) +class Component: + ERROR_CODE_NULL: ClassVar[str] = "Python code must be provided." + ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[str] = "The name of the entrypoint function must be provided." - code: Optional[str] - function_entrypoint_name = "build" + code: Optional[str] = None + _function_entrypoint_name: str = "build" field_config: dict = {} + _user_id: Optional[str] def __init__(self, **data): - super().__init__(**data) + self.cache = TTLCache(maxsize=1024, ttl=60) + for key, value in data.items(): + if key == "user_id": + setattr(self, "_user_id", value) + else: + setattr(self, key, value) + def __setattr__(self, key, value): + if key == "_user_id" and hasattr(self, "_user_id"): + warnings.warn("user_id is immutable and cannot be changed.") + super().__setattr__(key, value) + + @cachedmethod(cache=operator.attrgetter("cache")) def get_code_tree(self, code: str): parser = CodeParser(code) return parser.parse_code() @@ -39,7 +52,7 @@ class Component(BaseModel): detail={"error": self.ERROR_CODE_NULL, "traceback": ""}, ) - if not self.function_entrypoint_name: + if not self._function_entrypoint_name: raise ComponentFunctionEntrypointNameNullError( status_code=400, detail={ @@ -48,7 +61,7 @@ class Component(BaseModel): }, ) - return validate.create_function(self.code, self.function_entrypoint_name) + return validate.create_function(self.code, self._function_entrypoint_name) def build_template_config(self, attributes) -> dict: template_config = {} diff --git a/src/backend/langflow/interface/custom/custom_component.py b/src/backend/langflow/interface/custom/custom_component.py index ccc0b08b2..f857f3887 100644 --- a/src/backend/langflow/interface/custom/custom_component.py +++ b/src/backend/langflow/interface/custom/custom_component.py @@ -1,34 +1,42 @@ -from typing import Any, Callable, List, Optional, Union +import operator +from typing import Any, Callable, ClassVar, List, Optional, Union from uuid import UUID + +import yaml +from cachetools import TTLCache, cachedmethod from fastapi import HTTPException -from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES + from langflow.interface.custom.component import Component from langflow.interface.custom.directory_reader import DirectoryReader -from langflow.services.getters import get_db_service -from langflow.interface.custom.utils import extract_inner_type - +from langflow.interface.custom.utils import ( + extract_inner_type_from_generic_alias, + extract_union_types_from_generic_alias) +from langflow.services.database.models.flow import Flow +from langflow.services.database.utils import session_getter +from langflow.services.deps import get_credential_service, get_db_service from langflow.utils import validate -from langflow.services.database.utils import session_getter -from langflow.services.database.models.flow import Flow -from pydantic import Extra -import yaml - -class CustomComponent(Component, extra=Extra.allow): - code: Optional[str] +class CustomComponent(Component): + display_name: Optional[str] = None + description: Optional[str] = None + code: Optional[str] = None field_config: dict = {} - code_class_base_inheritance = "CustomComponent" - function_entrypoint_name = "build" + code_class_base_inheritance: ClassVar[str] = "CustomComponent" + function_entrypoint_name: ClassVar[str] = "build" function: Optional[Callable] = None - return_type_valid_list = list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys()) repr_value: Optional[Any] = "" user_id: Optional[Union[UUID, str]] = None + status: Optional[Any] = None + _tree: Optional[dict] = None def __init__(self, **data): + self.cache = TTLCache(maxsize=1024, ttl=60) super().__init__(**data) def custom_repr(self): + if self.repr_value == "": + self.repr_value = self.status if isinstance(self.repr_value, dict): return yaml.dump(self.repr_value) if isinstance(self.repr_value, str): @@ -53,47 +61,28 @@ class CustomComponent(Component, extra=Extra.allow): reader = DirectoryReader("", False) for type_hint in TYPE_HINT_LIST: - if reader._is_type_hint_used_in_args( + if reader._is_type_hint_used_in_args(type_hint, code) and not reader._is_type_hint_imported( type_hint, code - ) and not reader._is_type_hint_imported(type_hint, code): + ): error_detail = { "error": "Type hint Error", "traceback": f"Type hint '{type_hint}' is used but not imported in the code.", } raise HTTPException(status_code=400, detail=error_detail) + return True - def is_check_valid(self) -> bool: + def validate(self) -> bool: return self._class_template_validation(self.code) if self.code else False - def get_code_tree(self, code: str): - return super().get_code_tree(code) + @property + def tree(self): + return self.get_code_tree(self.code) @property - def get_function_entrypoint_args(self) -> str: - if not self.code: - return "" - tree = self.get_code_tree(self.code) - - component_classes = [ - cls - for cls in tree["classes"] - if self.code_class_base_inheritance in cls["bases"] - ] - if not component_classes: - return "" - - # Assume the first Component class is the one we're interested in - component_class = component_classes[0] - build_methods = [ - method - for method in component_class["methods"] - if method["name"] == self.function_entrypoint_name - ] - - if not build_methods: - return "" - - build_method = build_methods[0] + def get_function_entrypoint_args(self) -> list: + build_method = self.get_build_method() + if not build_method: + return [] args = build_method["args"] for arg in args: @@ -103,65 +92,69 @@ class CustomComponent(Component, extra=Extra.allow): detail={ "error": "Type hint Error", "traceback": ( - "Prompt type is not supported in the build method." - " Try using PromptTemplate instead." + "Prompt type is not supported in the build method." " Try using PromptTemplate instead." ), }, ) + elif not arg.get("type") and arg.get("name") != "self": + # Set the type to Data + arg["type"] = "Data" return args - @property - def get_function_entrypoint_return_type(self) -> List[str]: + @cachedmethod(operator.attrgetter("cache")) + def get_build_method(self): if not self.code: return [] - tree = self.get_code_tree(self.code) - component_classes = [ - cls - for cls in tree["classes"] - if self.code_class_base_inheritance in cls["bases"] - ] + component_classes = [cls for cls in self.tree["classes"] if self.code_class_base_inheritance in cls["bases"]] if not component_classes: return [] # Assume the first Component class is the one we're interested in component_class = component_classes[0] build_methods = [ - method - for method in component_class["methods"] - if method["name"] == self.function_entrypoint_name + method for method in component_class["methods"] if method["name"] == self.function_entrypoint_name ] if not build_methods: return [] - build_method = build_methods[0] - return_type = build_method["return_type"] - if not return_type: + return build_methods[0] + + @property + def get_function_entrypoint_return_type(self) -> List[Any]: + build_method = self.get_build_method() + if not build_method: return [] + elif not build_method["has_return"]: + return [] + + return_type = build_method["return_type"] + # If list or List is in the return type, then we remove it and return the inner type - if return_type.startswith("list") or return_type.startswith("List"): - return_type = extract_inner_type(return_type) + if hasattr(return_type, "__origin__") and return_type.__origin__ in [list, List]: + return_type = extract_inner_type_from_generic_alias(return_type) # If the return type is not a Union, then we just return it as a list - if "Union" not in return_type: - return [return_type] if return_type in self.return_type_valid_list else [] + if not hasattr(return_type, "__origin__") or return_type.__origin__ != Union: + if isinstance(return_type, list): + return return_type + return [return_type] - # If the return type is a Union, then we need to parse it - return_type = return_type.replace("Union", "").replace("[", "").replace("]", "") - return_type = return_type.split(",") - return_type = [item.strip() for item in return_type] - return [item for item in return_type if item in self.return_type_valid_list] + # If the return type is a Union, then we need to parse itx + return_type = extract_union_types_from_generic_alias(return_type) + return return_type @property def get_main_class_name(self): - tree = self.get_code_tree(self.code) + if not self.code: + return "" base_name = self.code_class_base_inheritance method_name = self.function_entrypoint_name classes = [] - for item in tree.get("classes"): + for item in self.tree.get("classes", []): if base_name in item["bases"]: method_names = [method["name"] for method in item["methods"]] if method_name in method_names: @@ -171,12 +164,16 @@ class CustomComponent(Component, extra=Extra.allow): return next(iter(classes), "") @property + def template_config(self): + return self.build_template_config() + def build_template_config(self): - tree = self.get_code_tree(self.code) + if not self.code: + return {} attributes = [ main_class["attributes"] - for main_class in tree.get("classes") + for main_class in self.tree.get("classes", []) if main_class["name"] == self.get_main_class_name ] # Get just the first item @@ -184,13 +181,44 @@ class CustomComponent(Component, extra=Extra.allow): return super().build_template_config(attributes) + @property + def keys(self): + def get_credential(name: str): + if hasattr(self, "_user_id") and not self._user_id: + raise ValueError(f"User id is not set for {self.__class__.__name__}") + credential_service = get_credential_service() # Get service instance + # Retrieve and decrypt the credential by name for the current user + db_service = get_db_service() + with session_getter(db_service) as session: + return credential_service.get_credential(user_id=self._user_id or "", name=name, session=session) + + return get_credential + + def list_key_names(self): + if hasattr(self, "_user_id") and not self._user_id: + raise ValueError(f"User id is not set for {self.__class__.__name__}") + credential_service = get_credential_service() + db_service = get_db_service() + with session_getter(db_service) as session: + return credential_service.list_credentials(user_id=self._user_id, session=session) + + def index(self, value: int = 0): + """Returns a function that returns the value at the given index in the iterable.""" + + def get_index(iterable: List[Any]): + if iterable: + return iterable[value] + return iterable + + return get_index + @property def get_function(self): return validate.create_function(self.code, self.function_entrypoint_name) - def load_flow(self, flow_id: str, tweaks: Optional[dict] = None) -> Any: - from langflow.processing.process import build_sorted_vertices - from langflow.processing.process import process_tweaks + async def load_flow(self, flow_id: str, tweaks: Optional[dict] = None) -> Any: + from langflow.processing.process import (build_sorted_vertices, + process_tweaks) db_service = get_db_service() with session_getter(db_service) as session: @@ -199,10 +227,10 @@ class CustomComponent(Component, extra=Extra.allow): raise ValueError(f"Flow {flow_id} not found") if tweaks: graph_data = process_tweaks(graph_data=graph_data, tweaks=tweaks) - return build_sorted_vertices(graph_data) + return await build_sorted_vertices(graph_data, self.user_id) def list_flows(self, *, get_session: Optional[Callable] = None) -> List[Flow]: - if not self.user_id: + if not self._user_id: raise ValueError("Session is invalid") try: get_session = get_session or session_getter @@ -213,7 +241,7 @@ class CustomComponent(Component, extra=Extra.allow): except Exception as e: raise ValueError("Session is invalid") from e - def get_flow( + async def get_flow( self, *, flow_name: Optional[str] = None, @@ -227,17 +255,13 @@ class CustomComponent(Component, extra=Extra.allow): if flow_id: flow = session.query(Flow).get(flow_id) elif flow_name: - flow = ( - session.query(Flow) - .filter(Flow.name == flow_name) - .filter(Flow.user_id == self.user_id) - ).first() + flow = (session.query(Flow).filter(Flow.name == flow_name).filter(Flow.user_id == self.user_id)).first() else: raise ValueError("Either flow_name or flow_id must be provided") if not flow: raise ValueError(f"Flow {flow_name or flow_id} not found") - return self.load_flow(flow.id, tweaks) + return await self.load_flow(flow.id, tweaks) def build(self, *args: Any, **kwargs: Any) -> Any: raise NotImplementedError diff --git a/src/backend/langflow/interface/custom/directory_reader.py b/src/backend/langflow/interface/custom/directory_reader.py index 01b11a4a6..e80f0bd28 100644 --- a/src/backend/langflow/interface/custom/directory_reader.py +++ b/src/backend/langflow/interface/custom/directory_reader.py @@ -76,9 +76,7 @@ class DirectoryReader: for menu in data["menu"] ] filtered = [menu for menu in items if menu["components"]] - logger.debug( - f'Filtered components {"with errors" if with_errors else ""}: {len(filtered)}' - ) + logger.debug(f'Filtered components {"with errors" if with_errors else ""}: {len(filtered)}') return {"menu": filtered} def validate_code(self, file_content): @@ -111,9 +109,7 @@ class DirectoryReader: Walk through the directory path and return a list of all .py files. """ if not (safe_path := self.get_safe_path()): - raise CustomComponentPathValueError( - f"The path needs to start with '{self.base_path}'." - ) + raise CustomComponentPathValueError(f"The path needs to start with '{self.base_path}'.") file_list = [] for root, _, files in os.walk(safe_path): @@ -158,9 +154,7 @@ class DirectoryReader: for node in ast.walk(module): if isinstance(node, ast.FunctionDef): for arg in node.args.args: - if self._is_type_hint_in_arg_annotation( - arg.annotation, type_hint_name - ): + if self._is_type_hint_in_arg_annotation(arg.annotation, type_hint_name): return True except SyntaxError: # Returns False if the code is not valid Python @@ -178,16 +172,14 @@ class DirectoryReader: and annotation.value.id == type_hint_name ) - def is_type_hint_used_but_not_imported( - self, type_hint_name: str, code: str - ) -> bool: + def is_type_hint_used_but_not_imported(self, type_hint_name: str, code: str) -> bool: """ Check if a type hint is used but not imported in the given code. """ try: - return self._is_type_hint_used_in_args( + return self._is_type_hint_used_in_args(type_hint_name, code) and not self._is_type_hint_imported( type_hint_name, code - ) and not self._is_type_hint_imported(type_hint_name, code) + ) except SyntaxError: # Returns True if there's something wrong with the code # TODO : Find a better way to handle this @@ -208,9 +200,9 @@ class DirectoryReader: return False, "Syntax error" elif not self.validate_build(file_content): return False, "Missing build function" - elif self._is_type_hint_used_in_args( + elif self._is_type_hint_used_in_args("Optional", file_content) and not self._is_type_hint_imported( "Optional", file_content - ) and not self._is_type_hint_imported("Optional", file_content): + ): return ( False, "Type hint 'Optional' is used but not imported in the code.", @@ -226,9 +218,7 @@ class DirectoryReader: from the .py files in the directory. """ response = {"menu": []} - logger.debug( - "-------------------- Building component menu list --------------------" - ) + logger.debug("-------------------- Building component menu list --------------------") for file_path in file_paths: menu_name = os.path.basename(os.path.dirname(file_path)) @@ -248,9 +238,7 @@ class DirectoryReader: # first check if it's already CamelCase if "_" in component_name: - component_name_camelcase = " ".join( - word.title() for word in component_name.split("_") - ) + component_name_camelcase = " ".join(word.title() for word in component_name.split("_")) else: component_name_camelcase = component_name @@ -266,7 +254,5 @@ class DirectoryReader: logger.debug(f"Component info: {component_info}") if menu_result not in response["menu"]: response["menu"].append(menu_result) - logger.debug( - "-------------------- Component menu list built --------------------" - ) + logger.debug("-------------------- Component menu list built --------------------") return response diff --git a/src/backend/langflow/interface/custom/schema.py b/src/backend/langflow/interface/custom/schema.py index 80d65405f..7c5975150 100644 --- a/src/backend/langflow/interface/custom/schema.py +++ b/src/backend/langflow/interface/custom/schema.py @@ -1,16 +1,15 @@ +from typing import Any, Optional + from pydantic import BaseModel, Field -from typing import Optional - - class ClassCodeDetails(BaseModel): """ A dataclass for storing details about a class. """ name: str - doc: Optional[str] + doc: Optional[str] = None bases: list attributes: list methods: list @@ -23,7 +22,8 @@ class CallableCodeDetails(BaseModel): """ name: str - doc: Optional[str] + doc: Optional[str] = None args: list body: list - return_type: Optional[str] + return_type: Optional[Any] = None + has_return: bool = False diff --git a/src/backend/langflow/interface/custom/utils.py b/src/backend/langflow/interface/custom/utils.py index 99b0d4bc6..e527670a0 100644 --- a/src/backend/langflow/interface/custom/utils.py +++ b/src/backend/langflow/interface/custom/utils.py @@ -1,4 +1,6 @@ import re +from types import GenericAlias +from typing import Any def extract_inner_type(return_type: str) -> str: @@ -8,3 +10,31 @@ def extract_inner_type(return_type: str) -> str: if match := re.match(r"list\[(.*)\]", return_type, re.IGNORECASE): return match[1] return return_type + + +def extract_inner_type_from_generic_alias(return_type: GenericAlias) -> Any: + """ + Extracts the inner type from a type hint that is a list. + """ + if return_type.__origin__ == list: + return list(return_type.__args__) + + return return_type + + +def extract_union_types_from_generic_alias(return_type: GenericAlias) -> list: + """ + Extracts the inner type from a type hint that is a Union. + """ + return list(return_type.__args__) + + +def extract_union_types(return_type: str) -> list[str]: + """ + Extracts the inner type from a type hint that is a list. + """ + # If the return type is a Union, then we need to parse it + return_type = return_type.replace("Union", "").replace("[", "").replace("]", "") + return_types = return_type.split(",") + return_types = [item.strip() for item in return_types] + return return_types diff --git a/src/backend/langflow/interface/custom_lists.py b/src/backend/langflow/interface/custom_lists.py index 5a22d989f..80a46c856 100644 --- a/src/backend/langflow/interface/custom_lists.py +++ b/src/backend/langflow/interface/custom_lists.py @@ -1,28 +1,21 @@ import inspect from typing import Any -from langchain import ( - document_loaders, - embeddings, - llms, - memory, - requests, - text_splitter, -) +from langchain import document_loaders, embeddings, llms, memory, requests, text_splitter from langchain.agents import agent_toolkits -from langchain.chat_models import ( - AzureChatOpenAI, - ChatOpenAI, - ChatVertexAI, - ChatAnthropic, -) - -from langflow.interface.importing.utils import import_class +from langchain.chat_models import AzureChatOpenAI, ChatAnthropic, ChatOpenAI, ChatVertexAI from langflow.interface.agents.custom import CUSTOM_AGENTS from langflow.interface.chains.custom import CUSTOM_CHAINS +from langflow.interface.importing.utils import import_class # LLMs -llm_type_to_cls_dict = llms.type_to_cls_dict +llm_type_to_cls_dict = {} + +for k, v in llms.get_type_to_cls_dict().items(): + try: + llm_type_to_cls_dict[k] = v() + except Exception: + pass llm_type_to_cls_dict["anthropic-chat"] = ChatAnthropic # type: ignore llm_type_to_cls_dict["azure-chat"] = AzureChatOpenAI # type: ignore llm_type_to_cls_dict["openai-chat"] = ChatOpenAI # type: ignore @@ -46,34 +39,26 @@ toolkit_type_to_cls_dict: dict[str, Any] = { # Memories memory_type_to_cls_dict: dict[str, Any] = { - memory_name: import_class(f"langchain.memory.{memory_name}") - for memory_name in memory.__all__ + memory_name: import_class(f"langchain.memory.{memory_name}") for memory_name in memory.__all__ } # Wrappers -wrapper_type_to_cls_dict: dict[str, Any] = { - wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper] -} +wrapper_type_to_cls_dict: dict[str, Any] = {wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper]} # Embeddings embedding_type_to_cls_dict: dict[str, Any] = { - embedding_name: import_class(f"langchain.embeddings.{embedding_name}") - for embedding_name in embeddings.__all__ + embedding_name: import_class(f"langchain.embeddings.{embedding_name}") for embedding_name in embeddings.__all__ } # Document Loaders documentloaders_type_to_cls_dict: dict[str, Any] = { - documentloader_name: import_class( - f"langchain.document_loaders.{documentloader_name}" - ) + documentloader_name: import_class(f"langchain.document_loaders.{documentloader_name}") for documentloader_name in document_loaders.__all__ } # Text Splitters -textsplitter_type_to_cls_dict: dict[str, Any] = dict( - inspect.getmembers(text_splitter, inspect.isclass) -) +textsplitter_type_to_cls_dict: dict[str, Any] = dict(inspect.getmembers(text_splitter, inspect.isclass)) # merge CUSTOM_AGENTS and CUSTOM_CHAINS CUSTOM_NODES = {**CUSTOM_AGENTS, **CUSTOM_CHAINS} # type: ignore diff --git a/src/backend/langflow/interface/document_loaders/base.py b/src/backend/langflow/interface/document_loaders/base.py index e2c379b67..6142d2fa2 100644 --- a/src/backend/langflow/interface/document_loaders/base.py +++ b/src/backend/langflow/interface/document_loaders/base.py @@ -1,7 +1,7 @@ from typing import Dict, List, Optional, Type from langflow.interface.base import LangChainTypeCreator -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.documentloaders import DocumentLoaderFrontNode from langflow.interface.custom_lists import documentloaders_type_to_cls_dict @@ -35,8 +35,7 @@ class DocumentLoaderCreator(LangChainTypeCreator): return [ documentloader.__name__ for documentloader in self.type_to_loader_dict.values() - if documentloader.__name__ in settings_service.settings.DOCUMENTLOADERS - or settings_service.settings.DEV + if documentloader.__name__ in settings_service.settings.DOCUMENTLOADERS or settings_service.settings.DEV ] diff --git a/src/backend/langflow/interface/embeddings/base.py b/src/backend/langflow/interface/embeddings/base.py index d280cf1c1..834ea61fa 100644 --- a/src/backend/langflow/interface/embeddings/base.py +++ b/src/backend/langflow/interface/embeddings/base.py @@ -2,7 +2,7 @@ from typing import Dict, List, Optional, Type from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import embedding_type_to_cls_dict -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.base import FrontendNode from langflow.template.frontend_node.embeddings import EmbeddingFrontendNode @@ -37,8 +37,7 @@ class EmbeddingCreator(LangChainTypeCreator): return [ embedding.__name__ for embedding in self.type_to_loader_dict.values() - if embedding.__name__ in settings_service.settings.EMBEDDINGS - or settings_service.settings.DEV + if embedding.__name__ in settings_service.settings.EMBEDDINGS or settings_service.settings.DEV ] diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py index 44d72df42..f3276d952 100644 --- a/src/backend/langflow/interface/importing/utils.py +++ b/src/backend/langflow/interface/importing/utils.py @@ -3,15 +3,15 @@ import importlib from typing import Any, Type -from langchain.prompts import PromptTemplate from langchain.agents import Agent from langchain.base_language import BaseLanguageModel from langchain.chains.base import Chain from langchain.chat_models.base import BaseChatModel +from langchain.prompts import PromptTemplate from langchain.tools import BaseTool from langflow.interface.custom.custom_component import CustomComponent -from langflow.utils import validate from langflow.interface.wrappers.base import wrapper_creator +from langflow.utils import validate def import_module(module_path: str) -> Any: @@ -104,10 +104,7 @@ def import_prompt(prompt: str) -> Type[PromptTemplate]: def import_wrapper(wrapper: str) -> Any: """Import wrapper from wrapper name""" - if ( - isinstance(wrapper_creator.type_dict, dict) - and wrapper in wrapper_creator.type_dict - ): + if isinstance(wrapper_creator.type_dict, dict) and wrapper in wrapper_creator.type_dict: return wrapper_creator.type_dict.get(wrapper) @@ -183,6 +180,7 @@ def get_function(code): return validate.create_function(code, function_name) -def get_function_custom(code): +def eval_custom_component_code(code: str) -> Type[CustomComponent]: + """Evaluate custom component code""" class_name = validate.extract_class_name(code) return validate.create_class(code, class_name) diff --git a/src/backend/langflow/interface/initialize/llm.py b/src/backend/langflow/interface/initialize/llm.py index eaed04b77..05219d7d3 100644 --- a/src/backend/langflow/interface/initialize/llm.py +++ b/src/backend/langflow/interface/initialize/llm.py @@ -2,8 +2,6 @@ def initialize_vertexai(class_object, params): if credentials_path := params.get("credentials"): from google.oauth2 import service_account # type: ignore - credentials_object = service_account.Credentials.from_service_account_file( - filename=credentials_path - ) + credentials_object = service_account.Credentials.from_service_account_file(filename=credentials_path) params["credentials"] = credentials_object return class_object(**params) diff --git a/src/backend/langflow/interface/initialize/loading.py b/src/backend/langflow/interface/initialize/loading.py index 4492cca1b..8fab13351 100644 --- a/src/backend/langflow/interface/initialize/loading.py +++ b/src/backend/langflow/interface/initialize/loading.py @@ -1,40 +1,30 @@ +import inspect import json +from typing import TYPE_CHECKING, Any, Callable, Dict, Sequence, Type + import orjson -from typing import Any, Callable, Dict, Sequence, Type, TYPE_CHECKING -from langchain.schema import Document from langchain.agents import agent as agent_module from langchain.agents.agent import AgentExecutor from langchain.agents.agent_toolkits.base import BaseToolkit from langchain.agents.tools import BaseTool -from langflow.interface.initialize.llm import initialize_vertexai -from langflow.interface.initialize.utils import ( - handle_format_kwargs, - handle_node_type, - handle_partial_variables, -) - -from langflow.interface.initialize.vector_store import vecstore_initializer - +from langchain.chains.base import Chain +from langchain.document_loaders.base import BaseLoader +from langchain.schema import Document +from langchain.vectorstores.base import VectorStore +from loguru import logger from pydantic import ValidationError -from langflow.interface.importing.utils import ( - get_function, - get_function_custom, - import_by_type, -) from langflow.interface.custom_lists import CUSTOM_NODES -from langflow.interface.agents.base import agent_creator -from langflow.interface.toolkits.base import toolkits_creator -from langflow.interface.chains.base import chain_creator +from langflow.interface.importing.utils import eval_custom_component_code, get_function, import_by_type +from langflow.interface.initialize.llm import initialize_vertexai +from langflow.interface.initialize.utils import handle_format_kwargs, handle_node_type, handle_partial_variables +from langflow.interface.initialize.vector_store import vecstore_initializer from langflow.interface.output_parsers.base import output_parser_creator from langflow.interface.retrievers.base import retriever_creator -from langflow.interface.wrappers.base import wrapper_creator +from langflow.interface.toolkits.base import toolkits_creator from langflow.interface.utils import load_file_into_dict +from langflow.interface.wrappers.base import wrapper_creator from langflow.utils import validate -from langchain.chains.base import Chain -from langchain.vectorstores.base import VectorStore -from langchain.document_loaders.base import BaseLoader -from loguru import logger if TYPE_CHECKING: from langflow import CustomComponent @@ -44,15 +34,10 @@ def build_vertex_in_params(params: Dict) -> Dict: from langflow.graph.vertex.base import Vertex # If any of the values in params is a Vertex, we will build it - return { - key: value.build() if isinstance(value, Vertex) else value - for key, value in params.items() - } + return {key: value.build() if isinstance(value, Vertex) else value for key, value in params.items()} -def instantiate_class( - node_type: str, base_type: str, params: Dict, user_id=None -) -> Any: +async def instantiate_class(node_type: str, base_type: str, params: Dict, user_id=None) -> Any: """Instantiate class from module type and key, and params""" params = convert_params_to_sets(params) params = convert_kwargs(params) @@ -64,9 +49,7 @@ def instantiate_class( return custom_node(**params) logger.debug(f"Instantiating {node_type} of type {base_type}") class_object = import_by_type(_type=base_type, name=node_type) - return instantiate_based_on_type( - class_object, base_type, node_type, params, user_id=user_id - ) + return await instantiate_based_on_type(class_object, base_type, node_type, params, user_id=user_id) def convert_params_to_sets(params): @@ -93,7 +76,7 @@ def convert_kwargs(params): return params -def instantiate_based_on_type(class_object, base_type, node_type, params, user_id): +async def instantiate_based_on_type(class_object, base_type, node_type, params, user_id): if base_type == "agents": return instantiate_agent(node_type, class_object, params) elif base_type == "prompts": @@ -127,20 +110,31 @@ def instantiate_based_on_type(class_object, base_type, node_type, params, user_i elif base_type == "memory": return instantiate_memory(node_type, class_object, params) elif base_type == "custom_components": - return instantiate_custom_component(node_type, class_object, params, user_id) + return await instantiate_custom_component(node_type, class_object, params, user_id) elif base_type == "wrappers": return instantiate_wrapper(node_type, class_object, params) else: return class_object(**params) -def instantiate_custom_component(node_type, class_object, params, user_id): - # we need to make a copy of the params because we will be - # modifying it +async def instantiate_custom_component(node_type, class_object, params, user_id): params_copy = params.copy() - class_object: "CustomComponent" = get_function_custom(params_copy.pop("code")) + class_object: "CustomComponent" = eval_custom_component_code(params_copy.pop("code")) custom_component = class_object(user_id=user_id) - built_object = custom_component.build(**params_copy) + + if "retriever" in params_copy and hasattr(params_copy["retriever"], "as_retriever"): + params_copy["retriever"] = params_copy["retriever"].as_retriever() + + # Determine if the build method is asynchronous + is_async = inspect.iscoroutinefunction(custom_component.build) + + if is_async: + # Await the build method directly if it's async + built_object = await custom_component.build(**params_copy) + else: + # Call the build method directly if it's sync + built_object = custom_component.build(**params_copy) + return built_object, {"repr": custom_component.custom_repr()} @@ -194,9 +188,7 @@ def instantiate_memory(node_type, class_object, params): # I want to catch a specific attribute error that happens # when the object does not have a cursor attribute except Exception as exc: - if "object has no attribute 'cursor'" in str( - exc - ) or 'object has no field "conn"' in str(exc): + if "object has no attribute 'cursor'" in str(exc) or 'object has no field "conn"' in str(exc): raise AttributeError( ( "Failed to build connection to database." @@ -218,6 +210,8 @@ def instantiate_retriever(node_type, class_object, params): def instantiate_chains(node_type, class_object: Type[Chain], params: Dict): + from langflow.interface.chains.base import chain_creator + if "retriever" in params and hasattr(params["retriever"], "as_retriever"): params["retriever"] = params["retriever"].as_retriever() if node_type in chain_creator.from_method_nodes: @@ -230,14 +224,14 @@ def instantiate_chains(node_type, class_object: Type[Chain], params: Dict): def instantiate_agent(node_type, class_object: Type[agent_module.Agent], params: Dict): + from langflow.interface.agents.base import agent_creator + if node_type in agent_creator.from_method_nodes: method = agent_creator.from_method_nodes[node_type] if class_method := getattr(class_object, method, None): agent = class_method(**params) tools = params.get("tools", []) - return AgentExecutor.from_agent_and_tools( - agent=agent, tools=tools, handle_parsing_errors=True - ) + return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, handle_parsing_errors=True) return load_agent_executor(class_object, params) @@ -287,14 +281,13 @@ def instantiate_embedding(node_type, class_object, params: Dict): if "VertexAI" in node_type: return initialize_vertexai(class_object=class_object, params=params) + if "OpenAIEmbedding" in node_type: + params["disallowed_special"] = () + try: return class_object(**params) except ValidationError: - params = { - key: value - for key, value in params.items() - if key in class_object.__fields__ - } + params = {key: value for key, value in params.items() if key in class_object.model_fields} return class_object(**params) @@ -306,9 +299,7 @@ def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict): if "texts" in params: params["documents"] = params.pop("texts") if "documents" in params: - params["documents"] = [ - doc for doc in params["documents"] if isinstance(doc, Document) - ] + params["documents"] = [doc for doc in params["documents"] if isinstance(doc, Document)] if initializer := vecstore_initializer.get(class_object.__name__): vecstore = initializer(class_object, params) else: @@ -323,9 +314,7 @@ def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict): return vecstore -def instantiate_documentloader( - node_type: str, class_object: Type[BaseLoader], params: Dict -): +def instantiate_documentloader(node_type: str, class_object: Type[BaseLoader], params: Dict): if "file_filter" in params: # file_filter will be a string but we need a function # that will be used to filter the files using file_filter @@ -334,17 +323,13 @@ def instantiate_documentloader( # in x and if it is, we will return True file_filter = params.pop("file_filter") extensions = file_filter.split(",") - params["file_filter"] = lambda x: any( - extension.strip() in x for extension in extensions - ) + params["file_filter"] = lambda x: any(extension.strip() in x for extension in extensions) metadata = params.pop("metadata", None) if metadata and isinstance(metadata, str): try: metadata = orjson.loads(metadata) except json.JSONDecodeError as exc: - raise ValueError( - "The metadata you provided is not a valid JSON string." - ) from exc + raise ValueError("The metadata you provided is not a valid JSON string.") from exc if node_type == "WebBaseLoader": if web_path := params.pop("web_path", None): @@ -377,16 +362,12 @@ def instantiate_textsplitter( "Try changing the chunk_size of the Text Splitter." ) from exc - if ( - "separator_type" in params and params["separator_type"] == "Text" - ) or "separator_type" not in params: + if ("separator_type" in params and params["separator_type"] == "Text") or "separator_type" not in params: params.pop("separator_type", None) # separators might come in as an escaped string like \\n # so we need to convert it to a string if "separators" in params: - params["separators"] = ( - params["separators"].encode().decode("unicode-escape") - ) + params["separators"] = params["separators"].encode().decode("unicode-escape") text_splitter = class_object(**params) else: from langchain.text_splitter import Language @@ -413,8 +394,7 @@ def replace_zero_shot_prompt_with_prompt_template(nodes): tools = [ tool for tool in nodes - if tool["type"] != "chatOutputNode" - and "Tool" in tool["data"]["node"]["base_classes"] + if tool["type"] != "chatOutputNode" and "Tool" in tool["data"]["node"]["base_classes"] ] node["data"] = build_prompt_template(prompt=node["data"], tools=tools) break @@ -428,9 +408,7 @@ def load_agent_executor(agent_class: type[agent_module.Agent], params, **kwargs) # agent has hidden args for memory. might need to be support # memory = params["memory"] # if allowed_tools is not a list or set, make it a list - if not isinstance(allowed_tools, (list, set)) and isinstance( - allowed_tools, BaseTool - ): + if not isinstance(allowed_tools, (list, set)) and isinstance(allowed_tools, BaseTool): allowed_tools = [allowed_tools] tool_names = [tool.name for tool in allowed_tools] # Agent class requires an output_parser but Agent classes @@ -458,10 +436,7 @@ def build_prompt_template(prompt, tools): format_instructions = prompt["node"]["template"]["format_instructions"]["value"] tool_strings = "\n".join( - [ - f"{tool['data']['node']['name']}: {tool['data']['node']['description']}" - for tool in tools - ] + [f"{tool['data']['node']['name']}: {tool['data']['node']['description']}" for tool in tools] ) tool_names = ", ".join([tool["data"]["node"]["name"] for tool in tools]) format_instructions = format_instructions.format(tool_names=tool_names) diff --git a/src/backend/langflow/interface/initialize/utils.py b/src/backend/langflow/interface/initialize/utils.py index 199626de5..69e436fcf 100644 --- a/src/backend/langflow/interface/initialize/utils.py +++ b/src/backend/langflow/interface/initialize/utils.py @@ -1,13 +1,11 @@ import contextlib import json -from langflow.services.database.models.base import orjson_dumps -import orjson from typing import Any, Dict, List +import orjson from langchain.agents import ZeroShotAgent - - -from langchain.schema import Document, BaseOutputParser +from langchain.schema import BaseOutputParser, Document +from langflow.services.database.models.base import orjson_dumps def handle_node_type(node_type, class_object, params: Dict): @@ -30,9 +28,7 @@ def check_tools_in_params(params: Dict): def instantiate_from_template(class_object, params: Dict): - from_template_params = { - "template": params.pop("prompt", params.pop("template", "")) - } + from_template_params = {"template": params.pop("prompt", params.pop("template", ""))} if not from_template_params.get("template"): raise ValueError("Prompt template is required") return class_object.from_template(**from_template_params) @@ -48,9 +44,7 @@ def handle_format_kwargs(prompt, params: Dict): def handle_partial_variables(prompt, format_kwargs: Dict): partial_variables = format_kwargs.copy() - partial_variables = { - key: value for key, value in partial_variables.items() if value - } + partial_variables = {key: value for key, value in partial_variables.items() if value} # Remove handle_keys otherwise LangChain raises an error partial_variables.pop("handle_keys", None) if partial_variables and hasattr(prompt, "partial"): @@ -62,9 +56,7 @@ def handle_variable(params: Dict, input_variable: str, format_kwargs: Dict): variable = params[input_variable] if isinstance(variable, str): format_kwargs[input_variable] = variable - elif isinstance(variable, BaseOutputParser) and hasattr( - variable, "get_format_instructions" - ): + elif isinstance(variable, BaseOutputParser) and hasattr(variable, "get_format_instructions"): format_kwargs[input_variable] = variable.get_format_instructions() elif is_instance_of_list_or_document(variable): format_kwargs = format_document(variable, input_variable, format_kwargs) @@ -91,8 +83,10 @@ def format_document(variable, input_variable: str, format_kwargs: Dict): def format_content(variable): if len(variable) > 1: return "\n".join([item.page_content for item in variable if item.page_content]) - content = variable[0].page_content - return try_to_load_json(content) + elif len(variable) == 1: + content = variable[0].page_content + return try_to_load_json(content) + return "" def try_to_load_json(content): @@ -107,8 +101,7 @@ def try_to_load_json(content): def needs_handle_keys(variable): return is_instance_of_list_or_document(variable) or ( - isinstance(variable, BaseOutputParser) - and hasattr(variable, "get_format_instructions") + isinstance(variable, BaseOutputParser) and hasattr(variable, "get_format_instructions") ) diff --git a/src/backend/langflow/interface/initialize/vector_store.py b/src/backend/langflow/interface/initialize/vector_store.py index 6548c8186..405f197e3 100644 --- a/src/backend/langflow/interface/initialize/vector_store.py +++ b/src/backend/langflow/interface/initialize/vector_store.py @@ -17,9 +17,7 @@ from langchain.vectorstores import ( def docs_in_params(params: dict) -> bool: """Check if params has documents OR texts and one of them is not an empty list, If any of them is not an empty list, return True, else return False""" - return ("documents" in params and params["documents"]) or ( - "texts" in params and params["texts"] - ) + return ("documents" in params and params["documents"]) or ("texts" in params and params["texts"]) def initialize_mongodb(class_object: Type[MongoDBAtlasVectorSearch], params: dict): @@ -31,9 +29,7 @@ def initialize_mongodb(class_object: Type[MongoDBAtlasVectorSearch], params: dic import certifi from pymongo import MongoClient - client: MongoClient = MongoClient( - MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where() - ) + client: MongoClient = MongoClient(MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where()) db_name = params.pop("db_name", None) collection_name = params.pop("collection_name", None) if not db_name or not collection_name: @@ -142,9 +138,7 @@ def initialize_pinecone(class_object: Type[Pinecone], params: dict): pinecone_env = os.getenv("PINECONE_ENV") if pinecone_api_key is None or pinecone_env is None: - raise ValueError( - "Pinecone API key and environment must be provided in the params" - ) + raise ValueError("Pinecone API key and environment must be provided in the params") # initialize pinecone pinecone.init( @@ -178,26 +172,20 @@ def initialize_chroma(class_object: Type[Chroma], params: dict): import chromadb # type: ignore settings_params = { - key: params[key] - for key, value_ in params.items() - if key.startswith("chroma_server_") and value_ + key: params[key] for key, value_ in params.items() if key.startswith("chroma_server_") and value_ } chroma_settings = chromadb.config.Settings(**settings_params) params["client_settings"] = chroma_settings else: # remove all chroma_server_ keys from params - params = { - key: value - for key, value in params.items() - if not key.startswith("chroma_server_") - } + params = {key: value for key, value in params.items() if not key.startswith("chroma_server_")} persist = params.pop("persist", False) if not docs_in_params(params): params.pop("documents", None) params.pop("texts", None) params["embedding_function"] = params.pop("embedding") - chromadb = class_object(**params) + chromadb_instance = class_object(**params) else: if "texts" in params: params["documents"] = params.pop("texts") @@ -212,10 +200,10 @@ def initialize_chroma(class_object: Type[Chroma], params: dict): if value is None: doc.metadata[key] = "" - chromadb = class_object.from_documents(**params) + chromadb_instance = class_object.from_documents(**params) if persist: - chromadb.persist() - return chromadb + chromadb_instance.persist() + return chromadb_instance def initialize_qdrant(class_object: Type[Qdrant], params: dict): diff --git a/src/backend/langflow/interface/listing.py b/src/backend/langflow/interface/listing.py index aa72e568e..79a7335d4 100644 --- a/src/backend/langflow/interface/listing.py +++ b/src/backend/langflow/interface/listing.py @@ -1,4 +1,4 @@ -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.utils.lazy_load import LazyLoadDictBase diff --git a/src/backend/langflow/interface/llms/base.py b/src/backend/langflow/interface/llms/base.py index ffb7fa2f2..4b0654a1a 100644 --- a/src/backend/langflow/interface/llms/base.py +++ b/src/backend/langflow/interface/llms/base.py @@ -2,7 +2,7 @@ from typing import Dict, List, Optional, Type from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import llm_type_to_cls_dict -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.llms import LLMFrontendNode from loguru import logger @@ -38,8 +38,7 @@ class LLMCreator(LangChainTypeCreator): return [ llm.__name__ for llm in self.type_to_loader_dict.values() - if llm.__name__ in settings_service.settings.LLMS - or settings_service.settings.DEV + if llm.__name__ in settings_service.settings.LLMS or settings_service.settings.DEV ] diff --git a/src/backend/langflow/interface/memories/base.py b/src/backend/langflow/interface/memories/base.py index 3f3658304..7508d6d64 100644 --- a/src/backend/langflow/interface/memories/base.py +++ b/src/backend/langflow/interface/memories/base.py @@ -1,8 +1,8 @@ -from typing import Dict, List, Optional, Type +from typing import ClassVar, Dict, List, Optional, Type from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import memory_type_to_cls_dict -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.base import FrontendNode from langflow.template.frontend_node.memories import MemoryFrontendNode @@ -14,7 +14,7 @@ from langflow.custom.customs import get_custom_nodes class MemoryCreator(LangChainTypeCreator): type_name: str = "memories" - from_method_nodes = { + from_method_nodes: ClassVar[Dict] = { "ZepChatMessageHistory": "__init__", "SQLiteEntityStore": "__init__", } @@ -53,8 +53,7 @@ class MemoryCreator(LangChainTypeCreator): return [ memory.__name__ for memory in self.type_to_loader_dict.values() - if memory.__name__ in settings_service.settings.MEMORIES - or settings_service.settings.DEV + if memory.__name__ in settings_service.settings.MEMORIES or settings_service.settings.DEV ] diff --git a/src/backend/langflow/interface/output_parsers/base.py b/src/backend/langflow/interface/output_parsers/base.py index 06dfdf4eb..3ae1e9d7b 100644 --- a/src/backend/langflow/interface/output_parsers/base.py +++ b/src/backend/langflow/interface/output_parsers/base.py @@ -1,10 +1,10 @@ -from typing import Dict, List, Optional, Type +from typing import ClassVar, Dict, List, Optional, Type from langchain import output_parsers from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.output_parsers import OutputParserFrontendNode from loguru import logger @@ -13,7 +13,7 @@ from langflow.utils.util import build_template_from_class, build_template_from_m class OutputParserCreator(LangChainTypeCreator): type_name: str = "output_parsers" - from_method_nodes = { + from_method_nodes: ClassVar[Dict] = { "StructuredOutputParser": "from_response_schemas", } @@ -26,17 +26,14 @@ class OutputParserCreator(LangChainTypeCreator): if self.type_dict is None: settings_service = get_settings_service() self.type_dict = { - output_parser_name: import_class( - f"langchain.output_parsers.{output_parser_name}" - ) + output_parser_name: import_class(f"langchain.output_parsers.{output_parser_name}") # if output_parser_name is not lower case it is a class for output_parser_name in output_parsers.__all__ } self.type_dict = { name: output_parser for name, output_parser in self.type_dict.items() - if name in settings_service.settings.OUTPUT_PARSERS - or settings_service.settings.DEV + if name in settings_service.settings.OUTPUT_PARSERS or settings_service.settings.DEV } return self.type_dict diff --git a/src/backend/langflow/interface/prompts/base.py b/src/backend/langflow/interface/prompts/base.py index 29d3e8ba8..164ea2b10 100644 --- a/src/backend/langflow/interface/prompts/base.py +++ b/src/backend/langflow/interface/prompts/base.py @@ -5,7 +5,7 @@ from langchain import prompts from langflow.custom.customs import get_custom_nodes from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.prompts import PromptFrontendNode from loguru import logger @@ -36,8 +36,7 @@ class PromptCreator(LangChainTypeCreator): self.type_dict = { name: prompt for name, prompt in self.type_dict.items() - if name in settings_service.settings.PROMPTS - or settings_service.settings.DEV + if name in settings_service.settings.PROMPTS or settings_service.settings.DEV } return self.type_dict diff --git a/src/backend/langflow/interface/prompts/custom.py b/src/backend/langflow/interface/prompts/custom.py index ef16f1474..202fbe409 100644 --- a/src/backend/langflow/interface/prompts/custom.py +++ b/src/backend/langflow/interface/prompts/custom.py @@ -1,7 +1,7 @@ from typing import Dict, List, Optional, Type from langchain.prompts import PromptTemplate -from pydantic import root_validator +from pydantic.v1 import root_validator from langflow.interface.utils import extract_input_variables_from_prompt @@ -42,17 +42,13 @@ class BaseCustomPrompt(PromptTemplate): values["template"] = values["template"].format(**format_dict) values["template"] = values["template"] - values["input_variables"] = extract_input_variables_from_prompt( - values["template"] - ) + values["input_variables"] = extract_input_variables_from_prompt(values["template"]) return values class SeriesCharacterPrompt(BaseCustomPrompt): # Add a very descriptive description for the prompt generator - description: Optional[ - str - ] = "A prompt that asks the AI to act like a character from a series." + description: Optional[str] = "A prompt that asks the AI to act like a character from a series." character: str series: str template: str = """I want you to act like {character} from {series}. @@ -68,6 +64,4 @@ Human: {input} input_variables: List[str] = ["character", "series"] -CUSTOM_PROMPTS: Dict[str, Type[BaseCustomPrompt]] = { - "SeriesCharacterPrompt": SeriesCharacterPrompt -} +CUSTOM_PROMPTS: Dict[str, Type[BaseCustomPrompt]] = {"SeriesCharacterPrompt": SeriesCharacterPrompt} diff --git a/src/backend/langflow/interface/retrievers/base.py b/src/backend/langflow/interface/retrievers/base.py index 4ee40e659..2439708a3 100644 --- a/src/backend/langflow/interface/retrievers/base.py +++ b/src/backend/langflow/interface/retrievers/base.py @@ -1,10 +1,10 @@ -from typing import Any, Dict, List, Optional, Type +from typing import Any, ClassVar, Dict, List, Optional, Type from langchain import retrievers from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.retrievers import RetrieverFrontendNode from loguru import logger @@ -14,7 +14,10 @@ from langflow.utils.util import build_template_from_method, build_template_from_ class RetrieverCreator(LangChainTypeCreator): type_name: str = "retrievers" - from_method_nodes = {"MultiQueryRetriever": "from_llm", "ZepRetriever": "__init__"} + from_method_nodes: ClassVar[Dict] = { + "MultiQueryRetriever": "from_llm", + "ZepRetriever": "__init__", + } @property def frontend_node_class(self) -> Type[RetrieverFrontendNode]: @@ -39,9 +42,7 @@ class RetrieverCreator(LangChainTypeCreator): method_name=self.from_method_nodes[name], ) else: - return build_template_from_class( - name, type_to_cls_dict=self.type_to_loader_dict - ) + return build_template_from_class(name, type_to_cls_dict=self.type_to_loader_dict) except ValueError as exc: raise ValueError(f"Retriever {name} not found") from exc except AttributeError as exc: @@ -53,8 +54,7 @@ class RetrieverCreator(LangChainTypeCreator): return [ retriever for retriever in self.type_to_loader_dict.keys() - if retriever in settings_service.settings.RETRIEVERS - or settings_service.settings.DEV + if retriever in settings_service.settings.RETRIEVERS or settings_service.settings.DEV ] diff --git a/src/backend/langflow/interface/run.py b/src/backend/langflow/interface/run.py index 63391204a..94cd922eb 100644 --- a/src/backend/langflow/interface/run.py +++ b/src/backend/langflow/interface/run.py @@ -1,9 +1,12 @@ -from typing import Dict, Tuple -from langflow.graph import Graph +from typing import Dict, Optional, Tuple, Union +from uuid import UUID + from loguru import logger +from langflow.graph import Graph -def build_sorted_vertices(data_graph) -> Tuple[Graph, Dict]: + +async def build_sorted_vertices(data_graph, user_id: Optional[Union[str, UUID]] = None) -> Tuple[Graph, Dict]: """ Build langchain object from data_graph. """ @@ -13,28 +16,12 @@ def build_sorted_vertices(data_graph) -> Tuple[Graph, Dict]: sorted_vertices = graph.topological_sort() artifacts = {} for vertex in sorted_vertices: - vertex.build() + await vertex.build(user_id=user_id) if vertex.artifacts: artifacts.update(vertex.artifacts) return graph, artifacts -def build_langchain_object(data_graph): - """ - Build langchain object from data_graph. - """ - - logger.debug("Building langchain object") - nodes = data_graph["nodes"] - # Add input variables - # nodes = payload.extract_input_variables(nodes) - # Nodes, edges and root node - edges = data_graph["edges"] - graph = Graph(nodes, edges) - - return graph.build() - - def get_memory_key(langchain_object): """ Given a LangChain object, this function retrieves the current memory key from the object's memory attribute. diff --git a/src/backend/langflow/interface/text_splitters/base.py b/src/backend/langflow/interface/text_splitters/base.py index 4f337817f..29c77a12f 100644 --- a/src/backend/langflow/interface/text_splitters/base.py +++ b/src/backend/langflow/interface/text_splitters/base.py @@ -1,7 +1,7 @@ from typing import Dict, List, Optional, Type from langflow.interface.base import LangChainTypeCreator -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.textsplitters import TextSplittersFrontendNode from langflow.interface.custom_lists import textsplitter_type_to_cls_dict @@ -35,8 +35,7 @@ class TextSplitterCreator(LangChainTypeCreator): return [ textsplitter.__name__ for textsplitter in self.type_to_loader_dict.values() - if textsplitter.__name__ in settings_service.settings.TEXTSPLITTERS - or settings_service.settings.DEV + if textsplitter.__name__ in settings_service.settings.TEXTSPLITTERS or settings_service.settings.DEV ] diff --git a/src/backend/langflow/interface/toolkits/base.py b/src/backend/langflow/interface/toolkits/base.py index 73ca4852f..7c57579d1 100644 --- a/src/backend/langflow/interface/toolkits/base.py +++ b/src/backend/langflow/interface/toolkits/base.py @@ -4,7 +4,7 @@ from langchain.agents import agent_toolkits from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class, import_module -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from loguru import logger from langflow.utils.util import build_template_from_class @@ -32,13 +32,10 @@ class ToolkitCreator(LangChainTypeCreator): if self.type_dict is None: settings_service = get_settings_service() self.type_dict = { - toolkit_name: import_class( - f"langchain.agents.agent_toolkits.{toolkit_name}" - ) + toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}") # if toolkit_name is not lower case it is a class for toolkit_name in agent_toolkits.__all__ - if not toolkit_name.islower() - and toolkit_name in settings_service.settings.TOOLKITS + if not toolkit_name.islower() and toolkit_name in settings_service.settings.TOOLKITS } return self.type_dict @@ -61,9 +58,7 @@ class ToolkitCreator(LangChainTypeCreator): def get_create_function(self, name: str) -> Callable: if loader_name := self.create_functions.get(name): - return import_module( - f"from langchain.agents.agent_toolkits import {loader_name[0]}" - ) + return import_module(f"from langchain.agents.agent_toolkits import {loader_name[0]}") else: raise ValueError("Toolkit not found") diff --git a/src/backend/langflow/interface/tools/base.py b/src/backend/langflow/interface/tools/base.py index 796d9d69c..20a08ee0a 100644 --- a/src/backend/langflow/interface/tools/base.py +++ b/src/backend/langflow/interface/tools/base.py @@ -16,12 +16,13 @@ from langflow.interface.tools.constants import ( OTHER_TOOLS, ) from langflow.interface.tools.util import get_tool_params -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.field.base import TemplateField from langflow.template.template.base import Template from langflow.utils import util from langflow.utils.util import build_template_from_class +from langflow.utils.logger import logger TOOL_INPUTS = { "str": TemplateField( @@ -32,11 +33,9 @@ TOOL_INPUTS = { placeholder="", value="", ), - "llm": TemplateField( - field_type="BaseLanguageModel", required=True, is_list=False, show=True - ), + "llm": TemplateField(field_type="BaseLanguageModel", required=True, is_list=False, show=True), "func": TemplateField( - field_type="function", + field_type="Callable", required=True, is_list=False, show=True, @@ -56,8 +55,7 @@ TOOL_INPUTS = { is_list=False, show=True, value="", - suffixes=[".json", ".yaml", ".yml"], - file_types=["json", "yaml", "yml"], + file_types=[".json", ".yaml", ".yml"], ), } @@ -73,14 +71,15 @@ class ToolCreator(LangChainTypeCreator): all_tools = {} for tool, tool_fcn in ALL_TOOLS_NAMES.items(): - tool_params = get_tool_params(tool_fcn) + try: + tool_params = get_tool_params(tool_fcn) + except Exception: + logger.error(f"Error getting params for tool {tool}") + continue tool_name = tool_params.get("name") or tool - if ( - tool_name in settings_service.settings.TOOLS - or settings_service.settings.DEV - ): + if tool_name in settings_service.settings.TOOLS or settings_service.settings.DEV: if tool_name == "JsonSpec": tool_params["path"] = tool_params.pop("dict_") # type: ignore all_tools[tool_name] = { @@ -122,7 +121,7 @@ class ToolCreator(LangChainTypeCreator): elif tool_type in CUSTOM_TOOLS: # Get custom tool params params = self.type_to_loader_dict[name]["params"] # type: ignore - base_classes = ["function"] + base_classes = ["Callable"] if node := customs.get_custom_nodes("tools").get(tool_type): return node elif tool_type in FILE_TOOLS: @@ -132,10 +131,15 @@ class ToolCreator(LangChainTypeCreator): tool_dict = build_template_from_class(tool_type, OTHER_TOOLS) fields = tool_dict["template"] + # _type is the only key in fields + # return None + if len(fields) == 1 and "_type" in fields: + return None + # Pop unnecessary fields and add name fields.pop("_type") # type: ignore - fields.pop("return_direct") # type: ignore - fields.pop("verbose") # type: ignore + fields.pop("return_direct", None) # type: ignore + fields.pop("verbose", None) # type: ignore tool_params = { "name": fields.pop("name")["value"], # type: ignore diff --git a/src/backend/langflow/interface/tools/custom.py b/src/backend/langflow/interface/tools/custom.py index 321298e34..73f5842df 100644 --- a/src/backend/langflow/interface/tools/custom.py +++ b/src/backend/langflow/interface/tools/custom.py @@ -1,7 +1,7 @@ from typing import Callable, Optional from langflow.interface.importing.utils import get_function -from pydantic import BaseModel, validator +from pydantic.v1 import BaseModel, validator from langflow.utils import validate from langchain.agents.tools import Tool diff --git a/src/backend/langflow/interface/tools/util.py b/src/backend/langflow/interface/tools/util.py index 8e4f582c1..7c8020aa9 100644 --- a/src/backend/langflow/interface/tools/util.py +++ b/src/backend/langflow/interface/tools/util.py @@ -21,16 +21,12 @@ def get_func_tool_params(func, **kwargs) -> Union[Dict, None]: for keyword in tool.keywords: if keyword.arg == "name": try: - tool_params["name"] = ast.literal_eval( - keyword.value - ) + tool_params["name"] = ast.literal_eval(keyword.value) except ValueError: break elif keyword.arg == "description": try: - tool_params["description"] = ast.literal_eval( - keyword.value - ) + tool_params["description"] = ast.literal_eval(keyword.value) except ValueError: continue @@ -43,9 +39,7 @@ def get_func_tool_params(func, **kwargs) -> Union[Dict, None]: else: # get the class object from the return statement try: - class_obj = eval( - compile(ast.Expression(tool), "", "eval") - ) + class_obj = eval(compile(ast.Expression(tool), "", "eval")) except Exception: return None diff --git a/src/backend/langflow/interface/types.py b/src/backend/langflow/interface/types.py index 6708c11c9..f13db2ba7 100644 --- a/src/backend/langflow/interface/types.py +++ b/src/backend/langflow/interface/types.py @@ -1,43 +1,40 @@ import ast import contextlib -from typing import Any, List -from langflow.api.utils import get_new_key +import re +import traceback +import warnings +from typing import Any, Dict, List, Optional, Union +from uuid import UUID + +from cachetools import LRUCache, cached +from fastapi import HTTPException +from loguru import logger + +from langflow.field_typing.range_spec import RangeSpec from langflow.interface.agents.base import agent_creator from langflow.interface.chains.base import chain_creator -from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES +from langflow.interface.custom.custom_component import CustomComponent +from langflow.interface.custom.directory_reader import DirectoryReader from langflow.interface.custom.utils import extract_inner_type from langflow.interface.document_loaders.base import documentloader_creator from langflow.interface.embeddings.base import embedding_creator -from langflow.interface.importing.utils import get_function_custom +from langflow.interface.importing.utils import eval_custom_component_code from langflow.interface.llms.base import llm_creator from langflow.interface.memories.base import memory_creator +from langflow.interface.output_parsers.base import output_parser_creator from langflow.interface.prompts.base import prompt_creator +from langflow.interface.retrievers.base import retriever_creator from langflow.interface.text_splitters.base import textsplitter_creator from langflow.interface.toolkits.base import toolkits_creator from langflow.interface.tools.base import tool_creator from langflow.interface.utilities.base import utility_creator from langflow.interface.vector_store.base import vectorstore_creator from langflow.interface.wrappers.base import wrapper_creator -from langflow.interface.output_parsers.base import output_parser_creator -from langflow.interface.custom.base import custom_component_creator -from langflow.interface.custom.custom_component import CustomComponent - from langflow.template.field.base import TemplateField -from langflow.template.frontend_node.constants import CLASSES_TO_REMOVE -from langflow.template.frontend_node.custom_components import ( - CustomComponentFrontendNode, -) -from langflow.interface.retrievers.base import retriever_creator - -from langflow.interface.custom.directory_reader import DirectoryReader -from loguru import logger +from langflow.template.frontend_node.custom_components import \ + CustomComponentFrontendNode from langflow.utils.util import get_base_classes -import re -import warnings -import traceback -from fastapi import HTTPException - # Used to get the base_classes list def get_type_list(): @@ -52,6 +49,7 @@ def get_type_list(): return all_types +@cached(LRUCache(maxsize=1)) def build_langchain_types_dict(): # sourcery skip: dict-assign-update-to-union """Build a dictionary of all langchain types""" all_types = {} @@ -72,7 +70,6 @@ def build_langchain_types_dict(): # sourcery skip: dict-assign-update-to-union utility_creator, output_parser_creator, retriever_creator, - custom_component_creator, ] all_types = {} @@ -92,7 +89,7 @@ def process_type(field_type: str): # TODO: Move to correct place def add_new_custom_field( - template, + frontend_node: CustomComponentFrontendNode, field_name: str, field_type: str, field_value: Any, @@ -114,16 +111,10 @@ def add_new_custom_field( # If options is a list, then it's a dropdown # If options is None, then it's a list of strings is_list = isinstance(field_config.get("options"), list) - field_config["is_list"] = ( - is_list or field_config.get("is_list", False) or field_contains_list - ) + field_config["is_list"] = is_list or field_config.get("is_list", False) or field_contains_list if "name" in field_config: - warnings.warn( - "The 'name' key in field_config is used to build the object and can't be changed." - ) - field_config.pop("name", None) - + warnings.warn("The 'name' key in field_config is used to build the object and can't be changed.") required = field_config.pop("required", field_required) placeholder = field_config.pop("placeholder", "") @@ -136,36 +127,39 @@ def add_new_custom_field( advanced=field_advanced, placeholder=placeholder, display_name=display_name, - **field_config, + **sanitize_field_config(field_config), ) - template.get("template")[field_name] = new_field.to_dict() - template.get("custom_fields")[field_name] = None + frontend_node.template.upsert_field(field_name, new_field) + if isinstance(frontend_node.custom_fields, dict): + frontend_node.custom_fields[field_name] = None - return template + return frontend_node + + +def sanitize_field_config(field_config: Dict): + # If any of the already existing keys are in field_config, remove them + for key in ["name", "field_type", "value", "required", "placeholder", "display_name", "advanced", "show"]: + field_config.pop(key, None) + return field_config # TODO: Move to correct place -def add_code_field(template, raw_code, field_config): - # Field with the Python code to allow update +def add_code_field(frontend_node: CustomComponentFrontendNode, raw_code, field_config): + code_field = TemplateField( + dynamic=True, + required=True, + placeholder="", + multiline=True, + value=raw_code, + password=False, + name="code", + advanced=field_config.pop("advanced", False), + field_type="code", + is_list=False, + ) + frontend_node.template.add_field(code_field) - code_field = { - "code": { - "dynamic": True, - "required": True, - "placeholder": "", - "show": field_config.pop("show", True), - "multiline": True, - "value": raw_code, - "password": False, - "name": "code", - "advanced": field_config.pop("advanced", False), - "type": "code", - "list": False, - } - } - template.get("template")["code"] = code_field.get("code") - - return template + return frontend_node def extract_type_from_optional(field_type): @@ -182,51 +176,108 @@ def extract_type_from_optional(field_type): return match[1] if match else None -def build_frontend_node(custom_component: CustomComponent): +def build_frontend_node(template_config): """Build a frontend node for a custom component""" try: - return ( - CustomComponentFrontendNode().to_dict().get(type(custom_component).__name__) - ) - + sanitized_template_config = sanitize_template_config(template_config) + return CustomComponentFrontendNode(**sanitized_template_config) except Exception as exc: logger.error(f"Error while building base frontend node: {exc}") - return None + raise exc -def update_attributes(frontend_node, template_config): - """Update the display name and description of a frontend node""" - attributes = [ +def sanitize_template_config(template_config): + """Sanitize the template config""" + attributes = { "display_name", "description", "beta", "documentation", "output_types", - ] - for attribute in attributes: - if attribute in template_config: - frontend_node[attribute] = template_config[attribute] + } + for key in template_config.copy(): + if key not in attributes: + template_config.pop(key, None) + + return template_config -def build_field_config(custom_component: CustomComponent): +def build_field_config( + custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None, update_field=None +): """Build the field configuration for a custom component""" try: - custom_class = get_function_custom(custom_component.code) + if custom_component.code is None: + return {} + elif isinstance(custom_component.code, str): + custom_class = eval_custom_component_code(custom_component.code) + else: + raise ValueError("Invalid code type") except Exception as exc: - logger.error(f"Error while getting custom function: {str(exc)}") - return {} + logger.error(f"Error while evaluating custom component code: {str(exc)}") + raise HTTPException( + status_code=400, + detail={ + "error": ("Invalid type convertion. Please check your code and try again."), + "traceback": traceback.format_exc(), + }, + ) from exc try: - return custom_class().build_config() + build_config: Dict = custom_class(user_id=user_id).build_config() + + for field_name, field in build_config.items(): + # Allow user to build TemplateField as well + # as a dict with the same keys as TemplateField + field_dict = get_field_dict(field) + if update_field is not None and field_name != update_field: + continue + try: + update_field_dict(field_dict) + build_config[field_name] = field_dict + except Exception as exc: + logger.error(f"Error while getting build_config: {str(exc)}") + + return build_config + except Exception as exc: logger.error(f"Error while building field config: {str(exc)}") - return {} + raise HTTPException( + status_code=400, + detail={ + "error": ("Invalid type convertion. Please check your code and try again."), + "traceback": traceback.format_exc(), + }, + ) from exc + + +def get_field_dict(field): + """Get the field dictionary from a TemplateField or a dict""" + if isinstance(field, TemplateField): + return field.model_dump(by_alias=True, exclude_none=True) + return field + + +def update_field_dict(field_dict): + """Update the field dictionary by calling options() or value() if they are callable""" + if "options" in field_dict and callable(field_dict["options"]): + field_dict["options"] = field_dict["options"]() + # Also update the "refresh" key + field_dict["refresh"] = True + + if "value" in field_dict and callable(field_dict["value"]): + field_dict["value"] = field_dict["value"](field_dict.get("options", [])) + field_dict["refresh"] = True + + # Let's check if "range_spec" is a RangeSpec object + if "rangeSpec" in field_dict and isinstance(field_dict["rangeSpec"], RangeSpec): + field_dict["rangeSpec"] = field_dict["rangeSpec"].model_dump() def add_extra_fields(frontend_node, field_config, function_args): """Add extra fields to the frontend node""" - if function_args is None or function_args == "": + if not function_args: return # sort function_args which is a list of dicts @@ -236,9 +287,7 @@ def add_extra_fields(frontend_node, field_config, function_args): if "name" not in extra_field or extra_field["name"] == "self": continue - field_name, field_type, field_value, field_required = get_field_properties( - extra_field - ) + field_name, field_type, field_value, field_required = get_field_properties(extra_field) config = field_config.get(field_name, {}) frontend_node = add_new_custom_field( frontend_node, @@ -259,72 +308,83 @@ def get_field_properties(extra_field): if not field_required: field_type = extract_type_from_optional(field_type) - - with contextlib.suppress(Exception): - field_value = ast.literal_eval(field_value) + if field_value is not None: + with contextlib.suppress(Exception): + field_value = ast.literal_eval(field_value) return field_name, field_type, field_value, field_required -def add_base_classes(frontend_node, return_types: List[str]): +def add_base_classes(frontend_node: CustomComponentFrontendNode, return_types: List[str]): """Add base classes to the frontend node""" - for return_type in return_types: - if return_type not in CUSTOM_COMPONENT_SUPPORTED_TYPES or return_type is None: + for return_type_instance in return_types: + if return_type_instance is None: raise HTTPException( status_code=400, detail={ - "error": ( - "Invalid return type should be one of: " - f"{list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())}" - ), + "error": ("Invalid return type. Please check your code and try again."), "traceback": traceback.format_exc(), }, ) - return_type_instance = CUSTOM_COMPONENT_SUPPORTED_TYPES.get(return_type) base_classes = get_base_classes(return_type_instance) for base_class in base_classes: - if base_class not in CLASSES_TO_REMOVE: - frontend_node.get("base_classes").append(base_class) + frontend_node.add_base_class(base_class) -def build_langchain_template_custom_component(custom_component: CustomComponent): +def add_output_types(frontend_node: CustomComponentFrontendNode, return_types: List[str]): + """Add output types to the frontend node""" + for return_type in return_types: + if return_type is None: + raise HTTPException( + status_code=400, + detail={ + "error": ("Invalid return type. Please check your code and try again."), + "traceback": traceback.format_exc(), + }, + ) + if hasattr(return_type, "__name__"): + return_type = return_type.__name__ + elif hasattr(return_type, "__class__"): + return_type = return_type.__class__.__name__ + else: + return_type = str(return_type) + + frontend_node.add_output_type(return_type) + + +def build_custom_component_template( + custom_component: CustomComponent, + user_id: Optional[Union[str, UUID]] = None, + update_field: Optional[str] = None, +) -> Optional[Dict[str, Any]]: """Build a custom component template for the langchain""" try: logger.debug("Building custom component template") - frontend_node = build_frontend_node(custom_component) + frontend_node = build_frontend_node(custom_component.template_config) - if frontend_node is None: - return None logger.debug("Built base frontend node") - template_config = custom_component.build_template_config - update_attributes(frontend_node, template_config) logger.debug("Updated attributes") - field_config = build_field_config(custom_component) + field_config = build_field_config(custom_component, user_id=user_id, update_field=update_field) logger.debug("Built field config") entrypoint_args = custom_component.get_function_entrypoint_args add_extra_fields(frontend_node, field_config, entrypoint_args) logger.debug("Added extra fields") - frontend_node = add_code_field( - frontend_node, custom_component.code, field_config.get("code", {}) - ) + frontend_node = add_code_field(frontend_node, custom_component.code, field_config.get("code", {})) logger.debug("Added code field") - add_base_classes( - frontend_node, custom_component.get_function_entrypoint_return_type - ) + add_base_classes(frontend_node, custom_component.get_function_entrypoint_return_type) + add_output_types(frontend_node, custom_component.get_function_entrypoint_return_type) logger.debug("Added base classes") - return frontend_node + return frontend_node.to_dict(add_name=False) except Exception as exc: if isinstance(exc, HTTPException): raise exc raise HTTPException( status_code=400, detail={ - "error": ( - "Invalid type convertion. Please check your code and try again." - ), + "error": ("Invalid type convertion. Please check your code and try again."), "traceback": traceback.format_exc(), }, ) from exc @@ -348,119 +408,147 @@ def build_and_validate_all_files(reader: DirectoryReader, file_list): def build_valid_menu(valid_components): - """Build the valid menu""" + """Build the valid menu.""" valid_menu = {} logger.debug("------------------- VALID COMPONENTS -------------------") for menu_item in valid_components["menu"]: menu_name = menu_item["name"] - valid_menu[menu_name] = {} - - for component in menu_item["components"]: - logger.debug( - f"Building component: {component.get('name'), component.get('output_types')}" - ) - try: - component_name = component["name"] - component_code = component["code"] - component_output_types = component["output_types"] - - component_extractor = CustomComponent(code=component_code) - component_extractor.is_check_valid() - - component_template = build_langchain_template_custom_component( - component_extractor - ) - component_template["output_types"] = component_output_types - if len(component_output_types) == 1: - component_name = component_output_types[0] - else: - file_name = component.get("file").split(".")[0] - if "_" in file_name: - # turn .py file into camelcase - component_name = "".join( - [word.capitalize() for word in file_name.split("_")] - ) - else: - component_name = file_name - - valid_menu[menu_name][component_name] = component_template - logger.debug(f"Added {component_name} to valid menu to {menu_name}") - - except Exception as exc: - logger.error(f"Error loading Component: {component['output_types']}") - logger.exception( - f"Error while building custom component {component_output_types}: {exc}" - ) - + valid_menu[menu_name] = build_menu_items(menu_item) return valid_menu +def build_menu_items(menu_item): + """Build menu items for a given menu.""" + menu_items = {} + for component in menu_item["components"]: + try: + component_name, component_template = build_component(component) + menu_items[component_name] = component_template + logger.debug(f"Added {component_name} to valid menu.") + except Exception as exc: + logger.error(f"Error loading Component: {component['output_types']}") + logger.exception(f"Error while building custom component {component['output_types']}: {exc}") + return menu_items + + +def build_component(component): + """Build a single component.""" + logger.debug(f"Building component: {component.get('name'), component.get('output_types')}") + component_name = determine_component_name(component) + component_template = create_component_template(component) + return component_name, component_template + + +def determine_component_name(component): + """Determine the name of the component.""" + component_output_types = component["output_types"] + if len(component_output_types) == 1: + return component_output_types[0] + else: + file_name = component.get("file").split(".")[0] + return "".join(word.capitalize() for word in file_name.split("_")) if "_" in file_name else file_name + + +def create_component_template(component): + """Create a template for a component.""" + component_code = component["code"] + component_output_types = component["output_types"] + + component_extractor = CustomComponent(code=component_code) + component_extractor.validate() + + component_template = build_custom_component_template(component_extractor) + component_template["output_types"] = component_output_types + return component_template + + def build_invalid_menu(invalid_components): - """Build the invalid menu""" - if invalid_components.get("menu"): - logger.debug("------------------- INVALID COMPONENTS -------------------") + """Build the invalid menu.""" + if not invalid_components.get("menu"): + return {} + + logger.debug("------------------- INVALID COMPONENTS -------------------") invalid_menu = {} for menu_item in invalid_components["menu"]: menu_name = menu_item["name"] - invalid_menu[menu_name] = {} - - for component in menu_item["components"]: - try: - component_name = component["name"] - component_code = component["code"] - - component_template = ( - CustomComponentFrontendNode( - description="ERROR - Check your Python Code", - display_name=f"ERROR - {component_name}", - ) - .to_dict() - .get(type(CustomComponent()).__name__) - ) - - component_template["error"] = component.get("error", None) - logger.debug(component) - logger.debug(f"Component Path: {component.get('path', None)}") - logger.debug(f"Component Error: {component.get('error', None)}") - component_template.get("template").get("code")["value"] = component_code - - invalid_menu[menu_name][component_name] = component_template - logger.debug(f"Added {component_name} to invalid menu to {menu_name}") - - except Exception as exc: - logger.exception( - f"Error while creating custom component [{component_name}]: {str(exc)}" - ) - + invalid_menu[menu_name] = build_invalid_menu_items(menu_item) return invalid_menu +def build_invalid_menu_items(menu_item): + """Build invalid menu items for a given menu.""" + menu_items = {} + for component in menu_item["components"]: + try: + component_name, component_template = build_invalid_component(component) + menu_items[component_name] = component_template + logger.debug(f"Added {component_name} to invalid menu.") + except Exception as exc: + logger.exception(f"Error while creating custom component [{component_name}]: {str(exc)}") + return menu_items + + +def build_invalid_component(component): + """Build a single invalid component.""" + component_name = component["name"] + component_template = create_invalid_component_template(component, component_name) + log_invalid_component_details(component) + return component_name, component_template + + +def create_invalid_component_template(component, component_name): + """Create a template for an invalid component.""" + component_code = component["code"] + component_template = ( + CustomComponentFrontendNode( + description="ERROR - Check your Python Code", + display_name=f"ERROR - {component_name}", + ) + .to_dict() + .get(type(CustomComponent()).__name__) + ) + + component_template["error"] = component.get("error", None) + component_template.get("template").get("code")["value"] = component_code + return component_template + + +def log_invalid_component_details(component): + """Log details of an invalid component.""" + logger.debug(component) + logger.debug(f"Component Path: {component.get('path', None)}") + logger.debug(f"Component Error: {component.get('error', None)}") + + +def get_new_key(dictionary, original_key): + counter = 1 + new_key = original_key + " (" + str(counter) + ")" + while new_key in dictionary: + counter += 1 + new_key = original_key + " (" + str(counter) + ")" + return new_key + + def merge_nested_dicts_with_renaming(dict1, dict2): for key, value in dict2.items(): - if ( - key in dict1 - and isinstance(value, dict) - and isinstance(dict1.get(key), dict) - ): + if key in dict1 and isinstance(value, dict) and isinstance(dict1.get(key), dict): for sub_key, sub_value in value.items(): - if sub_key in dict1[key]: - new_key = get_new_key(dict1[key], sub_key) - dict1[key][new_key] = sub_value - else: - dict1[key][sub_key] = sub_value + # if sub_key in dict1[key]: + # new_key = get_new_key(dict1[key], sub_key) + # dict1[key][new_key] = sub_value + # else: + dict1[key][sub_key] = sub_value else: dict1[key] = value return dict1 -def build_langchain_custom_component_list_from_path(path: str): +def build_custom_component_list_from_path(path: str): """Build a list of custom components for the langchain from a given path""" file_list = load_files_from_path(path) reader = DirectoryReader(path, False) - valid_components, invalid_components = build_and_validate_all_files( - reader, file_list - ) + valid_components, invalid_components = build_and_validate_all_files(reader, file_list) valid_menu = build_valid_menu(valid_components) invalid_menu = build_invalid_menu(invalid_components) @@ -469,42 +557,35 @@ def build_langchain_custom_component_list_from_path(path: str): def get_all_types_dict(settings_service): + """Get all types dictionary combining native and custom components.""" native_components = build_langchain_types_dict() - # custom_components is a list of dicts - # need to merge all the keys into one dict - custom_components_from_file: dict[str, Any] = {} - if settings_service.settings.COMPONENTS_PATH: - logger.info( - f"Building custom components from {settings_service.settings.COMPONENTS_PATH}" - ) + custom_components_from_file = build_custom_components(settings_service) + return merge_nested_dicts_with_renaming(native_components, custom_components_from_file) - custom_component_dicts = [] - processed_paths = [] - for path in settings_service.settings.COMPONENTS_PATH: - if str(path) in processed_paths: - continue - custom_component_dict = build_langchain_custom_component_list_from_path( - str(path) - ) - custom_component_dicts.append(custom_component_dict) - processed_paths.append(str(path)) - logger.info(f"Loading {len(custom_component_dicts)} category(ies)") - for custom_component_dict in custom_component_dicts: - # custom_component_dict is a dict of dicts - if not custom_component_dict: - continue - category = list(custom_component_dict.keys())[0] - logger.info( - f"Loading {len(custom_component_dict[category])} component(s) from category {category}" - ) +def build_custom_components(settings_service): + """Build custom components from the specified paths.""" + if not settings_service.settings.COMPONENTS_PATH: + return {} + + logger.info(f"Building custom components from {settings_service.settings.COMPONENTS_PATH}") + custom_components_from_file = {} + processed_paths = set() + for path in settings_service.settings.COMPONENTS_PATH: + path_str = str(path) + if path_str in processed_paths: + continue + + custom_component_dict = build_custom_component_list_from_path(path_str) + if custom_component_dict: + category = next(iter(custom_component_dict)) + logger.info(f"Loading {len(custom_component_dict[category])} component(s) from category {category}") custom_components_from_file = merge_nested_dicts_with_renaming( custom_components_from_file, custom_component_dict ) + processed_paths.add(path_str) - return merge_nested_dicts_with_renaming( - native_components, custom_components_from_file - ) + return custom_components_from_file def merge_nested_dicts(dict1, dict2): @@ -514,3 +595,9 @@ def merge_nested_dicts(dict1, dict2): else: dict1[key] = value return dict1 + + +def create_and_validate_component(code: str) -> CustomComponent: + component = CustomComponent(code=code) + component.validate() + return component diff --git a/src/backend/langflow/interface/utilities/base.py b/src/backend/langflow/interface/utilities/base.py index a404417dc..cfebf83a4 100644 --- a/src/backend/langflow/interface/utilities/base.py +++ b/src/backend/langflow/interface/utilities/base.py @@ -1,14 +1,13 @@ from typing import Dict, List, Optional, Type from langchain import utilities +from loguru import logger from langflow.custom.customs import get_custom_nodes from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class -from langflow.services.getters import get_settings_service - +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.utilities import UtilitiesFrontendNode -from loguru import logger from langflow.utils.util import build_template_from_class @@ -41,8 +40,7 @@ class UtilityCreator(LangChainTypeCreator): self.type_dict = { name: utility for name, utility in self.type_dict.items() - if name in settings_service.settings.UTILITIES - or settings_service.settings.DEV + if name in settings_service.settings.UTILITIES or settings_service.settings.DEV } return self.type_dict diff --git a/src/backend/langflow/interface/utils.py b/src/backend/langflow/interface/utils.py index b28a660bf..ef29911f5 100644 --- a/src/backend/langflow/interface/utils.py +++ b/src/backend/langflow/interface/utils.py @@ -10,7 +10,7 @@ from langchain.base_language import BaseLanguageModel from PIL.Image import Image from loguru import logger from langflow.services.chat.config import ChatConfig -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service def load_file_into_dict(file_path: str) -> dict: @@ -43,9 +43,7 @@ def try_setting_streaming_options(langchain_object): llm = None if hasattr(langchain_object, "llm"): llm = langchain_object.llm - elif hasattr(langchain_object, "llm_chain") and hasattr( - langchain_object.llm_chain, "llm" - ): + elif hasattr(langchain_object, "llm_chain") and hasattr(langchain_object.llm_chain, "llm"): llm = langchain_object.llm_chain.llm if isinstance(llm, BaseLanguageModel): @@ -74,17 +72,15 @@ def setup_llm_caching(): def set_langchain_cache(settings): - import langchain + from langchain.globals import set_llm_cache from langflow.interface.importing.utils import import_class if cache_type := os.getenv("LANGFLOW_LANGCHAIN_CACHE"): try: - cache_class = import_class( - f"langchain.cache.{cache_type or settings.LANGCHAIN_CACHE}" - ) + cache_class = import_class(f"langchain.cache.{cache_type or settings.LANGCHAIN_CACHE}") logger.debug(f"Setting up LLM caching with {cache_class.__name__}") - langchain.llm_cache = cache_class() + set_llm_cache(cache_class()) logger.info(f"LLM caching setup with {cache_class.__name__}") except ImportError: logger.warning(f"Could not import {cache_type}. ") diff --git a/src/backend/langflow/interface/vector_store/base.py b/src/backend/langflow/interface/vector_store/base.py index 786031a6b..d04689469 100644 --- a/src/backend/langflow/interface/vector_store/base.py +++ b/src/backend/langflow/interface/vector_store/base.py @@ -4,7 +4,7 @@ from langchain import vectorstores from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.vectorstores import VectorStoreFrontendNode from loguru import logger @@ -22,9 +22,7 @@ class VectorstoreCreator(LangChainTypeCreator): def type_to_loader_dict(self) -> Dict: if self.type_dict is None: self.type_dict: dict[str, Any] = { - vectorstore_name: import_class( - f"langchain.vectorstores.{vectorstore_name}" - ) + vectorstore_name: import_class(f"langchain.vectorstores.{vectorstore_name}") for vectorstore_name in vectorstores.__all__ } return self.type_dict @@ -48,8 +46,7 @@ class VectorstoreCreator(LangChainTypeCreator): return [ vectorstore for vectorstore in self.type_to_loader_dict.keys() - if vectorstore in settings_service.settings.VECTORSTORES - or settings_service.settings.DEV + if vectorstore in settings_service.settings.VECTORSTORES or settings_service.settings.DEV ] diff --git a/src/backend/langflow/interface/wrappers/base.py b/src/backend/langflow/interface/wrappers/base.py index de631101a..38d61af78 100644 --- a/src/backend/langflow/interface/wrappers/base.py +++ b/src/backend/langflow/interface/wrappers/base.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Optional +from typing import ClassVar, Dict, List, Optional from langchain.utilities import requests, sql_database @@ -10,14 +10,13 @@ from langflow.utils.util import build_template_from_class, build_template_from_m class WrapperCreator(LangChainTypeCreator): type_name: str = "wrappers" - from_method_nodes = {"SQLDatabase": "from_uri"} + from_method_nodes: ClassVar[Dict] = {"SQLDatabase": "from_uri"} @property def type_to_loader_dict(self) -> Dict: if self.type_dict is None: self.type_dict = { - wrapper.__name__: wrapper - for wrapper in [requests.TextRequestsWrapper, sql_database.SQLDatabase] + wrapper.__name__: wrapper for wrapper in [requests.TextRequestsWrapper, sql_database.SQLDatabase] } return self.type_dict diff --git a/src/backend/langflow/main.py b/src/backend/langflow/main.py index 9caa157d0..ea33acb61 100644 --- a/src/backend/langflow/main.py +++ b/src/backend/langflow/main.py @@ -1,29 +1,34 @@ +from contextlib import asynccontextmanager from pathlib import Path from typing import Optional -from fastapi import FastAPI +from urllib.parse import urlencode + +from fastapi import FastAPI, Request from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import FileResponse from fastapi.staticfiles import StaticFiles - from langflow.api import router - - from langflow.interface.utils import setup_llm_caching -from langflow.services.utils import initialize_services -from langflow.services.plugins.langfuse import LangfuseInstance -from langflow.services.utils import ( - teardown_services, -) +from langflow.services.plugins.langfuse_plugin import LangfuseInstance +from langflow.services.utils import initialize_services, teardown_services from langflow.utils.logger import configure +@asynccontextmanager +async def lifespan(app: FastAPI): + initialize_services() + setup_llm_caching() + LangfuseInstance.update() + yield + teardown_services() + + def create_app(): """Create the FastAPI app and include the router.""" configure() - app = FastAPI() - + app = FastAPI(lifespan=lifespan) origins = ["*"] app.add_middleware( @@ -34,19 +39,22 @@ def create_app(): allow_headers=["*"], ) + @app.middleware("http") + async def flatten_query_string_lists(request: Request, call_next): + flattened: list[tuple[str, str]] = [] + for key, value in request.query_params.multi_items(): + flattened.extend((key, entry) for entry in value.split(",")) + + request.scope["query_string"] = urlencode(flattened, doseq=True).encode("utf-8") + + return await call_next(request) + @app.get("/health") def health(): return {"status": "ok"} app.include_router(router) - app.on_event("startup")(initialize_services) - app.on_event("startup")(setup_llm_caching) - app.on_event("startup")(LangfuseInstance.update) - - app.on_event("shutdown")(teardown_services) - app.on_event("shutdown")(LangfuseInstance.teardown) - return app @@ -78,9 +86,7 @@ def get_static_files_dir(): return frontend_path / "frontend" -def setup_app( - static_files_dir: Optional[Path] = None, backend_only: bool = False -) -> FastAPI: +def setup_app(static_files_dir: Optional[Path] = None, backend_only: bool = False) -> FastAPI: """Setup the FastAPI app.""" # get the directory of the current file if not static_files_dir: diff --git a/src/backend/langflow/processing/base.py b/src/backend/langflow/processing/base.py index e5816306c..0af84ed14 100644 --- a/src/backend/langflow/processing/base.py +++ b/src/backend/langflow/processing/base.py @@ -1,12 +1,13 @@ -from typing import List, Union, TYPE_CHECKING -from langflow.api.v1.callback import ( - AsyncStreamingLLMCallbackHandler, - StreamingLLMCallbackHandler, -) -from langflow.processing.process import fix_memory_inputs, format_actions -from loguru import logger +from typing import TYPE_CHECKING, List, Union + from langchain.agents.agent import AgentExecutor from langchain.callbacks.base import BaseCallbackHandler +from loguru import logger + +from langflow.api.v1.callback import (AsyncStreamingLLMCallbackHandler, + StreamingLLMCallbackHandler) +from langflow.processing.process import fix_memory_inputs, format_actions +from langflow.services.deps import get_plugins_service if TYPE_CHECKING: from langfuse.callback import CallbackHandler # type: ignore @@ -20,21 +21,23 @@ def setup_callbacks(sync, trace_id, **kwargs): else: callbacks.append(AsyncStreamingLLMCallbackHandler(**kwargs)) - if langfuse_callback := get_langfuse_callback(trace_id=trace_id): - logger.debug("Langfuse callback loaded") - callbacks.append(langfuse_callback) + plugin_service = get_plugins_service() + plugin_callbacks = plugin_service.get_callbacks(_id=trace_id) + if plugin_callbacks: + callbacks.extend(plugin_callbacks) return callbacks def get_langfuse_callback(trace_id): - from langflow.services.plugins.langfuse import LangfuseInstance from langfuse.callback import CreateTrace + from langflow.services.deps import get_plugins_service + logger.debug("Initializing langfuse callback") - if langfuse := LangfuseInstance.get(): + if langfuse := get_plugins_service().get("langfuse"): logger.debug("Langfuse credentials found") try: - trace = langfuse.trace(CreateTrace(id=trace_id)) + trace = langfuse.trace(CreateTrace(name="langflow-" + trace_id, id=trace_id)) return trace.getNewHandler() except Exception as exc: logger.error(f"Error initializing langfuse callback: {exc}") @@ -42,14 +45,12 @@ def get_langfuse_callback(trace_id): return None -def flush_langfuse_callback_if_present( - callbacks: List[Union[BaseCallbackHandler, "CallbackHandler"]] -): +def flush_langfuse_callback_if_present(callbacks: List[Union[BaseCallbackHandler, "CallbackHandler"]]): """ If langfuse callback is present, run callback.langfuse.flush() """ for callback in callbacks: - if hasattr(callback, "langfuse"): + if hasattr(callback, "langfuse") and hasattr(callback.langfuse, "flush"): callback.langfuse.flush() break @@ -86,15 +87,9 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa # if langfuse callback is present, run callback.langfuse.flush() flush_langfuse_callback_if_present(callbacks) - intermediate_steps = ( - output.get("intermediate_steps", []) if isinstance(output, dict) else [] - ) + intermediate_steps = output.get("intermediate_steps", []) if isinstance(output, dict) else [] - result = ( - output.get(langchain_object.output_keys[0]) - if isinstance(output, dict) - else output - ) + result = output.get(langchain_object.output_keys[0]) if isinstance(output, dict) else output try: thought = format_actions(intermediate_steps) if intermediate_steps else "" except Exception as exc: @@ -103,4 +98,4 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa except Exception as exc: logger.exception(exc) raise ValueError(f"Error: {str(exc)}") from exc - return result, thought + return result, thought, output diff --git a/src/backend/langflow/processing/process.py b/src/backend/langflow/processing/process.py index ab3b7eca2..11e43e049 100644 --- a/src/backend/langflow/processing/process.py +++ b/src/backend/langflow/processing/process.py @@ -1,20 +1,20 @@ +import asyncio import json from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Coroutine, Dict, List, Optional, Tuple, Union +from langchain.agents import AgentExecutor from langchain.chains.base import Chain from langchain.schema import AgentAction, Document from langchain.vectorstores.base import VectorStore -from langflow.graph import Graph -from langflow.interface.run import ( - build_sorted_vertices, - get_memory_key, - update_memory_keys, -) -from langflow.services.getters import get_session_service from loguru import logger from pydantic import BaseModel +from langflow.graph import Graph +from langflow.interface.run import (build_sorted_vertices, get_memory_key, + update_memory_keys) +from langflow.services.deps import get_session_service + def fix_memory_inputs(langchain_object): """ @@ -69,7 +69,11 @@ def get_result_and_thought(langchain_object: Any, inputs: dict): if hasattr(langchain_object, "return_intermediate_steps"): langchain_object.return_intermediate_steps = False - fix_memory_inputs(langchain_object) + try: + if not isinstance(langchain_object, AgentExecutor): + fix_memory_inputs(langchain_object) + except Exception as exc: + logger.error(f"Error fixing memory inputs: {exc}") try: output = langchain_object(inputs, return_only_outputs=True) @@ -104,20 +108,6 @@ def get_build_result(data_graph, session_id): return build_sorted_vertices(data_graph) -def load_langchain_object( - data_graph: Dict[str, Any], session_id: str -) -> Tuple[Union[Chain, VectorStore], Dict[str, Any], str]: - langchain_object, artifacts = get_build_result(data_graph, session_id) - logger.debug("Loaded LangChain object") - - if langchain_object is None: - raise ValueError( - "There was an error loading the langchain_object. Please, check all the nodes and try again." - ) - - return langchain_object, artifacts, session_id - - def process_inputs(inputs: Optional[dict], artifacts: Dict[str, Any]) -> dict: if inputs is None: inputs = {} @@ -145,6 +135,8 @@ def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict): result = langchain_object.dict() else: logger.warning(f"Unknown langchain_object type: {type(langchain_object)}") + if isinstance(langchain_object, Coroutine): + result = asyncio.run(langchain_object) result = langchain_object return result @@ -165,13 +157,14 @@ async def process_graph_cached( if clear_cache: session_service.clear_session(session_id) if session_id is None: - session_id = session_service.generate_key( - session_id=session_id, data_graph=data_graph - ) + session_id = session_service.generate_key(session_id=session_id, data_graph=data_graph) # Load the graph using SessionService - graph, artifacts = session_service.load_session(session_id, data_graph) - built_object = graph.build() - processed_inputs = process_inputs(inputs, artifacts) + session = await session_service.load_session(session_id, data_graph) + graph, artifacts = session if session else (None, None) + if not graph: + raise ValueError("Graph not found in the session") + built_object = await graph.build() + processed_inputs = process_inputs(inputs, artifacts or {}) result = generate_result(built_object, processed_inputs) # langchain_object is now updated with the new memory # we need to update the cache with the updated langchain_object @@ -180,9 +173,7 @@ async def process_graph_cached( return Result(result=result, session_id=session_id) -def load_flow_from_json( - flow: Union[Path, str, dict], tweaks: Optional[dict] = None, build=True -): +def load_flow_from_json(flow: Union[Path, str, dict], tweaks: Optional[dict] = None, build=True): """ Load flow from a JSON file or a JSON object. @@ -199,9 +190,7 @@ def load_flow_from_json( elif isinstance(flow, dict): flow_graph = flow else: - raise TypeError( - "Input must be either a file path (str) or a JSON object (dict)" - ) + raise TypeError("Input must be either a file path (str) or a JSON object (dict)") graph_data = flow_graph["data"] if tweaks is not None: @@ -211,7 +200,7 @@ def load_flow_from_json( graph = Graph(nodes, edges) if build: - langchain_object = graph.build() + langchain_object = asyncio.run(graph.build()) if hasattr(langchain_object, "verbose"): langchain_object.verbose = True @@ -227,18 +216,14 @@ def load_flow_from_json( return graph -def validate_input( - graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]] -) -> List[Dict[str, Any]]: +def validate_input(graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]]) -> List[Dict[str, Any]]: if not isinstance(graph_data, dict) or not isinstance(tweaks, dict): raise ValueError("graph_data and tweaks should be dictionaries") nodes = graph_data.get("data", {}).get("nodes") or graph_data.get("nodes") if not isinstance(nodes, list): - raise ValueError( - "graph_data should contain a list of nodes under 'data' key or directly under 'nodes' key" - ) + raise ValueError("graph_data should contain a list of nodes under 'data' key or directly under 'nodes' key") return nodes @@ -247,9 +232,7 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None: template_data = node.get("data", {}).get("node", {}).get("template") if not isinstance(template_data, dict): - logger.warning( - f"Template data for node {node.get('id')} should be a dictionary" - ) + logger.warning(f"Template data for node {node.get('id')} should be a dictionary") return for tweak_name, tweak_value in node_tweaks.items(): @@ -258,9 +241,7 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None: template_data[tweak_name][key] = tweak_value -def process_tweaks( - graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]] -) -> Dict[str, Any]: +def process_tweaks(graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]]) -> Dict[str, Any]: """ This function is used to tweak the graph data using the node id and the tweaks dict. @@ -281,8 +262,6 @@ def process_tweaks( if node_tweaks := tweaks.get(node_id): apply_tweaks(node, node_tweaks) else: - logger.warning( - "Each node should be a dictionary with an 'id' key of type str" - ) + logger.warning("Each node should be a dictionary with an 'id' key of type str") return graph_data diff --git a/src/backend/langflow/server.py b/src/backend/langflow/server.py index 3a2943444..9fe432744 100644 --- a/src/backend/langflow/server.py +++ b/src/backend/langflow/server.py @@ -10,11 +10,7 @@ class LangflowApplication(BaseApplication): super().__init__() def load_config(self): - config = { - key: value - for key, value in self.options.items() - if key in self.cfg.settings and value is not None - } + config = {key: value for key, value in self.options.items() if key in self.cfg.settings and value is not None} for key, value in config.items(): self.cfg.set(key.lower(), value) diff --git a/src/backend/langflow/services/auth/service.py b/src/backend/langflow/services/auth/service.py index 5b0acf8c6..8beef8dcb 100644 --- a/src/backend/langflow/services/auth/service.py +++ b/src/backend/langflow/services/auth/service.py @@ -2,7 +2,7 @@ from langflow.services.base import Service from typing import TYPE_CHECKING if TYPE_CHECKING: - from langflow.services.settings.manager import SettingsService + from langflow.services.settings.service import SettingsService class AuthService(Service): diff --git a/src/backend/langflow/services/auth/utils.py b/src/backend/langflow/services/auth/utils.py index f88a1cd12..912d1fbe8 100644 --- a/src/backend/langflow/services/auth/utils.py +++ b/src/backend/langflow/services/auth/utils.py @@ -1,30 +1,25 @@ from datetime import datetime, timedelta, timezone +from typing import Annotated, Coroutine, Optional, Union +from uuid import UUID + +from cryptography.fernet import Fernet from fastapi import Depends, HTTPException, Security, status from fastapi.security import APIKeyHeader, APIKeyQuery, OAuth2PasswordBearer from jose import JWTError, jwt -from typing import Annotated, Coroutine, Optional, Union -from uuid import UUID -from langflow.services.database.models.api_key.api_key import ApiKey -from langflow.services.database.models.api_key.crud import check_key -from langflow.services.database.models.user.user import User -from langflow.services.database.models.user.crud import ( - get_user_by_id, - get_user_by_username, - update_user_last_login_at, -) -from langflow.services.getters import get_session, get_settings_service from sqlmodel import Session -oauth2_login = OAuth2PasswordBearer(tokenUrl="api/v1/login") +from langflow.services.database.models.api_key.model import ApiKey +from langflow.services.database.models.api_key.crud import check_key +from langflow.services.database.models.user.crud import get_user_by_id, get_user_by_username, update_user_last_login_at +from langflow.services.database.models.user.model import User +from langflow.services.deps import get_session, get_settings_service + +oauth2_login = OAuth2PasswordBearer(tokenUrl="api/v1/login", auto_error=False) API_KEY_NAME = "x-api-key" -api_key_query = APIKeyQuery( - name=API_KEY_NAME, scheme_name="API key query", auto_error=False -) -api_key_header = APIKeyHeader( - name=API_KEY_NAME, scheme_name="API key header", auto_error=False -) +api_key_query = APIKeyQuery(name=API_KEY_NAME, scheme_name="API key query", auto_error=False) +api_key_header = APIKeyHeader(name=API_KEY_NAME, scheme_name="API key header", auto_error=False) # Source: https://github.com/mrtolkien/fastapi_simple_security/blob/master/fastapi_simple_security/security_api_key.py @@ -69,6 +64,30 @@ async def api_key_security( async def get_current_user( + token: str = Security(oauth2_login), + query_param: str = Security(api_key_query), + header_param: str = Security(api_key_header), + db: Session = Depends(get_session), +) -> User: + if token: + return await get_current_user_by_jwt(token, db) + else: + if not query_param and not header_param: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="An API key as query or header, or a JWT token must be passed", + ) + user = await api_key_security(query_param, header_param, db) + if user: + return user + + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="Invalid or missing API key", + ) + + +async def get_current_user_by_jwt( token: Annotated[str, Depends(oauth2_login)], db: Session = Depends(get_session), ) -> User: @@ -117,23 +136,17 @@ def get_current_active_user(current_user: Annotated[User, Depends(get_current_us return current_user -def get_current_active_superuser( - current_user: Annotated[User, Depends(get_current_user)] -) -> User: +def get_current_active_superuser(current_user: Annotated[User, Depends(get_current_user)]) -> User: if not current_user.is_active: raise HTTPException(status_code=401, detail="Inactive user") if not current_user.is_superuser: - raise HTTPException( - status_code=400, detail="The user doesn't have enough privileges" - ) + raise HTTPException(status_code=400, detail="The user doesn't have enough privileges") return current_user def verify_password(plain_password, hashed_password): settings_service = get_settings_service() - return settings_service.auth_settings.pwd_context.verify( - plain_password, hashed_password - ) + return settings_service.auth_settings.pwd_context.verify(plain_password, hashed_password) def get_password_hash(password): @@ -222,22 +235,16 @@ def get_user_id_from_token(token: str) -> UUID: return UUID(int=0) -def create_user_tokens( - user_id: UUID, db: Session = Depends(get_session), update_last_login: bool = False -) -> dict: +def create_user_tokens(user_id: UUID, db: Session = Depends(get_session), update_last_login: bool = False) -> dict: settings_service = get_settings_service() - access_token_expires = timedelta( - minutes=settings_service.auth_settings.ACCESS_TOKEN_EXPIRE_MINUTES - ) + access_token_expires = timedelta(minutes=settings_service.auth_settings.ACCESS_TOKEN_EXPIRE_MINUTES) access_token = create_token( data={"sub": str(user_id)}, expires_delta=access_token_expires, ) - refresh_token_expires = timedelta( - minutes=settings_service.auth_settings.REFRESH_TOKEN_EXPIRE_MINUTES - ) + refresh_token_expires = timedelta(minutes=settings_service.auth_settings.REFRESH_TOKEN_EXPIRE_MINUTES) refresh_token = create_token( data={"sub": str(user_id), "type": "rf"}, expires_delta=refresh_token_expires, @@ -267,9 +274,7 @@ def create_refresh_token(refresh_token: str, db: Session = Depends(get_session)) token_type: str = payload.get("type") # type: ignore if user_id is None or token_type is None: - raise HTTPException( - status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid refresh token" - ) + raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid refresh token") return create_user_tokens(user_id, db) @@ -280,9 +285,7 @@ def create_refresh_token(refresh_token: str, db: Session = Depends(get_session)) ) from e -def authenticate_user( - username: str, password: str, db: Session = Depends(get_session) -) -> Optional[User]: +def authenticate_user(username: str, password: str, db: Session = Depends(get_session)) -> Optional[User]: user = get_user_by_username(db, username) if not user: @@ -294,3 +297,35 @@ def authenticate_user( raise HTTPException(status_code=400, detail="Inactive user") return user if verify_password(password, user.password) else None + + +def add_padding(s): + # Calculate the number of padding characters needed + padding_needed = 4 - len(s) % 4 + return s + "=" * padding_needed + + +def get_fernet(settings_service=Depends(get_settings_service)): + SECRET_KEY = settings_service.auth_settings.SECRET_KEY + # It's important that your secret key is 32 url-safe base64-encoded bytes + padded_secret_key = add_padding(SECRET_KEY) + fernet = Fernet(padded_secret_key) + return fernet + + +def encrypt_api_key(api_key: str, settings_service=Depends(get_settings_service)): + fernet = get_fernet(settings_service) + # Two-way encryption + encrypted_key = fernet.encrypt(api_key.encode()) + return encrypted_key + + +def decrypt_api_key(encrypted_api_key: str, settings_service=Depends(get_settings_service)): + fernet = get_fernet(settings_service) + # Two-way decryption + if isinstance(encrypted_api_key, str): + encoded_bytes = encrypted_api_key.encode() + else: + encoded_bytes = encrypted_api_key + decrypted_key = fernet.decrypt(encoded_bytes).decode() + return decrypted_key diff --git a/src/backend/langflow/services/cache/__init__.py b/src/backend/langflow/services/cache/__init__.py index 3b122aa9e..bf3a7c5ee 100644 --- a/src/backend/langflow/services/cache/__init__.py +++ b/src/backend/langflow/services/cache/__init__.py @@ -1,9 +1,9 @@ -from . import factory, manager -from langflow.services.cache.manager import InMemoryCache +from . import factory, service +from langflow.services.cache.service import InMemoryCache __all__ = [ "factory", - "manager", + "service", "InMemoryCache", ] diff --git a/src/backend/langflow/services/cache/base.py b/src/backend/langflow/services/cache/base.py index 4eee6639e..3b34e12f6 100644 --- a/src/backend/langflow/services/cache/base.py +++ b/src/backend/langflow/services/cache/base.py @@ -1,7 +1,9 @@ import abc +from langflow.services.base import Service -class BaseCacheService(abc.ABC): + +class BaseCacheService(Service): """ Abstract base class for a cache. """ diff --git a/src/backend/langflow/services/cache/factory.py b/src/backend/langflow/services/cache/factory.py index f00ab239f..10e657bc5 100644 --- a/src/backend/langflow/services/cache/factory.py +++ b/src/backend/langflow/services/cache/factory.py @@ -1,10 +1,10 @@ -from langflow.services.cache.manager import InMemoryCache, RedisCache, BaseCacheService +from langflow.services.cache.service import InMemoryCache, RedisCache, BaseCacheService from langflow.services.factory import ServiceFactory from langflow.utils.logger import logger from typing import TYPE_CHECKING if TYPE_CHECKING: - from langflow.services.settings.manager import SettingsService + from langflow.services.settings.service import SettingsService class CacheServiceFactory(ServiceFactory): @@ -26,9 +26,7 @@ class CacheServiceFactory(ServiceFactory): if redis_cache.is_connected(): logger.debug("Redis cache is connected") return redis_cache - logger.warning( - "Redis cache is not connected, falling back to in-memory cache" - ) + logger.warning("Redis cache is not connected, falling back to in-memory cache") return InMemoryCache() elif settings_service.settings.CACHE_TYPE == "memory": diff --git a/src/backend/langflow/services/cache/manager.py b/src/backend/langflow/services/cache/service.py similarity index 94% rename from src/backend/langflow/services/cache/manager.py rename to src/backend/langflow/services/cache/service.py index da76a2b5c..3ee8d001b 100644 --- a/src/backend/langflow/services/cache/manager.py +++ b/src/backend/langflow/services/cache/service.py @@ -68,10 +68,7 @@ class InMemoryCache(BaseCacheService, Service): Retrieve an item from the cache without acquiring the lock. """ if item := self._cache.get(key): - if ( - self.expiration_time is None - or time.time() - item["time"] < self.expiration_time - ): + if self.expiration_time is None or time.time() - item["time"] < self.expiration_time: # Move the key to the end to make it recently used self._cache.move_to_end(key) # Check if the value is pickled @@ -118,11 +115,7 @@ class InMemoryCache(BaseCacheService, Service): """ with self._lock: existing_value = self._get_without_lock(key) - if ( - existing_value is not None - and isinstance(existing_value, dict) - and isinstance(value, dict) - ): + if existing_value is not None and isinstance(existing_value, dict) and isinstance(value, dict): existing_value.update(value) value = existing_value @@ -276,9 +269,7 @@ class RedisCache(BaseCacheService, Service): if not result: raise ValueError("RedisCache could not set the value.") except TypeError as exc: - raise TypeError( - "RedisCache only accepts values that can be pickled. " - ) from exc + raise TypeError("RedisCache only accepts values that can be pickled. ") from exc def upsert(self, key, value): """ @@ -290,11 +281,7 @@ class RedisCache(BaseCacheService, Service): value: The value to insert or update. """ existing_value = self.get(key) - if ( - existing_value is not None - and isinstance(existing_value, dict) - and isinstance(value, dict) - ): + if existing_value is not None and isinstance(existing_value, dict) and isinstance(value, dict): existing_value.update(value) value = existing_value diff --git a/src/backend/langflow/services/cache/utils.py b/src/backend/langflow/services/cache/utils.py index bd6b4fb0a..129e9a6b7 100644 --- a/src/backend/langflow/services/cache/utils.py +++ b/src/backend/langflow/services/cache/utils.py @@ -1,24 +1,23 @@ import base64 import contextlib -import functools import hashlib import os import tempfile -from collections import OrderedDict from pathlib import Path from typing import TYPE_CHECKING, Any, Dict -from appdirs import user_cache_dir + from fastapi import UploadFile -from langflow.api.v1.schemas import BuildStatus -from langflow.services.database.models.base import orjson_dumps +from platformdirs import user_cache_dir if TYPE_CHECKING: - pass + from langflow.api.v1.schemas import BuildStatus CACHE: Dict[str, Any] = {} CACHE_DIR = user_cache_dir("langflow", "langflow") +PREFIX = "langflow_cache" + def create_cache_folder(func): def wrapper(*args, **kwargs): @@ -33,73 +32,19 @@ def create_cache_folder(func): return wrapper -def memoize_dict(maxsize=128): - cache = OrderedDict() - hash_to_key = {} # Mapping from hash to cache key - - def decorator(func): - @functools.wraps(func) - def wrapper(*args, **kwargs): - hashed = compute_dict_hash(args[0]) - key = (func.__name__, hashed, frozenset(kwargs.items())) - if key not in cache: - result = func(*args, **kwargs) - cache[key] = result - hash_to_key[hashed] = key # Store the mapping - if len(cache) > maxsize: - oldest_key = next(iter(cache)) - oldest_hash = oldest_key[1] - del cache[oldest_key] - del hash_to_key[oldest_hash] - else: - result = cache[key] - - wrapper.session_id = hashed # Store hash in the wrapper - return result - - def clear_cache(): - cache.clear() - hash_to_key.clear() - - def get_result_by_session_id(session_id): - key = hash_to_key.get(session_id) - return cache.get(key) if key is not None else None - - wrapper.clear_cache = clear_cache # type: ignore - wrapper.get_result_by_session_id = get_result_by_session_id # type: ignore - wrapper.hash = None - wrapper.cache = cache # type: ignore - return wrapper - - return decorator - - -PREFIX = "langflow_cache" - - @create_cache_folder def clear_old_cache_files(max_cache_size: int = 3): cache_dir = Path(tempfile.gettempdir()) / PREFIX cache_files = list(cache_dir.glob("*.dill")) if len(cache_files) > max_cache_size: - cache_files_sorted_by_mtime = sorted( - cache_files, key=lambda x: x.stat().st_mtime, reverse=True - ) + cache_files_sorted_by_mtime = sorted(cache_files, key=lambda x: x.stat().st_mtime, reverse=True) for cache_file in cache_files_sorted_by_mtime[max_cache_size:]: with contextlib.suppress(OSError): os.remove(cache_file) -def compute_dict_hash(graph_data): - graph_data = filter_json(graph_data) - - cleaned_graph_json = orjson_dumps(graph_data, sort_keys=True) - - return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest() - - def filter_json(json_data): filtered_data = json_data.copy() @@ -205,9 +150,11 @@ def save_uploaded_file(file: UploadFile, folder_name): return file_path -def update_build_status(cache_service, flow_id: str, status: BuildStatus): +def update_build_status(cache_service, flow_id: str, status: "BuildStatus"): cached_flow = cache_service[flow_id] if cached_flow is None: raise ValueError(f"Flow {flow_id} not found in cache") cached_flow["status"] = status cache_service[flow_id] = cached_flow + cached_flow["status"] = status + cache_service[flow_id] = cached_flow diff --git a/src/backend/langflow/services/chat/factory.py b/src/backend/langflow/services/chat/factory.py index 54af7fcca..337488e0f 100644 --- a/src/backend/langflow/services/chat/factory.py +++ b/src/backend/langflow/services/chat/factory.py @@ -1,4 +1,4 @@ -from langflow.services.chat.manager import ChatService +from langflow.services.chat.service import ChatService from langflow.services.factory import ServiceFactory diff --git a/src/backend/langflow/services/chat/manager.py b/src/backend/langflow/services/chat/service.py similarity index 86% rename from src/backend/langflow/services/chat/manager.py rename to src/backend/langflow/services/chat/service.py index 59488d431..825279ac3 100644 --- a/src/backend/langflow/services/chat/manager.py +++ b/src/backend/langflow/services/chat/service.py @@ -1,20 +1,20 @@ -from collections import defaultdict +import asyncio import uuid +from collections import defaultdict +from typing import Any, Dict, List + +import orjson from fastapi import WebSocket, status -from starlette.websockets import WebSocketState from langflow.api.v1.schemas import ChatMessage, ChatResponse, FileResponse from langflow.interface.utils import pil_to_base64 +from langflow.services import ServiceType, service_manager from langflow.services.base import Service from langflow.services.chat.cache import Subject from langflow.services.chat.utils import process_graph from loguru import logger +from starlette.websockets import WebSocketState from .cache import cache_service -import asyncio -from typing import Any, Dict, List - -from langflow.services import service_manager, ServiceType -import orjson class ChatHistory(Subject): @@ -59,9 +59,7 @@ class ChatService(Service): """Send the last chat message to the client.""" client_id = self.chat_cache.current_client_id if client_id in self.active_connections: - chat_response = self.chat_history.get_history( - client_id, filter_messages=False - )[-1] + chat_response = self.chat_history.get_history(client_id, filter_messages=False)[-1] if chat_response.is_bot: # Process FileResponse if isinstance(chat_response, FileResponse): @@ -88,9 +86,7 @@ class ChatService(Service): data_type=self.last_cached_object_dict["type"], ) - self.chat_history.add_message( - self.chat_cache.current_client_id, chat_response - ) + self.chat_history.add_message(self.chat_cache.current_client_id, chat_response) async def connect(self, client_id: str, websocket: WebSocket): self.active_connections[client_id] = websocket @@ -108,7 +104,7 @@ class ChatService(Service): async def send_json(self, client_id: str, message: ChatMessage): websocket = self.active_connections[client_id] - await websocket.send_json(message.dict()) + await websocket.send_json(message.model_dump()) async def close_connection(self, client_id: str, code: int, reason: str): if websocket := self.active_connections[client_id]: @@ -121,9 +117,7 @@ class ChatService(Service): if "after sending" in str(exc): logger.error(f"Error closing connection: {exc}") - async def process_message( - self, client_id: str, payload: Dict, langchain_object: Any - ): + async def process_message(self, client_id: str, payload: Dict, langchain_object: Any): # Process the graph data and chat message chat_inputs = payload.pop("inputs", {}) chatkey = payload.pop("chatKey", None) @@ -139,7 +133,7 @@ class ChatService(Service): try: logger.debug("Generating result and thought") - result, intermediate_steps = await process_graph( + result, intermediate_steps, raw_output = await process_graph( langchain_object=langchain_object, chat_inputs=chat_inputs, client_id=client_id, @@ -197,7 +191,7 @@ class ChatService(Service): try: chat_history = self.chat_history.get_history(client_id) # iterate and make BaseModel into dict - chat_history = [chat.dict() for chat in chat_history] + chat_history = [chat.model_dump() for chat in chat_history] await websocket.send_json(chat_history) while True: @@ -211,15 +205,11 @@ class ChatService(Service): continue with self.chat_cache.set_client_id(client_id): - if langchain_object := self.cache_service.get(client_id).get( - "result" - ): + if langchain_object := self.cache_service.get(client_id).get("result"): await self.process_message(client_id, payload, langchain_object) else: - raise RuntimeError( - f"Could not find a build result for client_id {client_id}" - ) + raise RuntimeError(f"Could not find a build result for client_id {client_id}") except Exception as exc: # Handle any exceptions that might occur logger.exception(f"Error handling websocket: {exc}") @@ -227,7 +217,7 @@ class ChatService(Service): await self.close_connection( client_id=client_id, code=status.WS_1011_INTERNAL_ERROR, - reason=str(exc)[:120], + reason=str(exc), ) elif websocket.client_state == WebSocketState.DISCONNECTED: self.disconnect(client_id) @@ -244,3 +234,26 @@ class ChatService(Service): except Exception as exc: logger.error(f"Error closing connection: {exc}") self.disconnect(client_id) + + +def dict_to_markdown_table(my_dict): + markdown_table = "| Key | Value |\n|---|---|\n" + for key, value in my_dict.items(): + markdown_table += f"| {key} | {value} |\n" + return markdown_table + + +def list_of_dicts_to_markdown_table(dict_list): + if not dict_list: + return "No data provided." + + # Extract headers from the keys of the first dictionary + headers = dict_list[0].keys() + markdown_table = "| " + " | ".join(headers) + " |\n" + markdown_table += "| " + " | ".join("---" for _ in headers) + " |\n" + + for row_dict in dict_list: + row = [str(row_dict.get(header, "")) for header in headers] + markdown_table += "| " + " | ".join(row) + " |\n" + + return markdown_table diff --git a/src/backend/langflow/services/chat/utils.py b/src/backend/langflow/services/chat/utils.py index 85b86a801..7970e5d89 100644 --- a/src/backend/langflow/services/chat/utils.py +++ b/src/backend/langflow/services/chat/utils.py @@ -1,8 +1,9 @@ -from langflow.api.v1.schemas import ChatMessage -from langflow.processing.base import get_result_and_steps -from langflow.interface.utils import try_setting_streaming_options from loguru import logger +from langflow.api.v1.schemas import ChatMessage +from langflow.interface.utils import try_setting_streaming_options +from langflow.processing.base import get_result_and_steps + async def process_graph( langchain_object, @@ -15,9 +16,7 @@ async def process_graph( if langchain_object is None: # Raise user facing error - raise ValueError( - "There was an error loading the langchain_object. Please, check all the nodes and try again." - ) + raise ValueError("There was an error loading the langchain_object. Please, check all the nodes and try again.") # Generate result and thought try: @@ -26,14 +25,14 @@ async def process_graph( chat_inputs.message = {} logger.debug("Generating result and thought") - result, intermediate_steps = await get_result_and_steps( + result, intermediate_steps, raw_output = await get_result_and_steps( langchain_object, chat_inputs.message, client_id=client_id, session_id=session_id, ) logger.debug("Generated result and intermediate_steps") - return result, intermediate_steps + return result, intermediate_steps, raw_output except Exception as e: # Log stack trace logger.exception(e) diff --git a/src/backend/langflow/services/credentials/__init__.py b/src/backend/langflow/services/credentials/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/services/credentials/factory.py b/src/backend/langflow/services/credentials/factory.py new file mode 100644 index 000000000..c44a43da4 --- /dev/null +++ b/src/backend/langflow/services/credentials/factory.py @@ -0,0 +1,15 @@ +from typing import TYPE_CHECKING + +from langflow.services.credentials.service import CredentialService +from langflow.services.factory import ServiceFactory + +if TYPE_CHECKING: + from langflow.services.settings.service import SettingsService + + +class CredentialServiceFactory(ServiceFactory): + def __init__(self): + super().__init__(CredentialService) + + def create(self, settings_service: "SettingsService"): + return CredentialService(settings_service) diff --git a/src/backend/langflow/services/credentials/service.py b/src/backend/langflow/services/credentials/service.py new file mode 100644 index 000000000..08e04fdaa --- /dev/null +++ b/src/backend/langflow/services/credentials/service.py @@ -0,0 +1,37 @@ +from typing import TYPE_CHECKING, Optional, Union +from uuid import UUID + +from fastapi import Depends +from langflow.services.auth import utils as auth_utils +from langflow.services.base import Service +from langflow.services.database.models.credential.model import Credential +from langflow.services.deps import get_session +from sqlmodel import Session, select + +if TYPE_CHECKING: + from langflow.services.settings.service import SettingsService + + +class CredentialService(Service): + name = "credential_service" + + def __init__(self, settings_service: "SettingsService"): + self.settings_service = settings_service + + def get_credential(self, user_id: Union[UUID, str], name: str, session: Session = Depends(get_session)) -> str: + # we get the credential from the database + # credential = session.query(Credential).filter(Credential.user_id == user_id, Credential.name == name).first() + credential = session.exec( + select(Credential).where(Credential.user_id == user_id, Credential.name == name) + ).first() + # we decrypt the value + if not credential or not credential.value: + raise ValueError(f"{name} credential not found.") + decrypted = auth_utils.decrypt_api_key(credential.value, settings_service=self.settings_service) + return decrypted + + def list_credentials( + self, user_id: Union[UUID, str], session: Session = Depends(get_session) + ) -> list[Optional[str]]: + credentials = session.exec(select(Credential).where(Credential.user_id == user_id)).all() + return [credential.name for credential in credentials] diff --git a/src/backend/langflow/services/database/factory.py b/src/backend/langflow/services/database/factory.py index 3726f520b..57bf1668d 100644 --- a/src/backend/langflow/services/database/factory.py +++ b/src/backend/langflow/services/database/factory.py @@ -1,9 +1,9 @@ from typing import TYPE_CHECKING -from langflow.services.database.manager import DatabaseService +from langflow.services.database.service import DatabaseService from langflow.services.factory import ServiceFactory if TYPE_CHECKING: - from langflow.services.settings.manager import SettingsService + from langflow.services.settings.service import SettingsService class DatabaseServiceFactory(ServiceFactory): diff --git a/src/backend/langflow/services/database/models/__init__.py b/src/backend/langflow/services/database/models/__init__.py index 3cc4231a3..9dd3e0285 100644 --- a/src/backend/langflow/services/database/models/__init__.py +++ b/src/backend/langflow/services/database/models/__init__.py @@ -1,5 +1,6 @@ +from .api_key import ApiKey +from .credential import Credential from .flow import Flow from .user import User -from .api_key import ApiKey -__all__ = ["Flow", "User", "ApiKey"] +__all__ = ["Flow", "User", "ApiKey", "Credential"] diff --git a/src/backend/langflow/services/database/models/api_key/__init__.py b/src/backend/langflow/services/database/models/api_key/__init__.py index fbb8265b9..001b0327e 100644 --- a/src/backend/langflow/services/database/models/api_key/__init__.py +++ b/src/backend/langflow/services/database/models/api_key/__init__.py @@ -1,3 +1,3 @@ -from .api_key import ApiKey, ApiKeyCreate, UnmaskedApiKeyRead, ApiKeyRead +from .model import ApiKey, ApiKeyCreate, UnmaskedApiKeyRead, ApiKeyRead __all__ = ["ApiKey", "ApiKeyCreate", "UnmaskedApiKeyRead", "ApiKeyRead"] diff --git a/src/backend/langflow/services/database/models/api_key/crud.py b/src/backend/langflow/services/database/models/api_key/crud.py index 0e0ae6137..e5c7d9ddd 100644 --- a/src/backend/langflow/services/database/models/api_key/crud.py +++ b/src/backend/langflow/services/database/models/api_key/crud.py @@ -1,26 +1,22 @@ import datetime import secrets import threading -from uuid import UUID from typing import List, Optional +from uuid import UUID + from sqlmodel import Session, select -from langflow.services.database.models.api_key import ( - ApiKey, - ApiKeyCreate, - UnmaskedApiKeyRead, - ApiKeyRead, -) +from sqlmodel.sql.expression import SelectOfScalar + +from langflow.services.database.models.api_key import ApiKey, ApiKeyCreate, ApiKeyRead, UnmaskedApiKeyRead def get_api_keys(session: Session, user_id: UUID) -> List[ApiKeyRead]: - query = select(ApiKey).where(ApiKey.user_id == user_id) + query: SelectOfScalar = select(ApiKey).where(ApiKey.user_id == user_id) api_keys = session.exec(query).all() - return [ApiKeyRead.from_orm(api_key) for api_key in api_keys] + return [ApiKeyRead.model_validate(api_key) for api_key in api_keys] -def create_api_key( - session: Session, api_key_create: ApiKeyCreate, user_id: UUID -) -> UnmaskedApiKeyRead: +def create_api_key(session: Session, api_key_create: ApiKeyCreate, user_id: UUID) -> UnmaskedApiKeyRead: # Generate a random API key with 32 bytes of randomness generated_api_key = f"sk-{secrets.token_urlsafe(32)}" @@ -48,7 +44,7 @@ def delete_api_key(session: Session, api_key_id: UUID) -> None: def check_key(session: Session, api_key: str) -> Optional[ApiKey]: """Check if the API key is valid.""" - query = select(ApiKey).where(ApiKey.api_key == api_key) + query: SelectOfScalar = select(ApiKey).where(ApiKey.api_key == api_key) api_key_object: Optional[ApiKey] = session.exec(query).first() if api_key_object is not None: threading.Thread( diff --git a/src/backend/langflow/services/database/models/api_key/api_key.py b/src/backend/langflow/services/database/models/api_key/model.py similarity index 83% rename from src/backend/langflow/services/database/models/api_key/api_key.py rename to src/backend/langflow/services/database/models/api_key/model.py index 684027ee2..226794cbe 100644 --- a/src/backend/langflow/services/database/models/api_key/api_key.py +++ b/src/backend/langflow/services/database/models/api_key/model.py @@ -1,16 +1,16 @@ -from pydantic import validator -from sqlmodel import Field, Relationship -from uuid import UUID, uuid4 -from typing import Optional, TYPE_CHECKING from datetime import datetime -from langflow.services.database.models.base import SQLModelSerializable +from typing import TYPE_CHECKING, Optional +from uuid import UUID, uuid4 + +from pydantic import validator +from sqlmodel import Field, Relationship, SQLModel if TYPE_CHECKING: from langflow.services.database.models.user import User -class ApiKeyBase(SQLModelSerializable): - name: Optional[str] = Field(index=True) +class ApiKeyBase(SQLModel): + name: Optional[str] = Field(index=True, nullable=True, default=None) created_at: datetime = Field(default_factory=datetime.utcnow) last_used_at: Optional[datetime] = Field(default=None, nullable=True) total_uses: int = Field(default=0) diff --git a/src/backend/langflow/services/database/models/base.py b/src/backend/langflow/services/database/models/base.py index a70999206..53ee2c37e 100644 --- a/src/backend/langflow/services/database/models/base.py +++ b/src/backend/langflow/services/database/models/base.py @@ -1,4 +1,3 @@ -from sqlmodel import SQLModel import orjson @@ -16,10 +15,3 @@ def orjson_dumps(v, *, default=None, sort_keys=False, indent_2=True): if default is None: return orjson.dumps(v, option=option).decode() return orjson.dumps(v, default=default, option=option).decode() - - -class SQLModelSerializable(SQLModel): - class Config: - orm_mode = True - json_loads = orjson.loads - json_dumps = orjson_dumps diff --git a/src/backend/langflow/services/database/models/component/__init__.py b/src/backend/langflow/services/database/models/component/__init__.py index c787c3e04..c5ad7d47f 100644 --- a/src/backend/langflow/services/database/models/component/__init__.py +++ b/src/backend/langflow/services/database/models/component/__init__.py @@ -1,3 +1,3 @@ -from .component import Component, ComponentModel +from .model import Component, ComponentModel __all__ = ["Component", "ComponentModel"] diff --git a/src/backend/langflow/services/database/models/component/component.py b/src/backend/langflow/services/database/models/component/model.py similarity index 85% rename from src/backend/langflow/services/database/models/component/component.py rename to src/backend/langflow/services/database/models/component/model.py index 5c4e6c13a..0a5afc440 100644 --- a/src/backend/langflow/services/database/models/component/component.py +++ b/src/backend/langflow/services/database/models/component/model.py @@ -1,11 +1,11 @@ -from langflow.services.database.models.base import SQLModelSerializable, SQLModel -from sqlmodel import Field -from typing import Optional -from datetime import datetime import uuid +from datetime import datetime +from typing import Optional + +from sqlmodel import Field, SQLModel -class Component(SQLModelSerializable, table=True): +class Component(SQLModel, table=True): id: uuid.UUID = Field(default_factory=uuid.uuid4, primary_key=True) frontend_node_id: uuid.UUID = Field(index=True) name: str = Field(index=True) diff --git a/src/backend/langflow/services/database/models/credential/__init__.py b/src/backend/langflow/services/database/models/credential/__init__.py new file mode 100644 index 000000000..2f8b6cd01 --- /dev/null +++ b/src/backend/langflow/services/database/models/credential/__init__.py @@ -0,0 +1,3 @@ +from .model import Credential, CredentialCreate, CredentialRead, CredentialUpdate + +__all__ = ["Credential", "CredentialCreate", "CredentialRead", "CredentialUpdate"] diff --git a/src/backend/langflow/services/database/models/credential/model.py b/src/backend/langflow/services/database/models/credential/model.py new file mode 100644 index 000000000..95bd4b829 --- /dev/null +++ b/src/backend/langflow/services/database/models/credential/model.py @@ -0,0 +1,43 @@ +from datetime import datetime +from typing import TYPE_CHECKING, Optional +from uuid import UUID, uuid4 + +from sqlmodel import Field, Relationship, SQLModel + +from langflow.services.database.models.credential.schema import CredentialType + +if TYPE_CHECKING: + from langflow.services.database.models.user import User + + +class CredentialBase(SQLModel): + name: Optional[str] = Field(None, description="Name of the credential") + value: Optional[str] = Field(None, description="Encrypted value of the credential") + provider: Optional[str] = Field(None, description="Provider of the credential (e.g OpenAI)") + + +class Credential(CredentialBase, table=True): + id: Optional[UUID] = Field(default_factory=uuid4, primary_key=True, description="Unique ID for the credential") + # name is unique per user + created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time of the credential") + updated_at: Optional[datetime] = Field(None, description="Last update time of the credential") + # foreign key to user table + user_id: UUID = Field(description="User ID associated with this credential", foreign_key="user.id") + user: "User" = Relationship(back_populates="credentials") + + +class CredentialCreate(CredentialBase): + # AcceptedProviders is a custom Enum + provider: CredentialType = Field(description="Provider of the credential (e.g OpenAI)") + + +class CredentialRead(SQLModel): + id: UUID + name: Optional[str] = Field(None, description="Name of the credential") + provider: Optional[str] = Field(None, description="Provider of the credential (e.g OpenAI)") + + +class CredentialUpdate(SQLModel): + id: UUID # Include the ID for updating + name: Optional[str] = Field(None, description="Name of the credential") + value: Optional[str] = Field(None, description="Encrypted value of the credential") diff --git a/src/backend/langflow/services/database/models/credential/schema.py b/src/backend/langflow/services/database/models/credential/schema.py new file mode 100644 index 000000000..985915340 --- /dev/null +++ b/src/backend/langflow/services/database/models/credential/schema.py @@ -0,0 +1,8 @@ +from enum import Enum + + +class CredentialType(str, Enum): + """CredentialType is an Enum of the accepted providers""" + + OPENAI_API_KEY = "OPENAI_API_KEY" + ANTHROPIC_API_KEY = "ANTHROPIC_API_KEY" diff --git a/src/backend/langflow/services/database/models/flow/__init__.py b/src/backend/langflow/services/database/models/flow/__init__.py index 7c7cc0172..3c861963c 100644 --- a/src/backend/langflow/services/database/models/flow/__init__.py +++ b/src/backend/langflow/services/database/models/flow/__init__.py @@ -1,3 +1,3 @@ -from .flow import Flow, FlowCreate, FlowRead, FlowUpdate +from .model import Flow, FlowCreate, FlowRead, FlowUpdate __all__ = ["Flow", "FlowCreate", "FlowRead", "FlowUpdate"] diff --git a/src/backend/langflow/services/database/models/flow/flow.py b/src/backend/langflow/services/database/models/flow/model.py similarity index 50% rename from src/backend/langflow/services/database/models/flow/flow.py rename to src/backend/langflow/services/database/models/flow/model.py index e578f37c4..d942fa93c 100644 --- a/src/backend/langflow/services/database/models/flow/flow.py +++ b/src/backend/langflow/services/database/models/flow/model.py @@ -1,22 +1,25 @@ # Path: src/backend/langflow/database/models/flow.py -from langflow.services.database.models.base import SQLModelSerializable -from pydantic import validator - -from sqlmodel import Field, JSON, Column, Relationship +from datetime import datetime +from typing import TYPE_CHECKING, Dict, Optional from uuid import UUID, uuid4 -from typing import Dict, Optional, TYPE_CHECKING + +from pydantic import field_serializer, field_validator +from sqlmodel import JSON, Column, Field, Relationship, SQLModel if TYPE_CHECKING: from langflow.services.database.models.user import User -class FlowBase(SQLModelSerializable): +class FlowBase(SQLModel): name: str = Field(index=True) - description: Optional[str] = Field(index=True) + description: Optional[str] = Field(index=True, nullable=True, default=None) data: Optional[Dict] = Field(default=None, nullable=True) + is_component: Optional[bool] = Field(default=False, nullable=True) + updated_at: Optional[datetime] = Field(default_factory=datetime.utcnow, nullable=True) + folder: Optional[str] = Field(default=None, nullable=True) - @validator("data") + @field_validator("data") def validate_json(v): if not v: return v @@ -31,11 +34,27 @@ class FlowBase(SQLModelSerializable): return v + # updated_at can be serialized to JSON + @field_serializer("updated_at") + def serialize_dt(self, dt: datetime, _info): + if dt is None: + return None + return dt.isoformat() + + @field_validator("updated_at", mode="before") + def validate_dt(cls, v): + if v is None: + return v + elif isinstance(v, datetime): + return v + + return datetime.fromisoformat(v) + class Flow(FlowBase, table=True): id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True) data: Optional[Dict] = Field(default=None, sa_column=Column(JSON)) - user_id: UUID = Field(index=True, foreign_key="user.id") + user_id: UUID = Field(index=True, foreign_key="user.id", nullable=True) user: "User" = Relationship(back_populates="flows") @@ -48,7 +67,7 @@ class FlowRead(FlowBase): user_id: UUID = Field() -class FlowUpdate(SQLModelSerializable): +class FlowUpdate(SQLModel): name: Optional[str] = None description: Optional[str] = None data: Optional[Dict] = None diff --git a/src/backend/langflow/services/database/models/user/__init__.py b/src/backend/langflow/services/database/models/user/__init__.py index da9170eb7..eecf6d0b1 100644 --- a/src/backend/langflow/services/database/models/user/__init__.py +++ b/src/backend/langflow/services/database/models/user/__init__.py @@ -1,4 +1,4 @@ -from .user import User, UserCreate, UserRead, UserUpdate +from .model import User, UserCreate, UserRead, UserUpdate __all__ = [ "User", diff --git a/src/backend/langflow/services/database/models/user/crud.py b/src/backend/langflow/services/database/models/user/crud.py index 36f03e684..e8f58735c 100644 --- a/src/backend/langflow/services/database/models/user/crud.py +++ b/src/backend/langflow/services/database/models/user/crud.py @@ -1,27 +1,24 @@ from datetime import datetime, timezone -from typing import Union +from typing import Optional, Union from uuid import UUID -from fastapi import Depends, HTTPException, status -from langflow.services.database.models.user.user import User, UserUpdate -from langflow.services.getters import get_session -from sqlalchemy.exc import IntegrityError -from sqlmodel import Session -from typing import Optional +from fastapi import Depends, HTTPException, status +from langflow.services.database.models.user.model import User, UserUpdate +from langflow.services.deps import get_session +from sqlalchemy.exc import IntegrityError from sqlalchemy.orm.attributes import flag_modified +from sqlmodel import Session, select def get_user_by_username(db: Session, username: str) -> Union[User, None]: - return db.query(User).filter(User.username == username).first() + return db.exec(select(User).where(User.username == username)).first() def get_user_by_id(db: Session, id: UUID) -> Union[User, None]: - return db.query(User).filter(User.id == id).first() + return db.exec(select(User).where(User.id == id)).first() -def update_user( - user_db: Optional[User], user: UserUpdate, db: Session = Depends(get_session) -) -> User: +def update_user(user_db: Optional[User], user: UserUpdate, db: Session = Depends(get_session)) -> User: if not user_db: raise HTTPException(status_code=404, detail="User not found") @@ -29,7 +26,7 @@ def update_user( # if user_db_by_username and user_db_by_username.id != user_id: # raise HTTPException(status_code=409, detail="Username already exists") - user_data = user.dict(exclude_unset=True) + user_data = user.model_dump(exclude_unset=True) changed = False for attr, value in user_data.items(): if hasattr(user_db, attr) and value is not None: @@ -37,9 +34,7 @@ def update_user( changed = True if not changed: - raise HTTPException( - status_code=status.HTTP_304_NOT_MODIFIED, detail="Nothing to update" - ) + raise HTTPException(status_code=status.HTTP_304_NOT_MODIFIED, detail="Nothing to update") user_db.updated_at = datetime.now(timezone.utc) flag_modified(user_db, "updated_at") @@ -54,6 +49,9 @@ def update_user( def update_user_last_login_at(user_id: UUID, db: Session = Depends(get_session)): - user_data = UserUpdate(last_login_at=datetime.now(timezone.utc)) # type: ignore - user = get_user_by_id(db, user_id) - return update_user(user, user_data, db) + try: + user_data = UserUpdate(last_login_at=datetime.now(timezone.utc)) # type: ignore + user = get_user_by_id(db, user_id) + return update_user(user, user_data, db) + except Exception: + pass diff --git a/src/backend/langflow/services/database/models/user/user.py b/src/backend/langflow/services/database/models/user/model.py similarity index 61% rename from src/backend/langflow/services/database/models/user/user.py rename to src/backend/langflow/services/database/models/user/model.py index b1f514531..dccd3c305 100644 --- a/src/backend/langflow/services/database/models/user/user.py +++ b/src/backend/langflow/services/database/models/user/model.py @@ -1,17 +1,16 @@ -from langflow.services.database.models.base import SQLModel, SQLModelSerializable -from sqlmodel import Field, Relationship - - from datetime import datetime -from typing import Optional, TYPE_CHECKING +from typing import TYPE_CHECKING, Optional from uuid import UUID, uuid4 +from sqlmodel import Field, Relationship, SQLModel + if TYPE_CHECKING: from langflow.services.database.models.api_key import ApiKey + from langflow.services.database.models.credential import Credential from langflow.services.database.models.flow import Flow -class User(SQLModelSerializable, table=True): +class User(SQLModel, table=True): id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True) username: str = Field(index=True, unique=True) password: str = Field() @@ -20,12 +19,17 @@ class User(SQLModelSerializable, table=True): is_superuser: bool = Field(default=False) create_at: datetime = Field(default_factory=datetime.utcnow) updated_at: datetime = Field(default_factory=datetime.utcnow) - last_login_at: Optional[datetime] = Field() + last_login_at: Optional[datetime] = Field(default=None, nullable=True) api_keys: list["ApiKey"] = Relationship( back_populates="user", sa_relationship_kwargs={"cascade": "delete"}, ) + store_api_key: Optional[str] = Field(default=None, nullable=True) flows: list["Flow"] = Relationship(back_populates="user") + credentials: list["Credential"] = Relationship( + back_populates="user", + sa_relationship_kwargs={"cascade": "delete"}, + ) class UserCreate(SQLModel): @@ -41,13 +45,13 @@ class UserRead(SQLModel): is_superuser: bool = Field() create_at: datetime = Field() updated_at: datetime = Field() - last_login_at: Optional[datetime] = Field() + last_login_at: Optional[datetime] = Field(nullable=True) class UserUpdate(SQLModel): - username: Optional[str] = Field() - profile_image: Optional[str] = Field() - password: Optional[str] = Field() - is_active: Optional[bool] = Field() - is_superuser: Optional[bool] = Field() - last_login_at: Optional[datetime] = Field() + username: Optional[str] = None + profile_image: Optional[str] = None + password: Optional[str] = None + is_active: Optional[bool] = None + is_superuser: Optional[bool] = None + last_login_at: Optional[datetime] = None diff --git a/src/backend/langflow/services/database/manager.py b/src/backend/langflow/services/database/service.py similarity index 74% rename from src/backend/langflow/services/database/manager.py rename to src/backend/langflow/services/database/service.py index 6513ca280..199e0dc34 100644 --- a/src/backend/langflow/services/database/manager.py +++ b/src/backend/langflow/services/database/service.py @@ -1,19 +1,21 @@ +import time from pathlib import Path from typing import TYPE_CHECKING import sqlalchemy as sa -from alembic import command +from alembic import command, util from alembic.config import Config +from loguru import logger +from sqlalchemy import inspect +from sqlalchemy.exc import OperationalError +from sqlmodel import Session, SQLModel, create_engine, select, text + from langflow.services.base import Service from langflow.services.database import models # noqa from langflow.services.database.models.user.crud import get_user_by_username from langflow.services.database.utils import Result, TableResults -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service from langflow.services.utils import teardown_superuser -from loguru import logger -from sqlalchemy import inspect -from sqlalchemy.exc import OperationalError -from sqlmodel import Session, SQLModel, create_engine if TYPE_CHECKING: from sqlalchemy.engine import Engine @@ -34,10 +36,7 @@ class DatabaseService(Service): def _create_engine(self) -> "Engine": """Create the engine for the database.""" settings_service = get_settings_service() - if ( - settings_service.settings.DATABASE_URL - and settings_service.settings.DATABASE_URL.startswith("sqlite") - ): + if settings_service.settings.DATABASE_URL and settings_service.settings.DATABASE_URL.startswith("sqlite"): connect_args = {"check_same_thread": False} else: connect_args = {} @@ -49,9 +48,7 @@ class DatabaseService(Service): def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None: # If an exception has been raised - logger.error( - f"Session rollback because of exception: {exc_type.__name__} {exc_value}" - ) + logger.error(f"Session rollback because of exception: {exc_type.__name__} {exc_value}") self._session.rollback() else: self._session.commit() @@ -68,15 +65,14 @@ class DatabaseService(Service): settings_service = get_settings_service() if settings_service.auth_settings.AUTO_LOGIN: with Session(self.engine) as session: - flows = ( - session.query(models.Flow) - .filter(models.Flow.user_id == None) # noqa - .all() - ) + flows = session.exec(select(models.Flow).where(models.Flow.user_id is None)).all() if flows: logger.debug("Migrating flows to default superuser") username = settings_service.auth_settings.SUPERUSER user = get_user_by_username(session, username) + if not user: + logger.error("Default superuser not found") + raise RuntimeError("Default superuser not found") for flow in flows: flow.user_id = user.id session.commit() @@ -96,12 +92,10 @@ class DatabaseService(Service): legacy_tables = ["flowstyle"] for table, model in model_mapping.items(): - expected_columns = list(model.__fields__.keys()) + expected_columns = list(model.model_fields.keys()) try: - available_columns = [ - col["name"] for col in inspector.get_columns(table) - ] + available_columns = [col["name"] for col in inspector.get_columns(table)] except sa.exc.NoSuchTableError: logger.error(f"Missing table: {table}") return False @@ -123,9 +117,10 @@ class DatabaseService(Service): alembic_cfg.set_main_option("script_location", str(self.script_location)) alembic_cfg.set_main_option("sqlalchemy.url", self.database_url) command.stamp(alembic_cfg, "head") + # command.upgrade(alembic_cfg, "head") logger.info("Alembic initialized") - def run_migrations(self): + def run_migrations(self, fix=False): # First we need to check if alembic has been initialized # If not, we need to initialize it # if not self.script_location.exists(): # this is not the correct way to check if alembic has been initialized @@ -135,7 +130,7 @@ class DatabaseService(Service): # If the table does not exist it throws an error # so we need to catch it try: - session.execute("SELECT * FROM alembic_version") + session.exec(text("SELECT * FROM alembic_version")) except Exception: logger.info("Alembic not initialized") try: @@ -150,27 +145,53 @@ class DatabaseService(Service): alembic_cfg = Config() alembic_cfg.set_main_option("script_location", str(self.script_location)) alembic_cfg.set_main_option("sqlalchemy.url", self.database_url) - command.upgrade(alembic_cfg, "head") + try: + command.check(alembic_cfg) + except Exception as exc: + if isinstance(exc, util.exc.CommandError) or isinstance(exc, util.exc.AutogenerateDiffsDetected): + command.upgrade(alembic_cfg, "head") + time.sleep(3) + + try: + command.check(alembic_cfg) + except util.exc.AutogenerateDiffsDetected: + logger.exception("AutogenerateDiffsDetected: {exc}") + if not fix: + raise RuntimeError("Something went wrong running migrations. Please, run `langflow migration --fix`") + + if fix: + self.try_downgrade_upgrade_until_success(alembic_cfg) + + def try_downgrade_upgrade_until_success(self, alembic_cfg, retries=5): + # Try -1 then head, if it fails, try -2 then head, etc. + # until we reach the number of retries + for i in range(1, retries + 1): + try: + command.check(alembic_cfg) + break + except util.exc.AutogenerateDiffsDetected as exc: + # downgrade to base and upgrade again + logger.warning(f"AutogenerateDiffsDetected: {exc}") + command.downgrade(alembic_cfg, f"-{i}") + # wait for the database to be ready + time.sleep(3) + command.upgrade(alembic_cfg, "head") def run_migrations_test(self): # This method is used for testing purposes only # We will check that all models are in the database # and that the database is up to date with all columns sql_models = [models.Flow, models.User, models.ApiKey] - return [ - TableResults(sql_model.__tablename__, self.check_table(sql_model)) - for sql_model in sql_models - ] + return [TableResults(sql_model.__tablename__, self.check_table(sql_model)) for sql_model in sql_models] def check_table(self, model): results = [] inspector = inspect(self.engine) table_name = model.__tablename__ expected_columns = list(model.__fields__.keys()) + available_columns = [] try: - available_columns = [ - col["name"] for col in inspector.get_columns(table_name) - ] + available_columns = [col["name"] for col in inspector.get_columns(table_name)] results.append(Result(name=table_name, type="table", success=True)) except sa.exc.NoSuchTableError: logger.error(f"Missing table: {table_name}") @@ -201,9 +222,7 @@ class DatabaseService(Service): try: table.create(self.engine, checkfirst=True) except OperationalError as oe: - logger.warning( - f"Table {table} already exists, skipping. Exception: {oe}" - ) + logger.warning(f"Table {table} already exists, skipping. Exception: {oe}") except Exception as exc: logger.error(f"Error creating table {table}: {exc}") raise RuntimeError(f"Error creating table {table}") from exc @@ -215,9 +234,7 @@ class DatabaseService(Service): if table not in table_names: logger.error("Something went wrong creating the database and tables.") logger.error("Please check your database settings.") - raise RuntimeError( - "Something went wrong creating the database and tables." - ) + raise RuntimeError("Something went wrong creating the database and tables.") logger.debug("Database and tables created successfully") diff --git a/src/backend/langflow/services/database/utils.py b/src/backend/langflow/services/database/utils.py index 968cfef59..c4ab1aac6 100644 --- a/src/backend/langflow/services/database/utils.py +++ b/src/backend/langflow/services/database/utils.py @@ -1,21 +1,20 @@ +from contextlib import contextmanager from dataclasses import dataclass from typing import TYPE_CHECKING -from loguru import logger -from contextlib import contextmanager + from alembic.util.exc import CommandError -from sqlmodel import Session +from loguru import logger +from sqlmodel import Session, text if TYPE_CHECKING: - from langflow.services.database.manager import DatabaseService + from langflow.services.database.service import DatabaseService -def initialize_database(): +def initialize_database(fix_migration: bool = False): logger.debug("Initializing database") - from langflow.services import service_manager, ServiceType + from langflow.services.deps import get_db_service - database_service: "DatabaseService" = service_manager.get( - ServiceType.DATABASE_SERVICE - ) + database_service: "DatabaseService" = get_db_service() try: database_service.create_db_and_tables() except Exception as exc: @@ -30,25 +29,23 @@ def initialize_database(): logger.error(f"Error checking schema health: {exc}") raise RuntimeError("Error checking schema health") from exc try: - database_service.run_migrations() + database_service.run_migrations(fix=fix_migration) except CommandError as exc: if "Can't locate revision identified by" not in str(exc): raise exc # This means there's wrong revision in the DB # We need to delete the alembic_version table # and run the migrations again - logger.warning( - "Wrong revision in DB, deleting alembic_version table and running migrations again" - ) + logger.warning("Wrong revision in DB, deleting alembic_version table and running migrations again") with session_getter(database_service) as session: - session.execute("DROP TABLE alembic_version") - database_service.run_migrations() + session.exec(text("DROP TABLE alembic_version")) + database_service.run_migrations(fix=fix_migration) except Exception as exc: # if the exception involves tables already existing # we can ignore it if "already exists" not in str(exc): - logger.error(f"Error running migrations: {exc}") - raise RuntimeError("Error running migrations") from exc + logger.error(exc) + raise exc logger.debug("Database initialized") diff --git a/src/backend/langflow/services/deps.py b/src/backend/langflow/services/deps.py new file mode 100644 index 000000000..1ac06738b --- /dev/null +++ b/src/backend/langflow/services/deps.py @@ -0,0 +1,63 @@ +from typing import TYPE_CHECKING, Generator + +from langflow.services import ServiceType, service_manager + +if TYPE_CHECKING: + from langflow.services.cache.service import BaseCacheService + from langflow.services.chat.service import ChatService + from langflow.services.credentials.service import CredentialService + from langflow.services.database.service import DatabaseService + from langflow.services.plugins.service import PluginService + from langflow.services.session.service import SessionService + from langflow.services.settings.service import SettingsService + from langflow.services.store.service import StoreService + from langflow.services.task.service import TaskService + from sqlmodel import Session + + +def get_credential_service() -> "CredentialService": + return service_manager.get(ServiceType.CREDENTIAL_SERVICE) # type: ignore + + +def get_plugins_service() -> "PluginService": + return service_manager.get(ServiceType.PLUGIN_SERVICE) # type: ignore + + +def get_settings_service() -> "SettingsService": + try: + return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore + except ValueError: + # initialize settings service + from langflow.services.manager import initialize_settings_service + + initialize_settings_service() + return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore + + +def get_db_service() -> "DatabaseService": + return service_manager.get(ServiceType.DATABASE_SERVICE) # type: ignore + + +def get_session() -> Generator["Session", None, None]: + db_service = get_db_service() + yield from db_service.get_session() + + +def get_cache_service() -> "BaseCacheService": + return service_manager.get(ServiceType.CACHE_SERVICE) # type: ignore + + +def get_session_service() -> "SessionService": + return service_manager.get(ServiceType.SESSION_SERVICE) # type: ignore + + +def get_task_service() -> "TaskService": + return service_manager.get(ServiceType.TASK_SERVICE) # type: ignore + + +def get_chat_service() -> "ChatService": + return service_manager.get(ServiceType.CHAT_SERVICE) # type: ignore + + +def get_store_service() -> "StoreService": + return service_manager.get(ServiceType.STORE_SERVICE) # type: ignore diff --git a/src/backend/langflow/services/factory.py b/src/backend/langflow/services/factory.py index c37f4e9c2..874d7374c 100644 --- a/src/backend/langflow/services/factory.py +++ b/src/backend/langflow/services/factory.py @@ -1,6 +1,12 @@ +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from langflow.services.base import Service + + class ServiceFactory: def __init__(self, service_class): self.service_class = service_class - def create(self, *args, **kwargs): + def create(self, *args, **kwargs) -> "Service": raise NotImplementedError diff --git a/src/backend/langflow/services/getters.py b/src/backend/langflow/services/getters.py deleted file mode 100644 index e88b998b5..000000000 --- a/src/backend/langflow/services/getters.py +++ /dev/null @@ -1,48 +0,0 @@ -from langflow.services import ServiceType, service_manager -from typing import TYPE_CHECKING, Generator - - -if TYPE_CHECKING: - from langflow.services.database.manager import DatabaseService - from langflow.services.settings.manager import SettingsService - from langflow.services.cache.manager import BaseCacheService - from langflow.services.session.manager import SessionService - from langflow.services.task.manager import TaskService - from langflow.services.chat.manager import ChatService - from sqlmodel import Session - - -def get_settings_service() -> "SettingsService": - try: - return service_manager.get(ServiceType.SETTINGS_SERVICE) - except ValueError: - # initialize settings service - from langflow.services.manager import initialize_settings_service - - initialize_settings_service() - return service_manager.get(ServiceType.SETTINGS_SERVICE) - - -def get_db_service() -> "DatabaseService": - return service_manager.get(ServiceType.DATABASE_SERVICE) - - -def get_session() -> Generator["Session", None, None]: - db_service = service_manager.get(ServiceType.DATABASE_SERVICE) - yield from db_service.get_session() - - -def get_cache_service() -> "BaseCacheService": - return service_manager.get(ServiceType.CACHE_SERVICE) - - -def get_session_service() -> "SessionService": - return service_manager.get(ServiceType.SESSION_SERVICE) - - -def get_task_service() -> "TaskService": - return service_manager.get(ServiceType.TASK_SERVICE) - - -def get_chat_service() -> "ChatService": - return service_manager.get(ServiceType.CHAT_SERVICE) diff --git a/src/backend/langflow/services/manager.py b/src/backend/langflow/services/manager.py index 10fd6b699..0adeefd29 100644 --- a/src/backend/langflow/services/manager.py +++ b/src/backend/langflow/services/manager.py @@ -1,10 +1,11 @@ -from langflow.services.schema import ServiceType from typing import TYPE_CHECKING, Dict, List, Optional + +from langflow.services.schema import ServiceType from loguru import logger if TYPE_CHECKING: - from langflow.services.factory import ServiceFactory from langflow.services.base import Service + from langflow.services.factory import ServiceFactory class ServiceManager: @@ -31,7 +32,7 @@ class ServiceManager: self.factories[service_name] = service_factory self.dependencies[service_name] = dependencies - def get(self, service_name: ServiceType): + def get(self, service_name: ServiceType) -> "Service": """ Get (or create) a service by its name. """ @@ -53,15 +54,10 @@ class ServiceManager: self._create_service(dependency) # Collect the dependent services - dependent_services = { - dep.value: self.services[dep] - for dep in self.dependencies.get(service_name, []) - } + dependent_services = {dep.value: self.services[dep] for dep in self.dependencies.get(service_name, [])} # Create the actual service - self.services[service_name] = self.factories[service_name].create( - **dependent_services - ) + self.services[service_name] = self.factories[service_name].create(**dependent_services) self.services[service_name].set_ready() def _validate_service_creation(self, service_name: ServiceType): @@ -69,9 +65,7 @@ class ServiceManager: Validate whether the service can be created. """ if service_name not in self.factories: - raise ValueError( - f"No factory registered for the service class '{service_name.name}'" - ) + raise ValueError(f"No factory registered for the service class '{service_name.name}'") def update(self, service_name: ServiceType): """ @@ -139,14 +133,12 @@ def initialize_session_service(): """ Initialize the session manager. """ - from langflow.services.session import factory as session_service_factory # type: ignore from langflow.services.cache import factory as cache_factory + from langflow.services.session import factory as session_service_factory # type: ignore initialize_settings_service() - service_manager.register_factory( - cache_factory.CacheServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE] - ) + service_manager.register_factory(cache_factory.CacheServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE]) service_manager.register_factory( session_service_factory.SessionServiceFactory(), diff --git a/src/backend/langflow/services/plugins/base.py b/src/backend/langflow/services/plugins/base.py new file mode 100644 index 000000000..a5ab14e54 --- /dev/null +++ b/src/backend/langflow/services/plugins/base.py @@ -0,0 +1,17 @@ +from typing import Any + + +class BasePlugin: + def initialize(self): + pass + + def teardown(self): + pass + + def get(self) -> Any: + pass + + +class CallbackPlugin(BasePlugin): + def get_callback(self, _id=None): + pass diff --git a/src/backend/langflow/services/plugins/factory.py b/src/backend/langflow/services/plugins/factory.py new file mode 100644 index 000000000..221d43500 --- /dev/null +++ b/src/backend/langflow/services/plugins/factory.py @@ -0,0 +1,16 @@ +from typing import TYPE_CHECKING + +from langflow.services.factory import ServiceFactory +from langflow.services.plugins.service import PluginService + +if TYPE_CHECKING: + from langflow.services.settings.service import SettingsService + + +class PluginServiceFactory(ServiceFactory): + def __init__(self): + super().__init__(PluginService) + + def create(self, settings_service: "SettingsService"): + service = PluginService(settings_service) + return service diff --git a/src/backend/langflow/services/plugins/langfuse.py b/src/backend/langflow/services/plugins/langfuse_plugin.py similarity index 53% rename from src/backend/langflow/services/plugins/langfuse.py rename to src/backend/langflow/services/plugins/langfuse_plugin.py index 7a1f60a48..424279342 100644 --- a/src/backend/langflow/services/plugins/langfuse.py +++ b/src/backend/langflow/services/plugins/langfuse_plugin.py @@ -1,8 +1,8 @@ -from langflow.services.getters import get_settings_service -from langflow.utils.logger import logger +from typing import Optional -### Temporary implementation -# This will be replaced by a plugin system once merged into 0.5.0 +from langflow.services.deps import get_settings_service +from langflow.services.plugins.base import CallbackPlugin +from loguru import logger class LangfuseInstance: @@ -23,10 +23,7 @@ class LangfuseInstance: settings_manager = get_settings_service() - if ( - settings_manager.settings.LANGFUSE_PUBLIC_KEY - and settings_manager.settings.LANGFUSE_SECRET_KEY - ): + if settings_manager.settings.LANGFUSE_PUBLIC_KEY and settings_manager.settings.LANGFUSE_SECRET_KEY: logger.debug("Langfuse credentials found") cls._instance = Langfuse( public_key=settings_manager.settings.LANGFUSE_PUBLIC_KEY, @@ -52,3 +49,33 @@ class LangfuseInstance: if cls._instance is not None: cls._instance.flush() cls._instance = None + + +class LangfusePlugin(CallbackPlugin): + def initialize(self): + LangfuseInstance.create() + + def teardown(self): + LangfuseInstance.teardown() + + def get(self): + return LangfuseInstance.get() + + def get_callback(self, _id: Optional[str] = None): + if _id is None: + _id = "default" + from langfuse.callback import CreateTrace # type: ignore + + logger.debug("Initializing langfuse callback") + + try: + langfuse_instance = self.get() + if langfuse_instance is not None and hasattr(langfuse_instance, "trace"): + trace = langfuse_instance.trace(CreateTrace(name="langflow-" + _id, id=_id)) + if trace: + return trace.getNewHandler() + + except Exception as exc: + logger.error(f"Error initializing langfuse callback: {exc}") + + return None diff --git a/src/backend/langflow/services/plugins/service.py b/src/backend/langflow/services/plugins/service.py new file mode 100644 index 000000000..f7b2895ec --- /dev/null +++ b/src/backend/langflow/services/plugins/service.py @@ -0,0 +1,66 @@ +import importlib +import inspect +import os +from typing import TYPE_CHECKING, Union + +from langflow.services.base import Service +from langflow.services.plugins.base import BasePlugin, CallbackPlugin +from loguru import logger + +if TYPE_CHECKING: + from langflow.services.settings.service import SettingsService + + +class PluginService(Service): + name = "plugin_service" + + def __init__(self, settings_service: "SettingsService"): + self.plugins: dict[str, BasePlugin] = {} + # plugin_dir = settings_service.settings.PLUGIN_DIR + self.plugin_dir = os.path.dirname(__file__) + self.plugins_base_module = "langflow.services.plugins" + self.load_plugins() + + def load_plugins(self): + base_files = ["base.py", "service.py", "factory.py", "__init__.py"] + for module in os.listdir(self.plugin_dir): + if module.endswith(".py") and module not in base_files: + plugin_name = module[:-3] + module_path = f"{self.plugins_base_module}.{plugin_name}" + try: + mod = importlib.import_module(module_path) + for attr_name in dir(mod): + attr = getattr(mod, attr_name) + if ( + inspect.isclass(attr) + and issubclass(attr, BasePlugin) + and attr not in [CallbackPlugin, BasePlugin] + ): + self.register_plugin(plugin_name, attr()) + except Exception as exc: + logger.error(f"Error loading plugin {plugin_name}: {exc}") + + def register_plugin(self, plugin_name, plugin_instance): + self.plugins[plugin_name] = plugin_instance + plugin_instance.initialize() + + def get_plugin(self, plugin_name) -> Union[BasePlugin, None]: + return self.plugins.get(plugin_name) + + def get(self, plugin_name): + if plugin := self.get_plugin(plugin_name): + return plugin.get() + return None + + def teardown(self): + for plugin in self.plugins.values(): + plugin.teardown() + + def get_callbacks(self, _id=None): + callbacks = [] + for plugin in self.plugins.values(): + if isinstance(plugin, CallbackPlugin): + callback = plugin.get_callback(_id=_id) + if callback: + callbacks.append(callback) + return callbacks diff --git a/src/backend/langflow/services/schema.py b/src/backend/langflow/services/schema.py index 8b3b41fcb..8265c1108 100644 --- a/src/backend/langflow/services/schema.py +++ b/src/backend/langflow/services/schema.py @@ -14,3 +14,6 @@ class ServiceType(str, Enum): CHAT_SERVICE = "chat_service" SESSION_SERVICE = "session_service" TASK_SERVICE = "task_service" + PLUGIN_SERVICE = "plugin_service" + STORE_SERVICE = "store_service" + CREDENTIAL_SERVICE = "credential_service" diff --git a/src/backend/langflow/services/session/factory.py b/src/backend/langflow/services/session/factory.py index 9abe025a8..beb0bd6bd 100644 --- a/src/backend/langflow/services/session/factory.py +++ b/src/backend/langflow/services/session/factory.py @@ -1,9 +1,9 @@ from typing import TYPE_CHECKING -from langflow.services.session.manager import SessionService +from langflow.services.session.service import SessionService from langflow.services.factory import ServiceFactory if TYPE_CHECKING: - from langflow.services.cache.manager import BaseCacheService + from langflow.services.cache.service import BaseCacheService class SessionServiceFactory(ServiceFactory): diff --git a/src/backend/langflow/services/session/manager.py b/src/backend/langflow/services/session/service.py similarity index 86% rename from src/backend/langflow/services/session/manager.py rename to src/backend/langflow/services/session/service.py index 6bdebf6b3..ac0f1fa1f 100644 --- a/src/backend/langflow/services/session/manager.py +++ b/src/backend/langflow/services/session/service.py @@ -1,8 +1,8 @@ from typing import TYPE_CHECKING + from langflow.interface.run import build_sorted_vertices from langflow.services.base import Service -from langflow.services.cache.utils import compute_dict_hash -from langflow.services.session.utils import session_id_generator +from langflow.services.session.utils import compute_dict_hash, session_id_generator if TYPE_CHECKING: from langflow.services.cache.base import BaseCacheService @@ -14,7 +14,7 @@ class SessionService(Service): def __init__(self, cache_service): self.cache_service: "BaseCacheService" = cache_service - def load_session(self, key, data_graph): + async def load_session(self, key, data_graph): # Check if the data is cached if key in self.cache_service: return self.cache_service.get(key) @@ -23,7 +23,7 @@ class SessionService(Service): key = self.generate_key(session_id=None, data_graph=data_graph) # If not cached, build the graph and cache it - graph, artifacts = build_sorted_vertices(data_graph) + graph, artifacts = await build_sorted_vertices(data_graph) self.cache_service.set(key, (graph, artifacts)) diff --git a/src/backend/langflow/services/session/utils.py b/src/backend/langflow/services/session/utils.py index 374d85540..95939b828 100644 --- a/src/backend/langflow/services/session/utils.py +++ b/src/backend/langflow/services/session/utils.py @@ -1,8 +1,18 @@ +import hashlib import random import string +from langflow.services.cache.utils import filter_json +from langflow.services.database.models.base import orjson_dumps + def session_id_generator(size=6): - return "".join( - random.SystemRandom().choices(string.ascii_uppercase + string.digits, k=size) - ) + return "".join(random.SystemRandom().choices(string.ascii_uppercase + string.digits, k=size)) + + +def compute_dict_hash(graph_data): + graph_data = filter_json(graph_data) + + cleaned_graph_json = orjson_dumps(graph_data, sort_keys=True) + + return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest() diff --git a/src/backend/langflow/services/settings/__init__.py b/src/backend/langflow/services/settings/__init__.py index 2191bf2cc..3c76ca0de 100644 --- a/src/backend/langflow/services/settings/__init__.py +++ b/src/backend/langflow/services/settings/__init__.py @@ -1,3 +1,3 @@ -from . import factory, manager +from . import factory, service -__all__ = ["factory", "manager"] +__all__ = ["factory", "service"] diff --git a/src/backend/langflow/services/settings/auth.py b/src/backend/langflow/services/settings/auth.py index 7ac7461a0..92a696cc5 100644 --- a/src/backend/langflow/services/settings/auth.py +++ b/src/backend/langflow/services/settings/auth.py @@ -1,15 +1,13 @@ +import secrets from pathlib import Path from typing import Optional -import secrets -from langflow.services.settings.constants import ( - DEFAULT_SUPERUSER, - DEFAULT_SUPERUSER_PASSWORD, -) -from langflow.services.settings.utils import read_secret_from_file, write_secret_to_file -from pydantic import BaseSettings, Field, validator -from passlib.context import CryptContext +from langflow.services.settings.constants import DEFAULT_SUPERUSER, DEFAULT_SUPERUSER_PASSWORD +from langflow.services.settings.utils import read_secret_from_file, write_secret_to_file from loguru import logger +from passlib.context import CryptContext +from pydantic import Field, validator +from pydantic_settings import BaseSettings class AuthSettings(BaseSettings): @@ -18,17 +16,14 @@ class AuthSettings(BaseSettings): SECRET_KEY: str = Field( default="", description="Secret key for JWT. If not provided, a random one will be generated.", - env="LANGFLOW_SECRET_KEY", - allow_mutation=False, + frozen=False, ) ALGORITHM: str = "HS256" ACCESS_TOKEN_EXPIRE_MINUTES: int = 60 - REFRESH_TOKEN_EXPIRE_MINUTES: int = 60 * 12 + REFRESH_TOKEN_EXPIRE_MINUTES: int = 60 * 12 * 7 # API Key to execute /process endpoint - API_KEY_SECRET_KEY: Optional[ - str - ] = "b82818e0ad4ff76615c5721ee21004b07d84cd9b87ba4d9cb42374da134b841a" + API_KEY_SECRET_KEY: Optional[str] = "b82818e0ad4ff76615c5721ee21004b07d84cd9b87ba4d9cb42374da134b841a" API_KEY_ALGORITHM: str = "HS256" API_V1_STR: str = "/api/v1" @@ -39,7 +34,7 @@ class AuthSettings(BaseSettings): SUPERUSER: str = DEFAULT_SUPERUSER SUPERUSER_PASSWORD: str = DEFAULT_SUPERUSER_PASSWORD - pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") + pwd_context: CryptContext = CryptContext(schemes=["bcrypt"], deprecated="auto") class Config: validate_assignment = True diff --git a/src/backend/langflow/services/settings/base.py b/src/backend/langflow/services/settings/base.py index 14e3f9928..c74d88a42 100644 --- a/src/backend/langflow/services/settings/base.py +++ b/src/backend/langflow/services/settings/base.py @@ -1,14 +1,15 @@ import contextlib import json -import orjson import os -from shutil import copy2 -from typing import Optional, List from pathlib import Path +from shutil import copy2 +from typing import List, Optional +import orjson import yaml -from pydantic import BaseSettings, root_validator, validator from loguru import logger +from pydantic import field_validator, validator +from pydantic_settings import BaseSettings, SettingsConfigDict # BASE_COMPONENTS_PATH = str(Path(__file__).parent / "components") BASE_COMPONENTS_PATH = str(Path(__file__).parent.parent.parent / "components") @@ -48,21 +49,30 @@ class Settings(BaseSettings): REDIS_DB: int = 0 REDIS_CACHE_EXPIRE: int = 3600 + # PLUGIN_DIR: Optional[str] = None + LANGFUSE_SECRET_KEY: Optional[str] = None LANGFUSE_PUBLIC_KEY: Optional[str] = None LANGFUSE_HOST: Optional[str] = None + STORE: Optional[bool] = True + STORE_URL: Optional[str] = "https://api.langflow.store" + DOWNLOAD_WEBHOOK_URL: Optional[ + str + ] = "https://api.langflow.store/flows/trigger/ec611a61-8460-4438-b187-a4f65e5559d4" + LIKE_WEBHOOK_URL: Optional[str] = "https://api.langflow.store/flows/trigger/64275852-ec00-45c1-984e-3bff814732da" + @validator("CONFIG_DIR", pre=True, allow_reuse=True) def set_langflow_dir(cls, value): if not value: - import appdirs + from platformdirs import user_cache_dir # Define the app name and author app_name = "langflow" app_author = "logspace" # Get the cache directory for the application - cache_dir = appdirs.user_cache_dir(app_name, app_author) + cache_dir = user_cache_dir(app_name, app_author) # Create a .langflow directory inside the cache directory value = Path(cache_dir) @@ -78,9 +88,7 @@ class Settings(BaseSettings): @validator("DATABASE_URL", pre=True) def set_database_url(cls, value, values): if not value: - logger.debug( - "No database_url provided, trying LANGFLOW_DATABASE_URL env variable" - ) + logger.debug("No database_url provided, trying LANGFLOW_DATABASE_URL env variable") if langflow_database_url := os.getenv("LANGFLOW_DATABASE_URL"): value = langflow_database_url logger.debug("Using LANGFLOW_DATABASE_URL env variable.") @@ -90,9 +98,7 @@ class Settings(BaseSettings): # so we need to migrate to the new format # if there is a database in that location if not values["CONFIG_DIR"]: - raise ValueError( - "CONFIG_DIR not set, please set it or provide a DATABASE_URL" - ) + raise ValueError("CONFIG_DIR not set, please set it or provide a DATABASE_URL") new_path = f"{values['CONFIG_DIR']}/langflow.db" if Path("./langflow.db").exists(): @@ -111,27 +117,20 @@ class Settings(BaseSettings): return value - @validator("COMPONENTS_PATH", pre=True) + @field_validator("COMPONENTS_PATH", mode="before") def set_components_path(cls, value): if os.getenv("LANGFLOW_COMPONENTS_PATH"): logger.debug("Adding LANGFLOW_COMPONENTS_PATH to components_path") langflow_component_path = os.getenv("LANGFLOW_COMPONENTS_PATH") - if ( - Path(langflow_component_path).exists() - and langflow_component_path not in value - ): + if Path(langflow_component_path).exists() and langflow_component_path not in value: if isinstance(langflow_component_path, list): for path in langflow_component_path: if path not in value: value.append(path) - logger.debug( - f"Extending {langflow_component_path} to components_path" - ) + logger.debug(f"Extending {langflow_component_path} to components_path") elif langflow_component_path not in value: value.append(langflow_component_path) - logger.debug( - f"Appending {langflow_component_path} to components_path" - ) + logger.debug(f"Appending {langflow_component_path} to components_path") if not value: value = [BASE_COMPONENTS_PATH] @@ -143,17 +142,15 @@ class Settings(BaseSettings): logger.debug(f"Components path: {value}") return value - class Config: - validate_assignment = True - extra = "ignore" - env_prefix = "LANGFLOW_" + model_config = SettingsConfigDict(validate_assignment=True, extra="ignore", env_prefix="LANGFLOW_") - @root_validator(allow_reuse=True) - def validate_lists(cls, values): - for key, value in values.items(): - if key != "dev" and not value: - values[key] = [] - return values + # @model_validator() + # @classmethod + # def validate_lists(cls, values): + # for key, value in values.items(): + # if key != "dev" and not value: + # values[key] = [] + # return values def update_from_yaml(self, file_path: str, dev: bool = False): new_settings = load_settings_from_yaml(file_path) @@ -210,7 +207,7 @@ class Settings(BaseSettings): def save_settings_to_yaml(settings: Settings, file_path: str): with open(file_path, "w") as f: - settings_dict = settings.dict() + settings_dict = settings.model_dump() yaml.dump(settings_dict, f) @@ -227,7 +224,7 @@ def load_settings_from_yaml(file_path: str) -> Settings: settings_dict = {k.upper(): v for k, v in settings_dict.items()} for key in settings_dict: - if key not in Settings.__fields__.keys(): + if key not in Settings.model_fields.keys(): raise KeyError(f"Key {key} not found in settings") logger.debug(f"Loading {len(settings_dict[key])} {key} from {file_path}") diff --git a/src/backend/langflow/services/settings/factory.py b/src/backend/langflow/services/settings/factory.py index 9202ae8c3..713f13f82 100644 --- a/src/backend/langflow/services/settings/factory.py +++ b/src/backend/langflow/services/settings/factory.py @@ -1,5 +1,5 @@ from pathlib import Path -from langflow.services.settings.manager import SettingsService +from langflow.services.settings.service import SettingsService from langflow.services.factory import ServiceFactory @@ -10,6 +10,4 @@ class SettingsServiceFactory(ServiceFactory): def create(self): # Here you would have logic to create and configure a SettingsService langflow_dir = Path(__file__).parent.parent.parent - return SettingsService.load_settings_from_yaml( - str(langflow_dir / "config.yaml") - ) + return SettingsService.load_settings_from_yaml(str(langflow_dir / "config.yaml")) diff --git a/src/backend/langflow/services/settings/manager.py b/src/backend/langflow/services/settings/service.py similarity index 87% rename from src/backend/langflow/services/settings/manager.py rename to src/backend/langflow/services/settings/service.py index cdededcea..a57a59eb8 100644 --- a/src/backend/langflow/services/settings/manager.py +++ b/src/backend/langflow/services/settings/service.py @@ -28,11 +28,9 @@ class SettingsService(Service): settings_dict = {k.upper(): v for k, v in settings_dict.items()} for key in settings_dict: - if key not in Settings.__fields__.keys(): + if key not in Settings.model_fields.keys(): raise KeyError(f"Key {key} not found in settings") - logger.debug( - f"Loading {len(settings_dict[key])} {key} from {file_path}" - ) + logger.debug(f"Loading {len(settings_dict[key])} {key} from {file_path}") settings = Settings(**settings_dict) if not settings.CONFIG_DIR: diff --git a/src/backend/langflow/services/settings/utils.py b/src/backend/langflow/services/settings/utils.py index fae96ff28..1fd308e72 100644 --- a/src/backend/langflow/services/settings/utils.py +++ b/src/backend/langflow/services/settings/utils.py @@ -14,9 +14,7 @@ def set_secure_permissions(file_path): import win32security user, domain, _ = win32security.LookupAccountName("", win32api.GetUserName()) - sd = win32security.GetFileSecurity( - file_path, win32security.DACL_SECURITY_INFORMATION - ) + sd = win32security.GetFileSecurity(file_path, win32security.DACL_SECURITY_INFORMATION) dacl = win32security.ACL() # Set the new DACL for the file: read and write access for the owner, no access for everyone else @@ -26,9 +24,7 @@ def set_secure_permissions(file_path): user, ) sd.SetSecurityDescriptorDacl(1, dacl, 0) - win32security.SetFileSecurity( - file_path, win32security.DACL_SECURITY_INFORMATION, sd - ) + win32security.SetFileSecurity(file_path, win32security.DACL_SECURITY_INFORMATION, sd) else: print("Unsupported OS") diff --git a/src/backend/langflow/services/store/__init__.py b/src/backend/langflow/services/store/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/services/store/exceptions.py b/src/backend/langflow/services/store/exceptions.py new file mode 100644 index 000000000..df86d59bc --- /dev/null +++ b/src/backend/langflow/services/store/exceptions.py @@ -0,0 +1,25 @@ +class CustomException(Exception): + def __init__(self, detail, status_code): + super().__init__(detail) + self.status_code = status_code + + +# Define custom exceptions with status codes +class UnauthorizedError(CustomException): + def __init__(self, detail="Unauthorized access"): + super().__init__(detail, 401) + + +class ForbiddenError(CustomException): + def __init__(self, detail="Forbidden"): + super().__init__(detail, 403) + + +class APIKeyError(CustomException): + def __init__(self, detail="API key error"): + super().__init__(detail, 400) #! Should be 401 + + +class FilterError(CustomException): + def __init__(self, detail="Filter error"): + super().__init__(detail, 400) diff --git a/src/backend/langflow/services/store/factory.py b/src/backend/langflow/services/store/factory.py new file mode 100644 index 000000000..a25ad78c7 --- /dev/null +++ b/src/backend/langflow/services/store/factory.py @@ -0,0 +1,14 @@ +from typing import TYPE_CHECKING +from langflow.services.store.service import StoreService +from langflow.services.factory import ServiceFactory + +if TYPE_CHECKING: + from langflow.services.settings.service import SettingsService + + +class StoreServiceFactory(ServiceFactory): + def __init__(self): + super().__init__(StoreService) + + def create(self, settings_service: "SettingsService"): + return StoreService(settings_service) diff --git a/src/backend/langflow/services/store/schema.py b/src/backend/langflow/services/store/schema.py new file mode 100644 index 000000000..0fe89de18 --- /dev/null +++ b/src/backend/langflow/services/store/schema.py @@ -0,0 +1,75 @@ +from typing import List, Optional +from uuid import UUID + +from pydantic import BaseModel, validator + + +class TagResponse(BaseModel): + id: UUID + name: Optional[str] + + +class UsersLikesResponse(BaseModel): + likes_count: Optional[int] + liked_by_user: Optional[bool] + + +class CreateComponentResponse(BaseModel): + id: UUID + + +class TagsIdResponse(BaseModel): + tags_id: Optional[TagResponse] + + +class ListComponentResponse(BaseModel): + id: Optional[UUID] = None + name: Optional[str] = None + description: Optional[str] = None + liked_by_count: Optional[int] = None + liked_by_user: Optional[bool] = None + is_component: Optional[bool] = None + metadata: Optional[dict] = {} + user_created: Optional[dict] = {} + tags: Optional[List[TagResponse]] = None + downloads_count: Optional[int] = None + last_tested_version: Optional[str] = None + private: Optional[bool] = None + + # tags comes as a TagsIdResponse but we want to return a list of TagResponse + @validator("tags", pre=True) + def tags_to_list(cls, v): + # Check if all values are have id and name + # if so, return v else transform to TagResponse + if not v: + return v + if all(["id" in tag and "name" in tag for tag in v]): + return v + else: + return [TagResponse(**tag.get("tags_id")) for tag in v if tag.get("tags_id")] + + +class ListComponentResponseModel(BaseModel): + count: Optional[int] = 0 + authorized: bool + results: Optional[List[ListComponentResponse]] + + +class DownloadComponentResponse(BaseModel): + id: UUID + name: Optional[str] + description: Optional[str] + data: Optional[dict] + is_component: Optional[bool] + metadata: Optional[dict] = {} + + +class StoreComponentCreate(BaseModel): + name: str + description: Optional[str] + data: dict + tags: Optional[List[str]] + parent: Optional[UUID] = None + is_component: Optional[bool] + last_tested_version: Optional[str] = None + private: Optional[bool] = True diff --git a/src/backend/langflow/services/store/service.py b/src/backend/langflow/services/store/service.py new file mode 100644 index 000000000..b25599297 --- /dev/null +++ b/src/backend/langflow/services/store/service.py @@ -0,0 +1,544 @@ +import json +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple +from uuid import UUID + +import httpx +from httpx import HTTPError, HTTPStatusError +from loguru import logger + +from langflow.services.base import Service +from langflow.services.store.exceptions import APIKeyError, FilterError, ForbiddenError +from langflow.services.store.schema import ( + CreateComponentResponse, + DownloadComponentResponse, + ListComponentResponse, + ListComponentResponseModel, + StoreComponentCreate, +) +from langflow.services.store.utils import ( + process_component_data, + process_tags_for_post, + update_components_with_user_data, +) + +if TYPE_CHECKING: + from langflow.services.settings.service import SettingsService + +from contextlib import asynccontextmanager +from contextvars import ContextVar + +user_data_var: ContextVar[Optional[Dict[str, Any]]] = ContextVar("user_data", default=None) + + +@asynccontextmanager +async def user_data_context(store_service: "StoreService", api_key: Optional[str] = None): + # Fetch and set user data to the context variable + if api_key: + try: + user_data, _ = await store_service._get( + f"{store_service.base_url}/users/me", api_key, params={"fields": "id"} + ) + user_data_var.set(user_data[0]) + except HTTPStatusError as exc: + if exc.response.status_code == 403: + raise ValueError("Invalid API key") + try: + yield + finally: + # Clear the user data from the context variable + user_data_var.set(None) + + +class StoreService(Service): + """This is a service that integrates langflow with the store which + is a Directus instance. It allows to search, get and post components to + the store.""" + + name = "store_service" + + def __init__(self, settings_service: "SettingsService"): + self.settings_service = settings_service + self.base_url = self.settings_service.settings.STORE_URL + self.download_webhook_url = self.settings_service.settings.DOWNLOAD_WEBHOOK_URL + self.like_webhook_url = self.settings_service.settings.LIKE_WEBHOOK_URL + self.components_url = f"{self.base_url}/items/components" + self.default_fields = [ + "id", + "name", + "description", + "user_created.username", + "is_component", + "tags.tags_id.name", + "tags.tags_id.id", + "count(liked_by)", + "count(downloads)", + "metadata", + "last_tested_version", + "private", + ] + + # Create a context manager that will use the api key to + # get the user data and all requests inside the context manager + # will make a property return that data + # Without making the request multiple times + + async def check_api_key(self, api_key: str): + # Check if the api key is valid + # If it is, return True + # If it is not, return False + try: + user_data, _ = await self._get(f"{self.base_url}/users/me", api_key, params={"fields": "id"}) + + return "id" in user_data[0] + except HTTPStatusError as exc: + if exc.response.status_code in [403, 401]: + return False + else: + raise ValueError(f"Unexpected status code: {exc.response.status_code}") + except Exception as exc: + raise ValueError(f"Unexpected error: {exc}") + + async def _get( + self, url: str, api_key: Optional[str] = None, params: Optional[Dict[str, Any]] = None + ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: + """Utility method to perform GET requests.""" + if api_key: + headers = {"Authorization": f"Bearer {api_key}"} + else: + headers = {} + async with httpx.AsyncClient() as client: + try: + response = await client.get(url, headers=headers, params=params) + response.raise_for_status() + except HTTPError as exc: + raise exc + except Exception as exc: + raise ValueError(f"GET failed: {exc}") + json_response = response.json() + result = json_response["data"] + metadata = {} + if "meta" in json_response: + metadata = json_response["meta"] + + if isinstance(result, dict): + return [result], metadata + return result, metadata + + async def call_webhook(self, api_key: str, webhook_url: str, component_id: UUID) -> None: + # The webhook is a POST request with the data in the body + # For now we are calling it just for testing + try: + headers = {"Authorization": f"Bearer {api_key}"} + async with httpx.AsyncClient() as client: + response = await client.post(webhook_url, headers=headers, json={"component_id": str(component_id)}) + response.raise_for_status() + return response.json() + except HTTPError as exc: + raise exc + except Exception as exc: + logger.debug(f"Webhook failed: {exc}") + + def build_tags_filter(self, tags: List[str]): + tags_filter: Dict[str, Any] = {"tags": {"_and": []}} + for tag in tags: + tags_filter["tags"]["_and"].append({"_some": {"tags_id": {"name": {"_eq": tag}}}}) + return tags_filter + + async def count_components( + self, + filter_conditions: List[Dict[str, Any]], + api_key: Optional[str] = None, + use_api_key: Optional[bool] = False, + ) -> int: + params = {"aggregate": json.dumps({"count": "*"})} + if filter_conditions: + params["filter"] = json.dumps({"_and": filter_conditions}) + + api_key = api_key if use_api_key else None + + results, _ = await self._get(self.components_url, api_key, params) + return int(results[0].get("count", 0)) + + @staticmethod + def build_search_filter_conditions(query: str): + # instead of build the param ?search=query, we will build the filter + # that will use _icontains (case insensitive) + conditions: Dict[str, Any] = {"_or": []} + conditions["_or"].append({"name": {"_icontains": query}}) + conditions["_or"].append({"description": {"_icontains": query}}) + conditions["_or"].append({"tags": {"tags_id": {"name": {"_icontains": query}}}}) + conditions["_or"].append({"user_created": {"username": {"_icontains": query}}}) + return conditions + + def build_filter_conditions( + self, + component_id: Optional[str] = None, + search: Optional[str] = None, + private: Optional[bool] = None, + tags: Optional[List[str]] = None, + is_component: Optional[bool] = None, + filter_by_user: Optional[bool] = False, + liked: Optional[bool] = False, + store_api_key: Optional[str] = None, + ): + filter_conditions = [] + + if search is not None: + search_conditions = self.build_search_filter_conditions(search) + filter_conditions.append(search_conditions) + + if private is not None: + filter_conditions.append({"private": {"_eq": private}}) + + if tags: + tags_filter = self.build_tags_filter(tags) + filter_conditions.append(tags_filter) + if component_id is not None: + filter_conditions.append({"id": {"_eq": component_id}}) + if is_component is not None: + filter_conditions.append({"is_component": {"_eq": is_component}}) + if liked and store_api_key: + liked_filter = self.build_liked_filter() + filter_conditions.append(liked_filter) + elif liked and not store_api_key: + raise APIKeyError("You must provide an API key to filter by likes") + + if filter_by_user and store_api_key: + user_data = user_data_var.get() + if not user_data: + raise ValueError("No user data") + filter_conditions.append({"user_created": {"_eq": user_data["id"]}}) + elif filter_by_user and not store_api_key: + raise APIKeyError("You must provide an API key to filter your components") + else: + filter_conditions.append({"private": {"_eq": False}}) + + return filter_conditions + + def build_liked_filter(self): + user_data = user_data_var.get() + # params["filter"] = json.dumps({"user_created": {"_eq": user_data["id"]}}) + if not user_data: + raise ValueError("No user data") + return {"liked_by": {"directus_users_id": {"_eq": user_data["id"]}}} + + async def query_components( + self, + api_key: Optional[str] = None, + sort: Optional[List[str]] = None, + page: int = 1, + limit: int = 15, + fields: Optional[List[str]] = None, + filter_conditions: Optional[List[Dict[str, Any]]] = None, + use_api_key: Optional[bool] = False, + ) -> Tuple[List[ListComponentResponse], Dict[str, Any]]: + params: Dict[str, Any] = { + "page": page, + "limit": limit, + "fields": ",".join(fields) if fields is not None else ",".join(self.default_fields), + "meta": "filter_count", # !This is DEPRECATED so we should remove it ASAP + } + # ?aggregate[count]=likes + + if sort: + params["sort"] = ",".join(sort) + + # Only public components or the ones created by the user + # check for "public" or "Public" + + if filter_conditions: + params["filter"] = json.dumps({"_and": filter_conditions}) + + # If not liked, this means we are getting public components + # so we don't need to risk passing an invalid api_key + # and getting 401 + api_key = api_key if use_api_key else None + results, metadata = await self._get(self.components_url, api_key, params) + if isinstance(results, dict): + results = [results] + + results_objects = [ListComponentResponse(**result) for result in results] + + return results_objects, metadata + + async def get_liked_by_user_components(self, component_ids: List[str], api_key: str) -> List[str]: + # Get fields id + # filter should be "id is in component_ids AND liked_by directus_users_id token is api_key" + # return the ids + user_data = user_data_var.get() + if not user_data: + raise ValueError("No user data") + params = { + "fields": "id", + "filter": json.dumps( + { + "_and": [ + {"id": {"_in": component_ids}}, + {"liked_by": {"directus_users_id": {"_eq": user_data["id"]}}}, + ] + } + ), + } + results, _ = await self._get(self.components_url, api_key, params) + return [result["id"] for result in results] + + # Which of the components is parent of the user's components + async def get_components_in_users_collection(self, component_ids: List[str], api_key: str): + user_data = user_data_var.get() + if not user_data: + raise ValueError("No user data") + params = { + "fields": "id", + "filter": json.dumps( + { + "_and": [ + {"user_created": {"_eq": user_data["id"]}}, + {"parent": {"_in": component_ids}}, + ] + } + ), + } + results, _ = await self._get(self.components_url, api_key, params) + return [result["id"] for result in results] + + async def download(self, api_key: str, component_id: UUID) -> DownloadComponentResponse: + url = f"{self.components_url}/{component_id}" + params = {"fields": ",".join(["id", "name", "description", "data", "is_component", "metadata"])} + if not self.download_webhook_url: + raise ValueError("DOWNLOAD_WEBHOOK_URL is not set") + component, _ = await self._get(url, api_key, params) + await self.call_webhook(api_key, self.download_webhook_url, component_id) + if len(component) > 1: + raise ValueError("Something went wrong while downloading the component") + component_dict = component[0] + + download_component = DownloadComponentResponse(**component_dict) + # Check if metadata is an empty dict + if download_component.metadata in [None, {}] and download_component.data is not None: + # If it is, we need to build the metadata + try: + download_component.metadata = process_component_data(download_component.data.get("nodes", [])) + except KeyError: + raise ValueError("Invalid component data. No nodes found") + return download_component + + async def upload(self, api_key: str, component_data: StoreComponentCreate) -> CreateComponentResponse: + headers = {"Authorization": f"Bearer {api_key}"} + component_dict = component_data.model_dump(exclude_unset=True) + # Parent is a UUID, but the store expects a string + response = None + if component_dict.get("parent"): + component_dict["parent"] = str(component_dict["parent"]) + + component_dict = process_tags_for_post(component_dict) + try: + # response = httpx.post(self.components_url, headers=headers, json=component_dict) + # response.raise_for_status() + async with httpx.AsyncClient() as client: + response = await client.post(self.components_url, headers=headers, json=component_dict) + response.raise_for_status() + component = response.json()["data"] + return CreateComponentResponse(**component) + except HTTPError as exc: + if response: + try: + errors = response.json() + message = errors["errors"][0]["message"] + if message == "An unexpected error occurred.": + # This is a bug in Directus that returns this error + # when an error was thrown in the flow + message = "You already have a component with this name. Please choose a different name." + raise FilterError(message) + except UnboundLocalError: + pass + raise ValueError(f"Upload failed: {exc}") + + async def update( + self, api_key: str, component_id: UUID, component_data: StoreComponentCreate + ) -> CreateComponentResponse: + # Patch is the same as post, but we need to add the id to the url + headers = {"Authorization": f"Bearer {api_key}"} + component_dict = component_data.model_dump(exclude_unset=True) + # Parent is a UUID, but the store expects a string + response = None + if component_dict.get("parent"): + component_dict["parent"] = str(component_dict["parent"]) + + component_dict = process_tags_for_post(component_dict) + try: + # response = httpx.post(self.components_url, headers=headers, json=component_dict) + # response.raise_for_status() + async with httpx.AsyncClient() as client: + response = await client.patch( + self.components_url + f"/{component_id}", headers=headers, json=component_dict + ) + response.raise_for_status() + component = response.json()["data"] + return CreateComponentResponse(**component) + except HTTPError as exc: + if response: + try: + errors = response.json() + message = errors["errors"][0]["message"] + if message == "An unexpected error occurred.": + # This is a bug in Directus that returns this error + # when an error was thrown in the flow + message = "You already have a component with this name. Please choose a different name." + raise FilterError(message) + except UnboundLocalError: + pass + raise ValueError(f"Upload failed: {exc}") + + async def get_tags(self) -> List[Dict[str, Any]]: + url = f"{self.base_url}/items/tags" + params = {"fields": ",".join(["id", "name"])} + tags, _ = await self._get(url, api_key=None, params=params) + return tags + + async def get_user_likes(self, api_key: str) -> List[Dict[str, Any]]: + url = f"{self.base_url}/users/me" + params = { + "fields": ",".join(["id", "likes"]), + } + likes, _ = await self._get(url, api_key, params) + return likes + + async def get_component_likes_count(self, component_id: str, api_key: Optional[str] = None) -> int: + url = f"{self.components_url}/{component_id}" + + params = { + "fields": ",".join(["id", "count(liked_by)"]), + } + result, _ = await self._get(url, api_key=api_key, params=params) + if len(result) == 0: + raise ValueError("Component not found") + likes = result[0]["liked_by_count"] + # likes_by_count is a string + # try to convert it to int + try: + likes = int(likes) + except ValueError: + raise ValueError(f"Unexpected value for likes count: {likes}") + return likes + + async def like_component(self, api_key: str, component_id: str) -> bool: + # if it returns a list with one id, it means the like was successful + # if it returns an int, it means the like was removed + if not self.like_webhook_url: + raise ValueError("LIKE_WEBHOOK_URL is not set") + headers = {"Authorization": f"Bearer {api_key}"} + # response = httpx.post( + # self.like_webhook_url, + # json={"component_id": str(component_id)}, + # headers=headers, + # ) + + # response.raise_for_status() + async with httpx.AsyncClient() as client: + response = await client.post( + self.like_webhook_url, + json={"component_id": str(component_id)}, + headers=headers, + ) + response.raise_for_status() + if response.status_code == 200: + result = response.json() + + if isinstance(result, list): + return True + elif isinstance(result, int): + return False + else: + raise ValueError(f"Unexpected result: {result}") + else: + raise ValueError(f"Unexpected status code: {response.status_code}") + + async def get_list_component_response_model( + self, + component_id: Optional[str] = None, + search: Optional[str] = None, + private: Optional[bool] = None, + tags: Optional[List[str]] = None, + is_component: Optional[bool] = None, + fields: Optional[List[str]] = None, + filter_by_user: bool = False, + liked: bool = False, + store_api_key: Optional[str] = None, + sort: Optional[List[str]] = None, + page: int = 1, + limit: int = 15, + ): + async with user_data_context(api_key=store_api_key, store_service=self): + filter_conditions: List[Dict[str, Any]] = self.build_filter_conditions( + component_id=component_id, + search=search, + private=private, + tags=tags, + is_component=is_component, + filter_by_user=filter_by_user, + liked=liked, + store_api_key=store_api_key, + ) + + result: List[ListComponentResponse] = [] + authorized = False + metadata: Dict = {} + comp_count = 0 + try: + result, metadata = await self.query_components( + api_key=store_api_key, + page=page, + limit=limit, + sort=sort, + fields=fields, + filter_conditions=filter_conditions, + use_api_key=liked or filter_by_user, + ) + if metadata: + comp_count = metadata.get("filter_count", 0) + except HTTPStatusError as exc: + if exc.response.status_code == 403: + raise ForbiddenError("You are not authorized to access this public resource") from exc + elif exc.response.status_code == 401: + raise APIKeyError( + "You are not authorized to access this resource. Please check your API key." + ) from exc + except Exception as exc: + raise ValueError(f"Unexpected error: {exc}") from exc + try: + if result and not metadata: + if len(result) >= limit: + comp_count = await self.count_components( + api_key=store_api_key, + filter_conditions=filter_conditions, + use_api_key=liked or filter_by_user, + ) + else: + comp_count = len(result) + elif not metadata: + comp_count = 0 + except HTTPStatusError as exc: + if exc.response.status_code == 403: + raise ForbiddenError("You are not authorized to access this public resource") + elif exc.response.status_code == 401: + raise APIKeyError("You are not authorized to access this resource. Please check your API key.") + + if store_api_key: + # Now, from the result, we need to get the components + # the user likes and set the liked_by_user to True + # if any of the components does not have an id, it means + # we should not update the components + + if not result or any(component.id is None for component in result): + authorized = await self.check_api_key(store_api_key) + else: + try: + updated_result = await update_components_with_user_data( + result, self, store_api_key, liked=liked + ) + authorized = True + result = updated_result + except Exception: + # If we get an error here, it means the user is not authorized + authorized = False + return ListComponentResponseModel(results=result, authorized=authorized, count=comp_count) diff --git a/src/backend/langflow/services/store/utils.py b/src/backend/langflow/services/store/utils.py new file mode 100644 index 000000000..9d8beb632 --- /dev/null +++ b/src/backend/langflow/services/store/utils.py @@ -0,0 +1,64 @@ +from typing import TYPE_CHECKING, List + +import httpx + +if TYPE_CHECKING: + from langflow.services.store.schema import ListComponentResponse + from langflow.services.store.service import StoreService + + +def process_tags_for_post(component_dict): + tags = component_dict.pop("tags", None) + if tags and all(isinstance(tag, str) for tag in tags): + component_dict["tags"] = [{"tags_id": tag} for tag in tags] + return component_dict + + +async def update_components_with_user_data( + components: List["ListComponentResponse"], + store_service: "StoreService", + store_api_key: str, + liked: bool, +): + """ + Updates the components with the user data (liked_by_user and in_users_collection) + """ + component_ids = [str(component.id) for component in components] + if liked: + # If liked is True, this means all we got were liked_by_user components + # So we can set liked_by_user to True for all components + liked_by_user_ids = component_ids + else: + liked_by_user_ids = await store_service.get_liked_by_user_components( + component_ids=component_ids, + api_key=store_api_key, + ) + # Now we need to set the liked_by_user attribute + for component in components: + component.liked_by_user = str(component.id) in liked_by_user_ids + + return components + + +# Get the latest released version of langflow (https://pypi.org/project/langflow/) +def get_lf_version_from_pypi(): + try: + response = httpx.get("https://pypi.org/pypi/langflow/json") + if response.status_code != 200: + return None + return response.json()["info"]["version"] + except Exception: + return None + + +def process_component_data(nodes_list): + names = [node["id"].split("-")[0] for node in nodes_list] + metadata = {} + for name in names: + if name in metadata: + metadata[name]["count"] += 1 + else: + metadata[name] = {"count": 1} + metadata["total"] = len(names) + + return metadata diff --git a/src/backend/langflow/services/task/backends/celery.py b/src/backend/langflow/services/task/backends/celery.py index eae985f3a..f23374549 100644 --- a/src/backend/langflow/services/task/backends/celery.py +++ b/src/backend/langflow/services/task/backends/celery.py @@ -10,9 +10,7 @@ class CeleryBackend(TaskBackend): def __init__(self): self.celery_app = celery_app - def launch_task( - self, task_func: Callable[..., Any], *args: Any, **kwargs: Any - ) -> tuple[str, AsyncResult]: + def launch_task(self, task_func: Callable[..., Any], *args: Any, **kwargs: Any) -> tuple[str, AsyncResult]: # I need to type the delay method to make it easier from celery import Task # type: ignore diff --git a/src/backend/langflow/services/task/factory.py b/src/backend/langflow/services/task/factory.py index efb6ac24d..e87eecc94 100644 --- a/src/backend/langflow/services/task/factory.py +++ b/src/backend/langflow/services/task/factory.py @@ -1,4 +1,4 @@ -from langflow.services.task.manager import TaskService +from langflow.services.task.service import TaskService from langflow.services.factory import ServiceFactory diff --git a/src/backend/langflow/services/task/manager.py b/src/backend/langflow/services/task/service.py similarity index 91% rename from src/backend/langflow/services/task/manager.py rename to src/backend/langflow/services/task/service.py index 807505c3a..3f7a81f2c 100644 --- a/src/backend/langflow/services/task/manager.py +++ b/src/backend/langflow/services/task/service.py @@ -1,10 +1,11 @@ from typing import Any, Callable, Coroutine, Union -from langflow.utils.logger import configure -from loguru import logger + from langflow.services.base import Service from langflow.services.task.backends.anyio import AnyIOBackend from langflow.services.task.backends.base import TaskBackend from langflow.services.task.utils import get_celery_worker_status +from langflow.utils.logger import configure +from loguru import logger def check_celery_availability(): @@ -60,12 +61,14 @@ class TaskService(Service): if not hasattr(task_func, "apply"): raise ValueError(f"Task function {task_func} does not have an apply method") task = task_func.apply(args=args, kwargs=kwargs) + result = task.get() + # if result is coroutine + if isinstance(result, Coroutine): + result = await result return task.id, result - async def launch_task( - self, task_func: Callable[..., Any], *args: Any, **kwargs: Any - ) -> Any: + async def launch_task(self, task_func: Callable[..., Any], *args: Any, **kwargs: Any) -> Any: logger.debug(f"Launching task {task_func} with args {args} and kwargs {kwargs}") logger.debug(f"Using backend {self.backend}") task = self.backend.launch_task(task_func, *args, **kwargs) diff --git a/src/backend/langflow/services/utils.py b/src/backend/langflow/services/utils.py index 74ea4f54c..cbba3add7 100644 --- a/src/backend/langflow/services/utils.py +++ b/src/backend/langflow/services/utils.py @@ -1,25 +1,27 @@ from loguru import logger -from sqlmodel import Session +from sqlmodel import Session, select from langflow.services.auth.utils import create_super_user, verify_password from langflow.services.database.utils import initialize_database from langflow.services.manager import service_manager from langflow.services.schema import ServiceType -from langflow.services.settings.constants import ( - DEFAULT_SUPERUSER, - DEFAULT_SUPERUSER_PASSWORD, -) +from langflow.services.settings.constants import (DEFAULT_SUPERUSER, + DEFAULT_SUPERUSER_PASSWORD) -from .getters import get_db_service, get_session, get_settings_service +from .deps import get_db_service, get_session, get_settings_service def get_factories_and_deps(): from langflow.services.auth import factory as auth_factory from langflow.services.cache import factory as cache_factory from langflow.services.chat import factory as chat_factory + from langflow.services.credentials import factory as credentials_factory from langflow.services.database import factory as database_factory - from langflow.services.session import factory as session_service_factory # type: ignore + from langflow.services.plugins import factory as plugins_factory + from langflow.services.session import \ + factory as session_service_factory # type: ignore from langflow.services.settings import factory as settings_factory + from langflow.services.store import factory as store_factory from langflow.services.task import factory as task_factory return [ @@ -42,13 +44,16 @@ def get_factories_and_deps(): session_service_factory.SessionServiceFactory(), [ServiceType.CACHE_SERVICE], ), + (plugins_factory.PluginServiceFactory(), [ServiceType.SETTINGS_SERVICE]), + (store_factory.StoreServiceFactory(), [ServiceType.SETTINGS_SERVICE]), + (credentials_factory.CredentialServiceFactory(), [ServiceType.SETTINGS_SERVICE]), ] def get_or_create_super_user(session: Session, username, password, is_default): - from langflow.services.database.models.user.user import User + from langflow.services.database.models.user.model import User - user = session.query(User).filter(User.username == username).first() + user = session.exec(select(User).where(User.username == username)).first() if user and user.is_superuser: return None # Superuser already exists @@ -71,16 +76,12 @@ def get_or_create_super_user(session: Session, username, password, is_default): ) return None else: - logger.debug( - "User with superuser credentials exists but is not a superuser." - ) + logger.debug("User with superuser credentials exists but is not a superuser.") return None if user: if verify_password(password, user.password): - raise ValueError( - "User with superuser credentials exists but is not a superuser." - ) + raise ValueError("User with superuser credentials exists but is not a superuser.") else: raise ValueError("Incorrect superuser credentials") @@ -102,25 +103,22 @@ def get_or_create_super_user(session: Session, username, password, is_default): def setup_superuser(settings_service, session: Session): if settings_service.auth_settings.AUTO_LOGIN: logger.debug("AUTO_LOGIN is set to True. Creating default superuser.") + else: + # Remove the default superuser if it exists + teardown_superuser(settings_service, session) username = settings_service.auth_settings.SUPERUSER password = settings_service.auth_settings.SUPERUSER_PASSWORD - is_default = (username == DEFAULT_SUPERUSER) and ( - password == DEFAULT_SUPERUSER_PASSWORD - ) + is_default = (username == DEFAULT_SUPERUSER) and (password == DEFAULT_SUPERUSER_PASSWORD) try: - user = get_or_create_super_user( - session=session, username=username, password=password, is_default=is_default - ) + user = get_or_create_super_user(session=session, username=username, password=password, is_default=is_default) if user is not None: logger.debug("Superuser created successfully.") except Exception as exc: logger.exception(exc) - raise RuntimeError( - "Could not create superuser. Please create a superuser manually." - ) from exc + raise RuntimeError("Could not create superuser. Please create a superuser manually.") from exc finally: settings_service.auth_settings.reset_credentials() @@ -134,14 +132,12 @@ def teardown_superuser(settings_service, session): if not settings_service.auth_settings.AUTO_LOGIN: try: - logger.debug( - "AUTO_LOGIN is set to False. Removing default superuser if exists." - ) + logger.debug("AUTO_LOGIN is set to False. Removing default superuser if exists.") username = DEFAULT_SUPERUSER - from langflow.services.database.models.user.user import User + from langflow.services.database.models.user.model import User - user = session.query(User).filter(User.username == username).first() - if user and user.is_superuser: + user = session.exec(select(User).where(User.username == username)).first() + if user and user.is_superuser is True: session.delete(user) session.commit() logger.debug("Default superuser removed successfully.") @@ -180,13 +176,12 @@ def initialize_session_service(): Initialize the session manager. """ from langflow.services.cache import factory as cache_factory - from langflow.services.session import factory as session_service_factory # type: ignore + from langflow.services.session import \ + factory as session_service_factory # type: ignore initialize_settings_service() - service_manager.register_factory( - cache_factory.CacheServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE] - ) + service_manager.register_factory(cache_factory.CacheServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE]) service_manager.register_factory( session_service_factory.SessionServiceFactory(), @@ -194,7 +189,7 @@ def initialize_session_service(): ) -def initialize_services(): +def initialize_services(fix_migration: bool = False): """ Initialize all the services needed. """ @@ -203,17 +198,17 @@ def initialize_services(): service_manager.register_factory(factory, dependencies=dependencies) except Exception as exc: logger.exception(exc) - raise RuntimeError( - "Could not initialize services. Please check your settings." - ) from exc + raise RuntimeError("Could not initialize services. Please check your settings.") from exc # Test cache connection service_manager.get(ServiceType.CACHE_SERVICE) # Setup the superuser - initialize_database() - setup_superuser( - service_manager.get(ServiceType.SETTINGS_SERVICE), next(get_session()) - ) + try: + initialize_database(fix_migration=fix_migration) + except Exception as exc: + logger.exception(exc) + raise exc + setup_superuser(service_manager.get(ServiceType.SETTINGS_SERVICE), next(get_session())) try: get_db_service().migrate_flows_if_auto_login() except Exception as exc: diff --git a/src/backend/langflow/settings.py b/src/backend/langflow/settings.py new file mode 100644 index 000000000..625bd56e5 --- /dev/null +++ b/src/backend/langflow/settings.py @@ -0,0 +1,169 @@ +import contextlib +import json +import os +from pathlib import Path +from typing import List, Optional + +import yaml +from pydantic import model_validator, validator +from pydantic_settings import BaseSettings + +from langflow.utils.logger import logger + +BASE_COMPONENTS_PATH = str(Path(__file__).parent / "components") + + +class Settings(BaseSettings): + CHAINS: dict = {} + AGENTS: dict = {} + PROMPTS: dict = {} + LLMS: dict = {} + TOOLS: dict = {} + MEMORIES: dict = {} + EMBEDDINGS: dict = {} + VECTORSTORES: dict = {} + DOCUMENTLOADERS: dict = {} + WRAPPERS: dict = {} + RETRIEVERS: dict = {} + TOOLKITS: dict = {} + TEXTSPLITTERS: dict = {} + UTILITIES: dict = {} + OUTPUT_PARSERS: dict = {} + CUSTOM_COMPONENTS: dict = {} + + DEV: bool = False + DATABASE_URL: Optional[str] = None + CACHE: str = "InMemoryCache" + REMOVE_API_KEYS: bool = False + COMPONENTS_PATH: List[str] = [] + + @validator("DATABASE_URL", pre=True) + def set_database_url(cls, value): + if not value: + logger.debug("No database_url provided, trying LANGFLOW_DATABASE_URL env variable") + if langflow_database_url := os.getenv("LANGFLOW_DATABASE_URL"): + value = langflow_database_url + logger.debug("Using LANGFLOW_DATABASE_URL env variable.") + else: + logger.debug("No DATABASE_URL env variable, using sqlite database") + value = "sqlite:///./langflow.db" + + return value + + @validator("COMPONENTS_PATH", pre=True) + def set_components_path(cls, value): + if os.getenv("LANGFLOW_COMPONENTS_PATH"): + logger.debug("Adding LANGFLOW_COMPONENTS_PATH to components_path") + langflow_component_path = os.getenv("LANGFLOW_COMPONENTS_PATH") + if Path(langflow_component_path).exists() and langflow_component_path not in value: + if isinstance(langflow_component_path, list): + for path in langflow_component_path: + if path not in value: + value.append(path) + logger.debug(f"Extending {langflow_component_path} to components_path") + elif langflow_component_path not in value: + value.append(langflow_component_path) + logger.debug(f"Appending {langflow_component_path} to components_path") + + if not value: + value = [BASE_COMPONENTS_PATH] + logger.debug("Setting default components path to components_path") + elif BASE_COMPONENTS_PATH not in value: + value.append(BASE_COMPONENTS_PATH) + logger.debug("Adding default components path to components_path") + + logger.debug(f"Components path: {value}") + return value + + class Config: + validate_assignment = True + extra = "ignore" + env_prefix = "LANGFLOW_" + + @model_validator(mode="after") + def validate_lists(cls, values): + for key, value in values.items(): + if key != "dev" and not value: + values[key] = [] + return values + + def update_from_yaml(self, file_path: str, dev: bool = False): + new_settings = load_settings_from_yaml(file_path) + self.CHAINS = new_settings.CHAINS or {} + self.AGENTS = new_settings.AGENTS or {} + self.PROMPTS = new_settings.PROMPTS or {} + self.LLMS = new_settings.LLMS or {} + self.TOOLS = new_settings.TOOLS or {} + self.MEMORIES = new_settings.MEMORIES or {} + self.WRAPPERS = new_settings.WRAPPERS or {} + self.TOOLKITS = new_settings.TOOLKITS or {} + self.TEXTSPLITTERS = new_settings.TEXTSPLITTERS or {} + self.UTILITIES = new_settings.UTILITIES or {} + self.EMBEDDINGS = new_settings.EMBEDDINGS or {} + self.VECTORSTORES = new_settings.VECTORSTORES or {} + self.DOCUMENTLOADERS = new_settings.DOCUMENTLOADERS or {} + self.RETRIEVERS = new_settings.RETRIEVERS or {} + self.OUTPUT_PARSERS = new_settings.OUTPUT_PARSERS or {} + self.CUSTOM_COMPONENTS = new_settings.CUSTOM_COMPONENTS or {} + self.COMPONENTS_PATH = new_settings.COMPONENTS_PATH or [] + self.DEV = dev + + def update_settings(self, **kwargs): + logger.debug("Updating settings") + for key, value in kwargs.items(): + # value may contain sensitive information, so we don't want to log it + if not hasattr(self, key): + logger.debug(f"Key {key} not found in settings") + continue + logger.debug(f"Updating {key}") + if isinstance(getattr(self, key), list): + # value might be a '[something]' string + with contextlib.suppress(json.decoder.JSONDecodeError): + value = json.loads(str(value)) + if isinstance(value, list): + for item in value: + if isinstance(item, Path): + item = str(item) + if item not in getattr(self, key): + getattr(self, key).append(item) + logger.debug(f"Extended {key}") + else: + if isinstance(value, Path): + value = str(value) + if value not in getattr(self, key): + getattr(self, key).append(value) + logger.debug(f"Appended {key}") + + else: + setattr(self, key, value) + logger.debug(f"Updated {key}") + logger.debug(f"{key}: {getattr(self, key)}") + + +def save_settings_to_yaml(settings: Settings, file_path: str): + with open(file_path, "w") as f: + settings_dict = settings.model_dump() + yaml.dump(settings_dict, f) + + +def load_settings_from_yaml(file_path: str) -> Settings: + # Check if a string is a valid path or a file name + if "/" not in file_path: + # Get current path + current_path = os.path.dirname(os.path.abspath(__file__)) + + file_path = os.path.join(current_path, file_path) + + with open(file_path, "r") as f: + settings_dict = yaml.safe_load(f) + settings_dict = {k.upper(): v for k, v in settings_dict.items()} + + for key in settings_dict: + if key not in Settings.model_fields.keys(): + raise KeyError(f"Key {key} not found in settings") + logger.debug(f"Loading {len(settings_dict[key])} {key} from {file_path}") + + return Settings(**settings_dict) + + +settings = load_settings_from_yaml("config.yaml") diff --git a/src/backend/langflow/template/field/base.py b/src/backend/langflow/template/field/base.py index 31c68d094..d59aaa68f 100644 --- a/src/backend/langflow/template/field/base.py +++ b/src/backend/langflow/template/field/base.py @@ -1,11 +1,13 @@ -from abc import ABC -from typing import Any, Optional, Union +from typing import Any, Callable, Optional, Union -from pydantic import BaseModel +from pydantic import BaseModel, ConfigDict, Field, field_serializer + +from langflow.field_typing.range_spec import RangeSpec -class TemplateFieldCreator(BaseModel, ABC): - field_type: str = "str" +class TemplateField(BaseModel): + model_config = ConfigDict() + field_type: str = Field(default="str", serialization_alias="type") """The type of field this is. Default is a string.""" required: bool = False @@ -14,7 +16,7 @@ class TemplateFieldCreator(BaseModel, ABC): placeholder: str = "" """A placeholder string for the field. Default is an empty string.""" - is_list: bool = False + is_list: bool = Field(default=False, serialization_alias="list") """Defines if the field is a list. Default is False.""" show: bool = True @@ -23,25 +25,22 @@ class TemplateFieldCreator(BaseModel, ABC): multiline: bool = False """Defines if the field will allow the user to open a text editor. Default is False.""" - value: Any = None + value: Any = "" """The value of the field. Default is None.""" - suffixes: list[str] = [] - """List of suffixes for a file field. Default is an empty list.""" - - file_types: list[str] = [] + file_types: list[str] = Field(default=[], serialization_alias="fileTypes") """List of file types associated with the field. Default is an empty list. (duplicate)""" - file_path: Union[str, None] = None + file_path: Optional[str] = "" """The file path of the field if it is a file. Defaults to None.""" password: bool = False """Specifies if the field is a password. Defaults to False.""" - options: list[str] = [] + options: Optional[Union[list[str], Callable]] = None """List of options for the field. Only used when is_list=True. Default is an empty list.""" - name: str = "" + name: Optional[str] = None """Name of the field. Default is an empty string.""" display_name: Optional[str] = None @@ -50,7 +49,7 @@ class TemplateFieldCreator(BaseModel, ABC): advanced: bool = False """Specifies if the field will an advanced parameter (hidden). Defaults to False.""" - input_types: list[str] = [] + input_types: Optional[list[str]] = None """List of input types for the handle when the field has more than one type. Default is an empty list.""" dynamic: bool = False @@ -59,22 +58,25 @@ class TemplateFieldCreator(BaseModel, ABC): info: Optional[str] = "" """Additional information about the field to be shown in the tooltip. Defaults to an empty string.""" + refresh: Optional[bool] = None + """Specifies if the field should be refreshed. Defaults to False.""" + + range_spec: Optional[RangeSpec] = Field(default=None, serialization_alias="rangeSpec") + """Range specification for the field. Defaults to None.""" + def to_dict(self): - result = self.dict() - # Remove key if it is None - for key in list(result.keys()): - if result[key] is None or result[key] == []: - del result[key] - result["type"] = result.pop("field_type") - result["list"] = result.pop("is_list") - - if result.get("file_types"): - result["fileTypes"] = result.pop("file_types") + return self.model_dump(by_alias=True, exclude_none=True) + @field_serializer("file_path") + def serialize_file_path(self, value): if self.field_type == "file": - result["file_path"] = self.file_path - return result + return value + return "" - -class TemplateField(TemplateFieldCreator): - pass + @field_serializer("field_type") + def serialize_field_type(self, value, _info): + if value == "float": + # check if range_spec is set + if self.range_spec is None: + self.range_spec = RangeSpec() + return value diff --git a/src/backend/langflow/template/frontend_node/agents.py b/src/backend/langflow/template/frontend_node/agents.py index b3f9b4545..b2a08d944 100644 --- a/src/backend/langflow/template/frontend_node/agents.py +++ b/src/backend/langflow/template/frontend_node/agents.py @@ -1,7 +1,6 @@ from typing import Optional from langchain.agents import types - from langflow.template.field.base import TemplateField from langflow.template.frontend_node.base import FrontendNode from langflow.template.template.base import Template @@ -29,17 +28,17 @@ class SQLAgentNode(FrontendNode): type_name="sql_agent", fields=[ TemplateField( - field_type="str", + field_type="str", # pyright: ignore required=True, placeholder="", - is_list=False, + is_list=False, # pyright: ignore show=True, multiline=False, value="", name="database_uri", ), TemplateField( - field_type="BaseLanguageModel", + field_type="BaseLanguageModel", # pyright: ignore required=True, show=True, name="llm", @@ -50,9 +49,6 @@ class SQLAgentNode(FrontendNode): description: str = """Construct an SQL agent from an LLM and tools.""" base_classes: list[str] = ["AgentExecutor"] - def to_dict(self): - return super().to_dict() - class VectorStoreRouterAgentNode(FrontendNode): name: str = "VectorStoreRouterAgent" @@ -60,14 +56,14 @@ class VectorStoreRouterAgentNode(FrontendNode): type_name="vectorstorerouter_agent", fields=[ TemplateField( - field_type="VectorStoreRouterToolkit", + field_type="VectorStoreRouterToolkit", # pyright: ignore required=True, show=True, name="vectorstoreroutertoolkit", display_name="Vector Store Router Toolkit", ), TemplateField( - field_type="BaseLanguageModel", + field_type="BaseLanguageModel", # pyright: ignore required=True, show=True, name="llm", @@ -78,9 +74,6 @@ class VectorStoreRouterAgentNode(FrontendNode): description: str = """Construct an agent from a Vector Store Router.""" base_classes: list[str] = ["AgentExecutor"] - def to_dict(self): - return super().to_dict() - class VectorStoreAgentNode(FrontendNode): name: str = "VectorStoreAgent" @@ -88,14 +81,14 @@ class VectorStoreAgentNode(FrontendNode): type_name="vectorstore_agent", fields=[ TemplateField( - field_type="VectorStoreInfo", + field_type="VectorStoreInfo", # pyright: ignore required=True, show=True, name="vectorstoreinfo", display_name="Vector Store Info", ), TemplateField( - field_type="BaseLanguageModel", + field_type="BaseLanguageModel", # pyright: ignore required=True, show=True, name="llm", @@ -106,9 +99,6 @@ class VectorStoreAgentNode(FrontendNode): description: str = """Construct an agent from a Vector Store.""" base_classes: list[str] = ["AgentExecutor"] - def to_dict(self): - return super().to_dict() - class SQLDatabaseNode(FrontendNode): name: str = "SQLDatabase" @@ -116,9 +106,9 @@ class SQLDatabaseNode(FrontendNode): type_name="sql_database", fields=[ TemplateField( - field_type="str", + field_type="str", # pyright: ignore required=True, - is_list=False, + is_list=False, # pyright: ignore show=True, multiline=False, value="", @@ -129,9 +119,6 @@ class SQLDatabaseNode(FrontendNode): description: str = """SQLAlchemy wrapper around a database.""" base_classes: list[str] = ["SQLDatabase"] - def to_dict(self): - return super().to_dict() - class CSVAgentNode(FrontendNode): name: str = "CSVAgent" @@ -139,16 +126,15 @@ class CSVAgentNode(FrontendNode): type_name="csv_agent", fields=[ TemplateField( - field_type="file", + field_type="file", # pyright: ignore required=True, show=True, name="path", value="", - suffixes=[".csv"], - file_types=["csv"], + file_types=[".csv"], # pyright: ignore ), TemplateField( - field_type="BaseLanguageModel", + field_type="BaseLanguageModel", # pyright: ignore required=True, show=True, name="llm", @@ -159,9 +145,6 @@ class CSVAgentNode(FrontendNode): description: str = """Construct a CSV agent from a CSV and tools.""" base_classes: list[str] = ["AgentExecutor"] - def to_dict(self): - return super().to_dict() - class InitializeAgentNode(FrontendNode): name: str = "AgentInitializer" @@ -170,9 +153,9 @@ class InitializeAgentNode(FrontendNode): type_name="initialize_agent", fields=[ TemplateField( - field_type="str", + field_type="str", # pyright: ignore required=True, - is_list=True, + is_list=True, # pyright: ignore show=True, multiline=False, options=list(NON_CHAT_AGENTS.keys()), @@ -181,22 +164,22 @@ class InitializeAgentNode(FrontendNode): advanced=False, ), TemplateField( - field_type="BaseChatMemory", + field_type="BaseChatMemory", # pyright: ignore required=False, show=True, name="memory", advanced=False, ), TemplateField( - field_type="Tool", + field_type="Tool", # pyright: ignore required=True, show=True, name="tools", - is_list=True, + is_list=True, # pyright: ignore advanced=False, ), TemplateField( - field_type="BaseLanguageModel", + field_type="BaseLanguageModel", # pyright: ignore required=True, show=True, name="llm", @@ -206,10 +189,7 @@ class InitializeAgentNode(FrontendNode): ], ) description: str = """Construct a zero shot agent from an LLM and tools.""" - base_classes: list[str] = ["AgentExecutor", "function"] - - def to_dict(self): - return super().to_dict() + base_classes: list[str] = ["AgentExecutor", "Callable"] @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: @@ -223,13 +203,13 @@ class JsonAgentNode(FrontendNode): type_name="json_agent", fields=[ TemplateField( - field_type="BaseToolkit", + field_type="BaseToolkit", # pyright: ignore required=True, show=True, name="toolkit", ), TemplateField( - field_type="BaseLanguageModel", + field_type="BaseLanguageModel", # pyright: ignore required=True, show=True, name="llm", @@ -239,6 +219,3 @@ class JsonAgentNode(FrontendNode): ) description: str = """Construct a json agent from an LLM and tools.""" base_classes: list[str] = ["AgentExecutor"] - - def to_dict(self): - return super().to_dict() diff --git a/src/backend/langflow/template/frontend_node/base.py b/src/backend/langflow/template/frontend_node/base.py index 36d651e78..33523e5cb 100644 --- a/src/backend/langflow/template/frontend_node/base.py +++ b/src/backend/langflow/template/frontend_node/base.py @@ -1,24 +1,21 @@ -from collections import defaultdict import re -from typing import List, Optional +from collections import defaultdict +from typing import ClassVar, Dict, List, Optional, Union -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_serializer, model_serializer -from langflow.template.frontend_node.formatter import field_formatters -from langflow.template.frontend_node.constants import ( - CLASSES_TO_REMOVE, - FORCE_SHOW_FIELDS, -) from langflow.template.field.base import TemplateField +from langflow.template.frontend_node.constants import CLASSES_TO_REMOVE, FORCE_SHOW_FIELDS +from langflow.template.frontend_node.formatter import field_formatters from langflow.template.template.base import Template from langflow.utils import constants class FieldFormatters(BaseModel): - formatters = { + formatters: ClassVar[Dict] = { "openai_api_key": field_formatters.OpenAIAPIKeyFormatter(), } - base_formatters = { + base_formatters: ClassVar[Dict] = { "kwargs": field_formatters.KwargsFormatter(), "optional": field_formatters.RemoveOptionalFormatter(), "list": field_formatters.ListTypeFormatter(), @@ -43,15 +40,18 @@ class FieldFormatters(BaseModel): class FrontendNode(BaseModel): + _format_template: bool = True template: Template description: str base_classes: List[str] name: str = "" - display_name: str = "" + display_name: Optional[str] = "" documentation: str = "" - custom_fields: defaultdict = defaultdict(list) + custom_fields: Optional[Dict] = defaultdict(list) output_types: List[str] = [] + full_path: Optional[str] = None field_formatters: FieldFormatters = Field(default_factory=FieldFormatters) + beta: bool = False error: Optional[str] = None @@ -65,30 +65,35 @@ class FrontendNode(BaseModel): """Sets the documentation of the frontend node.""" self.documentation = documentation - def process_base_classes(self) -> None: + @field_serializer("base_classes") + def process_base_classes(self, base_classes: List[str]) -> List[str]: """Removes unwanted base classes from the list of base classes.""" - self.base_classes = [ - base_class - for base_class in self.base_classes - if base_class not in CLASSES_TO_REMOVE - ] - def to_dict(self) -> dict: + return [base_class for base_class in base_classes if base_class not in CLASSES_TO_REMOVE] + + @field_serializer("display_name") + def process_display_name(self, display_name: str) -> str: + """Sets the display name of the frontend node.""" + + return display_name or self.name + + @model_serializer(mode="wrap") + def serialize_model(self, handler): + result = handler(self) + if hasattr(self, "template") and hasattr(self.template, "to_dict"): + format_func = self.format_field if self._format_template else None + result["template"] = self.template.to_dict(format_func) + name = result.pop("name") + + return {name: result} + + # For backwards compatibility + def to_dict(self, add_name=True) -> dict: """Returns a dict representation of the frontend node.""" - self.process_base_classes() - return { - self.name: { - "template": self.template.to_dict(self.format_field), - "description": self.description, - "base_classes": self.base_classes, - "display_name": self.display_name or self.name, - "custom_fields": self.custom_fields, - "output_types": self.output_types, - "documentation": self.documentation, - "beta": self.beta, - "error": self.error, - }, - } + dump = self.model_dump(by_alias=True, exclude_none=True) + if not add_name: + return dump.pop(self.name) + return dump def add_extra_fields(self) -> None: pass @@ -96,6 +101,20 @@ class FrontendNode(BaseModel): def add_extra_base_classes(self) -> None: pass + def add_base_class(self, base_class: Union[str, List[str]]) -> None: + """Adds a base class to the frontend node.""" + if isinstance(base_class, str): + self.base_classes.append(base_class) + elif isinstance(base_class, list): + self.base_classes.extend(base_class) + + def add_output_type(self, output_type: Union[str, List[str]]) -> None: + """Adds an output type to the frontend node.""" + if isinstance(output_type, str): + self.output_types.append(output_type) + elif isinstance(output_type, list): + self.output_types.extend(output_type) + @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: """Formats a given field based on its attributes and value.""" @@ -130,9 +149,7 @@ class FrontendNode(BaseModel): return _type @staticmethod - def handle_special_field( - field, key: str, _type: str, SPECIAL_FIELD_HANDLERS - ) -> str: + def handle_special_field(field, key: str, _type: str, SPECIAL_FIELD_HANDLERS) -> str: """Handles special field by using the respective handler if present.""" handler = SPECIAL_FIELD_HANDLERS.get(key) return handler(field) if handler else _type @@ -142,13 +159,8 @@ class FrontendNode(BaseModel): """Handles 'dict' type by replacing it with 'code' or 'file' based on the field name.""" if "dict" in _type.lower() and field.name == "dict_": field.field_type = "file" - field.suffixes = [".json", ".yaml", ".yml"] - field.file_types = ["json", "yaml", "yml"] - elif ( - _type.startswith("Dict") - or _type.startswith("Mapping") - or _type.startswith("dict") - ): + field.file_types = [".json", ".yaml", ".yml"] + elif _type.startswith("Dict") or _type.startswith("Mapping") or _type.startswith("dict"): field.field_type = "dict" return _type @@ -159,9 +171,7 @@ class FrontendNode(BaseModel): field.value = value["default"] @staticmethod - def handle_specific_field_values( - field: TemplateField, key: str, name: Optional[str] = None - ) -> None: + def handle_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: """Handles specific field values for certain fields.""" if key == "headers": field.value = """{"Authorization": "Bearer "}""" @@ -169,9 +179,7 @@ class FrontendNode(BaseModel): FrontendNode._handle_api_key_specific_field_values(field, key, name) @staticmethod - def _handle_model_specific_field_values( - field: TemplateField, key: str, name: Optional[str] = None - ) -> None: + def _handle_model_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: """Handles specific field values related to models.""" model_dict = { "OpenAI": constants.OPENAI_MODELS, @@ -184,9 +192,7 @@ class FrontendNode(BaseModel): field.is_list = True @staticmethod - def _handle_api_key_specific_field_values( - field: TemplateField, key: str, name: Optional[str] = None - ) -> None: + def _handle_api_key_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: """Handles specific field values related to API keys.""" if "api_key" in key and "OpenAI" in str(name): field.display_name = "OpenAI API Key" @@ -197,7 +203,8 @@ class FrontendNode(BaseModel): @staticmethod def handle_kwargs_field(field: TemplateField) -> None: """Handles kwargs field by setting certain attributes.""" - if "kwargs" in field.name.lower(): + + if "kwargs" in (field.name or "").lower(): field.advanced = True field.required = False field.show = False @@ -225,10 +232,7 @@ class FrontendNode(BaseModel): @staticmethod def should_be_password(key: str, show: bool) -> bool: """Determines whether the field should be a password field.""" - return ( - any(text in key.lower() for text in {"password", "token", "api", "key"}) - and show - ) + return any(text in key.lower() for text in {"password", "token", "api", "key"}) and show @staticmethod def should_be_multiline(key: str) -> bool: diff --git a/src/backend/langflow/template/frontend_node/chains.py b/src/backend/langflow/template/frontend_node/chains.py index b678dec3b..acacd1753 100644 --- a/src/backend/langflow/template/frontend_node/chains.py +++ b/src/backend/langflow/template/frontend_node/chains.py @@ -48,16 +48,16 @@ class ChainFrontendNode(FrontendNode): @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: FrontendNode.format_field(field, name) - - if "name" == "RetrievalQA" and field.name == "memory": + key = field.name or "" + if "name" == "RetrievalQA" and key == "memory": field.show = False field.required = False field.advanced = False - if "key" in field.name: + if "key" in key: field.password = False field.show = False - if field.name in ["input_key", "output_key"]: + if key in ["input_key", "output_key"]: field.required = True field.show = True field.advanced = True @@ -71,24 +71,24 @@ class ChainFrontendNode(FrontendNode): # field.value = field.value.template # Separated for possible future changes - if field.name == "prompt" and field.value is None: + if key == "prompt" and field.value is None: field.required = True field.show = True field.advanced = False - if field.name == "memory": + if key == "memory": # field.required = False field.show = True field.advanced = False - if field.name == "verbose": + if key == "verbose": field.required = False field.show = False field.advanced = True - if field.name == "llm": + if key == "llm": field.required = True field.show = True field.advanced = False - if field.name == "return_source_documents": + if key == "return_source_documents": field.required = False field.show = True field.advanced = True @@ -133,14 +133,16 @@ class SeriesCharacterChainNode(FrontendNode): ), ], ) - description: str = "SeriesCharacterChain is a chain you can use to have a conversation with a character from a series." # noqa + description: str = ( + "SeriesCharacterChain is a chain you can use to have a conversation with a character from a series." # noqa + ) base_classes: list[str] = [ "LLMChain", "BaseCustomChain", "Chain", "ConversationChain", "SeriesCharacterChain", - "function", + "Callable", ] @@ -241,10 +243,7 @@ class CombineDocsChainNode(FrontendNode): ], ) description: str = """Load question answering chain.""" - base_classes: list[str] = ["BaseCombineDocumentsChain", "function"] - - def to_dict(self): - return super().to_dict() + base_classes: list[str] = ["BaseCombineDocumentsChain", "Callable"] @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: diff --git a/src/backend/langflow/template/frontend_node/custom_components.py b/src/backend/langflow/template/frontend_node/custom_components.py index 4f36a1c9f..b1d2e6156 100644 --- a/src/backend/langflow/template/frontend_node/custom_components.py +++ b/src/backend/langflow/template/frontend_node/custom_components.py @@ -1,12 +1,53 @@ +from typing import Optional + from langflow.template.field.base import TemplateField from langflow.template.frontend_node.base import FrontendNode from langflow.template.template.base import Template -from langflow.interface.custom.constants import DEFAULT_CUSTOM_COMPONENT_CODE + +DEFAULT_CUSTOM_COMPONENT_CODE = """from langflow import CustomComponent +from typing import Optional, List, Dict, Union +from langflow.field_typing import ( + AgentExecutor, + BaseChatMemory, + BaseLanguageModel, + BaseLLM, + BaseLoader, + BaseMemory, + BaseOutputParser, + BasePromptTemplate, + BaseRetriever, + Callable, + Chain, + ChatPromptTemplate, + Data, + Document, + Embeddings, + NestedDict, + Object, + PromptTemplate, + TextSplitter, + Tool, + VectorStore, +) + + +class Component(CustomComponent): + display_name: str = "Custom Component" + description: str = "Create any custom component you want!" + + def build_config(self): + return {"param": {"display_name": "Parameter"}} + + def build(self, param: Data) -> Data: + return param + +""" class CustomComponentFrontendNode(FrontendNode): + _format_template: bool = False name: str = "CustomComponent" - display_name: str = "Custom Component" + display_name: Optional[str] = "CustomComponent" beta: bool = True template: Template = Template( type_name="CustomComponent", @@ -26,6 +67,3 @@ class CustomComponentFrontendNode(FrontendNode): ) description: str = "Create any custom component you want!" base_classes: list[str] = [] - - def to_dict(self): - return super().to_dict() diff --git a/src/backend/langflow/template/frontend_node/documentloaders.py b/src/backend/langflow/template/frontend_node/documentloaders.py index 0be2ebe98..31e13894a 100644 --- a/src/backend/langflow/template/frontend_node/documentloaders.py +++ b/src/backend/langflow/template/frontend_node/documentloaders.py @@ -1,11 +1,10 @@ -from typing import Optional +from typing import ClassVar, Dict, Optional + from langflow.template.field.base import TemplateField from langflow.template.frontend_node.base import FrontendNode -def build_file_field( - suffixes: list, fileTypes: list, name: str = "file_path" -) -> TemplateField: +def build_file_field(fileTypes: list, name: str = "file_path") -> TemplateField: """Build a template field for a document loader.""" return TemplateField( field_type="file", @@ -13,7 +12,6 @@ def build_file_field( show=True, name=name, value="", - suffixes=suffixes, file_types=fileTypes, ) @@ -23,35 +21,52 @@ class DocumentLoaderFrontNode(FrontendNode): self.base_classes = ["Document"] self.output_types = ["Document"] - file_path_templates = { - "AirbyteJSONLoader": build_file_field(suffixes=[".json"], fileTypes=["json"]), - "CoNLLULoader": build_file_field(suffixes=[".csv"], fileTypes=["csv"]), - "CSVLoader": build_file_field(suffixes=[".csv"], fileTypes=["csv"]), + file_path_templates: ClassVar[Dict] = { + "AirbyteJSONLoader": build_file_field( + fileTypes=[".json"], + ), + "CoNLLULoader": build_file_field( + fileTypes=[".csv"], + ), + "CSVLoader": build_file_field( + fileTypes=[".csv"], + ), "UnstructuredEmailLoader": build_file_field( - suffixes=[".eml"], fileTypes=["eml"] + fileTypes=[".eml"], ), - "EverNoteLoader": build_file_field(suffixes=[".xml"], fileTypes=["xml"]), - "FacebookChatLoader": build_file_field(suffixes=[".json"], fileTypes=["json"]), - "BSHTMLLoader": build_file_field(suffixes=[".html"], fileTypes=["html"]), - "UnstructuredHTMLLoader": build_file_field( - suffixes=[".html"], fileTypes=["html"] + "EverNoteLoader": build_file_field( + fileTypes=[".xml"], ), + "FacebookChatLoader": build_file_field( + fileTypes=[".json"], + ), + "BSHTMLLoader": build_file_field( + fileTypes=[".html"], + ), + "UnstructuredHTMLLoader": build_file_field(fileTypes=[".html"]), "UnstructuredImageLoader": build_file_field( - suffixes=[".jpg", ".jpeg", ".png", ".gif", ".bmp"], - fileTypes=["jpg", "jpeg", "png", "gif", "bmp"], + fileTypes=[".jpg", ".jpeg", ".png", ".gif", ".bmp"], ), "UnstructuredMarkdownLoader": build_file_field( - suffixes=[".md"], fileTypes=["md"] + fileTypes=[".md"], + ), + "PyPDFLoader": build_file_field( + fileTypes=[".pdf"], ), - "PyPDFLoader": build_file_field(suffixes=[".pdf"], fileTypes=["pdf"]), "UnstructuredPowerPointLoader": build_file_field( - suffixes=[".pptx", ".ppt"], fileTypes=["pptx", "ppt"] + fileTypes=[".pptx", ".ppt"], + ), + "SRTLoader": build_file_field( + fileTypes=[".srt"], + ), + "TelegramChatLoader": build_file_field( + fileTypes=[".json"], + ), + "TextLoader": build_file_field( + fileTypes=[".txt"], ), - "SRTLoader": build_file_field(suffixes=[".srt"], fileTypes=["srt"]), - "TelegramChatLoader": build_file_field(suffixes=[".json"], fileTypes=["json"]), - "TextLoader": build_file_field(suffixes=[".txt"], fileTypes=["txt"]), "UnstructuredWordDocumentLoader": build_file_field( - suffixes=[".docx", ".doc"], fileTypes=["docx", "doc"] + fileTypes=[".docx", ".doc"], ), } @@ -113,8 +128,7 @@ class DocumentLoaderFrontNode(FrontendNode): name="zip_path", value="", display_name="Path to zip file", - suffixes=[".zip"], - file_types=["zip"], + file_types=[".zip"], ) ) self.template.add_field( diff --git a/src/backend/langflow/template/frontend_node/embeddings.py b/src/backend/langflow/template/frontend_node/embeddings.py index 665328e78..a2974487e 100644 --- a/src/backend/langflow/template/frontend_node/embeddings.py +++ b/src/backend/langflow/template/frontend_node/embeddings.py @@ -15,21 +15,21 @@ class EmbeddingFrontendNode(FrontendNode): show=True, name="credentials", value="", - suffixes=[".json"], - file_types=["json"], + file_types=[".json"], ) ) @staticmethod def format_vertex_field(field: TemplateField, name: str): if "VertexAI" in name: + key = field.name or "" advanced_fields = [ "verbose", "top_p", "top_k", "max_output_tokens", ] - if field.name in advanced_fields: + if key in advanced_fields: field.advanced = True show_fields = [ "verbose", @@ -43,21 +43,22 @@ class EmbeddingFrontendNode(FrontendNode): "top_k", ] - if field.name in show_fields: + if key in show_fields: field.show = True @staticmethod def format_jina_fields(field: TemplateField): - if "jina" in field.name: + name = field.name or "" + if "jina" in name: field.show = True field.advanced = False - if "auth" in field.name or "token" in field.name: + if "auth" in name or "token" in name: field.password = True field.show = True field.advanced = False - if field.name == "jina_api_url": + if name == "jina_api_url": field.show = True field.advanced = True field.display_name = "Jina API URL" @@ -65,16 +66,15 @@ class EmbeddingFrontendNode(FrontendNode): @staticmethod def format_openai_fields(field: TemplateField): - if "openai" in field.name: + name = field.name or "" + if "openai" in name: field.show = True field.advanced = True - split_name = field.name.split("_") + split_name = name.split("_") title_name = " ".join([s.capitalize() for s in split_name]) - field.display_name = title_name.replace("Openai", "OpenAI").replace( - "Api", "API" - ) + field.display_name = title_name.replace("Openai", "OpenAI").replace("Api", "API") - if "api_key" in field.name: + if "api_key" in name: field.password = True field.show = True field.advanced = False @@ -86,13 +86,14 @@ class EmbeddingFrontendNode(FrontendNode): EmbeddingFrontendNode.format_vertex_field(field, name) field.advanced = not field.required field.show = True - if field.name == "headers": + key = field.name or "" + if key == "headers": field.show = False - if field.name == "model_kwargs": + if key == "model_kwargs": field.field_type = "dict" field.advanced = True field.show = True - elif field.name in [ + elif key in [ "model_name", "temperature", "model_file", @@ -102,9 +103,9 @@ class EmbeddingFrontendNode(FrontendNode): ]: field.advanced = False field.show = True - if field.name == "credentials": + if key == "credentials": field.field_type = "file" - if name == "VertexAI" and field.name not in [ + if name == "VertexAI" and key not in [ "callbacks", "client", "stop", diff --git a/src/backend/langflow/template/frontend_node/formatter/field_formatters.py b/src/backend/langflow/template/frontend_node/formatter/field_formatters.py index a67387df7..42d3321ff 100644 --- a/src/backend/langflow/template/frontend_node/formatter/field_formatters.py +++ b/src/backend/langflow/template/frontend_node/formatter/field_formatters.py @@ -1,19 +1,15 @@ -from typing import Optional +import re +from typing import ClassVar, Dict, Optional + from langflow.template.field.base import TemplateField from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS from langflow.template.frontend_node.formatter.base import FieldFormatter -import re - -from langflow.utils.constants import ( - ANTHROPIC_MODELS, - CHAT_OPENAI_MODELS, - OPENAI_MODELS, -) +from langflow.utils.constants import ANTHROPIC_MODELS, CHAT_OPENAI_MODELS, OPENAI_MODELS class OpenAIAPIKeyFormatter(FieldFormatter): def format(self, field: TemplateField, name: Optional[str] = None) -> None: - if "api_key" in field.name and "OpenAI" in str(name): + if field.name and "api_key" in field.name and "OpenAI" in str(name): field.display_name = "OpenAI API Key" field.required = False if field.value is None: @@ -21,7 +17,7 @@ class OpenAIAPIKeyFormatter(FieldFormatter): class ModelSpecificFieldFormatter(FieldFormatter): - MODEL_DICT = { + MODEL_DICT: ClassVar[Dict] = { "OpenAI": OPENAI_MODELS, "ChatOpenAI": CHAT_OPENAI_MODELS, "Anthropic": ANTHROPIC_MODELS, @@ -29,14 +25,14 @@ class ModelSpecificFieldFormatter(FieldFormatter): } def format(self, field: TemplateField, name: Optional[str] = None) -> None: - if name in self.MODEL_DICT and field.name == "model_name": + if field.name and name in self.MODEL_DICT and field.name == "model_name": field.options = self.MODEL_DICT[name] field.is_list = True class KwargsFormatter(FieldFormatter): def format(self, field: TemplateField, name: Optional[str] = None) -> None: - if "kwargs" in field.name.lower(): + if field.name and "kwargs" in field.name.lower(): field.advanced = True field.required = False field.show = False @@ -44,11 +40,11 @@ class KwargsFormatter(FieldFormatter): class APIKeyFormatter(FieldFormatter): def format(self, field: TemplateField, name: Optional[str] = None) -> None: - if "api" in field.name.lower() and "key" in field.name.lower(): + if field.name and "api" in field.name.lower() and "key" in field.name.lower(): field.required = False field.advanced = False - field.display_name = field.name.replace("_", " ").title() + field.display_name = (field.name or "").replace("_", " ").title() field.display_name = field.display_name.replace("Api", "API") @@ -86,7 +82,7 @@ class UnionTypeFormatter(FieldFormatter): class SpecialFieldFormatter(FieldFormatter): - SPECIAL_FIELD_HANDLERS = { + SPECIAL_FIELD_HANDLERS: ClassVar[Dict] = { "allowed_tools": lambda field: "Tool", "max_value_length": lambda field: "int", } @@ -98,7 +94,7 @@ class SpecialFieldFormatter(FieldFormatter): class ShowFieldFormatter(FieldFormatter): def format(self, field: TemplateField, name: Optional[str] = None) -> None: - key = field.name + key = field.name or "" required = field.required field.show = ( (required and key not in ["input_variables"]) @@ -110,18 +106,15 @@ class ShowFieldFormatter(FieldFormatter): class PasswordFieldFormatter(FieldFormatter): def format(self, field: TemplateField, name: Optional[str] = None) -> None: - key = field.name + key = field.name or "" show = field.show - if ( - any(text in key.lower() for text in {"password", "token", "api", "key"}) - and show - ): + if any(text in key.lower() for text in {"password", "token", "api", "key"}) and show: field.password = True class MultilineFieldFormatter(FieldFormatter): def format(self, field: TemplateField, name: Optional[str] = None) -> None: - key = field.name + key = field.name or "" if key in { "suffix", "prefix", @@ -136,7 +129,7 @@ class MultilineFieldFormatter(FieldFormatter): class DefaultValueFormatter(FieldFormatter): def format(self, field: TemplateField, name: Optional[str] = None) -> None: - value = field.to_dict() + value = field.model_dump(by_alias=True, exclude_none=True) if "default" in value: field.value = value["default"] @@ -151,15 +144,10 @@ class HeadersDefaultValueFormatter(FieldFormatter): class DictCodeFileFormatter(FieldFormatter): def format(self, field: TemplateField, name: Optional[str] = None) -> None: key = field.name - value = field.to_dict() + value = field.model_dump(by_alias=True, exclude_none=True) _type = value["type"] if "dict" in _type.lower() and key == "dict_": field.field_type = "file" - field.suffixes = [".json", ".yaml", ".yml"] - field.file_types = ["json", "yaml", "yml"] - elif ( - _type.startswith("Dict") - or _type.startswith("Mapping") - or _type.startswith("dict") - ): + field.file_types = [".json", ".yaml", ".yml"] + elif _type.startswith("Dict") or _type.startswith("Mapping") or _type.startswith("dict"): field.field_type = "dict" diff --git a/src/backend/langflow/template/frontend_node/llms.py b/src/backend/langflow/template/frontend_node/llms.py index b8e007a27..e33c4a60a 100644 --- a/src/backend/langflow/template/frontend_node/llms.py +++ b/src/backend/langflow/template/frontend_node/llms.py @@ -1,10 +1,9 @@ from typing import Optional -from langflow.services.database.models.base import orjson_dumps +from langflow.services.database.models.base import orjson_dumps from langflow.template.field.base import TemplateField from langflow.template.frontend_node.base import FrontendNode -from langflow.template.frontend_node.constants import CTRANSFORMERS_DEFAULT_CONFIG -from langflow.template.frontend_node.constants import OPENAI_API_BASE_INFO +from langflow.template.frontend_node.constants import CTRANSFORMERS_DEFAULT_CONFIG, OPENAI_API_BASE_INFO class LLMFrontendNode(FrontendNode): @@ -18,13 +17,13 @@ class LLMFrontendNode(FrontendNode): show=True, name="credentials", value="", - suffixes=[".json"], - file_types=["json"], + file_types=[".json"], ) ) @staticmethod def format_vertex_field(field: TemplateField, name: str): + key = field.name or "" if "VertexAI" in name: advanced_fields = [ "tuned_model_name", @@ -33,7 +32,7 @@ class LLMFrontendNode(FrontendNode): "top_k", "max_output_tokens", ] - if field.name in advanced_fields: + if key in advanced_fields: field.advanced = True show_fields = [ "tuned_model_name", @@ -48,20 +47,19 @@ class LLMFrontendNode(FrontendNode): "top_k", ] - if field.name in show_fields: + if key in show_fields: field.show = True @staticmethod def format_openai_field(field: TemplateField): - if "openai" in field.name.lower(): - field.display_name = ( - field.name.title().replace("Openai", "OpenAI").replace("_", " ") - ).replace("Api", "API") + key = field.name or "" + if "openai" in key.lower(): + field.display_name = (key.title().replace("Openai", "OpenAI").replace("_", " ")).replace("Api", "API") - if "key" not in field.name.lower() and "token" not in field.name.lower(): + if "key" not in key.lower() and "token" not in key.lower(): field.password = False - if field.name == "openai_api_base": + if key == "openai_api_base": field.info = OPENAI_API_BASE_INFO def add_extra_base_classes(self) -> None: @@ -70,13 +68,14 @@ class LLMFrontendNode(FrontendNode): @staticmethod def format_azure_field(field: TemplateField): - if field.name == "model_name": + key = field.name or "" + if key == "model_name": field.show = False # Azure uses deployment_name instead of model_name. - elif field.name == "openai_api_type": + elif key == "openai_api_type": field.show = False field.password = False field.value = "azure" - elif field.name == "openai_api_version": + elif key == "openai_api_version": field.password = False @staticmethod @@ -86,7 +85,8 @@ class LLMFrontendNode(FrontendNode): @staticmethod def format_ctransformers_field(field: TemplateField): - if field.name == "config": + key = field.name or "" + if key == "config": field.show = True field.advanced = True field.value = orjson_dumps(CTRANSFORMERS_DEFAULT_CONFIG, indent_2=True) @@ -106,13 +106,11 @@ class LLMFrontendNode(FrontendNode): if name and "vertex" in name.lower(): LLMFrontendNode.format_vertex_field(field, name) SHOW_FIELDS = ["repo_id"] - if field.name in SHOW_FIELDS: + key = field.name or "" + if key in SHOW_FIELDS: field.show = True - if "api" in field.name and ( - "key" in field.name - or ("token" in field.name and "tokens" not in field.name) - ): + if "api" in key and ("key" in key or ("token" in key and "tokens" not in key)): field.password = True field.show = True # Required should be False to support @@ -120,7 +118,7 @@ class LLMFrontendNode(FrontendNode): field.required = False field.advanced = False - if field.name == "task": + if key == "task": field.required = True field.show = True field.is_list = True @@ -128,13 +126,13 @@ class LLMFrontendNode(FrontendNode): field.value = field.options[0] field.advanced = True - if display_name := display_names_dict.get(field.name): + if display_name := display_names_dict.get(key): field.display_name = display_name - if field.name == "model_kwargs": + if key == "model_kwargs": field.field_type = "dict" field.advanced = True field.show = True - elif field.name in [ + elif key in [ "model_name", "temperature", "model_file", @@ -144,9 +142,9 @@ class LLMFrontendNode(FrontendNode): ]: field.advanced = False field.show = True - if field.name == "credentials": + if key == "credentials": field.field_type = "file" - if name == "VertexAI" and field.name not in [ + if name == "VertexAI" and key not in [ "callbacks", "client", "stop", diff --git a/src/backend/langflow/template/frontend_node/memories.py b/src/backend/langflow/template/frontend_node/memories.py index 019dc0fa8..bbf1c9a8d 100644 --- a/src/backend/langflow/template/frontend_node/memories.py +++ b/src/backend/langflow/template/frontend_node/memories.py @@ -76,9 +76,7 @@ class MemoryFrontendNode(FrontendNode): field.show = True field.advanced = False field.value = "" - field.info = ( - INPUT_KEY_INFO if field.name == "input_key" else OUTPUT_KEY_INFO - ) + field.info = INPUT_KEY_INFO if field.name == "input_key" else OUTPUT_KEY_INFO if field.name == "memory_key": field.value = "chat_history" diff --git a/src/backend/langflow/template/frontend_node/prompts.py b/src/backend/langflow/template/frontend_node/prompts.py index c52b1901c..956114cc6 100644 --- a/src/backend/langflow/template/frontend_node/prompts.py +++ b/src/backend/langflow/template/frontend_node/prompts.py @@ -2,13 +2,9 @@ from typing import Optional from langchain.agents.mrkl import prompt -from langflow.template.frontend_node.constants import ( - DEFAULT_PROMPT, - HUMAN_PROMPT, - SYSTEM_PROMPT, -) from langflow.template.field.base import TemplateField from langflow.template.frontend_node.base import FrontendNode +from langflow.template.frontend_node.constants import DEFAULT_PROMPT, HUMAN_PROMPT, SYSTEM_PROMPT from langflow.template.template.base import Template @@ -25,21 +21,19 @@ class PromptFrontendNode(FrontendNode): "examples", "format_instructions", ] + key = field.name or "" if field.field_type == "StringPromptTemplate" and "Message" in str(name): field.field_type = "prompt" field.multiline = True - field.value = HUMAN_PROMPT if "Human" in field.name else SYSTEM_PROMPT - if field.name == "template" and field.value == "": + field.value = HUMAN_PROMPT if "Human" in key else SYSTEM_PROMPT + if key == "template" and field.value == "": field.value = DEFAULT_PROMPT - if field.name in PROMPT_FIELDS: + if key and key in PROMPT_FIELDS: field.field_type = "prompt" field.advanced = False - if ( - "Union" in field.field_type - and "BaseMessagePromptTemplate" in field.field_type - ): + if "Union" in field.field_type and "BaseMessagePromptTemplate" in field.field_type: field.field_type = "BaseMessagePromptTemplate" # All prompt fields should be password=False @@ -52,13 +46,11 @@ class PromptTemplateNode(FrontendNode): description: str base_classes: list[str] = ["BasePromptTemplate"] - def to_dict(self): - return super().to_dict() - @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: FrontendNode.format_field(field, name) - if field.name == "examples": + + if (field.name or "") == "examples": field.advanced = False @@ -68,9 +60,6 @@ class BasePromptFrontendNode(FrontendNode): description: str base_classes: list[str] - def to_dict(self): - return super().to_dict() - class ZeroShotPromptNode(BasePromptFrontendNode): name: str = "ZeroShotPrompt" @@ -112,9 +101,6 @@ class ZeroShotPromptNode(BasePromptFrontendNode): description: str = "Prompt template for Zero Shot Agent." base_classes: list[str] = ["BasePromptTemplate"] - def to_dict(self): - return super().to_dict() - @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: PromptFrontendNode.format_field(field, name) diff --git a/src/backend/langflow/template/frontend_node/tools.py b/src/backend/langflow/template/frontend_node/tools.py index 579b32da3..5bed90c05 100644 --- a/src/backend/langflow/template/frontend_node/tools.py +++ b/src/backend/langflow/template/frontend_node/tools.py @@ -1,9 +1,7 @@ from langflow.template.field.base import TemplateField from langflow.template.frontend_node.base import FrontendNode from langflow.template.template.base import Template -from langflow.utils.constants import ( - DEFAULT_PYTHON_FUNCTION, -) +from langflow.utils.constants import DEFAULT_PYTHON_FUNCTION class ToolNode(FrontendNode): @@ -35,7 +33,7 @@ class ToolNode(FrontendNode): ), TemplateField( name="func", - field_type="function", + field_type="Callable", required=True, is_list=False, show=True, @@ -57,9 +55,6 @@ class ToolNode(FrontendNode): description: str = "Converts a chain, agent or function into a tool." base_classes: list[str] = ["Tool", "BaseTool"] - def to_dict(self): - return super().to_dict() - class PythonFunctionToolNode(FrontendNode): name: str = "PythonFunctionTool" @@ -113,9 +108,6 @@ class PythonFunctionToolNode(FrontendNode): description: str = "Python function to be executed." base_classes: list[str] = ["BaseTool", "Tool"] - def to_dict(self): - return super().to_dict() - class PythonFunctionNode(FrontendNode): name: str = "PythonFunction" @@ -135,7 +127,4 @@ class PythonFunctionNode(FrontendNode): ], ) description: str = "Python function to be executed." - base_classes: list[str] = ["function"] - - def to_dict(self): - return super().to_dict() + base_classes: list[str] = ["Callable"] diff --git a/src/backend/langflow/template/frontend_node/vectorstores.py b/src/backend/langflow/template/frontend_node/vectorstores.py index 73e9aaaca..170c3c766 100644 --- a/src/backend/langflow/template/frontend_node/vectorstores.py +++ b/src/backend/langflow/template/frontend_node/vectorstores.py @@ -3,7 +3,6 @@ from typing import List, Optional from langflow.template.field.base import TemplateField from langflow.template.frontend_node.base import FrontendNode - BASIC_FIELDS = [ "work_dir", "collection_name", @@ -313,7 +312,7 @@ class VectorStoreFrontendNode(FrontendNode): field.show = True field.advanced = False field.is_list = True - elif "embedding" in field.name: + elif field.name and "embedding" in field.name: # for backwards compatibility field.name = "embedding" field.required = True diff --git a/src/backend/langflow/template/template/base.py b/src/backend/langflow/template/template/base.py index c680fd468..d7632e239 100644 --- a/src/backend/langflow/template/template/base.py +++ b/src/backend/langflow/template/template/base.py @@ -1,6 +1,6 @@ -from typing import Callable, Optional, Union +from typing import Callable, Union -from pydantic import BaseModel +from pydantic import BaseModel, model_serializer from langflow.template.field.base import TemplateField from langflow.utils.constants import DIRECT_TYPES @@ -12,12 +12,11 @@ class Template(BaseModel): def process_fields( self, - name: Optional[str] = None, format_field_func: Union[Callable, None] = None, ): if format_field_func: for field in self.fields: - format_field_func(field, name) + format_field_func(field, self.type_name) def sort_fields(self): # first sort alphabetically @@ -25,12 +24,41 @@ class Template(BaseModel): self.fields.sort(key=lambda x: x.name) self.fields.sort(key=lambda x: x.field_type in DIRECT_TYPES, reverse=False) - def to_dict(self, format_field_func=None): - self.process_fields(self.type_name, format_field_func) - self.sort_fields() - result = {field.name: field.to_dict() for field in self.fields} - result["_type"] = self.type_name # type: ignore + @model_serializer(mode="wrap") + def serialize_model(self, handler): + result = handler(self) + for field in self.fields: + result[field.name] = field.model_dump(by_alias=True, exclude_none=True) + result["_type"] = result.pop("type_name") return result + # For backwards compatibility + def to_dict(self, format_field_func=None): + self.process_fields(format_field_func) + self.sort_fields() + return self.model_dump(by_alias=True, exclude_none=True, exclude={"fields"}) + def add_field(self, field: TemplateField) -> None: self.fields.append(field) + + def get_field(self, field_name: str) -> TemplateField: + """Returns the field with the given name.""" + field = next((field for field in self.fields if field.name == field_name), None) + if field is None: + raise ValueError(f"Field {field_name} not found in template {self.type_name}") + return field + + def update_field(self, field_name: str, field: TemplateField) -> None: + """Updates the field with the given name.""" + for idx, template_field in enumerate(self.fields): + if template_field.name == field_name: + self.fields[idx] = field + return + raise ValueError(f"Field {field_name} not found in template {self.type_name}") + + def upsert_field(self, field_name: str, field: TemplateField) -> None: + """Updates the field with the given name or adds it if it doesn't exist.""" + try: + self.update_field(field_name, field) + except ValueError: + self.add_field(field) diff --git a/src/backend/langflow/utils/constants.py b/src/backend/langflow/utils/constants.py index 283f44406..b2d00f8e6 100644 --- a/src/backend/langflow/utils/constants.py +++ b/src/backend/langflow/utils/constants.py @@ -1,3 +1,5 @@ +from typing import Any, Dict, List + OPENAI_MODELS = [ "text-davinci-003", "text-davinci-002", @@ -7,6 +9,7 @@ OPENAI_MODELS = [ ] CHAT_OPENAI_MODELS = [ "gpt-4-1106-preview", + "gpt-4-vision-preview", "gpt-4", "gpt-4-32k", "gpt-3.5-turbo", @@ -59,3 +62,117 @@ DIRECT_TYPES = [ "code", "NestedDict", ] + + +LOADERS_INFO: List[Dict[str, Any]] = [ + { + "loader": "AirbyteJSONLoader", + "name": "Airbyte JSON (.jsonl)", + "import": "langchain.document_loaders.AirbyteJSONLoader", + "defaultFor": ["jsonl"], + "allowdTypes": ["jsonl"], + }, + { + "loader": "JSONLoader", + "name": "JSON (.json)", + "import": "langchain.document_loaders.JSONLoader", + "defaultFor": ["json"], + "allowdTypes": ["json"], + }, + { + "loader": "BSHTMLLoader", + "name": "BeautifulSoup4 HTML (.html, .htm)", + "import": "langchain.document_loaders.BSHTMLLoader", + "allowdTypes": ["html", "htm"], + }, + { + "loader": "CSVLoader", + "name": "CSV (.csv)", + "import": "langchain.document_loaders.CSVLoader", + "defaultFor": ["csv"], + "allowdTypes": ["csv"], + }, + { + "loader": "CoNLLULoader", + "name": "CoNLL-U (.conllu)", + "import": "langchain.document_loaders.CoNLLULoader", + "defaultFor": ["conllu"], + "allowdTypes": ["conllu"], + }, + { + "loader": "EverNoteLoader", + "name": "EverNote (.enex)", + "import": "langchain.document_loaders.EverNoteLoader", + "defaultFor": ["enex"], + "allowdTypes": ["enex"], + }, + { + "loader": "FacebookChatLoader", + "name": "Facebook Chat (.json)", + "import": "langchain.document_loaders.FacebookChatLoader", + "allowdTypes": ["json"], + }, + { + "loader": "OutlookMessageLoader", + "name": "Outlook Message (.msg)", + "import": "langchain.document_loaders.OutlookMessageLoader", + "defaultFor": ["msg"], + "allowdTypes": ["msg"], + }, + { + "loader": "PyPDFLoader", + "name": "PyPDF (.pdf)", + "import": "langchain.document_loaders.PyPDFLoader", + "defaultFor": ["pdf"], + "allowdTypes": ["pdf"], + }, + { + "loader": "STRLoader", + "name": "Subtitle (.str)", + "import": "langchain.document_loaders.STRLoader", + "defaultFor": ["str"], + "allowdTypes": ["str"], + }, + { + "loader": "TextLoader", + "name": "Text (.txt)", + "import": "langchain.document_loaders.TextLoader", + "defaultFor": ["txt"], + "allowdTypes": ["txt"], + }, + { + "loader": "UnstructuredEmailLoader", + "name": "Unstructured Email (.eml)", + "import": "langchain.document_loaders.UnstructuredEmailLoader", + "defaultFor": ["eml"], + "allowdTypes": ["eml"], + }, + { + "loader": "UnstructuredHTMLLoader", + "name": "Unstructured HTML (.html, .htm)", + "import": "langchain.document_loaders.UnstructuredHTMLLoader", + "defaultFor": ["html", "htm"], + "allowdTypes": ["html", "htm"], + }, + { + "loader": "UnstructuredMarkdownLoader", + "name": "Unstructured Markdown (.md)", + "import": "langchain.document_loaders.UnstructuredMarkdownLoader", + "defaultFor": ["md"], + "allowdTypes": ["md"], + }, + { + "loader": "UnstructuredPowerPointLoader", + "name": "Unstructured PowerPoint (.pptx)", + "import": "langchain.document_loaders.UnstructuredPowerPointLoader", + "defaultFor": ["pptx"], + "allowdTypes": ["pptx"], + }, + { + "loader": "UnstructuredWordLoader", + "name": "Unstructured Word (.docx)", + "import": "langchain.document_loaders.UnstructuredWordLoader", + "defaultFor": ["docx"], + "allowdTypes": ["docx"], + }, +] diff --git a/src/backend/langflow/utils/logger.py b/src/backend/langflow/utils/logger.py index b08621410..060ad9731 100644 --- a/src/backend/langflow/utils/logger.py +++ b/src/backend/langflow/utils/logger.py @@ -1,11 +1,11 @@ -from typing import Optional -from loguru import logger -from pathlib import Path -from rich.logging import RichHandler import os -import orjson -import appdirs +from pathlib import Path +from typing import Optional +import orjson +from loguru import logger +from platformdirs import user_cache_dir +from rich.logging import RichHandler VALID_LOG_LEVELS = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] @@ -50,7 +50,7 @@ def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None): ) if not log_file: - cache_dir = Path(appdirs.user_cache_dir("langflow")) + cache_dir = Path(user_cache_dir("langflow")) log_file = cache_dir / "langflow.log" log_file = Path(log_file) @@ -66,4 +66,4 @@ def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None): logger.debug(f"Logger set up with log level: {log_level}") if log_file: - logger.info(f"Log file: {log_file}") + logger.debug(f"Log file: {log_file}") diff --git a/src/backend/langflow/utils/payload.py b/src/backend/langflow/utils/payload.py index cac23a0d6..0e2f0fc7a 100644 --- a/src/backend/langflow/utils/payload.py +++ b/src/backend/langflow/utils/payload.py @@ -28,16 +28,16 @@ def extract_input_variables(nodes): return nodes -def get_root_node(graph): +def get_root_vertex(graph): """ Returns the root node of the template. """ - incoming_edges = {edge.source for edge in graph.edges} + incoming_edges = {edge.source_id for edge in graph.edges} - if not incoming_edges and len(graph.nodes) == 1: - return graph.nodes[0] + if not incoming_edges and len(graph.vertices) == 1: + return graph.vertices[0] - return next((node for node in graph.nodes if node not in incoming_edges), None) + return next((node for node in graph.vertices if node.id not in incoming_edges), None) def build_json(root, graph) -> Dict: @@ -81,9 +81,7 @@ def build_json(root, graph) -> Dict: raise ValueError(f"No child with type {node_type} found") values = [build_json(child, graph) for child in children] value = ( - list(values) - if value["list"] - else next(iter(values), None) # type: ignore + list(values) if value["list"] else next(iter(values), None) # type: ignore ) final_dict[key] = value diff --git a/src/backend/langflow/utils/util.py b/src/backend/langflow/utils/util.py index b563c5973..e65010ff8 100644 --- a/src/backend/langflow/utils/util.py +++ b/src/backend/langflow/utils/util.py @@ -15,12 +15,8 @@ def remove_ansi_escape_codes(text): return re.sub(r"\x1b\[[0-9;]*[a-zA-Z]", "", text) -def build_template_from_function( - name: str, type_to_loader_dict: Dict, add_function: bool = False -): - classes = [ - item.__annotations__["return"].__name__ for item in type_to_loader_dict.values() - ] +def build_template_from_function(name: str, type_to_loader_dict: Dict, add_function: bool = False): + classes = [item.__annotations__["return"].__name__ for item in type_to_loader_dict.values()] # Raise error if name is not in chains if name not in classes: @@ -34,16 +30,14 @@ def build_template_from_function( docs = parse(_class.__doc__) variables = {"_type": _type} - for class_field_items, value in _class.__fields__.items(): + for class_field_items, value in _class.model_fields.items(): if class_field_items in ["callback_manager"]: continue variables[class_field_items] = {} for name_, value_ in value.__repr_args__(): if name_ == "default_factory": try: - variables[class_field_items][ - "default" - ] = get_default_factory( + variables[class_field_items]["default"] = get_default_factory( module=_class.__base__.__module__, function=value_ ) except Exception: @@ -52,15 +46,13 @@ def build_template_from_function( variables[class_field_items][name_] = value_ variables[class_field_items]["placeholder"] = ( - docs.params[class_field_items] - if class_field_items in docs.params - else "" + docs.params[class_field_items] if class_field_items in docs.params else "" ) # Adding function to base classes to allow # the output to be a function base_classes = get_base_classes(_class) if add_function: - base_classes.append("function") + base_classes.append("Callable") return { "template": format_dict(variables, name), @@ -69,9 +61,7 @@ def build_template_from_function( } -def build_template_from_class( - name: str, type_to_cls_dict: Dict, add_function: bool = False -): +def build_template_from_class(name: str, type_to_cls_dict: Dict, add_function: bool = False): classes = [item.__name__ for item in type_to_cls_dict.values()] # Raise error if name is not in chains @@ -95,9 +85,7 @@ def build_template_from_class( for name_, value_ in value.__repr_args__(): if name_ == "default_factory": try: - variables[class_field_items][ - "default" - ] = get_default_factory( + variables[class_field_items]["default"] = get_default_factory( module=_class.__base__.__module__, function=value_ ) except Exception: @@ -106,15 +94,13 @@ def build_template_from_class( variables[class_field_items][name_] = value_ variables[class_field_items]["placeholder"] = ( - docs.params[class_field_items] - if class_field_items in docs.params - else "" + docs.params[class_field_items] if class_field_items in docs.params else "" ) base_classes = get_base_classes(_class) # Adding function to base classes to allow # the output to be a function if add_function: - base_classes.append("function") + base_classes.append("Callable") return { "template": format_dict(variables, name), "description": docs.short_description or "", @@ -140,9 +126,7 @@ def build_template_from_method( # Check if the method exists in this class if not hasattr(_class, method_name): - raise ValueError( - f"Method {method_name} not found in class {class_name}" - ) + raise ValueError(f"Method {method_name} not found in class {class_name}") # Get the method method = getattr(_class, method_name) @@ -161,12 +145,8 @@ def build_template_from_method( "_type": _type, **{ name: { - "default": param.default - if param.default != param.empty - else None, - "type": param.annotation - if param.annotation != param.empty - else None, + "default": param.default if param.default != param.empty else None, + "type": param.annotation if param.annotation != param.empty else None, "required": param.default == param.empty, } for name, param in params.items() @@ -178,7 +158,7 @@ def build_template_from_method( # Adding function to base classes to allow the output to be a function if add_function: - base_classes.append("function") + base_classes.append("Callable") return { "template": format_dict(variables, class_name), @@ -251,9 +231,7 @@ def sync_to_async(func): return async_wrapper -def format_dict( - dictionary: Dict[str, Any], class_name: Optional[str] = None -) -> Dict[str, Any]: +def format_dict(dictionary: Dict[str, Any], class_name: Optional[str] = None) -> Dict[str, Any]: """ Formats a dictionary by removing certain keys and modifying the values of other keys. @@ -293,6 +271,15 @@ def format_dict( return dictionary +# "Union[Literal['f-string'], Literal['jinja2']]" -> "str" +def get_type_from_union_literal(union_literal: str) -> str: + # if types are literal strings + # the type is a string + if "Literal" in union_literal: + return "str" + return union_literal + + def get_type(value: Any) -> Union[str, type]: """ Retrieves the type value from the dictionary. @@ -300,7 +287,8 @@ def get_type(value: Any) -> Union[str, type]: Returns: The type value. """ - _type = value["type"] + # get "type" or "annotation" from the value + _type = value.get("type") or value.get("annotation") return _type if isinstance(_type, str) else _type.__name__ @@ -328,9 +316,7 @@ def check_list_type(_type: str, value: Dict[str, Any]) -> str: The modified type string. """ if any(list_type in _type for list_type in ["List", "Sequence", "Set"]): - _type = ( - _type.replace("List[", "").replace("Sequence[", "").replace("Set[", "")[:-1] - ) + _type = _type.replace("List[", "").replace("Sequence[", "").replace("Set[", "")[:-1] value["list"] = True else: value["list"] = False @@ -414,8 +400,7 @@ def set_dict_file_attributes(value: Dict[str, Any]) -> None: Sets the file attributes for the 'dict_' key. """ value["type"] = "file" - value["suffixes"] = [".json", ".yaml", ".yml"] - value["fileTypes"] = ["json", "yaml", "yml"] + value["fileTypes"] = [".json", ".yaml", ".yml"] def replace_default_value_with_actual(value: Dict[str, Any]) -> None: @@ -434,9 +419,7 @@ def set_headers_value(value: Dict[str, Any]) -> None: value["value"] = """{"Authorization": "Bearer "}""" -def add_options_to_field( - value: Dict[str, Any], class_name: Optional[str], key: str -) -> None: +def add_options_to_field(value: Dict[str, Any], class_name: Optional[str], key: str) -> None: """ Adds options to the field based on the class name and key. """ diff --git a/src/backend/langflow/utils/validate.py b/src/backend/langflow/utils/validate.py index f8a9c1d1d..785055dba 100644 --- a/src/backend/langflow/utils/validate.py +++ b/src/backend/langflow/utils/validate.py @@ -1,9 +1,11 @@ import ast import contextlib import importlib -import types +from types import FunctionType from typing import Dict +from langflow.field_typing.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES + def add_type_ignores(): if not hasattr(ast, "TypeIgnore"): @@ -41,9 +43,7 @@ def validate_code(code): # Evaluate the function definition for node in tree.body: if isinstance(node, ast.FunctionDef): - code_obj = compile( - ast.Module(body=[node], type_ignores=[]), "", "exec" - ) + code_obj = compile(ast.Module(body=[node], type_ignores=[]), "", "exec") try: exec(code_obj) except Exception as e: @@ -63,8 +63,7 @@ def eval_function(function_string: str): ( obj for name, obj in namespace.items() - if isinstance(obj, types.FunctionType) - and obj.__code__.co_filename == "" + if isinstance(obj, FunctionType) and obj.__code__.co_filename == "" ), None, ) @@ -88,23 +87,15 @@ def execute_function(code, function_name, *args, **kwargs): exec_globals, locals(), ) - exec_globals[alias.asname or alias.name] = importlib.import_module( - alias.name - ) + exec_globals[alias.asname or alias.name] = importlib.import_module(alias.name) except ModuleNotFoundError as e: - raise ModuleNotFoundError( - f"Module {alias.name} not found. Please install it and try again." - ) from e + raise ModuleNotFoundError(f"Module {alias.name} not found. Please install it and try again.") from e function_code = next( - node - for node in module.body - if isinstance(node, ast.FunctionDef) and node.name == function_name + node for node in module.body if isinstance(node, ast.FunctionDef) and node.name == function_name ) function_code.parent = None - code_obj = compile( - ast.Module(body=[function_code], type_ignores=[]), "", "exec" - ) + code_obj = compile(ast.Module(body=[function_code], type_ignores=[]), "", "exec") try: exec(code_obj, exec_globals, locals()) except Exception as exc: @@ -131,23 +122,15 @@ def create_function(code, function_name): if isinstance(node, ast.Import): for alias in node.names: try: - exec_globals[alias.asname or alias.name] = importlib.import_module( - alias.name - ) + exec_globals[alias.asname or alias.name] = importlib.import_module(alias.name) except ModuleNotFoundError as e: - raise ModuleNotFoundError( - f"Module {alias.name} not found. Please install it and try again." - ) from e + raise ModuleNotFoundError(f"Module {alias.name} not found. Please install it and try again.") from e function_code = next( - node - for node in module.body - if isinstance(node, ast.FunctionDef) and node.name == function_name + node for node in module.body if isinstance(node, ast.FunctionDef) and node.name == function_name ) function_code.parent = None - code_obj = compile( - ast.Module(body=[function_code], type_ignores=[]), "", "exec" - ) + code_obj = compile(ast.Module(body=[function_code], type_ignores=[]), "", "exec") with contextlib.suppress(Exception): exec(code_obj, exec_globals, locals()) exec_globals[function_name] = locals()[function_name] @@ -164,53 +147,104 @@ def create_function(code, function_name): def create_class(code, class_name): + """ + Dynamically create a class from a string of code and a specified class name. + + :param code: String containing the Python code defining the class + :param class_name: Name of the class to be created + :return: A function that, when called, returns an instance of the created class + """ if not hasattr(ast, "TypeIgnore"): - - class TypeIgnore(ast.AST): - _fields = () - - ast.TypeIgnore = TypeIgnore + ast.TypeIgnore = create_type_ignore_class() module = ast.parse(code) - exec_globals = globals().copy() + exec_globals = prepare_global_scope(code, module) + class_code = extract_class_code(module, class_name) + compiled_class = compile_class_code(class_code) + + return build_class_constructor(compiled_class, exec_globals, class_name) + + +def create_type_ignore_class(): + """ + Create a TypeIgnore class for AST module if it doesn't exist. + + :return: TypeIgnore class + """ + + class TypeIgnore(ast.AST): + _fields = () + + return TypeIgnore + + +def prepare_global_scope(code, module): + """ + Prepares the global scope with necessary imports from the provided code module. + + :param module: AST parsed module + :return: Dictionary representing the global scope with imported modules + """ + exec_globals = globals().copy() + exec_globals.update(get_default_imports(code)) for node in module.body: if isinstance(node, ast.Import): for alias in node.names: try: - exec_globals[alias.asname or alias.name] = importlib.import_module( - alias.name - ) + exec_globals[alias.asname or alias.name] = importlib.import_module(alias.name) except ModuleNotFoundError as e: - raise ModuleNotFoundError( - f"Module {alias.name} not found. Please install it and try again." - ) from e - elif isinstance(node, ast.ImportFrom): + raise ModuleNotFoundError(f"Module {alias.name} not found. Please install it and try again.") from e + elif isinstance(node, ast.ImportFrom) and node.module is not None: try: 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." - ) from e + raise ModuleNotFoundError(f"Module {node.module} not found. Please install it and try again.") from e + return exec_globals + + +def extract_class_code(module, class_name): + """ + Extracts the AST node for the specified class from the module. + + :param module: AST parsed module + :param class_name: Name of the class to extract + :return: AST node of the specified class + """ + class_code = next(node for node in module.body if isinstance(node, ast.ClassDef) and node.name == class_name) - class_code = next( - node - for node in module.body - if isinstance(node, ast.ClassDef) and node.name == class_name - ) class_code.parent = None - code_obj = compile( - ast.Module(body=[class_code], type_ignores=[]), "", "exec" - ) - # This suppresses import errors - # with contextlib.suppress(Exception): - exec(code_obj, exec_globals, locals()) + return class_code + + +def compile_class_code(class_code): + """ + Compiles the AST node of a class into a code object. + + :param class_code: AST node of the class + :return: Compiled code object of the class + """ + code_obj = compile(ast.Module(body=[class_code], type_ignores=[]), "", "exec") + return code_obj + + +def build_class_constructor(compiled_class, exec_globals, class_name): + """ + Builds a constructor function for the dynamically created class. + + :param compiled_class: Compiled code object of the class + :param exec_globals: Global scope with necessary imports + :param class_name: Name of the class + :return: Constructor function for the class + """ + + exec(compiled_class, exec_globals, locals()) exec_globals[class_name] = locals()[class_name] # Return a function that imports necessary modules and creates an instance of the target class - def build_my_class(*args, **kwargs): + def build_custom_class(*args, **kwargs): for module_name, module in exec_globals.items(): if isinstance(module, type(importlib)): globals()[module_name] = module @@ -218,9 +252,40 @@ def create_class(code, class_name): instance = exec_globals[class_name](*args, **kwargs) return instance - build_my_class.__globals__.update(exec_globals) + build_custom_class.__globals__.update(exec_globals) + return build_custom_class - return build_my_class + +def get_default_imports(code_string): + """ + Returns a dictionary of default imports for the dynamic class constructor. + """ + typing_module = importlib.import_module("typing") + default_imports = { + "Optional": typing_module.Optional, + "List": typing_module.List, + "Dict": typing_module.Dict, + "Union": typing_module.Union, + } + + langflow_imports = list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys()) + necessary_imports = find_names_in_code(code_string, langflow_imports) + langflow_module = importlib.import_module("langflow.field_typing") + default_imports.update({name: getattr(langflow_module, name) for name in necessary_imports}) + + return default_imports + + +def find_names_in_code(code, names): + """ + Finds if any of the specified names are present in the given code string. + + :param code: The source code as a string. + :param names: A list of names to check for in the code. + :return: A set of names that are found in the code. + """ + found_names = {name for name in names if name in code} + return found_names def extract_function_name(code): diff --git a/src/backend/langflow/worker.py b/src/backend/langflow/worker.py index 2eeba14a5..a66c35b78 100644 --- a/src/backend/langflow/worker.py +++ b/src/backend/langflow/worker.py @@ -1,15 +1,13 @@ -from langflow.core.celery_app import celery_app -from typing import Any, Dict, Optional -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any, Dict, Optional +from asgiref.sync import async_to_sync from celery.exceptions import SoftTimeLimitExceeded # type: ignore -from langflow.processing.process import ( - Result, - generate_result, - process_inputs, -) +from langflow.core.celery_app import celery_app +from langflow.processing.process import Result, generate_result, process_inputs +from langflow.services.deps import get_session_service from langflow.services.manager import initialize_session_service -from langflow.services.getters import get_session_service +from loguru import logger +from rich import print if TYPE_CHECKING: from langflow.graph.vertex.base import Vertex @@ -27,12 +25,10 @@ def build_vertex(self, vertex: "Vertex") -> "Vertex": """ try: vertex.task_id = self.request.id - vertex.build() + async_to_sync(vertex.build)() return vertex except SoftTimeLimitExceeded as e: - raise self.retry( - exc=SoftTimeLimitExceeded("Task took too long"), countdown=2 - ) from e + raise self.retry(exc=SoftTimeLimitExceeded("Task took too long"), countdown=2) from e @celery_app.task(acks_late=True) @@ -42,21 +38,38 @@ def process_graph_cached_task( clear_cache=False, session_id=None, ) -> Dict[str, Any]: - initialize_session_service() - session_service = get_session_service() - if clear_cache: - session_service.clear_session(session_id) - if session_id is None: - session_id = session_service.generate_key( - session_id=session_id, data_graph=data_graph - ) - # Load the graph using SessionService - graph, artifacts = session_service.load_session(session_id, data_graph) - built_object = graph.build() - processed_inputs = process_inputs(inputs, artifacts) - result = generate_result(built_object, processed_inputs) - # langchain_object is now updated with the new memory - # we need to update the cache with the updated langchain_object - session_service.update_session(session_id, (graph, artifacts)) + try: + initialize_session_service() + session_service = get_session_service() - return Result(result=result, session_id=session_id).dict() + if clear_cache: + session_service.clear_session(session_id) + + if session_id is None: + session_id = session_service.generate_key(session_id=session_id, data_graph=data_graph) + + # Use async_to_sync to handle the asynchronous part of the session service + session_data = async_to_sync(session_service.load_session, force_new_loop=True)(session_id, data_graph) + logger.warning(f"session_data: {session_data}") + graph, artifacts = session_data if session_data else (None, None) + + if not graph: + raise ValueError("Graph not found in the session") + + # Use async_to_sync for the asynchronous build method + built_object = async_to_sync(graph.build, force_new_loop=True)() + + logger.debug(f"Built object: {built_object}") + + processed_inputs = process_inputs(inputs, artifacts or {}) + result = generate_result(built_object, processed_inputs) + + # Update the session with the new data + session_service.update_session(session_id, (graph, artifacts)) + result_object = Result(result=result, session_id=session_id).model_dump() + print(f"Result object: {result_object}") + return result_object + except Exception as e: + logger.error(f"Error in process_graph_cached_task: {e}") + # Handle the exception as needed, maybe re-raise or return an error message + raise diff --git a/src/frontend/src/pages/ViewPage/index.tsx b/src/frontend/src/pages/ViewPage/index.tsx index 88bb55ec3..2f1af3049 100644 --- a/src/frontend/src/pages/ViewPage/index.tsx +++ b/src/frontend/src/pages/ViewPage/index.tsx @@ -1,10 +1,11 @@ import { useContext, useEffect } from "react"; import { useParams } from "react-router-dom"; +import { FlowsContext } from "../../contexts/flowsContext"; import Page from "../FlowPage/components/PageComponent"; export default function ViewPage() { - const { setDark } = useContext(darkContext); - const { id, theme } = useParams(); + const { flows, tabId, setTabId } = useContext(FlowsContext); + const { id } = useParams(); // Set flow tab id useEffect(() => { diff --git a/tests/conftest.py b/tests/conftest.py index c58f35cf5..f8db5a67f 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,46 +1,41 @@ -from contextlib import contextmanager import json -from contextlib import suppress +# we need to import tmpdir +import tempfile +from contextlib import contextmanager, suppress from pathlib import Path -from typing import AsyncGenerator, TYPE_CHECKING +from typing import TYPE_CHECKING, AsyncGenerator -from langflow.graph.graph.base import Graph -from langflow.services.auth.utils import get_password_hash -from langflow.services.database.models.flow.flow import Flow, FlowCreate -from langflow.services.database.models.user.user import User, UserCreate import orjson -from langflow.services.database.utils import session_getter -from langflow.services.getters import get_db_service import pytest from fastapi.testclient import TestClient from httpx import AsyncClient -from sqlmodel import SQLModel, Session, create_engine +from sqlmodel import Session, SQLModel, create_engine from sqlmodel.pool import StaticPool from typer.testing import CliRunner -# we need to import tmpdir -import tempfile +from langflow.graph.graph.base import Graph +from langflow.services.auth.utils import get_password_hash +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 if TYPE_CHECKING: - from langflow.services.database.manager import DatabaseService + from langflow.services.database.service import DatabaseService def pytest_configure(): - pytest.BASIC_EXAMPLE_PATH = ( - Path(__file__).parent.absolute() / "data" / "basic_example.json" - ) - pytest.COMPLEX_EXAMPLE_PATH = ( - Path(__file__).parent.absolute() / "data" / "complex_example.json" - ) - pytest.OPENAPI_EXAMPLE_PATH = ( - Path(__file__).parent.absolute() / "data" / "Openapi.json" - ) + pytest.BASIC_EXAMPLE_PATH = Path(__file__).parent.absolute() / "data" / "basic_example.json" + pytest.COMPLEX_EXAMPLE_PATH = Path(__file__).parent.absolute() / "data" / "complex_example.json" + pytest.OPENAPI_EXAMPLE_PATH = Path(__file__).parent.absolute() / "data" / "Openapi.json" + pytest.GROUPED_CHAT_EXAMPLE_PATH = Path(__file__).parent.absolute() / "data" / "grouped_chat.json" + pytest.ONE_GROUPED_CHAT_EXAMPLE_PATH = Path(__file__).parent.absolute() / "data" / "one_group_chat.json" + pytest.VECTOR_STORE_GROUPED_EXAMPLE_PATH = Path(__file__).parent.absolute() / "data" / "vector_store_grouped.json" + pytest.BASIC_CHAT_WITH_PROMPT_AND_HISTORY = ( Path(__file__).parent.absolute() / "data" / "BasicChatwithPromptandHistory.json" ) - pytest.VECTOR_STORE_PATH = ( - Path(__file__).parent.absolute() / "data" / "Vector_store.json" - ) + pytest.VECTOR_STORE_PATH = Path(__file__).parent.absolute() / "data" / "Vector_store.json" pytest.CODE_WITH_SYNTAX_ERROR = """ def get_text(): retun "Hello World" @@ -58,9 +53,7 @@ async def async_client() -> AsyncGenerator: @pytest.fixture(name="session") def session_fixture(): - engine = create_engine( - "sqlite://", connect_args={"check_same_thread": False}, poolclass=StaticPool - ) + engine = create_engine("sqlite://", connect_args={"check_same_thread": False}, poolclass=StaticPool) SQLModel.metadata.create_all(engine) with Session(engine) as session: yield session @@ -96,9 +89,7 @@ def distributed_client_fixture(session: Session, monkeypatch, distributed_env): monkeypatch.setenv("LANGFLOW_AUTO_LOGIN", "false") # monkeypatch langflow.services.task.manager.USE_CELERY to True # monkeypatch.setattr(manager, "USE_CELERY", True) - monkeypatch.setattr( - celery_app, "celery_app", celery_app.make_celery("langflow", Config) - ) + monkeypatch.setattr(celery_app, "celery_app", celery_app.make_celery("langflow", Config)) # def get_session_override(): # return session @@ -215,7 +206,7 @@ def test_user(client): username="testuser", password="testpassword", ) - response = client.post("/api/v1/users", json=user_data.dict()) + response = client.post("/api/v1/users", json=user_data.model_dump()) assert response.status_code == 201 return response.json() @@ -231,11 +222,7 @@ def active_user(client): is_superuser=False, ) # check if user exists - if ( - active_user := session.query(User) - .filter(User.username == user.username) - .first() - ): + if active_user := session.query(User).filter(User.username == user.username).first(): return active_user session.add(user) session.commit() @@ -255,13 +242,16 @@ def logged_in_headers(client, active_user): @pytest.fixture def flow(client, json_flow: str, active_user): - from langflow.services.database.models.flow.flow import FlowCreate + from langflow.services.database.models.flow.model import FlowCreate loaded_json = json.loads(json_flow) flow_data = FlowCreate( - name="test_flow", data=loaded_json.get("data"), user_id=active_user.id + name="test_flow", + data=loaded_json.get("data"), + user_id=active_user.id, + description="description", ) - flow = Flow(**flow_data.dict()) + flow = Flow.model_validate(flow_data.model_dump()) with session_getter(get_db_service()) as session: session.add(flow) session.commit() @@ -275,7 +265,7 @@ def added_flow(client, json_flow_with_prompt_and_history, logged_in_headers): flow = orjson.loads(json_flow_with_prompt_and_history) data = flow["data"] flow = FlowCreate(name="Basic Chat", description="description", data=data) - response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers) + response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers) assert response.status_code == 201 assert response.json()["name"] == flow.name assert response.json()["data"] == flow.data @@ -287,10 +277,24 @@ def added_vector_store(client, json_vector_store, logged_in_headers): vector_store = orjson.loads(json_vector_store) data = vector_store["data"] vector_store = FlowCreate(name="Vector Store", description="description", data=data) - response = client.post( - "api/v1/flows/", json=vector_store.dict(), headers=logged_in_headers - ) + response = client.post("api/v1/flows/", json=vector_store.model_dump(), headers=logged_in_headers) assert response.status_code == 201 assert response.json()["name"] == vector_store.name assert response.json()["data"] == vector_store.data return response.json() + + +@pytest.fixture +def test_component_code(): + path = Path(__file__).parent.absolute() / "data" / "component.py" + # load the content as a string + with open(path, "r") as f: + return f.read() + + +@pytest.fixture +def test_component_with_templatefield_code(): + path = Path(__file__).parent.absolute() / "data" / "component_with_templatefield.py" + # load the content as a string + with open(path, "r") as f: + return f.read() diff --git a/tests/data/component.py b/tests/data/component.py new file mode 100644 index 000000000..e801d2feb --- /dev/null +++ b/tests/data/component.py @@ -0,0 +1,16 @@ +import random + +from langflow import CustomComponent + + +class TestComponent(CustomComponent): + def refresh_values(self): + # This is a function that will be called every time the component is updated + # and should return a list of random strings + return [f"Random {random.randint(1, 100)}" for _ in range(5)] + + def build_config(self): + return {"param": {"display_name": "Param", "options": self.refresh_values}} + + def build(self, param: int): + return param diff --git a/tests/data/component_with_templatefield.py b/tests/data/component_with_templatefield.py new file mode 100644 index 000000000..6b2ce011b --- /dev/null +++ b/tests/data/component_with_templatefield.py @@ -0,0 +1,17 @@ +import random + +from langflow import CustomComponent +from langflow.field_typing import TemplateField + + +class TestComponent(CustomComponent): + def refresh_values(self): + # This is a function that will be called every time the component is updated + # and should return a list of random strings + return [f"Random {random.randint(1, 100)}" for _ in range(5)] + + def build_config(self): + return {"param": TemplateField(display_name="Param", options=self.refresh_values)} + + def build(self, param: int): + return param diff --git a/tests/locust/locustfile.py b/tests/locust/locustfile.py index aca0d1de9..1fc91ee2c 100644 --- a/tests/locust/locustfile.py +++ b/tests/locust/locustfile.py @@ -66,9 +66,7 @@ class NameTest(FastHttpUser): result1, session_id = self.process(name, self.flow_id, payload1) payload2 = { - "inputs": { - "text": "What is my name? Please, answer like this: Your name is " - }, + "inputs": {"text": "What is my name? Please, answer like this: Your name is "}, "session_id": session_id, "sync": False, } @@ -88,9 +86,7 @@ class NameTest(FastHttpUser): logged_in_headers = {"Authorization": f"Bearer {a_token}"} print("Logged in") with open( - Path(__file__).parent.parent - / "data" - / "BasicChatwithPromptandHistory.json", + Path(__file__).parent.parent / "data" / "BasicChatwithPromptandHistory.json", "r", ) as f: json_flow = f.read() @@ -115,11 +111,7 @@ class NameTest(FastHttpUser): ) print(response.json()) user_id = next( - ( - user["id"] - for user in response.json()["users"] - if user["username"] == "superuser" - ), + (user["id"] for user in response.json()["users"] if user["username"] == "superuser"), None, ) # Create api key diff --git a/tests/test_agents_template.py b/tests/test_agents_template.py index e354d4a16..01891ec05 100644 --- a/tests/test_agents_template.py +++ b/tests/test_agents_template.py @@ -12,7 +12,7 @@ def test_zero_shot_agent(client: TestClient, logged_in_headers): "ZeroShotAgent", "BaseSingleActionAgent", "Agent", - "function", + "Callable", } template = zero_shot_agent["template"] @@ -28,6 +28,7 @@ def test_zero_shot_agent(client: TestClient, logged_in_headers): "list": True, "advanced": False, "info": "", + "fileTypes": [], } # Additional assertions for other template variables @@ -43,6 +44,7 @@ def test_zero_shot_agent(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["llm"] == { "required": True, @@ -56,6 +58,7 @@ def test_zero_shot_agent(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["output_parser"] == { "required": False, @@ -69,6 +72,7 @@ def test_zero_shot_agent(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["input_variables"] == { "required": False, @@ -82,6 +86,7 @@ def test_zero_shot_agent(client: TestClient, logged_in_headers): "list": True, "advanced": False, "info": "", + "fileTypes": [], } assert template["prefix"] == { "required": False, @@ -96,6 +101,7 @@ def test_zero_shot_agent(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["suffix"] == { "required": False, @@ -110,6 +116,7 @@ def test_zero_shot_agent(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } @@ -135,6 +142,9 @@ def test_json_agent(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "file_path": "", + "fileTypes": [], + "value": "", } assert template["llm"] == { "required": True, @@ -149,6 +159,9 @@ def test_json_agent(client: TestClient, logged_in_headers): "advanced": False, "display_name": "LLM", "info": "", + "file_path": "", + "fileTypes": [], + "value": "", } @@ -169,87 +182,12 @@ def test_csv_agent(client: TestClient, logged_in_headers): "show": True, "multiline": False, "value": "", - "suffixes": [".csv"], - "fileTypes": ["csv"], + "fileTypes": [".csv"], "password": False, "name": "path", "type": "file", "list": False, - "file_path": None, - "advanced": False, - "info": "", - } - assert template["llm"] == { - "required": True, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "password": False, - "name": "llm", - "type": "BaseLanguageModel", - "list": False, - "advanced": False, - "display_name": "LLM", - "info": "", - } - - -def test_initialize_agent(client: TestClient, logged_in_headers): - response = client.get("api/v1/all", headers=logged_in_headers) - assert response.status_code == 200 - json_response = response.json() - agents = json_response["agents"] - - initialize_agent = agents["AgentInitializer"] - assert initialize_agent["base_classes"] == ["AgentExecutor", "function"] - template = initialize_agent["template"] - - assert template["agent"] == { - "required": True, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "value": "zero-shot-react-description", - "password": False, - "options": [ - "zero-shot-react-description", - "react-docstore", - "self-ask-with-search", - "conversational-react-description", - "openai-functions", - "openai-multi-functions", - ], - "name": "agent", - "type": "str", - "list": True, - "advanced": False, - "info": "", - } - assert template["memory"] == { - "required": False, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "password": False, - "name": "memory", - "type": "BaseChatMemory", - "list": False, - "advanced": False, - "info": "", - } - assert template["tools"] == { - "required": True, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "password": False, - "name": "tools", - "type": "Tool", - "list": True, + "file_path": "", "advanced": False, "info": "", } @@ -266,4 +204,7 @@ def test_initialize_agent(client: TestClient, logged_in_headers): "advanced": False, "display_name": "LLM", "info": "", + "file_path": "", + "fileTypes": [], + "value": "", } diff --git a/tests/test_api_key.py b/tests/test_api_key.py index 43b91fa43..7988793d4 100644 --- a/tests/test_api_key.py +++ b/tests/test_api_key.py @@ -6,9 +6,7 @@ from langflow.services.database.models.api_key import ApiKeyCreate def api_key(client, logged_in_headers, active_user): api_key = ApiKeyCreate(name="test-api-key") - response = client.post( - "api/v1/api_key", data=api_key.json(), headers=logged_in_headers - ) + response = client.post("api/v1/api_key", data=api_key.json(), headers=logged_in_headers) assert response.status_code == 200, response.text return response.json() @@ -28,9 +26,7 @@ def test_get_api_keys(client, logged_in_headers, api_key): def test_create_api_key(client, logged_in_headers): api_key_name = "test-api-key" - response = client.post( - "api/v1/api_key", json={"name": api_key_name}, headers=logged_in_headers - ) + response = client.post("api/v1/api_key", json={"name": api_key_name}, headers=logged_in_headers) assert response.status_code == 200 data = response.json() assert "name" in data and data["name"] == api_key_name diff --git a/tests/test_cache.py b/tests/test_cache.py index c2c706ee9..8cfebe230 100644 --- a/tests/test_cache.py +++ b/tests/test_cache.py @@ -1,8 +1,9 @@ import json -from langflow.graph import Graph import pytest +from langflow.graph import Graph + def get_graph(_type="basic"): """Get a graph from a json file""" @@ -41,5 +42,5 @@ def langchain_objects_are_equal(obj1, obj2): def test_build_graph(client, basic_data_graph): graph = Graph.from_payload(basic_data_graph) assert graph is not None - assert len(graph.nodes) == len(basic_data_graph["nodes"]) + assert len(graph.vertices) == len(basic_data_graph["nodes"]) assert len(graph.edges) == len(basic_data_graph["edges"]) diff --git a/tests/test_chains_template.py b/tests/test_chains_template.py index eb20a0571..2e705ac00 100644 --- a/tests/test_chains_template.py +++ b/tests/test_chains_template.py @@ -1,6 +1,5 @@ from fastapi.testclient import TestClient - # def test_chains_settings(client: TestClient, logged_in_headers): # response = client.get("api/v1/all", headers=logged_in_headers) # assert response.status_code == 200 @@ -9,170 +8,6 @@ from fastapi.testclient import TestClient # assert set(chains.keys()) == set(settings.chains) -# Test the ConversationChain object -def test_conversation_chain(client: TestClient, logged_in_headers): - response = client.get("api/v1/all", headers=logged_in_headers) - assert response.status_code == 200 - json_response = response.json() - chains = json_response["chains"] - - chain = chains["ConversationChain"] - # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects - assert set(chain["base_classes"]) == { - "ConversationChain", - "LLMChain", - "Chain", - "function", - } - - template = chain["template"] - assert template["memory"] == { - "required": False, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "password": False, - "name": "memory", - "type": "BaseMemory", - "list": False, - "advanced": False, - "info": "", - } - assert template["verbose"] == { - "required": False, - "dynamic": False, - "placeholder": "", - "show": False, - "multiline": False, - "password": False, - "name": "verbose", - "type": "bool", - "list": False, - "advanced": True, - "info": "", - } - assert template["llm"] == { - "required": True, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "password": False, - "name": "llm", - "type": "BaseLanguageModel", - "list": False, - "advanced": False, - "info": "", - } - assert template["input_key"] == { - "required": True, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "value": "input", - "password": False, - "name": "input_key", - "type": "str", - "list": False, - "advanced": True, - "info": "", - } - assert template["output_key"] == { - "required": True, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "value": "response", - "password": False, - "name": "output_key", - "type": "str", - "list": False, - "advanced": True, - "info": "", - } - assert template["_type"] == "ConversationChain" - - # Test the description object - assert ( - chain["description"] - == "Chain to have a conversation and load context from memory." - ) - - -def test_llm_chain(client: TestClient, logged_in_headers): - response = client.get("api/v1/all", headers=logged_in_headers) - assert response.status_code == 200 - json_response = response.json() - chains = json_response["chains"] - chain = chains["LLMChain"] - - # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects - assert set(chain["base_classes"]) == { - "function", - "LLMChain", - "Chain", - } - - template = chain["template"] - assert template["memory"] == { - "required": False, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "password": False, - "name": "memory", - "type": "BaseMemory", - "list": False, - "advanced": False, - "info": "", - } - assert template["verbose"] == { - "required": False, - "dynamic": False, - "placeholder": "", - "show": False, - "multiline": False, - "value": False, - "password": False, - "name": "verbose", - "type": "bool", - "list": False, - "advanced": True, - "info": "", - } - assert template["llm"] == { - "required": True, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "password": False, - "name": "llm", - "type": "BaseLanguageModel", - "list": False, - "advanced": False, - "info": "", - } - assert template["output_key"] == { - "required": True, - "dynamic": False, - "placeholder": "", - "show": True, - "multiline": False, - "value": "text", - "password": False, - "name": "output_key", - "type": "str", - "list": False, - "advanced": True, - "info": "", - } - - def test_llm_checker_chain(client: TestClient, logged_in_headers): response = client.get("api/v1/all", headers=logged_in_headers) assert response.status_code == 200 @@ -182,7 +17,7 @@ def test_llm_checker_chain(client: TestClient, logged_in_headers): # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects assert set(chain["base_classes"]) == { - "function", + "Callable", "LLMCheckerChain", "Chain", } @@ -200,6 +35,7 @@ def test_llm_checker_chain(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["_type"] == "LLMCheckerChain" @@ -216,7 +52,7 @@ def test_llm_math_chain(client: TestClient, logged_in_headers): chain = chains["LLMMathChain"] # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects assert set(chain["base_classes"]) == { - "function", + "Callable", "LLMMathChain", "Chain", } @@ -234,6 +70,7 @@ def test_llm_math_chain(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["verbose"] == { "required": False, @@ -248,6 +85,7 @@ def test_llm_math_chain(client: TestClient, logged_in_headers): "list": False, "advanced": True, "info": "", + "fileTypes": [], } assert template["llm"] == { "required": True, @@ -261,6 +99,7 @@ def test_llm_math_chain(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["input_key"] == { "required": True, @@ -275,6 +114,7 @@ def test_llm_math_chain(client: TestClient, logged_in_headers): "list": False, "advanced": True, "info": "", + "fileTypes": [], } assert template["output_key"] == { "required": True, @@ -289,14 +129,12 @@ def test_llm_math_chain(client: TestClient, logged_in_headers): "list": False, "advanced": True, "info": "", + "fileTypes": [], } assert template["_type"] == "LLMMathChain" # Test the description object - assert ( - chain["description"] - == "Chain that interprets a prompt and executes python code to do math." - ) + assert chain["description"] == "Chain that interprets a prompt and executes python code to do math." def test_series_character_chain(client: TestClient, logged_in_headers): @@ -309,7 +147,7 @@ def test_series_character_chain(client: TestClient, logged_in_headers): # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects assert set(chain["base_classes"]) == { - "function", + "Callable", "LLMChain", "BaseCustomChain", "Chain", @@ -331,6 +169,9 @@ def test_series_character_chain(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], + "file_path": "", + "value": "", } assert template["character"] == { "required": True, @@ -344,6 +185,9 @@ def test_series_character_chain(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], + "file_path": "", + "value": "", } assert template["series"] == { "required": True, @@ -357,6 +201,9 @@ def test_series_character_chain(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], + "file_path": "", + "value": "", } assert template["_type"] == "SeriesCharacterChain" @@ -400,12 +247,12 @@ def test_mid_journey_prompt_chain(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "file_path": "", + "fileTypes": [], + "value": "", } # Test the description object - assert ( - chain["description"] - == "MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts." - ) + assert chain["description"] == "MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts." def test_time_travel_guide_chain(client: TestClient, logged_in_headers): @@ -441,6 +288,9 @@ def test_time_travel_guide_chain(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "file_path": "", + "fileTypes": [], + "value": "", } assert template["memory"] == { "required": False, @@ -454,6 +304,9 @@ def test_time_travel_guide_chain(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "file_path": "", + "fileTypes": [], + "value": "", } assert chain["description"] == "Time travel guide chain." diff --git a/tests/test_cli.py b/tests/test_cli.py index ee938db12..ee95a271c 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -1,9 +1,10 @@ from pathlib import Path from tempfile import tempdir -from langflow.__main__ import app + import pytest -from langflow.services import getters +from langflow.__main__ import app +from langflow.services import deps @pytest.fixture(scope="module") @@ -26,11 +27,12 @@ def test_components_path(runner, client, default_settings): ["run", "--components-path", str(temp_dir), *default_settings], ) assert result.exit_code == 0, result.stdout - settings_service = getters.get_settings_service() + settings_service = deps.get_settings_service() assert str(temp_dir) in settings_service.settings.COMPONENTS_PATH def test_superuser(runner, client, session): result = runner.invoke(app, ["superuser"], input="admin\nadmin\n") assert result.exit_code == 0, result.stdout - assert "Superuser created successfully." in result.stdout + assert "Superuser creation failed." not in result.output, result.output + assert "Superuser created successfully." in result.output, result.output diff --git a/tests/test_custom_component.py b/tests/test_custom_component.py index 47c9cbfb2..35eaba00e 100644 --- a/tests/test_custom_component.py +++ b/tests/test_custom_component.py @@ -1,19 +1,15 @@ import ast -import pytest import types from uuid import uuid4 - +import pytest from fastapi import HTTPException -from langflow.services.database.models.flow import Flow, FlowCreate -from langflow.interface.custom.base import CustomComponent -from langflow.interface.custom.component import ( - Component, - ComponentCodeNullError, - ComponentFunctionEntrypointNameNullError, -) -from langflow.interface.custom.code_parser import CodeParser, CodeSyntaxError +from langflow.interface.custom.base import CustomComponent +from langflow.interface.custom.code_parser import CodeParser, CodeSyntaxError +from langflow.interface.custom.component import Component, ComponentCodeNullError +from langflow.interface.types import build_custom_component_template, create_and_validate_component +from langflow.services.database.models.flow import Flow, FlowCreate code_default = """ from langflow import Prompt @@ -73,16 +69,16 @@ def test_component_init(): """ Test the initialization of the Component class. """ - component = Component(code=code_default, function_entrypoint_name="build") + component = Component(code=code_default, _function_entrypoint_name="build") assert component.code == code_default - assert component.function_entrypoint_name == "build" + assert component._function_entrypoint_name == "build" def test_component_get_code_tree(): """ Test the get_code_tree method of the Component class. """ - component = Component(code=code_default, function_entrypoint_name="build") + component = Component(code=code_default, _function_entrypoint_name="build") tree = component.get_code_tree(component.code) assert "imports" in tree @@ -92,19 +88,20 @@ def test_component_code_null_error(): Test the get_function method raises the ComponentCodeNullError when the code is empty. """ - component = Component(code="", function_entrypoint_name="") + component = Component(code="", _function_entrypoint_name="") with pytest.raises(ComponentCodeNullError): component.get_function() -def test_component_function_entrypoint_name_null_error(): - """ - Test the get_function method raises the ComponentFunctionEntrypointNameNullError - when the function_entrypoint_name is empty. - """ - component = Component(code=code_default, function_entrypoint_name="") - with pytest.raises(ComponentFunctionEntrypointNameNullError): - component.get_function() +# TODO: Validate if we should remove this +# def test_component_function_entrypoint_name_null_error(): +# """ +# Test the get_function method raises the ComponentFunctionEntrypointNameNullError +# when the function_entrypoint_name is empty. +# """ +# component = Component(code=code_default, _function_entrypoint_name="") +# with pytest.raises(ComponentFunctionEntrypointNameNullError): +# component.get_function() def test_custom_component_init(): @@ -113,9 +110,7 @@ def test_custom_component_init(): """ function_entrypoint_name = "build" - custom_component = CustomComponent( - code=code_default, function_entrypoint_name=function_entrypoint_name - ) + custom_component = CustomComponent(code=code_default, function_entrypoint_name=function_entrypoint_name) assert custom_component.code == code_default assert custom_component.function_entrypoint_name == function_entrypoint_name @@ -124,10 +119,8 @@ def test_custom_component_build_template_config(): """ Test the build_template_config property of the CustomComponent class. """ - custom_component = CustomComponent( - code=code_default, function_entrypoint_name="build" - ) - config = custom_component.build_template_config + custom_component = CustomComponent(code=code_default, function_entrypoint_name="build") + config = custom_component.template_config assert isinstance(config, dict) @@ -135,9 +128,7 @@ def test_custom_component_get_function(): """ Test the get_function property of the CustomComponent class. """ - custom_component = CustomComponent( - code="def build(): pass", function_entrypoint_name="build" - ) + custom_component = CustomComponent(code="def build(): pass", function_entrypoint_name="build") my_function = custom_component.get_function assert isinstance(my_function, types.FunctionType) @@ -212,7 +203,7 @@ def test_component_get_function_valid(): Test the get_function method of the Component class with valid code and function_entrypoint_name. """ - component = Component(code="def build(): pass", function_entrypoint_name="build") + component = Component(code="def build(): pass", _function_entrypoint_name="build") my_function = component.get_function() assert callable(my_function) @@ -222,9 +213,7 @@ def test_custom_component_get_function_entrypoint_args(): Test the get_function_entrypoint_args property of the CustomComponent class. """ - custom_component = CustomComponent( - code=code_default, function_entrypoint_name="build" - ) + custom_component = CustomComponent(code=code_default, function_entrypoint_name="build") args = custom_component.get_function_entrypoint_args assert len(args) == 4 assert args[0]["name"] == "self" @@ -237,20 +226,18 @@ def test_custom_component_get_function_entrypoint_return_type(): Test the get_function_entrypoint_return_type property of the CustomComponent class. """ - custom_component = CustomComponent( - code=code_default, function_entrypoint_name="build" - ) + from langchain.schema import Document + + custom_component = CustomComponent(code=code_default, function_entrypoint_name="build") return_type = custom_component.get_function_entrypoint_return_type - assert return_type == ["Document"] + assert return_type == [Document] def test_custom_component_get_main_class_name(): """ Test the get_main_class_name property of the CustomComponent class. """ - custom_component = CustomComponent( - code=code_default, function_entrypoint_name="build" - ) + custom_component = CustomComponent(code=code_default, function_entrypoint_name="build") class_name = custom_component.get_main_class_name assert class_name == "YourComponent" @@ -260,9 +247,7 @@ def test_custom_component_get_function_valid(): Test the get_function property of the CustomComponent class with valid code and function_entrypoint_name. """ - custom_component = CustomComponent( - code="def build(): pass", function_entrypoint_name="build" - ) + custom_component = CustomComponent(code="def build(): pass", function_entrypoint_name="build") my_function = custom_component.get_function assert callable(my_function) @@ -297,9 +282,7 @@ def test_code_parser_parse_callable_details_no_args(): parser = CodeParser("") node = ast.FunctionDef( name="test", - args=ast.arguments( - args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[] - ), + args=ast.arguments(args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]), body=[], decorator_list=[], returns=None, @@ -345,9 +328,7 @@ def test_code_parser_parse_function_def_not_init(): parser = CodeParser("") stmt = ast.FunctionDef( name="test", - args=ast.arguments( - args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[] - ), + args=ast.arguments(args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]), body=[], decorator_list=[], returns=None, @@ -365,9 +346,7 @@ def test_code_parser_parse_function_def_init(): parser = CodeParser("") stmt = ast.FunctionDef( name="__init__", - args=ast.arguments( - args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[] - ), + args=ast.arguments(args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]), body=[], decorator_list=[], returns=None, @@ -382,7 +361,7 @@ def test_component_get_code_tree_syntax_error(): Test the get_code_tree method of the Component class raises the CodeSyntaxError when given incorrect syntax. """ - component = Component(code="import os as", function_entrypoint_name="build") + component = Component(code="import os as", _function_entrypoint_name="build") with pytest.raises(CodeSyntaxError): component.get_code_tree(component.code) @@ -402,9 +381,7 @@ def test_custom_component_get_code_tree_syntax_error(): Test the get_code_tree method of the CustomComponent class raises the CodeSyntaxError when given incorrect syntax. """ - custom_component = CustomComponent( - code="import os as", function_entrypoint_name="build" - ) + custom_component = CustomComponent(code="import os as", function_entrypoint_name="build") with pytest.raises(CodeSyntaxError): custom_component.get_code_tree(custom_component.code) @@ -458,9 +435,7 @@ def test_custom_component_build_not_implemented(): Test the build method of the CustomComponent class raises the NotImplementedError. """ - custom_component = CustomComponent( - code="def build(): pass", function_entrypoint_name="build" - ) + custom_component = CustomComponent(code="def build(): pass", function_entrypoint_name="build") with pytest.raises(NotImplementedError): custom_component.build() @@ -468,7 +443,7 @@ def test_custom_component_build_not_implemented(): def test_build_config_no_code(): component = CustomComponent(code=None) - assert component.get_function_entrypoint_args == "" + assert component.get_function_entrypoint_args == [] assert component.get_function_entrypoint_return_type == [] @@ -494,9 +469,7 @@ def test_flow(db): } # Create flow - flow = FlowCreate( - id=uuid4(), name="Test Flow", description="Fixture flow", data=flow_data - ) + flow = FlowCreate(id=uuid4(), name="Test Flow", description="Fixture flow", data=flow_data) # Add to database db.add(flow) @@ -557,3 +530,36 @@ def test_build_config_field_value_keys(component): config = component.build_config() field_values = config["fields"].values() assert all("type" in value for value in field_values) + + +def test_create_and_validate_component_valid_code(test_component_code): + component = create_and_validate_component(test_component_code) + assert isinstance(component, CustomComponent) + + +def test_build_langchain_template_custom_component_valid_code(test_component_code): + component = create_and_validate_component(test_component_code) + frontend_node = build_custom_component_template(component) + assert isinstance(frontend_node, dict) + template = frontend_node["template"] + assert isinstance(template, dict) + assert "param" in template + param_options = template["param"]["options"] + # Now run it again with an update field + frontend_node = build_custom_component_template(component, update_field="param") + new_param_options = frontend_node["template"]["param"]["options"] + assert param_options != new_param_options + + +def test_build_langchain_template_custom_component_templatefield(test_component_with_templatefield_code): + component = create_and_validate_component(test_component_with_templatefield_code) + frontend_node = build_custom_component_template(component) + assert isinstance(frontend_node, dict) + template = frontend_node["template"] + assert isinstance(template, dict) + assert "param" in template + param_options = template["param"]["options"] + # Now run it again with an update field + frontend_node = build_custom_component_template(component, update_field="param") + new_param_options = frontend_node["template"]["param"]["options"] + assert param_options != new_param_options diff --git a/tests/test_custom_types.py b/tests/test_custom_types.py index b65f58d0a..ba54b7023 100644 --- a/tests/test_custom_types.py +++ b/tests/test_custom_types.py @@ -18,9 +18,7 @@ def test_python_function_tool(): with pytest.raises(SyntaxError): code = pytest.CODE_WITH_SYNTAX_ERROR func = get_function(code) - func = PythonFunctionTool( - name="Test", description="Testing", code=code, func=func - ) + func = PythonFunctionTool(name="Test", description="Testing", code=code, func=func) def test_python_function(): diff --git a/tests/test_database.py b/tests/test_database.py index 21f0cec17..f52252856 100644 --- a/tests/test_database.py +++ b/tests/test_database.py @@ -1,16 +1,14 @@ -from langflow.services.database.models.base import orjson_dumps -from langflow.services.database.utils import session_getter -from langflow.services.getters import get_db_service +from uuid import UUID, uuid4 + import orjson import pytest - -from uuid import UUID, uuid4 -from sqlmodel import Session - from fastapi.testclient import TestClient - from langflow.api.v1.schemas import FlowListCreate +from langflow.services.database.models.base import orjson_dumps from langflow.services.database.models.flow import Flow, FlowCreate, FlowUpdate +from langflow.services.database.utils import session_getter +from langflow.services.deps import get_db_service +from sqlmodel import Session @pytest.fixture(scope="module") @@ -27,21 +25,17 @@ def json_style(): ) -def test_create_flow( - client: TestClient, json_flow: str, active_user, logged_in_headers -): +def test_create_flow(client: TestClient, json_flow: str, active_user, logged_in_headers): flow = orjson.loads(json_flow) data = flow["data"] flow = FlowCreate(name="Test Flow", description="description", data=data) - response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers) + response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers) assert response.status_code == 201 assert response.json()["name"] == flow.name assert response.json()["data"] == flow.data # flow is optional so we can create a flow without a flow - flow = FlowCreate(name="Test Flow") - response = client.post( - "api/v1/flows/", json=flow.dict(exclude_unset=True), headers=logged_in_headers - ) + flow = FlowCreate(name="Test Flow", description="description") + response = client.post("api/v1/flows/", json=flow.model_dump(exclude_unset=True), headers=logged_in_headers) assert response.status_code == 201 assert response.json()["name"] == flow.name assert response.json()["data"] == flow.data @@ -51,13 +45,13 @@ def test_read_flows(client: TestClient, json_flow: str, active_user, logged_in_h flow_data = orjson.loads(json_flow) data = flow_data["data"] flow = FlowCreate(name="Test Flow", description="description", data=data) - response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers) + response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers) assert response.status_code == 201 assert response.json()["name"] == flow.name assert response.json()["data"] == flow.data flow = FlowCreate(name="Test Flow", description="description", data=data) - response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers) + response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers) assert response.status_code == 201 assert response.json()["name"] == flow.name assert response.json()["data"] == flow.data @@ -71,7 +65,7 @@ def test_read_flow(client: TestClient, json_flow: str, active_user, logged_in_he flow = orjson.loads(json_flow) data = flow["data"] flow = FlowCreate(name="Test Flow", description="description", data=data) - response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers) + response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers) flow_id = response.json()["id"] # flow_id should be a UUID but is a string # turn it into a UUID flow_id = UUID(flow_id) @@ -82,14 +76,12 @@ def test_read_flow(client: TestClient, json_flow: str, active_user, logged_in_he assert response.json()["data"] == flow.data -def test_update_flow( - client: TestClient, json_flow: str, active_user, logged_in_headers -): +def test_update_flow(client: TestClient, json_flow: str, active_user, logged_in_headers): flow = orjson.loads(json_flow) data = flow["data"] flow = FlowCreate(name="Test Flow", description="description", data=data) - response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers) + response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers) flow_id = response.json()["id"] updated_flow = FlowUpdate( @@ -97,9 +89,7 @@ def test_update_flow( description="updated description", data=data, ) - response = client.patch( - f"api/v1/flows/{flow_id}", json=updated_flow.dict(), headers=logged_in_headers - ) + response = client.patch(f"api/v1/flows/{flow_id}", json=updated_flow.model_dump(), headers=logged_in_headers) assert response.status_code == 200 assert response.json()["name"] == updated_flow.name @@ -107,22 +97,18 @@ def test_update_flow( # assert response.json()["data"] == updated_flow.data -def test_delete_flow( - client: TestClient, json_flow: str, active_user, logged_in_headers -): +def test_delete_flow(client: TestClient, json_flow: str, active_user, logged_in_headers): flow = orjson.loads(json_flow) data = flow["data"] flow = FlowCreate(name="Test Flow", description="description", data=data) - response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers) + response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers) flow_id = response.json()["id"] response = client.delete(f"api/v1/flows/{flow_id}", headers=logged_in_headers) assert response.status_code == 200 assert response.json()["message"] == "Flow deleted successfully" -def test_create_flows( - client: TestClient, session: Session, json_flow: str, logged_in_headers -): +def test_create_flows(client: TestClient, session: Session, json_flow: str, logged_in_headers): flow = orjson.loads(json_flow) data = flow["data"] # Create test data @@ -133,9 +119,7 @@ def test_create_flows( ] ) # Make request to endpoint - response = client.post( - "api/v1/flows/batch/", json=flow_list.dict(), headers=logged_in_headers - ) + response = client.post("api/v1/flows/batch/", json=flow_list.model_dump(), headers=logged_in_headers) # Check response status code assert response.status_code == 201 # Check response data @@ -149,9 +133,7 @@ def test_create_flows( assert response_data[1]["data"] == data -def test_upload_file( - client: TestClient, session: Session, json_flow: str, logged_in_headers -): +def test_upload_file(client: TestClient, session: Session, json_flow: str, logged_in_headers): flow = orjson.loads(json_flow) data = flow["data"] # Create test data @@ -161,7 +143,7 @@ def test_upload_file( FlowCreate(name="Flow 2", description="description", data=data), ] ) - file_contents = orjson_dumps(flow_list.dict()) + file_contents = orjson_dumps(flow_list.model_dump()) response = client.post( "api/v1/flows/upload/", files={"file": ("examples.json", file_contents, "application/json")}, @@ -200,7 +182,7 @@ def test_download_file( with session_getter(db_manager) as session: for flow in flow_list.flows: flow.user_id = active_user.id - db_flow = Flow.from_orm(flow) + db_flow = Flow.model_validate(flow, from_attributes=True) session.add(db_flow) session.commit() # Make request to endpoint @@ -218,9 +200,7 @@ def test_download_file( assert response_data[1]["data"] == data -def test_create_flow_with_invalid_data( - client: TestClient, active_user, logged_in_headers -): +def test_create_flow_with_invalid_data(client: TestClient, active_user, logged_in_headers): flow = {"name": "a" * 256, "data": "Invalid flow data"} response = client.post("api/v1/flows/", json=flow, headers=logged_in_headers) assert response.status_code == 422 @@ -232,29 +212,19 @@ def test_get_nonexistent_flow(client: TestClient, active_user, logged_in_headers assert response.status_code == 404 -def test_update_flow_idempotency( - client: TestClient, json_flow: str, active_user, logged_in_headers -): +def test_update_flow_idempotency(client: TestClient, json_flow: str, active_user, logged_in_headers): flow_data = orjson.loads(json_flow) data = flow_data["data"] flow_data = FlowCreate(name="Test Flow", description="description", data=data) - response = client.post( - "api/v1/flows/", json=flow_data.dict(), headers=logged_in_headers - ) + response = client.post("api/v1/flows/", json=flow_data.model_dump(), headers=logged_in_headers) flow_id = response.json()["id"] updated_flow = FlowCreate(name="Updated Flow", description="description", data=data) - response1 = client.put( - f"api/v1/flows/{flow_id}", json=updated_flow.dict(), headers=logged_in_headers - ) - response2 = client.put( - f"api/v1/flows/{flow_id}", json=updated_flow.dict(), headers=logged_in_headers - ) + response1 = client.put(f"api/v1/flows/{flow_id}", json=updated_flow.model_dump(), headers=logged_in_headers) + response2 = client.put(f"api/v1/flows/{flow_id}", json=updated_flow.model_dump(), headers=logged_in_headers) assert response1.json() == response2.json() -def test_update_nonexistent_flow( - client: TestClient, json_flow: str, active_user, logged_in_headers -): +def test_update_nonexistent_flow(client: TestClient, json_flow: str, active_user, logged_in_headers): flow_data = orjson.loads(json_flow) data = flow_data["data"] uuid = uuid4() @@ -263,9 +233,7 @@ def test_update_nonexistent_flow( description="description", data=data, ) - response = client.patch( - f"api/v1/flows/{uuid}", json=updated_flow.dict(), headers=logged_in_headers - ) + response = client.patch(f"api/v1/flows/{uuid}", json=updated_flow.model_dump(), headers=logged_in_headers) assert response.status_code == 404 diff --git a/tests/test_endpoints.py b/tests/test_endpoints.py index 1de7c9deb..338224004 100644 --- a/tests/test_endpoints.py +++ b/tests/test_endpoints.py @@ -1,18 +1,17 @@ -from collections import namedtuple +import time import uuid -from langflow.processing.process import Result -from langflow.services.auth.utils import get_password_hash -from langflow.services.database.models.api_key.api_key import ApiKey -from langflow.services.getters import get_settings_service -from langflow.services.database.utils import session_getter -from langflow.services.getters import get_db_service +from collections import namedtuple + import pytest from fastapi.testclient import TestClient from langflow.interface.tools.constants import CUSTOM_TOOLS +from langflow.processing.process import Result +from langflow.services.auth.utils import get_password_hash +from langflow.services.database.models.api_key.model import ApiKey +from langflow.services.database.utils import session_getter +from langflow.services.deps import get_db_service, get_settings_service from langflow.template.frontend_node.chains import TimeTravelGuideChainNode -import time - def run_post(client, flow_id, headers, post_data): response = client.post( @@ -25,16 +24,13 @@ def run_post(client, flow_id, headers, post_data): # Helper function to poll task status -def poll_task_status(client, headers, href, max_attempts=20, sleep_time=1): +def poll_task_status(client, headers, href, max_attempts=20, sleep_time=2): for _ in range(max_attempts): task_status_response = client.get( href, headers=headers, ) - if ( - task_status_response.status_code == 200 - and task_status_response.json()["status"] == "SUCCESS" - ): + if task_status_response.status_code == 200 and task_status_response.json()["status"] == "SUCCESS": return task_status_response.json() time.sleep(sleep_time) return None # Return None if task did not complete in time @@ -130,11 +126,7 @@ def created_api_key(active_user): ) db_manager = get_db_service() with session_getter(db_manager) as session: - if ( - existing_api_key := session.query(ApiKey) - .filter(ApiKey.api_key == api_key.api_key) - .first() - ): + if existing_api_key := session.query(ApiKey).filter(ApiKey.api_key == api_key.api_key).first(): return existing_api_key session.add(api_key) session.commit() @@ -193,9 +185,7 @@ def test_process_flow_invalid_id(client, monkeypatch, created_api_key): } invalid_id = uuid.uuid4() - response = client.post( - f"api/v1/process/{invalid_id}", headers=headers, json=post_data - ) + response = client.post(f"api/v1/process/{invalid_id}", headers=headers, json=post_data) assert response.status_code == 404 assert f"Flow {invalid_id} not found" in response.json()["detail"] @@ -236,9 +226,7 @@ def test_process_flow_without_autologin(client, flow, monkeypatch, created_api_k monkeypatch.setattr(endpoints, "process_graph_cached", mock_process_graph_cached) monkeypatch.setattr(crud, "update_total_uses", mock_update_total_uses) - monkeypatch.setattr( - endpoints, "process_graph_cached_task", mock_process_graph_cached_task - ) + monkeypatch.setattr(endpoints, "process_graph_cached_task", mock_process_graph_cached_task) api_key = created_api_key.api_key headers = {"x-api-key": api_key} @@ -510,9 +498,7 @@ def test_basic_chat_with_two_session_ids_and_names(client, added_flow, created_a @pytest.mark.async_test -def test_vector_store_in_process( - distributed_client, added_vector_store, created_api_key -): +def test_vector_store_in_process(distributed_client, added_vector_store, created_api_key): # Run the /api/v1/process/{flow_id} endpoint headers = {"x-api-key": created_api_key.api_key} post_data = {"inputs": {"input": "What is Langflow?"}} @@ -563,9 +549,7 @@ def test_async_task_processing(distributed_client, added_flow, created_api_key): # Test function without loop @pytest.mark.async_test -def test_async_task_processing_vector_store( - client, added_vector_store, created_api_key -): +def test_async_task_processing_vector_store(client, added_vector_store, created_api_key): headers = {"x-api-key": created_api_key.api_key} post_data = {"inputs": {"input": "How do I upload examples?"}} @@ -594,6 +578,4 @@ def test_async_task_processing_vector_store( # Validate that the task completed successfully and the result is as expected assert "result" in task_status_json, task_status_json assert "output" in task_status_json["result"], task_status_json["result"] - assert "Langflow" in task_status_json["result"]["output"], task_status_json[ - "result" - ] + assert "Langflow" in task_status_json["result"]["output"], task_status_json["result"] diff --git a/tests/test_frontend_nodes.py b/tests/test_frontend_nodes.py index 00fe9fcb1..e92ad1fe4 100644 --- a/tests/test_frontend_nodes.py +++ b/tests/test_frontend_nodes.py @@ -31,17 +31,14 @@ def test_template_field_defaults(sample_template_field: TemplateField): assert sample_template_field.is_list is False assert sample_template_field.show is True assert sample_template_field.multiline is False - assert sample_template_field.value is None - assert sample_template_field.suffixes == [] + assert sample_template_field.value == "" assert sample_template_field.file_types == [] - assert sample_template_field.file_path is None + assert sample_template_field.file_path == "" assert sample_template_field.password is False assert sample_template_field.name == "test_field" -def test_template_to_dict( - sample_template: Template, sample_template_field: TemplateField -): +def test_template_to_dict(sample_template: Template, sample_template_field: TemplateField): template_dict = sample_template.to_dict() assert template_dict["_type"] == "test_template" assert len(template_dict) == 2 # _type and test_field diff --git a/tests/test_graph.py b/tests/test_graph.py index fdb249eda..ad5f8c07a 100644 --- a/tests/test_graph.py +++ b/tests/test_graph.py @@ -1,22 +1,26 @@ import json import os -from pathlib import Path import pickle +from pathlib import Path from typing import Type, Union -from langflow.graph.edge.base import Edge -from langflow.graph.vertex.base import Vertex -from langchain.agents import AgentExecutor + import pytest +from langchain.agents import AgentExecutor from langchain.chains.base import Chain from langchain.llms.fake import FakeListLLM + from langflow.graph import Graph -from langflow.graph.vertex.types import ( - FileToolVertex, - LLMVertex, - ToolkitVertex, -) +from langflow.graph.edge.base import Edge +from langflow.graph.graph.utils import (find_last_node, process_flow, + set_new_target_handle, ungroup_node, + update_source_handle, + update_target_handle, update_template) +from langflow.graph.utils import UnbuiltObject +from langflow.graph.vertex.base import Vertex +from langflow.graph.vertex.types import (FileToolVertex, LLMVertex, + ToolkitVertex) from langflow.processing.process import get_result_and_thought -from langflow.utils.payload import get_root_node +from langflow.utils.payload import get_root_vertex # Test cases for the graph module @@ -24,21 +28,57 @@ from langflow.utils.payload import get_root_node # BASIC_EXAMPLE_PATH, COMPLEX_EXAMPLE_PATH, OPENAPI_EXAMPLE_PATH +@pytest.fixture +def sample_template(): + return { + "field1": {"proxy": {"field": "some_field", "id": "node1"}}, + "field2": {"proxy": {"field": "other_field", "id": "node2"}}, + } + + +@pytest.fixture +def sample_nodes(): + return [ + { + "id": "node1", + "data": {"node": {"template": {"some_field": {"show": True, "advanced": False, "name": "Name1"}}}}, + }, + { + "id": "node2", + "data": { + "node": { + "template": { + "other_field": { + "show": False, + "advanced": True, + "display_name": "DisplayName2", + } + } + } + }, + }, + { + "id": "node3", + "data": {"node": {"template": {"unrelated_field": {"show": True, "advanced": True}}}}, + }, + ] + + def get_node_by_type(graph, node_type: Type[Vertex]) -> Union[Vertex, None]: """Get a node by type""" - return next((node for node in graph.nodes if isinstance(node, node_type)), None) + return next((node for node in graph.vertices if isinstance(node, node_type)), None) def test_graph_structure(basic_graph): assert isinstance(basic_graph, Graph) - assert len(basic_graph.nodes) > 0 + assert len(basic_graph.vertices) > 0 assert len(basic_graph.edges) > 0 - for node in basic_graph.nodes: + for node in basic_graph.vertices: assert isinstance(node, Vertex) for edge in basic_graph.edges: assert isinstance(edge, Edge) - assert edge.source in basic_graph.nodes - assert edge.target in basic_graph.nodes + assert edge.source_id in basic_graph.vertex_map.keys() + assert edge.target_id in basic_graph.vertex_map.keys() def test_circular_dependencies(basic_graph): @@ -46,7 +86,7 @@ def test_circular_dependencies(basic_graph): def check_circular(node, visited): visited.add(node) - neighbors = basic_graph.get_nodes_with_target(node) + neighbors = basic_graph.get_vertices_with_target(node) for neighbor in neighbors: if neighbor in visited: return True @@ -54,7 +94,7 @@ def test_circular_dependencies(basic_graph): return True return False - for node in basic_graph.nodes: + for node in basic_graph.vertices: assert not check_circular(node, set()) @@ -79,13 +119,13 @@ def test_invalid_node_types(): Graph(graph_data["nodes"], graph_data["edges"]) -def test_get_nodes_with_target(basic_graph): +def test_get_vertices_with_target(basic_graph): """Test getting connected nodes""" assert isinstance(basic_graph, Graph) # Get root node - root = get_root_node(basic_graph) + root = get_root_vertex(basic_graph) assert root is not None - connected_nodes = basic_graph.get_nodes_with_target(root) + connected_nodes = basic_graph.get_vertices_with_target(root.id) assert connected_nodes is not None @@ -94,23 +134,17 @@ def test_get_node_neighbors_basic(basic_graph): assert isinstance(basic_graph, Graph) # Get root node - root = get_root_node(basic_graph) + root = get_root_vertex(basic_graph) assert root is not None - neighbors = basic_graph.get_node_neighbors(root) + neighbors = basic_graph.get_vertex_neighbors(root) assert neighbors is not None assert isinstance(neighbors, dict) # Root Node is an Agent, it requires an LLMChain and tools # We need to check if there is a Chain in the one of the neighbors' # data attribute in the type key - assert any( - "ConversationBufferMemory" in neighbor.data["type"] - for neighbor, val in neighbors.items() - if val - ) + assert any("ConversationBufferMemory" in neighbor.data["type"] for neighbor, val in neighbors.items() if val) - assert any( - "OpenAI" in neighbor.data["type"] for neighbor, val in neighbors.items() if val - ) + assert any("OpenAI" in neighbor.data["type"] for neighbor, val in neighbors.items() if val) # def test_get_node_neighbors_complex(complex_graph): @@ -164,8 +198,8 @@ def test_get_node_neighbors_basic(basic_graph): def test_get_node(basic_graph): """Test getting a single node""" - node_id = basic_graph.nodes[0].id - node = basic_graph.get_node(node_id) + node_id = basic_graph.vertices[0].id + node = basic_graph.get_vertex(node_id) assert isinstance(node, Vertex) assert node.id == node_id @@ -173,8 +207,8 @@ def test_get_node(basic_graph): def test_build_nodes(basic_graph): """Test building nodes""" - assert len(basic_graph.nodes) == len(basic_graph._nodes) - for node in basic_graph.nodes: + assert len(basic_graph.vertices) == len(basic_graph._vertices) + for node in basic_graph.vertices: assert isinstance(node, Vertex) @@ -183,20 +217,21 @@ def test_build_edges(basic_graph): assert len(basic_graph.edges) == len(basic_graph._edges) for edge in basic_graph.edges: assert isinstance(edge, Edge) - assert isinstance(edge.source, Vertex) - assert isinstance(edge.target, Vertex) + + assert isinstance(edge.source_id, str) + assert isinstance(edge.target_id, str) -def test_get_root_node(client, basic_graph, complex_graph): +def test_get_root_vertex(client, basic_graph, complex_graph): """Test getting root node""" assert isinstance(basic_graph, Graph) - root = get_root_node(basic_graph) + root = get_root_vertex(basic_graph) assert root is not None assert isinstance(root, Vertex) assert root.data["type"] == "TimeTravelGuideChain" # For complex example, the root node is a ZeroShotAgent too assert isinstance(complex_graph, Graph) - root = get_root_node(complex_graph) + root = get_root_vertex(complex_graph) assert root is not None assert isinstance(root, Vertex) assert root.data["type"] == "ZeroShotAgent" @@ -232,7 +267,7 @@ def test_build_params(basic_graph): # The matched_type attribute should be in the source_types attr assert all(edge.matched_type in edge.source_types for edge in basic_graph.edges) # Get the root node - root = get_root_node(basic_graph) + root = get_root_vertex(basic_graph) # Root node is a TimeTravelGuideChain # which requires an llm and memory assert root is not None @@ -241,29 +276,32 @@ def test_build_params(basic_graph): assert "memory" in root.params -def test_build(basic_graph): +@pytest.mark.asyncio +async def test_build(basic_graph): """Test Node's build method""" - assert_agent_was_built(basic_graph) + await assert_agent_was_built(basic_graph) -def assert_agent_was_built(graph): +async def assert_agent_was_built(graph): """Assert that the agent was built""" assert isinstance(graph, Graph) # Now we test the build method # Build the Agent - result = graph.build() + result = await graph.build() # The agent should be a AgentExecutor assert isinstance(result, Chain) -def test_llm_node_build(basic_graph): +@pytest.mark.asyncio +async def test_llm_node_build(basic_graph): llm_node = get_node_by_type(basic_graph, LLMVertex) assert llm_node is not None - built_object = llm_node.build() - assert built_object is not None + built_object = await llm_node.build() + assert built_object is not UnbuiltObject() -def test_toolkit_node_build(client, openapi_graph): +@pytest.mark.asyncio +async def test_toolkit_node_build(client, openapi_graph): # Write a file to the disk file_path = "api-with-examples.yaml" with open(file_path, "w") as f: @@ -271,36 +309,31 @@ def test_toolkit_node_build(client, openapi_graph): toolkit_node = get_node_by_type(openapi_graph, ToolkitVertex) assert toolkit_node is not None - built_object = toolkit_node.build() - assert built_object is not None + built_object = await toolkit_node.build() + assert built_object is not UnbuiltObject # Remove the file os.remove(file_path) assert not Path(file_path).exists() -def test_file_tool_node_build(client, openapi_graph): +@pytest.mark.asyncio +async def test_file_tool_node_build(client, openapi_graph): file_path = "api-with-examples.yaml" with open(file_path, "w") as f: f.write("openapi: 3.0.0") assert Path(file_path).exists() file_tool_node = get_node_by_type(openapi_graph, FileToolVertex) - assert file_tool_node is not None - built_object = file_tool_node.build() - assert built_object is not None + assert file_tool_node is not UnbuiltObject and file_tool_node is not None + built_object = await file_tool_node.build() + assert built_object is not UnbuiltObject # Remove the file os.remove(file_path) assert not Path(file_path).exists() -# def test_wrapper_node_build(openapi_graph): -# wrapper_node = get_node_by_type(openapi_graph, WrapperVertex) -# assert wrapper_node is not None -# built_object = wrapper_node.build() -# assert built_object is not None - - -def test_get_result_and_thought(basic_graph): +@pytest.mark.asyncio +async def test_get_result_and_thought(basic_graph): """Test the get_result_and_thought method""" responses = [ "Final Answer: I am a response", @@ -312,9 +345,9 @@ def test_get_result_and_thought(basic_graph): assert llm_node is not None llm_node._built_object = FakeListLLM(responses=responses) llm_node._built = True - langchain_object = basic_graph.build() + langchain_object = await basic_graph.build() # assert all nodes are built - assert all(node._built for node in basic_graph.nodes) + assert all(node._built for node in basic_graph.vertices) # now build again and check if FakeListLLM was used # Get the result and thought @@ -322,27 +355,204 @@ def test_get_result_and_thought(basic_graph): assert isinstance(result, dict) -def test_pickle_graph(json_vector_store): +def test_find_last_node(grouped_chat_json_flow): + grouped_chat_data = json.loads(grouped_chat_json_flow).get("data") + nodes, edges = grouped_chat_data["nodes"], grouped_chat_data["edges"] + last_node = find_last_node(nodes, edges) + assert last_node is not None # Replace with the actual expected value + assert last_node["id"] == "LLMChain-pimAb" # Replace with the actual expected value + + +def test_ungroup_node(grouped_chat_json_flow): + grouped_chat_data = json.loads(grouped_chat_json_flow).get("data") + group_node = grouped_chat_data["nodes"][2] # Assuming the first node is a group node + base_flow = copy.deepcopy(grouped_chat_data) + ungroup_node(group_node["data"], base_flow) + # after ungroup_node is called, the base_flow and grouped_chat_data should be different + assert base_flow != grouped_chat_data + # assert node 2 is not a group node anymore + assert base_flow["nodes"][2]["data"]["node"].get("flow") is None + # assert the edges are updated + assert len(base_flow["edges"]) > len(grouped_chat_data["edges"]) + assert base_flow["edges"][0]["source"] == "ConversationBufferMemory-kUMif" + assert base_flow["edges"][0]["target"] == "LLMChain-2P369" + assert base_flow["edges"][1]["source"] == "PromptTemplate-Wjk4g" + assert base_flow["edges"][1]["target"] == "LLMChain-2P369" + assert base_flow["edges"][2]["source"] == "ChatOpenAI-rUJ1b" + assert base_flow["edges"][2]["target"] == "LLMChain-2P369" + + +def test_process_flow(grouped_chat_json_flow): + grouped_chat_data = json.loads(grouped_chat_json_flow).get("data") + + processed_flow = process_flow(grouped_chat_data) + assert processed_flow is not None + assert isinstance(processed_flow, dict) + assert "nodes" in processed_flow + assert "edges" in processed_flow + + +def test_process_flow_one_group(one_grouped_chat_json_flow): + grouped_chat_data = json.loads(one_grouped_chat_json_flow).get("data") + # There should be only one node + assert len(grouped_chat_data["nodes"]) == 1 + # Get the node, it should be a group node + group_node = grouped_chat_data["nodes"][0] + node_data = group_node["data"]["node"] + assert node_data.get("flow") is not None + template_data = node_data["template"] + assert any("openai_api_key" in key for key in template_data.keys()) + # Get the openai_api_key dict + openai_api_key = next( + (template_data[key] for key in template_data.keys() if "openai_api_key" in key), + None, + ) + assert openai_api_key is not None + assert openai_api_key["value"] == "test" + + processed_flow = process_flow(grouped_chat_data) + assert processed_flow is not None + assert isinstance(processed_flow, dict) + assert "nodes" in processed_flow + assert "edges" in processed_flow + + # Now get the node that has ChatOpenAI in its id + chat_openai_node = next((node for node in processed_flow["nodes"] if "ChatOpenAI" in node["id"]), None) + assert chat_openai_node is not None + assert chat_openai_node["data"]["node"]["template"]["openai_api_key"]["value"] == "test" + + +def test_process_flow_vector_store_grouped(vector_store_grouped_json_flow): + grouped_chat_data = json.loads(vector_store_grouped_json_flow).get("data") + nodes = grouped_chat_data["nodes"] + assert len(nodes) == 4 + # There are two group nodes in this flow + # One of them is inside the other totalling 7 nodes + # 4 nodes grouped, one of these turns into 1 normal node and 1 group node + # This group node has 2 nodes inside it + + processed_flow = process_flow(grouped_chat_data) + assert processed_flow is not None + processed_nodes = processed_flow["nodes"] + assert len(processed_nodes) == 7 + assert isinstance(processed_flow, dict) + assert "nodes" in processed_flow + assert "edges" in processed_flow + edges = processed_flow["edges"] + # Expected keywords in source and target fields + expected_keywords = [ + {"source": "VectorStoreInfo", "target": "VectorStoreAgent"}, + {"source": "ChatOpenAI", "target": "VectorStoreAgent"}, + {"source": "OpenAIEmbeddings", "target": "Chroma"}, + {"source": "Chroma", "target": "VectorStoreInfo"}, + {"source": "WebBaseLoader", "target": "RecursiveCharacterTextSplitter"}, + {"source": "RecursiveCharacterTextSplitter", "target": "Chroma"}, + ] + + for idx, expected_keyword in enumerate(expected_keywords): + for key, value in expected_keyword.items(): + assert ( + value in edges[idx][key].split("-")[0] + ), f"Edge {idx}, key {key} expected to contain {value} but got {edges[idx][key]}" + + +def test_update_template(sample_template, sample_nodes): + # Making a deep copy to keep original sample_nodes unchanged + nodes_copy = copy.deepcopy(sample_nodes) + update_template(sample_template, nodes_copy) + + # Now, validate the updates. + node1_updated = next((n for n in nodes_copy if n["id"] == "node1"), None) + node2_updated = next((n for n in nodes_copy if n["id"] == "node2"), None) + node3_updated = next((n for n in nodes_copy if n["id"] == "node3"), None) + + assert node1_updated is not None + assert node1_updated["data"]["node"]["template"]["some_field"]["show"] is True + assert node1_updated["data"]["node"]["template"]["some_field"]["advanced"] is False + assert node1_updated["data"]["node"]["template"]["some_field"]["display_name"] == "Name1" + + assert node2_updated is not None + assert node2_updated["data"]["node"]["template"]["other_field"]["show"] is False + assert node2_updated["data"]["node"]["template"]["other_field"]["advanced"] is True + assert node2_updated["data"]["node"]["template"]["other_field"]["display_name"] == "DisplayName2" + + # Ensure node3 remains unchanged + assert node3_updated == sample_nodes[2] + + +# Test `update_target_handle` +def test_update_target_handle_proxy(): + new_edge = { + "data": { + "targetHandle": { + "type": "some_type", + "proxy": {"id": "some_id", "field": ""}, + } + } + } + g_nodes = [{"id": "some_id", "data": {"node": {"flow": None}}}] + group_node_id = "group_id" + updated_edge = update_target_handle(new_edge, g_nodes, group_node_id) + assert updated_edge["data"]["targetHandle"] == new_edge["data"]["targetHandle"] + + +# Test `set_new_target_handle` +def test_set_new_target_handle(): + proxy_id = "proxy_id" + new_edge = {"target": None, "data": {"targetHandle": {}}} + target_handle = {"type": "type_1", "proxy": {"field": "field_1"}} + node = { + "data": { + "node": { + "flow": True, + "template": {"field_1": {"proxy": {"field": "new_field", "id": "new_id"}}}, + } + } + } + set_new_target_handle(proxy_id, new_edge, target_handle, node) + assert new_edge["target"] == "proxy_id" + assert new_edge["data"]["targetHandle"]["fieldName"] == "field_1" + assert new_edge["data"]["targetHandle"]["proxy"] == { + "field": "new_field", + "id": "new_id", + } + + +# Test `update_source_handle` +def test_update_source_handle(): + new_edge = {"source": None, "data": {"sourceHandle": {"id": None}}} + flow_data = { + "nodes": [{"id": "some_node"}, {"id": "last_node"}], + "edges": [{"source": "some_node"}], + } + updated_edge = update_source_handle(new_edge, flow_data["nodes"], flow_data["edges"]) + assert updated_edge["source"] == "last_node" + assert updated_edge["data"]["sourceHandle"]["id"] == "last_node" + + +@pytest.mark.asyncio +async def test_pickle_graph(json_vector_store): loaded_json = json.loads(json_vector_store) graph = Graph.from_payload(loaded_json) assert isinstance(graph, Graph) - first_result = graph.build() + first_result = await graph.build() assert isinstance(first_result, AgentExecutor) pickled = pickle.dumps(graph) - assert pickled is not None + assert pickled is not UnbuiltObject unpickled = pickle.loads(pickled) - assert unpickled is not None - result = unpickled.build() + assert unpickled is not UnbuiltObject + result = await unpickled.build() assert isinstance(result, AgentExecutor) -def test_pickle_each_vertex(json_vector_store): +@pytest.mark.asyncio +async def test_pickle_each_vertex(json_vector_store): loaded_json = json.loads(json_vector_store) graph = Graph.from_payload(loaded_json) assert isinstance(graph, Graph) - for vertex in graph.nodes: - vertex.build() + for vertex in graph.vertices: + await vertex.build() pickled = pickle.dumps(vertex) - assert pickled is not None + assert pickled is not UnbuiltObject unpickled = pickle.loads(pickled) - assert unpickled is not None + assert unpickled is not UnbuiltObject diff --git a/tests/test_llms_template.py b/tests/test_llms_template.py index 78131cb05..30a15c932 100644 --- a/tests/test_llms_template.py +++ b/tests/test_llms_template.py @@ -22,6 +22,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["verbose"] == { "required": False, @@ -35,6 +36,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["client"] == { "required": False, @@ -48,6 +50,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["model_name"] == { "required": False, @@ -69,6 +72,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": True, "advanced": False, "info": "", + "fileTypes": [], } # Add more assertions for other properties here assert template["temperature"] == { @@ -84,6 +88,8 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "rangeSpec": {"max": 1.0, "min": -1.0, "step": 0.1}, + "fileTypes": [], } assert template["max_tokens"] == { "required": False, @@ -98,6 +104,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["top_p"] == { "required": False, @@ -112,6 +119,8 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "rangeSpec": {"max": 1.0, "min": -1.0, "step": 0.1}, + "fileTypes": [], } assert template["frequency_penalty"] == { "required": False, @@ -126,6 +135,8 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "rangeSpec": {"max": 1.0, "min": -1.0, "step": 0.1}, + "fileTypes": [], } assert template["presence_penalty"] == { "required": False, @@ -140,6 +151,8 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "rangeSpec": {"max": 1.0, "min": -1.0, "step": 0.1}, + "fileTypes": [], } assert template["n"] == { "required": False, @@ -154,6 +167,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["best_of"] == { "required": False, @@ -168,6 +182,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["model_kwargs"] == { "required": False, @@ -181,6 +196,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": True, "info": "", + "fileTypes": [], } assert template["openai_api_key"] == { "required": False, @@ -196,6 +212,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["batch_size"] == { "required": False, @@ -210,6 +227,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["request_timeout"] == { "required": False, @@ -223,6 +241,8 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "rangeSpec": {"max": 1.0, "min": -1.0, "step": 0.1}, + "fileTypes": [], } assert template["logit_bias"] == { "required": False, @@ -236,6 +256,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["max_retries"] == { "required": False, @@ -243,13 +264,14 @@ def test_openai(client: TestClient, logged_in_headers): "placeholder": "", "show": False, "multiline": False, - "value": 6, + "value": 2, "password": False, "name": "max_retries", "type": "int", "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["streaming"] == { "required": False, @@ -264,6 +286,7 @@ def test_openai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } @@ -289,6 +312,7 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["client"] == { "required": False, @@ -302,6 +326,7 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["model_name"] == { "required": False, @@ -313,6 +338,7 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "password": False, "options": [ "gpt-4-1106-preview", + "gpt-4-vision-preview", "gpt-4", "gpt-4-32k", "gpt-3.5-turbo", @@ -323,6 +349,7 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": True, "advanced": False, "info": "", + "fileTypes": [], } assert template["temperature"] == { "required": False, @@ -337,6 +364,8 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "rangeSpec": {"max": 1.0, "min": -1.0, "step": 0.1}, + "fileTypes": [], } assert template["model_kwargs"] == { "required": False, @@ -350,6 +379,7 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": False, "advanced": True, "info": "", + "fileTypes": [], } assert template["openai_api_key"] == { "required": False, @@ -365,6 +395,7 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["request_timeout"] == { "required": False, @@ -378,6 +409,8 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "rangeSpec": {"max": 1.0, "min": -1.0, "step": 0.1}, + "fileTypes": [], } assert template["max_retries"] == { "required": False, @@ -385,13 +418,14 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "placeholder": "", "show": False, "multiline": False, - "value": 6, + "value": 2, "password": False, "name": "max_retries", "type": "int", "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["streaming"] == { "required": False, @@ -406,6 +440,7 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["n"] == { "required": False, @@ -420,6 +455,7 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["max_tokens"] == { @@ -434,6 +470,7 @@ def test_chat_open_ai(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["_type"] == "ChatOpenAI" assert ( diff --git a/tests/test_loading.py b/tests/test_loading.py index e5c409c93..94cf34d8e 100644 --- a/tests/test_loading.py +++ b/tests/test_loading.py @@ -1,9 +1,10 @@ import json + import pytest from langchain.chains.base import Chain -from langflow.processing.process import load_flow_from_json from langflow.graph import Graph -from langflow.utils.payload import get_root_node +from langflow.processing.process import load_flow_from_json +from langflow.utils.payload import get_root_vertex def test_load_flow_from_json(): @@ -22,14 +23,15 @@ def test_load_flow_from_json_with_tweaks(): assert loaded.llm.model_name == "test model" -def test_get_root_node(): +def test_get_root_vertex(): with open(pytest.BASIC_EXAMPLE_PATH, "r") as f: flow_graph = json.load(f) data_graph = flow_graph["data"] nodes = data_graph["nodes"] edges = data_graph["edges"] graph = Graph(nodes, edges) - root = get_root_node(graph) + root = get_root_vertex(graph) assert root is not None assert hasattr(root, "id") assert hasattr(root, "data") + assert hasattr(root, "data") diff --git a/tests/test_login.py b/tests/test_login.py index f505f4100..399c7b761 100644 --- a/tests/test_login.py +++ b/tests/test_login.py @@ -1,5 +1,5 @@ from langflow.services.database.utils import session_getter -from langflow.services.getters import get_db_service +from langflow.services.deps import get_db_service import pytest from langflow.services.database.models.user import User from langflow.services.auth.utils import get_password_hash @@ -9,9 +9,7 @@ from langflow.services.auth.utils import get_password_hash def test_user(): return User( username="testuser", - password=get_password_hash( - "testpassword" - ), # Assuming password needs to be hashed + password=get_password_hash("testpassword"), # Assuming password needs to be hashed is_active=True, is_superuser=False, ) @@ -23,17 +21,13 @@ def test_login_successful(client, test_user): session.add(test_user) session.commit() - response = client.post( - "api/v1/login", data={"username": "testuser", "password": "testpassword"} - ) + response = client.post("api/v1/login", data={"username": "testuser", "password": "testpassword"}) assert response.status_code == 200 assert "access_token" in response.json() def test_login_unsuccessful_wrong_username(client): - response = client.post( - "api/v1/login", data={"username": "wrongusername", "password": "testpassword"} - ) + response = client.post("api/v1/login", data={"username": "wrongusername", "password": "testpassword"}) assert response.status_code == 401 assert response.json()["detail"] == "Incorrect username or password" @@ -43,8 +37,6 @@ def test_login_unsuccessful_wrong_password(client, test_user, session): session.add(test_user) session.commit() - response = client.post( - "api/v1/login", data={"username": "testuser", "password": "wrongpassword"} - ) + response = client.post("api/v1/login", data={"username": "testuser", "password": "wrongpassword"}) assert response.status_code == 401 assert response.json()["detail"] == "Incorrect username or password" diff --git a/tests/test_process.py b/tests/test_process.py index 0588800dc..c8e4ec9cc 100644 --- a/tests/test_process.py +++ b/tests/test_process.py @@ -1,5 +1,6 @@ +import pytest from langflow.processing.process import process_tweaks -from langflow.services.getters import get_session_service +from langflow.services.deps import get_session_service def test_no_tweaks(): @@ -197,39 +198,42 @@ def test_tweak_not_in_template(): assert result == graph_data -def test_load_langchain_object_with_cached_session(client, basic_graph_data): +@pytest.mark.asyncio +async def test_load_langchain_object_with_cached_session(client, basic_graph_data): # Provide a non-existent session_id session_service = get_session_service() session_id1 = "non-existent-session-id" - graph1, artifacts1 = session_service.load_session(session_id1, basic_graph_data) + graph1, artifacts1 = await session_service.load_session(session_id1, basic_graph_data) # Use the new session_id to get the langchain_object again - graph2, artifacts2 = session_service.load_session(session_id1, basic_graph_data) + graph2, artifacts2 = await session_service.load_session(session_id1, basic_graph_data) assert graph1 == graph2 assert artifacts1 == artifacts2 -def test_load_langchain_object_with_no_cached_session(client, basic_graph_data): +@pytest.mark.asyncio +async def test_load_langchain_object_with_no_cached_session(client, basic_graph_data): # Provide a non-existent session_id session_service = get_session_service() session_id1 = "non-existent-session-id" session_id = session_service.build_key(session_id1, basic_graph_data) - graph1, artifacts1 = session_service.load_session(session_id, basic_graph_data) + graph1, artifacts1 = await session_service.load_session(session_id, basic_graph_data) # Clear the cache session_service.clear_session(session_id) # Use the new session_id to get the langchain_object again - graph2, artifacts2 = session_service.load_session(session_id, basic_graph_data) + graph2, artifacts2 = await session_service.load_session(session_id, basic_graph_data) assert id(graph1) != id(graph2) # Since the cache was cleared, objects should be different -def test_load_langchain_object_without_session_id(client, basic_graph_data): +@pytest.mark.asyncio +async def test_load_langchain_object_without_session_id(client, basic_graph_data): # Provide a non-existent session_id session_service = get_session_service() session_id1 = None - graph1, artifacts1 = session_service.load_session(session_id1, basic_graph_data) + graph1, artifacts1 = await session_service.load_session(session_id1, basic_graph_data) # Use the new session_id to get the langchain_object again - graph2, artifacts2 = session_service.load_session(session_id1, basic_graph_data) + graph2, artifacts2 = await session_service.load_session(session_id1, basic_graph_data) assert graph1 == graph2 diff --git a/tests/test_prompts_template.py b/tests/test_prompts_template.py index f9292f441..491a18d69 100644 --- a/tests/test_prompts_template.py +++ b/tests/test_prompts_template.py @@ -1,5 +1,6 @@ from fastapi.testclient import TestClient -from langflow.services.getters import get_settings_service + +from langflow.services.deps import get_settings_service def test_prompts_settings(client: TestClient, logged_in_headers): @@ -31,6 +32,7 @@ def test_prompt_template(client: TestClient, logged_in_headers): "list": True, "advanced": False, "info": "", + "fileTypes": [], } assert template["output_parser"] == { @@ -45,6 +47,7 @@ def test_prompt_template(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["partial_variables"] == { @@ -59,6 +62,7 @@ def test_prompt_template(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } assert template["template"] == { @@ -73,6 +77,23 @@ def test_prompt_template(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], + } + + assert template["template_format"] == { + "required": False, + "dynamic": True, + "placeholder": "", + "show": False, + "multiline": False, + "value": "f-string", + "password": False, + "name": "template_format", + "type": "str", + "list": False, + "advanced": False, + "info": "", + "fileTypes": [], } assert template["validate_template"] == { @@ -88,4 +109,5 @@ def test_prompt_template(client: TestClient, logged_in_headers): "list": False, "advanced": False, "info": "", + "fileTypes": [], } diff --git a/tests/test_setup_superuser.py b/tests/test_setup_superuser.py index 8cdcfc0c8..03d3882fb 100644 --- a/tests/test_setup_superuser.py +++ b/tests/test_setup_superuser.py @@ -1,17 +1,11 @@ -from unittest.mock import patch, MagicMock -from langflow.services.database.models.user.user import User -from langflow.services.settings.constants import ( - DEFAULT_SUPERUSER, - DEFAULT_SUPERUSER_PASSWORD, -) -from langflow.services.utils import ( - teardown_superuser, -) +from unittest.mock import MagicMock, patch +from langflow.services.settings.constants import DEFAULT_SUPERUSER, DEFAULT_SUPERUSER_PASSWORD +from langflow.services.utils import teardown_superuser -# @patch("langflow.services.getters.get_session") +# @patch("langflow.services.deps.get_session") # @patch("langflow.services.utils.create_super_user") -# @patch("langflow.services.getters.get_settings_service") +# @patch("langflow.services.deps.get_settings_service") # # @patch("langflow.services.utils.verify_password") # def test_setup_superuser( # mock_get_session, mock_create_super_user, mock_get_settings_service @@ -92,11 +86,9 @@ from langflow.services.utils import ( # assert str(actual_expr) == str(expected_expr) -@patch("langflow.services.getters.get_settings_service") -@patch("langflow.services.getters.get_session") -def test_teardown_superuser_default_superuser( - mock_get_session, mock_get_settings_service -): +@patch("langflow.services.deps.get_settings_service") +@patch("langflow.services.deps.get_session") +def test_teardown_superuser_default_superuser(mock_get_session, mock_get_settings_service): mock_settings_service = MagicMock() mock_settings_service.auth_settings.AUTO_LOGIN = True mock_settings_service.auth_settings.SUPERUSER = DEFAULT_SUPERUSER @@ -111,20 +103,12 @@ def test_teardown_superuser_default_superuser( teardown_superuser(mock_settings_service, mock_session) - mock_session.query.assert_called_once_with(User) - actual_expr = mock_session.query.return_value.filter.call_args[0][0] - expected_expr = User.username == DEFAULT_SUPERUSER - - assert str(actual_expr) == str(expected_expr) - mock_session.delete.assert_called_once_with(mock_user) - mock_session.commit.assert_called_once() + mock_session.query.assert_not_called() -@patch("langflow.services.getters.get_settings_service") -@patch("langflow.services.getters.get_session") -def test_teardown_superuser_no_default_superuser( - mock_get_session, mock_get_settings_service -): +@patch("langflow.services.deps.get_settings_service") +@patch("langflow.services.deps.get_session") +def test_teardown_superuser_no_default_superuser(mock_get_session, mock_get_settings_service): ADMIN_USER_NAME = "admin_user" mock_settings_service = MagicMock() mock_settings_service.auth_settings.AUTO_LOGIN = False @@ -135,11 +119,11 @@ def test_teardown_superuser_no_default_superuser( mock_session = MagicMock() mock_user = MagicMock() mock_user.is_superuser = False - mock_session.query.return_value.filter.return_value.first.return_value = mock_user + mock_session.exec.return_value.filter.return_value.first.return_value = mock_user mock_get_session.return_value = [mock_session] teardown_superuser(mock_settings_service, mock_session) - mock_session.query.assert_not_called() + mock_session.exec.assert_called_once() mock_session.delete.assert_not_called() mock_session.commit.assert_not_called() diff --git a/tests/test_template.py b/tests/test_template.py index 81f2a6020..b7a3e1441 100644 --- a/tests/test_template.py +++ b/tests/test_template.py @@ -65,11 +65,9 @@ def test_build_template_from_function(): assert "base_classes" in result # Test with add_function=True - result_with_function = build_template_from_function( - "ExampleClass1", type_to_loader_dict, add_function=True - ) + result_with_function = build_template_from_function("ExampleClass1", type_to_loader_dict, add_function=True) assert result_with_function is not None - assert "function" in result_with_function["base_classes"] + assert "Callable" in result_with_function["base_classes"] # Test with invalid name with pytest.raises(ValueError, match=r".* not found"): diff --git a/tests/test_user.py b/tests/test_user.py index 49962c8d1..d7e814845 100644 --- a/tests/test_user.py +++ b/tests/test_user.py @@ -1,11 +1,12 @@ from datetime import datetime -from langflow.services.auth.utils import create_super_user, get_password_hash -from langflow.services.database.models.user.user import User -from langflow.services.database.utils import session_getter -from langflow.services.getters import get_db_service, get_settings_service import pytest + +from langflow.services.auth.utils import create_super_user, get_password_hash from langflow.services.database.models.user import UserUpdate +from langflow.services.database.models.user.model import User +from langflow.services.database.utils import session_getter +from langflow.services.deps import get_db_service, get_settings_service @pytest.fixture @@ -85,15 +86,11 @@ def test_deactivated_user_cannot_access(client, deactivated_user, logged_in_head assert response.json()["detail"] == "The user doesn't have enough privileges" -def test_data_consistency_after_update( - client, active_user, logged_in_headers, super_user_headers -): +def test_data_consistency_after_update(client, active_user, logged_in_headers, super_user_headers): user_id = active_user.id update_data = UserUpdate(is_active=False) - response = client.patch( - f"/api/v1/users/{user_id}", json=update_data.dict(), headers=super_user_headers - ) + response = client.patch(f"/api/v1/users/{user_id}", json=update_data.model_dump(), headers=super_user_headers) assert response.status_code == 200, response.json() # Fetch the updated user from the database @@ -120,7 +117,7 @@ def test_inactive_user(client): username="inactiveuser", password=get_password_hash("testpassword"), is_active=False, - last_login_at="2023-01-01T00:00:00", # Set to a valid datetime string + last_login_at=datetime.now(), ) session.add(user) session.commit() @@ -167,17 +164,13 @@ def test_patch_user(client, active_user, logged_in_headers): username="newname", ) - response = client.patch( - f"/api/v1/users/{user_id}", json=update_data.dict(), headers=logged_in_headers - ) + response = client.patch(f"/api/v1/users/{user_id}", json=update_data.model_dump(), headers=logged_in_headers) assert response.status_code == 200, response.json() update_data = UserUpdate( profile_image="new_image", ) - response = client.patch( - f"/api/v1/users/{user_id}", json=update_data.dict(), headers=logged_in_headers - ) + response = client.patch(f"/api/v1/users/{user_id}", json=update_data.model_dump(), headers=logged_in_headers) assert response.status_code == 200, response.json() @@ -189,7 +182,7 @@ def test_patch_reset_password(client, active_user, logged_in_headers): response = client.patch( f"/api/v1/users/{user_id}/reset-password", - json=update_data.dict(), + json=update_data.model_dump(), headers=logged_in_headers, ) assert response.status_code == 200, response.json() @@ -205,19 +198,13 @@ def test_patch_user_wrong_id(client, active_user, logged_in_headers): username="newname", ) - response = client.patch( - f"/api/v1/users/{user_id}", json=update_data.dict(), headers=logged_in_headers - ) + response = client.patch(f"/api/v1/users/{user_id}", json=update_data.model_dump(), headers=logged_in_headers) assert response.status_code == 422, response.json() - assert response.json() == { - "detail": [ - { - "loc": ["path", "user_id"], - "msg": "value is not a valid uuid", - "type": "type_error.uuid", - } - ] - } + json_response = response.json() + detail = json_response["detail"] + assert detail[0]["type"] == "uuid_parsing" + assert detail[0]["loc"] == ["path", "user_id"] + assert detail[0]["input"] == "wrong_id" def test_delete_user(client, test_user, super_user_headers): @@ -231,15 +218,11 @@ def test_delete_user_wrong_id(client, test_user, super_user_headers): user_id = "wrong_id" response = client.delete(f"/api/v1/users/{user_id}", headers=super_user_headers) assert response.status_code == 422 - assert response.json() == { - "detail": [ - { - "loc": ["path", "user_id"], - "msg": "value is not a valid uuid", - "type": "type_error.uuid", - } - ] - } + json_response = response.json() + detail = json_response["detail"] + assert detail[0]["type"] == "uuid_parsing" + assert detail[0]["loc"] == ["path", "user_id"] + assert detail[0]["input"] == "wrong_id" def test_normal_user_cant_delete_user(client, test_user, logged_in_headers): diff --git a/tests/test_vectorstore_template.py b/tests/test_vectorstore_template.py index 9dd131dbc..3b5c7ed42 100644 --- a/tests/test_vectorstore_template.py +++ b/tests/test_vectorstore_template.py @@ -1,5 +1,5 @@ from fastapi.testclient import TestClient -from langflow.services.getters import get_settings_service +from langflow.services.deps import get_settings_service # check that all agents are in settings.agents diff --git a/tests/test_websocket.py b/tests/test_websocket.py index 5016eb704..c4c9ee322 100644 --- a/tests/test_websocket.py +++ b/tests/test_websocket.py @@ -31,9 +31,7 @@ def test_websocket_endpoint(client: TestClient, active_user, logged_in_headers): # Assuming your websocket_endpoint uses chat_service which caches data from stream_build access_token = logged_in_headers["Authorization"].split(" ")[1] with pytest.raises(WebSocketDisconnect): - with client.websocket_connect( - f"api/v1/chat/non_existing_client_id?token={access_token}" - ) as websocket: + with client.websocket_connect(f"api/v1/chat/non_existing_client_id?token={access_token}") as websocket: websocket.send_json({"type": "test"}) data = websocket.receive_json() assert "Please, build the flow before sending messages" in data["message"]