diff --git a/.env.example b/.env.example index b3e592bdf..ccab7c28e 100644 --- a/.env.example +++ b/.env.example @@ -56,6 +56,14 @@ LANGFLOW_REMOVE_API_KEYS= # LANGFLOW_REDIS_CACHE_EXPIRE (default: 3600) LANGFLOW_CACHE_TYPE= +# Auto login +# If set to true then a superuser will be logged in automatically +# and the login page will be skipped, keeping the +# default experience of Langflow +# Values: true, false +# Example: LANGFLOW_AUTO_LOGIN=true +LANGFLOW_AUTO_LOGIN= + # Superuser username # Example: LANGFLOW_SUPERUSER=admin LANGFLOW_SUPERUSER= diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d81146d19..ae1c613e2 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -4,10 +4,18 @@ on: push: branches: - dev + paths: + - "tests/**" + - "src/backend/**" + - "pyproject.toml" pull_request: branches: - dev - main + paths: + - "tests/**" + - "src/backend/**" + - "pyproject.toml" jobs: build-and-test: diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index f32cbfc6b..3ec6881ff 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -3,7 +3,16 @@ name: lint on: push: branches: [main] + paths: + - "tests/**" + - "src/backend/**" + - "pyproject.toml" + pull_request: + paths: + - "tests/**" + - "src/backend/**" + - "pyproject.toml" env: POETRY_VERSION: "1.4.0" diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index bc2e193c6..e818715d9 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -3,9 +3,17 @@ name: test on: push: branches: [main] + paths: + - "tests/**" + - "src/backend/**" + - "pyproject.toml" + pull_request: branches: [dev] - + paths: + - "tests/**" + - "src/backend/**" + - "pyproject.toml" env: POETRY_VERSION: "1.5.0" diff --git a/.gitignore b/.gitignore index 9b8ec1cc1..0f9c499a0 100644 --- a/.gitignore +++ b/.gitignore @@ -254,4 +254,6 @@ langflow.db /tmp/* src/backend/langflow/frontend/ -.docker \ No newline at end of file +.docker + +.idea \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index afe112a40..520c407de 100644 --- a/Dockerfile +++ b/Dockerfile @@ -12,4 +12,4 @@ WORKDIR $HOME/app COPY --chown=user . $HOME/app RUN pip install langflow>==0.0.86 -U --user -CMD ["python", "-m", "langflow", "--host", "0.0.0.0", "--port", "7860"] +CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"] diff --git a/Makefile b/Makefile index 1fbd7403c..51c9d4897 100644 --- a/Makefile +++ b/Makefile @@ -22,6 +22,13 @@ tests: @make install_backend poetry run pytest tests +tests_frontend: +ifeq ($(UI), true) + cd src/frontend && ./run-tests.sh --ui +else + cd src/frontend && ./run-tests.sh +endif + format: poetry run black . poetry run ruff . --fix @@ -53,8 +60,8 @@ setup_devcontainer: poetry run langflow --path src/frontend/build frontend: - make install_frontend - make run_frontend + @-make install_frontend || (echo "An error occurred while installing frontend dependencies. Attempting to fix." && make install_frontendc) + @make run_frontend frontendc: make install_frontendc @@ -65,7 +72,13 @@ install_backend: backend: make install_backend - poetry run uvicorn --factory src.backend.langflow.main:create_app --port 7860 --reload --log-level debug +ifeq ($(login),1) + @echo "Running backend without autologin"; + poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser +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 +endif build_and_run: echo 'Removing dist folder' diff --git a/README.md b/README.md index 538d53bef..133f41a6c 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@

- +

@@ -76,7 +76,7 @@ python -m langflow or ```shell -langflow # or langflow --help +langflow run # or langflow --help ``` ### HuggingFace Spaces @@ -92,7 +92,7 @@ Langflow provides a command-line interface (CLI) for easy management and configu You can run the Langflow using the following command: ```shell -langflow [OPTIONS] +langflow run [OPTIONS] ``` Each option is detailed below: @@ -131,7 +131,6 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google [![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md) - ## Deploy on Railway [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/JMXEWp?referralCode=MnPSdg) @@ -144,7 +143,7 @@ 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://docs.langchain.com/docs/category/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/docs/docs/components/llms.mdx b/docs/docs/components/llms.mdx index 088556d2f..412f38681 100644 --- a/docs/docs/components/llms.mdx +++ b/docs/docs/components/llms.mdx @@ -217,4 +217,40 @@ Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). - **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`. - **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`. - **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored. -- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`. \ No newline at end of file +- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`. + +--- + +### QianfanLLMEndpoint + +Wrapper around [Baidu Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) large language models. + +:::info +The Qianfan Big Model Platform is a one-stop platform for enterprise developers to develop and operate large models and services. It provides data management based on ERNIE Bot's underlying model (Ernie Bot), automatic model customization and fine-tuning, and one-stop large-scale model customization services for cloud deployment of prediction services, and provides ERNIE Bot's enterprise level service API that can be quickly called, helping to implement the demand for generative AI applications in various industries. +::: + +- **Model Name:** Model name. you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu preset models are mapping to an endpoint. `Model Name` will be ignored if `Endpoint` is set. +- **Qianfan Ak:** which you could get from https://cloud.baidu.com/product/wenxinworkshop. +- **Qianfan Sk:** which you could get from https://cloud.baidu.com/product/wenxinworkshop. +- **Top p:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. The diversity of the output text is affected, and the larger the value, the stronger the diversity of the generated text - defaults to `0.8`. +- **Temperature:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. Higher values make the output more random, while lower values make it more concentrated and deterministic - defaults to `0.95`. +- **Penalty Score:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. By increasing the penalty for generated tokens, the phenomenon of duplicate generation is reduced. A higher value indicates a higher penalty - defaults to `1.0`. +- **Endpoint:** Endpoint of the Qianfan LLM, required if custom model used. + +--- + +### QianfanChatEndpoint + +Wrapper around [Baidu Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) chat large language models. This component supports some of the LLMs (Large Language Models) available by Baidu qianfan and is used for tasks such as chatbots, Generative Question-Answering (GQA), and summarization. + +:::info +The Qianfan Big Model Platform is a one-stop platform for enterprise developers to develop and operate large models and services. It provides data management based on ERNIE Bot's underlying model (Ernie Bot), automatic model customization and fine-tuning, and one-stop large-scale model customization services for cloud deployment of prediction services, and provides ERNIE Bot's enterprise level service API that can be quickly called, helping to implement the demand for generative AI applications in various industries. +::: + +- **Model Name:** Model name. you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu preset models are mapping to an endpoint. `Model Name` will be ignored if `Endpoint` is set. +- **Qianfan Ak:** which you could get from https://cloud.baidu.com/product/wenxinworkshop. +- **Qianfan Sk:** which you could get from https://cloud.baidu.com/product/wenxinworkshop. +- **Top p:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. The diversity of the output text is affected, and the larger the value, the stronger the diversity of the generated text - defaults to `0.8`. +- **Temperature:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. Higher values make the output more random, while lower values make it more concentrated and deterministic - defaults to `0.95`. +- **Penalty Score:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. By increasing the penalty for generated tokens, the phenomenon of duplicate generation is reduced. A higher value indicates a higher penalty - defaults to `1.0`. +- **Endpoint:** Endpoint of the Qianfan LLM, required if custom model used. \ No newline at end of file diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx index 7be04549c..f0fee5009 100644 --- a/docs/docs/index.mdx +++ b/docs/docs/index.mdx @@ -7,10 +7,11 @@ import useBaseUrl from "@docusaurus/useBaseUrl"; import ZoomableImage from "/src/theme/ZoomableImage.js"; {" "} + diff --git a/docs/package-lock.json b/docs/package-lock.json index c0c8a73fd..2cc1ec39b 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -28,7 +28,7 @@ "medium-zoom": "^1.0.8", "node-fetch": "^3.3.1", "path-browserify": "^1.0.1", - "postcss": "^8.4.24", + "postcss": "^8.4.31", "prism-react-renderer": "^1.3.5", "react": "^17.0.2", "react-dom": "^17.0.2", @@ -13956,9 +13956,9 @@ } }, "node_modules/postcss": { - "version": "8.4.25", - "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.25.tgz", - "integrity": 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deleted file mode 100644 index 587450de2..000000000 Binary files a/docs/static/img/new_langflow2.gif and /dev/null differ diff --git a/docs/static/videos/langflow_fork.mp4 b/docs/static/videos/langflow_fork.mp4 index c9b75bc23..3e0b114ba 100644 Binary files a/docs/static/videos/langflow_fork.mp4 and b/docs/static/videos/langflow_fork.mp4 differ diff --git a/docs/static/videos/langflow_parameters.mp4 b/docs/static/videos/langflow_parameters.mp4 index c7599e649..96f689655 100644 Binary files a/docs/static/videos/langflow_parameters.mp4 and b/docs/static/videos/langflow_parameters.mp4 differ diff --git a/docs/static/videos/langflow_widget.mp4 b/docs/static/videos/langflow_widget.mp4 index d5514a948..3125acf74 100644 Binary files a/docs/static/videos/langflow_widget.mp4 and b/docs/static/videos/langflow_widget.mp4 differ diff --git a/img/langflow-demo.gif b/img/langflow-demo.gif index 4cea58628..19b63eeb1 100644 Binary files a/img/langflow-demo.gif and b/img/langflow-demo.gif differ diff 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otherwise we get an error when running gunicorn + + if platform.system() in ["Darwin"]: + import os + + os.environ["OBJC_DISABLE_INITIALIZE_FORK_SAFETY"] = "YES" + # https://stackoverflow.com/questions/75747888/uwsgi-segmentation-fault-with-flask-python-app-behind-nginx-after-running-for-2 # noqa + os.environ["no_proxy"] = "*" # to avoid error with gunicorn + logger.debug("Set OBJC_DISABLE_INITIALIZE_FORK_SAFETY to YES to avoid error") + + def update_settings( config: str, cache: Optional[str] = None, @@ -84,7 +98,7 @@ def run( "127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST" ), workers: int = typer.Option( - 2, help="Number of worker processes.", envvar="LANGFLOW_WORKERS" + 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"), @@ -143,7 +157,10 @@ def run( """ Run the Langflow. """ + + set_var_for_macos_issue() # override env variables with .env file + if env_file: load_dotenv(env_file, override=True) @@ -165,7 +182,6 @@ def run( options = { "bind": f"{host}:{port}", "workers": get_number_of_workers(workers), - "worker_class": "uvicorn.workers.UvicornWorker", "timeout": timeout, } 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 new file mode 100644 index 000000000..cb126c926 --- /dev/null +++ b/src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py @@ -0,0 +1,85 @@ +"""Change columns to be nullable + +Revision ID: eb5866d51fd2 +Revises: 67cc006d50bf +Create Date: 2023-10-04 10:18:25.640458 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +from sqlalchemy import exc +import sqlmodel # noqa: F401 + +# revision identifiers, used by Alembic. +revision: str = "eb5866d51fd2" +down_revision: Union[str, None] = "67cc006d50bf" +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! ### + connection = op.get_bind() + try: + op.drop_table("flowstyle") + 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") + except exc.SQLAlchemyError: + connection.execute("ROLLBACK") + except Exception: + pass + + try: + op.drop_table("component") + except exc.SQLAlchemyError: + connection.execute("ROLLBACK") + except Exception: + pass + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + try: + 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: + 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"), + ) + except Exception: + pass + # ### end Alembic commands ### diff --git a/src/backend/langflow/api/v1/callback.py b/src/backend/langflow/api/v1/callback.py index 2a16a0bd2..787ca9680 100644 --- a/src/backend/langflow/api/v1/callback.py +++ b/src/backend/langflow/api/v1/callback.py @@ -1,15 +1,17 @@ import asyncio +from uuid import UUID from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler -from langflow.api.v1.schemas import ChatResponse +from langflow.api.v1.schemas import ChatResponse, PromptResponse -from typing import Any, Dict, List, Union -from fastapi import WebSocket +from typing import Any, Dict, List, Optional +from langflow.services.getters import get_chat_service -from langchain.schema import AgentAction, LLMResult, AgentFinish +from langflow.utils.util import remove_ansi_escape_codes +from langchain.schema import AgentAction, AgentFinish from loguru import logger @@ -17,39 +19,15 @@ from loguru import logger class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): """Callback handler for streaming LLM responses.""" - def __init__(self, websocket: WebSocket): - self.websocket = websocket + def __init__(self, client_id: str): + self.chat_service = get_chat_service() + self.client_id = client_id + self.websocket = self.chat_service.active_connections[self.client_id] 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()) - async def on_llm_start( - self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any - ) -> Any: - """Run when LLM starts running.""" - - async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any: - """Run when LLM ends running.""" - - async def on_llm_error( - self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any - ) -> Any: - """Run when LLM errors.""" - - async def on_chain_start( - self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any - ) -> Any: - """Run when chain starts running.""" - - async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any: - """Run when chain ends running.""" - - async def on_chain_error( - self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any - ) -> Any: - """Run when chain errors.""" - async def on_tool_start( self, serialized: Dict[str, Any], input_str: str, **kwargs: Any ) -> Any: @@ -95,8 +73,14 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): logger.error(f"Error sending response: {exc}") async def on_tool_error( - self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any - ) -> Any: + self, + error: BaseException, + *, + run_id: UUID, + parent_run_id: Optional[UUID] = None, + tags: Optional[List[str]] = None, + **kwargs: Any, + ) -> None: """Run when tool errors.""" async def on_text(self, text: str, **kwargs: Any) -> Any: @@ -104,6 +88,14 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): # This runs when first sending the prompt # to the LLM, adding it will send the final prompt # to the frontend + if "Prompt after formatting" in text: + text = text.replace("Prompt after formatting:\n", "") + text = remove_ansi_escape_codes(text) + resp = PromptResponse( + prompt=text, + ) + await self.websocket.send_json(resp.dict()) + self.chat_service.chat_history.add_message(self.client_id, resp) async def on_agent_action(self, action: AgentAction, **kwargs: Any): log = f"Thought: {action.log}" @@ -131,8 +123,10 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler): class StreamingLLMCallbackHandler(BaseCallbackHandler): """Callback handler for streaming LLM responses.""" - def __init__(self, websocket): - self.websocket = websocket + def __init__(self, client_id: str): + self.chat_service = get_chat_service() + self.client_id = client_id + self.websocket = self.chat_service.active_connections[self.client_id] def on_llm_new_token(self, token: str, **kwargs: Any) -> None: resp = ChatResponse(message=token, type="stream", intermediate_steps="") diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py index c25c4bde2..0f277fbb7 100644 --- a/src/backend/langflow/api/v1/chat.py +++ b/src/backend/langflow/api/v1/chat.py @@ -187,14 +187,18 @@ 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_nodes: + response = { + "valid": valid, + "params": params, + "id": vertex_id, + "progress": round(i / number_of_nodes, 2), + } - yield str(StreamData(event="message", data=response)) + yield str(StreamData(event="message", data=response)) langchain_object = graph.build() # Now we need to check the input_keys to send them to the client @@ -239,8 +243,7 @@ def try_running_celery_task(vertex): task = build_vertex.delay(vertex) vertex.task_id = task.id except Exception as exc: - logger.exception(exc) - logger.error("Error running task in celery, running locally") + logger.debug(f"Error running task in celery: {exc}") vertex.task_id = None vertex.build() return vertex diff --git a/src/backend/langflow/api/v1/endpoints.py b/src/backend/langflow/api/v1/endpoints.py index 864467f9b..9b1213c2c 100644 --- a/src/backend/langflow/api/v1/endpoints.py +++ b/src/backend/langflow/api/v1/endpoints.py @@ -69,7 +69,7 @@ def get_all( "/process/{flow_id}", response_model=ProcessResponse, ) -async def process_flow( +async def process( session: Annotated[Session, Depends(get_session)], flow_id: str, inputs: Optional[dict] = None, @@ -156,7 +156,7 @@ async def process_flow( result=task_result, task=task_response, session_id=session_id, - backend=str(type(task_service.backend)), + backend=task_service.backend_name, ) except sa.exc.StatementError as exc: # StatementError('(builtins.ValueError) badly formed hexadecimal UUID string') diff --git a/src/backend/langflow/api/v1/schemas.py b/src/backend/langflow/api/v1/schemas.py index c53f6cb78..54bd31659 100644 --- a/src/backend/langflow/api/v1/schemas.py +++ b/src/backend/langflow/api/v1/schemas.py @@ -78,6 +78,7 @@ class ChatMessage(BaseModel): is_bot: bool = False message: Union[str, None, dict] = None + chatKey: Optional[str] = None type: str = "human" @@ -85,6 +86,7 @@ class ChatResponse(ChatMessage): """Chat response schema.""" intermediate_steps: str + type: str is_bot: bool = True files: list = [] @@ -97,6 +99,14 @@ class ChatResponse(ChatMessage): return v +class PromptResponse(ChatMessage): + """Prompt response schema.""" + + prompt: str + type: str = "prompt" + is_bot: bool = True + + class FileResponse(ChatMessage): """File response schema.""" diff --git a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py b/src/backend/langflow/components/agents/OpenAIConversationalAgent.py index ff1a993b1..364abf962 100644 --- a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py +++ b/src/backend/langflow/components/agents/OpenAIConversationalAgent.py @@ -79,4 +79,5 @@ class ConversationalAgent(CustomComponent): memory=memory, verbose=True, return_intermediate_steps=True, + handle_parsing_errors=True, ) diff --git a/src/backend/langflow/components/chains/PromptRunner.py b/src/backend/langflow/components/chains/PromptRunner.py index b8eaf8677..34ee1f6da 100644 --- a/src/backend/langflow/components/chains/PromptRunner.py +++ b/src/backend/langflow/components/chains/PromptRunner.py @@ -1,7 +1,7 @@ from langflow import CustomComponent from langchain.llms.base import BaseLLM -from langchain import PromptTemplate +from langchain.prompts import PromptTemplate from langchain.schema import Document diff --git a/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py b/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py new file mode 100644 index 000000000..1ea679b47 --- /dev/null +++ b/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py @@ -0,0 +1,92 @@ +from typing import Optional +from langflow import CustomComponent +from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint +from langchain.llms.base import BaseLLM + + +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=qianfan_ak, + qianfan_sk=qianfan_sk, + 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/llms/BaiduQianfanLLMEndpoints.py b/src/backend/langflow/components/llms/BaiduQianfanLLMEndpoints.py new file mode 100644 index 000000000..786c4516b --- /dev/null +++ b/src/backend/langflow/components/llms/BaiduQianfanLLMEndpoints.py @@ -0,0 +1,92 @@ +from typing import Optional +from langflow import CustomComponent +from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint +from langchain.llms.base import BaseLLM + + +class QianfanLLMEndpointComponent(CustomComponent): + display_name: str = "QianfanLLMEndpoint" + description: str = ( + "Baidu Qianfan hosted open source or customized 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 = QianfanLLMEndpoint( # type: ignore + model=model, + qianfan_ak=qianfan_ak, + qianfan_sk=qianfan_sk, + 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/llms/HuggingFaceEndpoints.py b/src/backend/langflow/components/llms/HuggingFaceEndpoints.py index ea2b4f20b..0d28d5b9b 100644 --- a/src/backend/langflow/components/llms/HuggingFaceEndpoints.py +++ b/src/backend/langflow/components/llms/HuggingFaceEndpoints.py @@ -1,6 +1,6 @@ from typing import Optional from langflow import CustomComponent -from langchain.llms import HuggingFaceEndpoint +from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint from langchain.llms.base import BaseLLM @@ -13,7 +13,6 @@ class HuggingFaceEndpointsComponent(CustomComponent): "endpoint_url": {"display_name": "Endpoint URL", "password": True}, "task": { "display_name": "Task", - "type": "select", "options": ["text2text-generation", "text-generation", "summarization"], }, "huggingfacehub_api_token": {"display_name": "API token", "password": True}, @@ -27,7 +26,7 @@ class HuggingFaceEndpointsComponent(CustomComponent): def build( self, endpoint_url: str, - task="text2text-generation", + task: str = "text2text-generation", huggingfacehub_api_token: Optional[str] = None, model_kwargs: Optional[dict] = None, ) -> BaseLLM: @@ -36,6 +35,7 @@ class HuggingFaceEndpointsComponent(CustomComponent): endpoint_url=endpoint_url, task=task, huggingfacehub_api_token=huggingfacehub_api_token, + model_kwargs=model_kwargs, ) except Exception as e: raise ValueError("Could not connect to HuggingFace Endpoints API.") from e diff --git a/src/backend/langflow/components/vectorstores/Vectara.py b/src/backend/langflow/components/vectorstores/Vectara.py index df3b66395..28cd4e3a9 100644 --- a/src/backend/langflow/components/vectorstores/Vectara.py +++ b/src/backend/langflow/components/vectorstores/Vectara.py @@ -5,7 +5,6 @@ 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 class VectaraComponent(CustomComponent): @@ -22,7 +21,6 @@ class VectaraComponent(CustomComponent): "vectara_api_key": {"display_name": "Vectara API Key", "password": True}, "code": {"show": False}, "documents": {"display_name": "Documents"}, - "embedding": {"display_name": "Embedding"}, } def build( @@ -30,21 +28,21 @@ class VectaraComponent(CustomComponent): vectara_customer_id: str, vectara_corpus_id: str, vectara_api_key: str, - embedding: Optional[Embeddings] = None, documents: Optional[Document] = None, ) -> Union[VectorStore, BaseRetriever]: # If documents, then we need to create a Vectara instance using .from_documents - if documents is not None and embedding is not None: + if documents is not None: return Vectara.from_documents( documents=documents, # type: ignore vectara_customer_id=vectara_customer_id, vectara_corpus_id=vectara_corpus_id, vectara_api_key=vectara_api_key, - embedding=embedding, + source="langflow", ) return Vectara( vectara_customer_id=vectara_customer_id, vectara_corpus_id=vectara_corpus_id, vectara_api_key=vectara_api_key, + source="langflow", ) diff --git a/src/backend/langflow/field_typing/__init__.py b/src/backend/langflow/field_typing/__init__.py index 927716b11..9a2161d3f 100644 --- a/src/backend/langflow/field_typing/__init__.py +++ b/src/backend/langflow/field_typing/__init__.py @@ -1,3 +1,53 @@ -from .base import NestedDict +# LANGCHAIN_BASE_TYPES = { +# "Chain": Chain, +# "AgentExecutor": AgentExecutor, +# "Tool": Tool, +# "BaseLLM": BaseLLM, +# "PromptTemplate": PromptTemplate, +# "BaseLoader": BaseLoader, +# "Document": Document, +# "TextSplitter": TextSplitter, +# "VectorStore": VectorStore, +# "Embeddings": Embeddings, +# "BaseRetriever": BaseRetriever, +# "BaseOutputParser": BaseOutputParser, +# "BaseMemory": BaseMemory, +# "BaseChatMemory": BaseChatMemory, +# } +from .constants import ( + Tool, + PromptTemplate, + Chain, + BaseChatMemory, + BaseLLM, + BaseLoader, + BaseMemory, + BaseOutputParser, + BaseRetriever, + VectorStore, + Embeddings, + TextSplitter, + Document, + AgentExecutor, + NestedDict, + Data, +) -__all__ = ["NestedDict"] +__all__ = [ + "NestedDict", + "Data", + "Tool", + "PromptTemplate", + "Chain", + "BaseChatMemory", + "BaseLLM", + "BaseLoader", + "BaseMemory", + "BaseOutputParser", + "BaseRetriever", + "VectorStore", + "Embeddings", + "TextSplitter", + "Document", + "AgentExecutor", +] diff --git a/src/backend/langflow/field_typing/base.py b/src/backend/langflow/field_typing/base.py deleted file mode 100644 index ed3219888..000000000 --- a/src/backend/langflow/field_typing/base.py +++ /dev/null @@ -1,4 +0,0 @@ -from typing import Union, Dict - -# Type alias for more complex dicts -NestedDict = Dict[str, Union[str, Dict]] diff --git a/src/backend/langflow/field_typing/constants.py b/src/backend/langflow/field_typing/constants.py new file mode 100644 index 000000000..3ce429548 --- /dev/null +++ b/src/backend/langflow/field_typing/constants.py @@ -0,0 +1,50 @@ +from langchain.agents.agent import AgentExecutor +from langchain.chains.base import Chain +from langchain.document_loaders.base import BaseLoader +from langchain.llms.base import BaseLLM +from langchain.memory.chat_memory import BaseChatMemory +from langchain.prompts import PromptTemplate +from langchain.schema import BaseOutputParser, BaseRetriever, Document +from langchain.schema.embeddings import Embeddings +from langchain.schema.memory import BaseMemory +from langchain.text_splitter import TextSplitter +from langchain.tools import Tool +from langchain.vectorstores.base import VectorStore +from typing import Union, Dict + +# Type alias for more complex dicts +NestedDict = Dict[str, Union[str, Dict]] + + +class Data: + pass + + +LANGCHAIN_BASE_TYPES = { + "Chain": Chain, + "AgentExecutor": AgentExecutor, + "Tool": Tool, + "BaseLLM": BaseLLM, + "PromptTemplate": PromptTemplate, + "BaseLoader": BaseLoader, + "Document": Document, + "TextSplitter": TextSplitter, + "VectorStore": VectorStore, + "Embeddings": Embeddings, + "BaseRetriever": BaseRetriever, + "BaseOutputParser": BaseOutputParser, + "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, +} diff --git a/src/backend/langflow/graph/edge/base.py b/src/backend/langflow/graph/edge/base.py index 2c60b0288..82714e395 100644 --- a/src/backend/langflow/graph/edge/base.py +++ b/src/backend/langflow/graph/edge/base.py @@ -1,28 +1,79 @@ from loguru import logger from typing import TYPE_CHECKING +from pydantic import BaseModel, Field +from typing import List, Optional 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] - + 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() + 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() + def validate_handles(self) -> 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 {self.source.vertex_type} and {self.target.vertex_type} " + f"has invalid handles" + ) + def __setstate__(self, state): self.source = state["source"] self.target = state["target"] self.target_param = state["target_param"] - self.source_handle = state["source_handle"] - self.target_handle = state["target_handle"] + self.source_handle = state.get("source_handle") + self.target_handle = state.get("target_handle") def reset(self) -> None: self.source._build_params() diff --git a/src/backend/langflow/graph/graph/base.py b/src/backend/langflow/graph/graph/base.py index 227b04bf9..83976ae49 100644 --- a/src/backend/langflow/graph/graph/base.py +++ b/src/backend/langflow/graph/graph/base.py @@ -2,6 +2,7 @@ from typing import Dict, Generator, List, Type, Union 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, @@ -19,11 +20,21 @@ class Graph: def __init__( self, - nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]], + nodes: List[Dict], edges: List[Dict[str, str]], ) -> None: self._nodes = nodes self._edges = edges + self.raw_graph_data = {"nodes": nodes, "edges": edges} + + self.top_level_nodes = [] + for node in self._nodes: + if node_id := node.get("id"): + self.top_level_nodes.append(node_id) + + self._graph_data = process_flow(self.raw_graph_data) + self._nodes = self._graph_data["nodes"] + self._edges = self._graph_data["edges"] self._build_graph() def __setstate__(self, state): @@ -50,6 +61,7 @@ class Graph: edges = payload["edges"] return cls(nodes, edges) except KeyError as exc: + logger.exception(exc) raise ValueError( f"Invalid payload. Expected keys 'nodes' and 'edges'. Found {list(payload.keys())}" ) from exc @@ -215,7 +227,9 @@ class Graph: node_lc_type: str = node_data["node"]["template"]["_type"] # type: ignore VertexClass = self._get_vertex_class(node_type, node_lc_type) - nodes.append(VertexClass(node)) + vertex = VertexClass(node) + vertex.set_top_level(self.top_level_nodes) + nodes.append(vertex) return nodes diff --git a/src/backend/langflow/graph/graph/utils.py b/src/backend/langflow/graph/graph/utils.py index e69de29bb..71b81fea1 100644 --- a/src/backend/langflow/graph/graph/utils.py +++ b/src/backend/langflow/graph/graph/utils.py @@ -0,0 +1,230 @@ +from collections import deque +import copy + + +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 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) + + nodes_to_process = deque(cloned_flow["nodes"]) + + 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) + 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..88a4db26e 100644 --- a/src/backend/langflow/graph/vertex/base.py +++ b/src/backend/langflow/graph/vertex/base.py @@ -38,6 +38,8 @@ class Vertex: self.task_id: Optional[str] = None self.is_task = is_task self.params = params or {} + self.parent_node_id: Optional[str] = self._data.get("parent_node_id") + self.parent_is_top_level = False def reset_params(self): for edge in self.edges: @@ -88,6 +90,11 @@ 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_nodes: List[str]) -> None: + self.parent_is_top_level = self.parent_node_id in top_level_nodes def _parse_data(self) -> None: self.data = self._data["data"] @@ -209,6 +216,16 @@ class Vertex: } elif isinstance(_value, dict): params[key] = _value + elif value.get("type") == "int" and value.get("value") is not None: + try: + params[key] = int(value.get("value")) + except ValueError: + params[key] = value.get("value") + elif value.get("type") == "float" and value.get("value") is not None: + try: + params[key] = float(value.get("value")) + except ValueError: + params[key] = value.get("value") else: params[key] = value.get("value") @@ -342,7 +359,7 @@ class Vertex: except Exception as exc: logger.exception(exc) raise ValueError( - f"Error building node {self.vertex_type}: {str(exc)}" + f"Error building node {self.vertex_type}(ID:{self.id}): {str(exc)}" ) from exc def _update_built_object_and_artifacts(self, result): diff --git a/src/backend/langflow/interface/agents/custom.py b/src/backend/langflow/interface/agents/custom.py index b4e2b9bac..735b27917 100644 --- a/src/backend/langflow/interface/agents/custom.py +++ b/src/backend/langflow/interface/agents/custom.py @@ -1,6 +1,6 @@ from typing import Any, List, Optional -from langchain import LLMChain +from langchain.chains.llm import LLMChain from langchain.agents import ( AgentExecutor, Tool, diff --git a/src/backend/langflow/interface/agents/prebuilt.py b/src/backend/langflow/interface/agents/prebuilt.py index 5b81b7713..ec4799a81 100644 --- a/src/backend/langflow/interface/agents/prebuilt.py +++ b/src/backend/langflow/interface/agents/prebuilt.py @@ -1,4 +1,4 @@ -from langchain import LLMChain +from langchain.chains.llm import LLMChain from langchain.agents import AgentExecutor, ZeroShotAgent from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit diff --git a/src/backend/langflow/interface/custom/constants.py b/src/backend/langflow/interface/custom/constants.py index 39a1e9f3b..6df72500c 100644 --- a/src/backend/langflow/interface/custom/constants.py +++ b/src/backend/langflow/interface/custom/constants.py @@ -1,65 +1,33 @@ -from langchain import PromptTemplate -from langchain.chains.base import Chain -from langchain.document_loaders.base import BaseLoader -from langchain.embeddings.base import Embeddings -from langchain.llms.base import BaseLLM -from langchain.schema import BaseRetriever, Document -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 - -LANGCHAIN_BASE_TYPES = { - "Chain": Chain, - "AgentExecutor": AgentExecutor, - "Tool": Tool, - "BaseLLM": BaseLLM, - "PromptTemplate": PromptTemplate, - "BaseLoader": BaseLoader, - "Document": Document, - "TextSplitter": TextSplitter, - "VectorStore": VectorStore, - "Embeddings": Embeddings, - "BaseRetriever": BaseRetriever, - "BaseOutputParser": BaseOutputParser, - "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, -} - - DEFAULT_CUSTOM_COMPONENT_CODE = """from langflow import CustomComponent -from langchain.llms.base import BaseLLM -from langchain.chains import LLMChain -from langchain import PromptTemplate -from langchain.schema import Document +from langflow.field_typing import ( + Tool, + PromptTemplate, + Chain, + BaseChatMemory, + BaseLLM, + BaseLoader, + BaseMemory, + BaseOutputParser, + BaseRetriever, + VectorStore, + Embeddings, + TextSplitter, + Document, + AgentExecutor, + NestedDict, + Data, +) -import requests -class YourComponent(CustomComponent): +class Component(CustomComponent): display_name: str = "Custom Component" description: str = "Create any custom component you want!" def build_config(self): - return { "url": { "multiline": True, "required": True } } + return {"param": {"display_name": "Parameter"}} + + def build(self, param: Data) -> Data: + return param - 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/interface/custom/custom_component.py b/src/backend/langflow/interface/custom/custom_component.py index 84ee3f99a..c734543ec 100644 --- a/src/backend/langflow/interface/custom/custom_component.py +++ b/src/backend/langflow/interface/custom/custom_component.py @@ -1,7 +1,7 @@ from typing import Any, Callable, ClassVar, List, Optional, Union, Dict from uuid import UUID from fastapi import HTTPException -from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES +from langflow.field_typing.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 @@ -108,6 +108,9 @@ class CustomComponent(Component, extra=Extra.allow): ), }, ) + elif not arg.get("type"): + # Set the type to Data + arg["type"] = "Data" return args @property diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py index 000eed205..44d72df42 100644 --- a/src/backend/langflow/interface/importing/utils.py +++ b/src/backend/langflow/interface/importing/utils.py @@ -3,7 +3,7 @@ import importlib from typing import Any, Type -from langchain import PromptTemplate +from langchain.prompts import PromptTemplate from langchain.agents import Agent from langchain.base_language import BaseLanguageModel from langchain.chains.base import Chain diff --git a/src/backend/langflow/interface/initialize/loading.py b/src/backend/langflow/interface/initialize/loading.py index 39677db52..04cbcd4fc 100644 --- a/src/backend/langflow/interface/initialize/loading.py +++ b/src/backend/langflow/interface/initialize/loading.py @@ -111,7 +111,7 @@ def instantiate_based_on_type(class_object, base_type, node_type, params, user_i elif base_type == "vectorstores": return instantiate_vectorstore(class_object, params) elif base_type == "documentloaders": - return instantiate_documentloader(class_object, params) + return instantiate_documentloader(node_type, class_object, params) elif base_type == "textsplitters": return instantiate_textsplitter(class_object, params) elif base_type == "utilities": @@ -321,7 +321,9 @@ def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict): return vecstore -def instantiate_documentloader(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 @@ -341,6 +343,11 @@ def instantiate_documentloader(class_object: Type[BaseLoader], params: Dict): 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): + params["web_paths"] = [web_path] + docs = class_object(**params).load() # Now if metadata is an empty dict, we will not add it to the documents if metadata: diff --git a/src/backend/langflow/interface/types.py b/src/backend/langflow/interface/types.py index 6708c11c9..0046c84fb 100644 --- a/src/backend/langflow/interface/types.py +++ b/src/backend/langflow/interface/types.py @@ -4,7 +4,7 @@ from typing import Any, List from langflow.api.utils import get_new_key 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.field_typing.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES 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 @@ -288,6 +288,24 @@ def add_base_classes(frontend_node, return_types: List[str]): frontend_node.get("base_classes").append(base_class) +def add_output_types(frontend_node, return_types: List[str]): + """Add output types to the frontend node""" + for return_type in return_types: + if return_type not in CUSTOM_COMPONENT_SUPPORTED_TYPES or return_type is None: + raise HTTPException( + status_code=400, + detail={ + "error": ( + "Invalid return type should be one of: " + f"{list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())}" + ), + "traceback": traceback.format_exc(), + }, + ) + + frontend_node.get("output_types").append(return_type) + + def build_langchain_template_custom_component(custom_component: CustomComponent): """Build a custom component template for the langchain""" try: @@ -314,6 +332,9 @@ def build_langchain_template_custom_component(custom_component: CustomComponent) 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 except Exception as exc: diff --git a/src/backend/langflow/interface/utilities/base.py b/src/backend/langflow/interface/utilities/base.py index 665143da2..bfc7cb11e 100644 --- a/src/backend/langflow/interface/utilities/base.py +++ b/src/backend/langflow/interface/utilities/base.py @@ -1,6 +1,6 @@ from typing import Dict, List, Optional, Type -from langchain import SQLDatabase, utilities +from langchain import utilities from langflow.custom.customs import get_custom_nodes from langflow.interface.base import LangChainTypeCreator @@ -32,7 +32,7 @@ class UtilityCreator(LangChainTypeCreator): utility_name: import_class(f"langchain.utilities.{utility_name}") for utility_name in utilities.__all__ } - self.type_dict["SQLDatabase"] = SQLDatabase + self.type_dict["SQLDatabase"] = utilities.SQLDatabase # Filter according to settings.utilities self.type_dict = { name: utility diff --git a/src/backend/langflow/interface/utils.py b/src/backend/langflow/interface/utils.py index 7b78e75c5..b28a660bf 100644 --- a/src/backend/langflow/interface/utils.py +++ b/src/backend/langflow/interface/utils.py @@ -36,7 +36,7 @@ def pil_to_base64(image: Image) -> str: return img_str.decode("utf-8") -def try_setting_streaming_options(langchain_object, websocket): +def try_setting_streaming_options(langchain_object): # If the LLM type is OpenAI or ChatOpenAI, # set streaming to True # First we need to find the LLM diff --git a/src/backend/langflow/interface/wrappers/base.py b/src/backend/langflow/interface/wrappers/base.py index 30625c264..e0e762e99 100644 --- a/src/backend/langflow/interface/wrappers/base.py +++ b/src/backend/langflow/interface/wrappers/base.py @@ -1,6 +1,6 @@ from typing import ClassVar, Dict, List, Optional -from langchain import requests, sql_database +from langchain.utilities import requests, sql_database from langflow.interface.base import LangChainTypeCreator from loguru import logger diff --git a/src/backend/langflow/processing/base.py b/src/backend/langflow/processing/base.py index e5816306c..6c56a59dd 100644 --- a/src/backend/langflow/processing/base.py +++ b/src/backend/langflow/processing/base.py @@ -34,7 +34,9 @@ def get_langfuse_callback(trace_id): if langfuse := LangfuseInstance.get(): 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}") diff --git a/src/backend/langflow/server.py b/src/backend/langflow/server.py index a96f8b7e0..3a2943444 100644 --- a/src/backend/langflow/server.py +++ b/src/backend/langflow/server.py @@ -4,6 +4,8 @@ from gunicorn.app.base import BaseApplication # type: ignore class LangflowApplication(BaseApplication): def __init__(self, app, options=None): self.options = options or {} + + self.options["worker_class"] = "uvicorn.workers.UvicornWorker" self.application = app super().__init__() diff --git a/src/backend/langflow/services/chat/manager.py b/src/backend/langflow/services/chat/manager.py index 3fe937ce8..59488d431 100644 --- a/src/backend/langflow/services/chat/manager.py +++ b/src/backend/langflow/services/chat/manager.py @@ -1,6 +1,7 @@ from collections import defaultdict import uuid 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.base import Service @@ -125,7 +126,8 @@ class ChatService(Service): ): # Process the graph data and chat message chat_inputs = payload.pop("inputs", {}) - chat_inputs = ChatMessage(message=chat_inputs) + chatkey = payload.pop("chatKey", None) + chat_inputs = ChatMessage(message=chat_inputs, chatKey=chatkey) self.chat_history.add_message(client_id, chat_inputs) # graph_data = payload @@ -140,7 +142,7 @@ class ChatService(Service): result, intermediate_steps = await process_graph( langchain_object=langchain_object, chat_inputs=chat_inputs, - websocket=self.active_connections[client_id], + client_id=client_id, session_id=self.connection_ids[client_id], ) self.set_cache(client_id, langchain_object) @@ -200,11 +202,11 @@ class ChatService(Service): while True: json_payload = await websocket.receive_json() - try: + if isinstance(json_payload, str): payload = orjson.loads(json_payload) - except Exception: + elif isinstance(json_payload, dict): payload = json_payload - if "clear_history" in payload: + if "clear_history" in payload and payload["clear_history"]: self.chat_history.history[client_id] = [] continue @@ -216,23 +218,29 @@ class ChatService(Service): else: raise RuntimeError( - f"Could not find a LangChain object for client_id {client_id}" + f"Could not find a build result for client_id {client_id}" ) except Exception as exc: # Handle any exceptions that might occur - logger.error(f"Error handling websocket: {exc}") - await self.close_connection( - client_id=client_id, - code=status.WS_1011_INTERNAL_ERROR, - reason=str(exc)[:120], - ) - finally: - try: + logger.exception(f"Error handling websocket: {exc}") + if websocket.client_state == WebSocketState.CONNECTED: await self.close_connection( client_id=client_id, - code=status.WS_1000_NORMAL_CLOSURE, - reason="Client disconnected", + code=status.WS_1011_INTERNAL_ERROR, + reason=str(exc)[:120], ) + elif websocket.client_state == WebSocketState.DISCONNECTED: + self.disconnect(client_id) + + finally: + try: + # first check if the connection is still open + if websocket.client_state == WebSocketState.CONNECTED: + await self.close_connection( + client_id=client_id, + code=status.WS_1000_NORMAL_CLOSURE, + reason="Client disconnected", + ) except Exception as exc: logger.error(f"Error closing connection: {exc}") self.disconnect(client_id) diff --git a/src/backend/langflow/services/chat/utils.py b/src/backend/langflow/services/chat/utils.py index a332e381e..85b86a801 100644 --- a/src/backend/langflow/services/chat/utils.py +++ b/src/backend/langflow/services/chat/utils.py @@ -1,4 +1,3 @@ -from fastapi import WebSocket 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 @@ -8,10 +7,10 @@ from loguru import logger async def process_graph( langchain_object, chat_inputs: ChatMessage, - websocket: WebSocket, + client_id: str, session_id: str, ): - langchain_object = try_setting_streaming_options(langchain_object, websocket) + langchain_object = try_setting_streaming_options(langchain_object) logger.debug("Loaded langchain object") if langchain_object is None: @@ -30,7 +29,7 @@ async def process_graph( result, intermediate_steps = await get_result_and_steps( langchain_object, chat_inputs.message, - websocket=websocket, + client_id=client_id, session_id=session_id, ) logger.debug("Generated result and intermediate_steps") diff --git a/src/backend/langflow/services/database/manager.py b/src/backend/langflow/services/database/manager.py index 3a388f694..7465c804f 100644 --- a/src/backend/langflow/services/database/manager.py +++ b/src/backend/langflow/services/database/manager.py @@ -6,6 +6,7 @@ from langflow.services.database.utils import Result, TableResults from langflow.services.getters import get_settings_service from sqlalchemy import inspect import sqlalchemy as sa +from sqlalchemy.exc import OperationalError from sqlmodel import SQLModel, Session, create_engine from loguru import logger from alembic.config import Config @@ -58,6 +59,27 @@ class DatabaseService(Service): with Session(self.engine) as session: yield session + def migrate_flows_if_auto_login(self): + # if auto_login is enabled, we need to migrate the flows + # to the default superuser if they don't have a user id + # associated with them + 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() + ) + if flows: + logger.debug("Migrating flows to default superuser") + username = settings_service.auth_settings.SUPERUSER + user = get_user_by_username(session, username) + for flow in flows: + flow.user_id = user.id + session.commit() + logger.debug("Flows migrated successfully") + def check_schema_health(self) -> bool: inspector = inspect(self.engine) @@ -93,7 +115,35 @@ class DatabaseService(Service): return True + def init_alembic(self): + logger.info("Initializing alembic") + 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.stamp(alembic_cfg, "head") + logger.info("Alembic initialized") + def run_migrations(self): + # 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 + # We need to check if the alembic_version table exists + # if not, we need to initialize alembic + with Session(self.engine) as session: + # If the table does not exist it throws an error + # so we need to catch it + try: + session.execute("SELECT * FROM alembic_version") + except Exception: + logger.info("Alembic not initialized") + try: + self.init_alembic() + except Exception as exc: + logger.error(f"Error initializing alembic: {exc}") + raise RuntimeError("Error initializing alembic") from exc + else: + logger.info("Alembic already initialized") + logger.info(f"Running DB migrations in {self.script_location}") alembic_cfg = Config() alembic_cfg.set_main_option("script_location", str(self.script_location)) @@ -133,19 +183,31 @@ class DatabaseService(Service): return results def create_db_and_tables(self): - logger.debug("Creating database and tables") - try: - SQLModel.metadata.create_all(self.engine) - except Exception as exc: - logger.error(f"Error creating database and tables: {exc}") - raise RuntimeError("Error creating database and tables") from exc - - # Now check if the table "flow" exists, if not, something went wrong - # and we need to create the tables again. from sqlalchemy import inspect inspector = inspect(self.engine) + table_names = inspector.get_table_names() current_tables = ["flow", "user", "apikey"] + + if table_names and all(table in table_names for table in current_tables): + logger.debug("Database and tables already exist") + return + + logger.debug("Creating database and tables") + + for table in SQLModel.metadata.sorted_tables: + try: + table.create(self.engine, checkfirst=True) + except OperationalError as 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 + + # Now check if the required tables exist, if not, something went wrong. + inspector = inspect(self.engine) table_names = inspector.get_table_names() for table in current_tables: if table not in table_names: diff --git a/src/backend/langflow/services/database/models/api_key/api_key.py b/src/backend/langflow/services/database/models/api_key/api_key.py index 35aa6c7a9..684027ee2 100644 --- a/src/backend/langflow/services/database/models/api_key/api_key.py +++ b/src/backend/langflow/services/database/models/api_key/api_key.py @@ -12,7 +12,7 @@ if TYPE_CHECKING: class ApiKeyBase(SQLModelSerializable): name: Optional[str] = Field(index=True) created_at: datetime = Field(default_factory=datetime.utcnow) - last_used_at: Optional[datetime] = Field(default=None) + last_used_at: Optional[datetime] = Field(default=None, nullable=True) total_uses: int = Field(default=0) is_active: bool = Field(default=True) 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 c1c6f786e..0e0ae6137 100644 --- a/src/backend/langflow/services/database/models/api_key/crud.py +++ b/src/backend/langflow/services/database/models/api_key/crud.py @@ -22,7 +22,7 @@ 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"lf-{secrets.token_urlsafe(32)}" + generated_api_key = f"sk-{secrets.token_urlsafe(32)}" api_key = ApiKey( api_key=generated_api_key, diff --git a/src/backend/langflow/services/database/models/flow/flow.py b/src/backend/langflow/services/database/models/flow/flow.py index 2a07b7edc..e578f37c4 100644 --- a/src/backend/langflow/services/database/models/flow/flow.py +++ b/src/backend/langflow/services/database/models/flow/flow.py @@ -2,6 +2,7 @@ from langflow.services.database.models.base import SQLModelSerializable from pydantic import validator + from sqlmodel import Field, JSON, Column, Relationship from uuid import UUID, uuid4 from typing import Dict, Optional, TYPE_CHECKING @@ -12,8 +13,8 @@ if TYPE_CHECKING: class FlowBase(SQLModelSerializable): name: str = Field(index=True) - description: Optional[str] = Field(index=True, default="") - data: Optional[Dict] = Field(default=None) + description: Optional[str] = Field(index=True) + data: Optional[Dict] = Field(default=None, nullable=True) @validator("data") def validate_json(v): diff --git a/src/backend/langflow/services/database/models/user/user.py b/src/backend/langflow/services/database/models/user/user.py index 411026d89..1f530466b 100644 --- a/src/backend/langflow/services/database/models/user/user.py +++ b/src/backend/langflow/services/database/models/user/user.py @@ -15,7 +15,7 @@ class User(SQLModelSerializable, table=True): id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True) username: str = Field(index=True, unique=True) password: str = Field() - profile_image: Optional[str] = Field(default=None) + profile_image: Optional[str] = Field(default=None, nullable=True) is_active: bool = Field(default=False) is_superuser: bool = Field(default=False) create_at: datetime = Field(default_factory=datetime.utcnow) diff --git a/src/backend/langflow/services/database/utils.py b/src/backend/langflow/services/database/utils.py index ce4990fa1..b457b70e2 100644 --- a/src/backend/langflow/services/database/utils.py +++ b/src/backend/langflow/services/database/utils.py @@ -13,7 +13,17 @@ def initialize_database(): logger.debug("Initializing database") from langflow.services import service_manager, ServiceType - database_service = service_manager.get(ServiceType.DATABASE_SERVICE) + database_service: "DatabaseService" = service_manager.get( + ServiceType.DATABASE_SERVICE + ) + try: + database_service.create_db_and_tables() + 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 creating DB and tables: {exc}") + raise RuntimeError("Error creating DB and tables") from exc try: database_service.check_schema_health() except Exception as exc: @@ -22,7 +32,11 @@ def initialize_database(): try: database_service.run_migrations() except CommandError as exc: - if "Can't locate revision identified by" not in str(exc): + # if "overlaps with other requested revisions" or "Can't locate revision identified by" + # are not in the exception, we can't handle it + if "overlaps with other requested revisions" not in str( + exc + ) and "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 @@ -39,7 +53,6 @@ def initialize_database(): if "already exists" not in str(exc): logger.error(f"Error running migrations: {exc}") raise RuntimeError("Error running migrations") from exc - database_service.create_db_and_tables() logger.debug("Database initialized") diff --git a/src/backend/langflow/services/manager.py b/src/backend/langflow/services/manager.py index e74146a20..10fd6b699 100644 --- a/src/backend/langflow/services/manager.py +++ b/src/backend/langflow/services/manager.py @@ -90,7 +90,10 @@ class ServiceManager: if service is None: continue logger.debug(f"Teardown service {service.name}") - service.teardown() + try: + service.teardown() + except Exception as exc: + logger.exception(exc) self.services = {} self.factories = {} self.dependencies = {} @@ -99,51 +102,6 @@ class ServiceManager: service_manager = ServiceManager() -def initialize_services(): - """ - Initialize all the services needed. - """ - from langflow.services.database import factory as database_factory - from langflow.services.cache import factory as cache_factory - from langflow.services.chat import factory as chat_factory - from langflow.services.settings import factory as settings_factory - from langflow.services.session import factory as session_service_factory - from langflow.services.auth import factory as auth_factory - from langflow.services.task import factory as task_factory - - service_manager.register_factory(settings_factory.SettingsServiceFactory()) - service_manager.register_factory( - database_factory.DatabaseServiceFactory(), - dependencies=[ServiceType.SETTINGS_SERVICE], - ) - service_manager.register_factory( - cache_factory.CacheServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE] - ) - - service_manager.register_factory( - auth_factory.AuthServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE] - ) - - service_manager.register_factory(chat_factory.ChatServiceFactory()) - service_manager.register_factory( - session_service_factory.SessionServiceFactory(), - dependencies=[ServiceType.CACHE_SERVICE], - ) - service_manager.register_factory( - task_factory.TaskServiceFactory(), - ) - - # Test cache connection - service_manager.get(ServiceType.CACHE_SERVICE) - # Test database connection - service_manager.get(ServiceType.DATABASE_SERVICE) - - # Test cache connection - service_manager.get(ServiceType.CACHE_SERVICE) - # Test database connection - service_manager.get(ServiceType.DATABASE_SERVICE) - - def reinitialize_services(): """ Reinitialize all the services needed. @@ -194,10 +152,3 @@ def initialize_session_service(): session_service_factory.SessionServiceFactory(), dependencies=[ServiceType.CACHE_SERVICE], ) - - -def teardown_services(): - """ - Teardown all the services. - """ - service_manager.teardown() diff --git a/src/backend/langflow/services/settings/auth.py b/src/backend/langflow/services/settings/auth.py index 167b75ad4..823f242bc 100644 --- a/src/backend/langflow/services/settings/auth.py +++ b/src/backend/langflow/services/settings/auth.py @@ -35,7 +35,7 @@ class AuthSettings(BaseSettings): # If AUTO_LOGIN = True # > The application does not request login and logs in automatically as a super user. - AUTO_LOGIN: bool = False + AUTO_LOGIN: bool = True NEW_USER_IS_ACTIVE: bool = False SUPERUSER: str = DEFAULT_SUPERUSER SUPERUSER_PASSWORD: str = DEFAULT_SUPERUSER_PASSWORD diff --git a/src/backend/langflow/services/task/backends/anyio.py b/src/backend/langflow/services/task/backends/anyio.py index ca91eca19..49c9c0e4f 100644 --- a/src/backend/langflow/services/task/backends/anyio.py +++ b/src/backend/langflow/services/task/backends/anyio.py @@ -34,6 +34,8 @@ class AnyIOTaskResult: class AnyIOBackend(TaskBackend): + name = "anyio" + def __init__(self): self.tasks = {} diff --git a/src/backend/langflow/services/task/backends/celery.py b/src/backend/langflow/services/task/backends/celery.py index 5b0a035ef..eae985f3a 100644 --- a/src/backend/langflow/services/task/backends/celery.py +++ b/src/backend/langflow/services/task/backends/celery.py @@ -5,6 +5,8 @@ from langflow.worker import celery_app class CeleryBackend(TaskBackend): + name = "celery" + def __init__(self): self.celery_app = celery_app diff --git a/src/backend/langflow/services/task/manager.py b/src/backend/langflow/services/task/manager.py index 422b34faa..807505c3a 100644 --- a/src/backend/langflow/services/task/manager.py +++ b/src/backend/langflow/services/task/manager.py @@ -35,6 +35,10 @@ class TaskService(Service): self.backend = self.get_backend() self.use_celery = USE_CELERY + @property + def backend_name(self) -> str: + return self.backend.name + def get_backend(self) -> TaskBackend: if USE_CELERY: from langflow.services.task.backends.celery import CeleryBackend diff --git a/src/backend/langflow/services/utils.py b/src/backend/langflow/services/utils.py index 02d9816f5..adbf072fe 100644 --- a/src/backend/langflow/services/utils.py +++ b/src/backend/langflow/services/utils.py @@ -1,4 +1,4 @@ -from langflow.services.auth.utils import create_super_user +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 @@ -6,50 +6,121 @@ from langflow.services.settings.constants import ( DEFAULT_SUPERUSER, DEFAULT_SUPERUSER_PASSWORD, ) -from .getters import get_session, get_settings_service +from sqlmodel import Session +from .getters import get_db_service, get_session, get_settings_service from loguru import logger -def setup_superuser(settings_service, session): - """ - Setup the superuser. - """ - # We will use the SUPERUSER and SUPERUSER_PASSWORD - # vars on settings_manager.auth_settings to create the superuser - # if it does not exist. +def get_factories_and_deps(): + from langflow.services.database import factory as database_factory + from langflow.services.cache import factory as cache_factory + from langflow.services.chat import factory as chat_factory + from langflow.services.settings import factory as settings_factory + from langflow.services.auth import factory as auth_factory + from langflow.services.task import factory as task_factory + from langflow.services.session import factory as session_service_factory # type: ignore + + return [ + (settings_factory.SettingsServiceFactory(), []), + ( + auth_factory.AuthServiceFactory(), + [ServiceType.SETTINGS_SERVICE], + ), + ( + database_factory.DatabaseServiceFactory(), + [ServiceType.SETTINGS_SERVICE], + ), + ( + cache_factory.CacheServiceFactory(), + [ServiceType.SETTINGS_SERVICE], + ), + (chat_factory.ChatServiceFactory(), []), + (task_factory.TaskServiceFactory(), []), + ( + session_service_factory.SessionServiceFactory(), + [ServiceType.CACHE_SERVICE], + ), + ] + + +def get_or_create_super_user(session: Session, username, password, is_default): + from langflow.services.database.models.user.user import User + + user = session.query(User).filter(User.username == username).first() + + if user and user.is_superuser: + return None # Superuser already exists + + if user and is_default: + if user.is_superuser: + if verify_password(password, user.password): + return None + else: + # Superuser exists but password is incorrect + # which means that the user has changed the + # base superuser credentials. + # This means that the user has already created + # a superuser and changed the password in the UI + # so we don't need to do anything. + logger.debug( + "Superuser exists but password is incorrect. " + "This means that the user has changed the " + "base superuser credentials." + ) + return None + else: + 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." + ) + else: + raise ValueError("Incorrect superuser credentials") + + if is_default: + logger.debug("Creating default superuser.") + else: + logger.debug("Creating superuser.") + try: + return create_super_user(username, password, db=session) + except Exception as exc: + if "UNIQUE constraint failed: user.username" in str(exc): + # This is to deal with workers running this + # at startup and trying to create the superuser + # at the same time. + logger.debug("Superuser already exists.") + return None + + +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.") username = settings_service.auth_settings.SUPERUSER password = settings_service.auth_settings.SUPERUSER_PASSWORD - if username == DEFAULT_SUPERUSER and password == DEFAULT_SUPERUSER_PASSWORD: - logger.debug("Default superuser credentials detected.") - logger.debug("Creating default superuser.") - else: - logger.debug("Creating superuser.") + + is_default = (username == DEFAULT_SUPERUSER) and ( + password == DEFAULT_SUPERUSER_PASSWORD + ) try: - from langflow.services.database.models.user.user import User - - user = session.query(User).filter(User.username == username).first() - if user and user.is_superuser is True: - return + 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 - try: - # create superuser - create_super_user(db=session, username=username, password=password) - except Exception as exc: - logger.exception(exc) - raise RuntimeError( - "Could not create superuser. Please create a superuser manually." - ) from exc - # reset superuser credentials - settings_service.auth_settings.reset_credentials() - logger.debug("Superuser created successfully.") + finally: + settings_service.auth_settings.reset_credentials() def teardown_superuser(settings_service, session): @@ -60,17 +131,21 @@ def teardown_superuser(settings_service, session): # from the database. if settings_service.auth_settings.AUTO_LOGIN: - logger.debug("AUTO_LOGIN is set to True. Removing default superuser.") - username = settings_service.auth_settings.SUPERUSER - from langflow.services.database.models.user.user import User + try: + logger.debug("AUTO_LOGIN is set to True. Removing default superuser.") + username = settings_service.auth_settings.SUPERUSER + from langflow.services.database.models.user.user import User - user = session.query(User).filter(User.username == username).first() - if user and user.is_superuser: - session.delete(user) - session.commit() - logger.debug("Default superuser removed successfully.") - else: - logger.debug("Default superuser not found.") + user = session.query(User).filter(User.username == username).first() + if user and user.is_superuser: + session.delete(user) + session.commit() + logger.debug("Default superuser removed successfully.") + else: + logger.debug("Default superuser not found.") + except Exception as exc: + logger.exception(exc) + raise RuntimeError("Could not remove default superuser.") from exc def teardown_services(): @@ -79,6 +154,9 @@ def teardown_services(): """ try: teardown_superuser(get_settings_service(), next(get_session())) + except Exception as exc: + logger.exception(exc) + try: service_manager.teardown() except Exception as exc: logger.exception(exc) @@ -116,38 +194,24 @@ def initialize_services(): """ Initialize all the services needed. """ - from langflow.services.database import factory as database_factory - from langflow.services.cache import factory as cache_factory - from langflow.services.chat import factory as chat_factory - from langflow.services.settings import factory as settings_factory - from langflow.services.auth import factory as auth_factory - from langflow.services.task import factory as task_factory - from langflow.services.session import factory as session_service_factory # type: ignore + for factory, dependencies in get_factories_and_deps(): + try: + 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 - service_manager.register_factory(settings_factory.SettingsServiceFactory()) - service_manager.register_factory( - auth_factory.AuthServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE] - ) - service_manager.register_factory( - database_factory.DatabaseServiceFactory(), - dependencies=[ServiceType.SETTINGS_SERVICE], - ) - service_manager.register_factory( - cache_factory.CacheServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE] - ) - service_manager.register_factory(chat_factory.ChatServiceFactory()) - - service_manager.register_factory(task_factory.TaskServiceFactory()) - - service_manager.register_factory( - session_service_factory.SessionServiceFactory(), - dependencies=[ServiceType.CACHE_SERVICE], - ) # Test cache connection service_manager.get(ServiceType.CACHE_SERVICE) - # Test database connection - service_manager.get(ServiceType.DATABASE_SERVICE) # Setup the superuser initialize_database() - session = next(get_session()) - setup_superuser(service_manager.get(ServiceType.SETTINGS_SERVICE), session) + setup_superuser( + service_manager.get(ServiceType.SETTINGS_SERVICE), next(get_session()) + ) + try: + get_db_service().migrate_flows_if_auto_login() + except Exception as exc: + logger.error(f"Error migrating flows: {exc}") + raise RuntimeError("Error migrating flows") from exc diff --git a/src/backend/langflow/template/frontend_node/agents.py b/src/backend/langflow/template/frontend_node/agents.py index 63c8a4d5e..b3f9b4545 100644 --- a/src/backend/langflow/template/frontend_node/agents.py +++ b/src/backend/langflow/template/frontend_node/agents.py @@ -189,7 +189,7 @@ class InitializeAgentNode(FrontendNode): ), TemplateField( field_type="Tool", - required=False, + required=True, show=True, name="tools", is_list=True, diff --git a/src/backend/langflow/template/frontend_node/base.py b/src/backend/langflow/template/frontend_node/base.py index 6b05b6554..4b1c151a3 100644 --- a/src/backend/langflow/template/frontend_node/base.py +++ b/src/backend/langflow/template/frontend_node/base.py @@ -44,7 +44,7 @@ class FieldFormatters(BaseModel): class FrontendNode(BaseModel): template: Template - description: str + description: Optional[str] = None base_classes: List[str] name: str = "" display_name: str = "" @@ -164,7 +164,7 @@ class FrontendNode(BaseModel): ) -> None: """Handles specific field values for certain fields.""" if key == "headers": - field.value = """{'Authorization': 'Bearer '}""" + field.value = """{"Authorization": "Bearer "}""" FrontendNode._handle_model_specific_field_values(field, key, name) FrontendNode._handle_api_key_specific_field_values(field, key, name) @@ -249,4 +249,4 @@ class FrontendNode(BaseModel): if "default" in value: field.value = value["default"] if key == "headers": - field.value = """{'Authorization': 'Bearer '}""" + field.value = """{"Authorization": "Bearer "}""" diff --git a/src/backend/langflow/template/frontend_node/constants.py b/src/backend/langflow/template/frontend_node/constants.py index 8800a3755..a213a2744 100644 --- a/src/backend/langflow/template/frontend_node/constants.py +++ b/src/backend/langflow/template/frontend_node/constants.py @@ -65,4 +65,11 @@ INPUT_KEY_INFO = """The variable to be used as Chat Input when more than one var OUTPUT_KEY_INFO = """The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)""" -CLASSES_TO_REMOVE = ["Serializable", "BaseModel", "object", "Runnable", "Generic"] +CLASSES_TO_REMOVE = [ + "RunnableSerializable", + "Serializable", + "BaseModel", + "object", + "Runnable", + "Generic", +] diff --git a/src/backend/langflow/template/frontend_node/custom_components.py b/src/backend/langflow/template/frontend_node/custom_components.py index 4f36a1c9f..e239775bc 100644 --- a/src/backend/langflow/template/frontend_node/custom_components.py +++ b/src/backend/langflow/template/frontend_node/custom_components.py @@ -2,6 +2,7 @@ from langflow.template.field.base import TemplateField from langflow.template.frontend_node.base import FrontendNode from langflow.template.template.base import Template from langflow.interface.custom.constants import DEFAULT_CUSTOM_COMPONENT_CODE +from typing import Optional class CustomComponentFrontendNode(FrontendNode): @@ -24,7 +25,7 @@ class CustomComponentFrontendNode(FrontendNode): ) ], ) - description: str = "Create any custom component you want!" + description: Optional[str] = None base_classes: list[str] = [] def to_dict(self): 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 dd289f453..654a92c2f 100644 --- a/src/backend/langflow/template/frontend_node/formatter/field_formatters.py +++ b/src/backend/langflow/template/frontend_node/formatter/field_formatters.py @@ -145,7 +145,7 @@ class HeadersDefaultValueFormatter(FieldFormatter): def format(self, field: TemplateField, name: Optional[str] = None) -> None: key = field.name if key == "headers": - field.value = """{'Authorization': 'Bearer '}""" + field.value = """{"Authorization": "Bearer "}""" class DictCodeFileFormatter(FieldFormatter): diff --git a/src/backend/langflow/template/frontend_node/prompts.py b/src/backend/langflow/template/frontend_node/prompts.py index c52b1901c..f0ebc35aa 100644 --- a/src/backend/langflow/template/frontend_node/prompts.py +++ b/src/backend/langflow/template/frontend_node/prompts.py @@ -44,6 +44,7 @@ class PromptFrontendNode(FrontendNode): # All prompt fields should be password=False field.password = False + field.dynamic = True class PromptTemplateNode(FrontendNode): diff --git a/src/backend/langflow/utils/util.py b/src/backend/langflow/utils/util.py index fce9a1a21..4d96709b5 100644 --- a/src/backend/langflow/utils/util.py +++ b/src/backend/langflow/utils/util.py @@ -11,6 +11,10 @@ from langflow.utils import constants from langchain.schema import Document +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 ): @@ -187,7 +191,9 @@ def get_base_classes(cls): """Get the base classes of a class. These are used to determine the output of the nodes. """ - if bases := cls.__bases__: + + if hasattr(cls, "__bases__") and cls.__bases__: + bases = cls.__bases__ result = [] for base in bases: if any(type in base.__module__ for type in ["pydantic", "abc"]): @@ -428,7 +434,7 @@ def set_headers_value(value: Dict[str, Any]) -> None: """ Sets the value for the 'headers' key. """ - value["value"] = """{'Authorization': 'Bearer '}""" + value["value"] = """{"Authorization": "Bearer "}""" def add_options_to_field( diff --git a/src/frontend/.gitignore b/src/frontend/.gitignore index 9ad9e7fb4..9dfae2bb0 100644 --- a/src/frontend/.gitignore +++ b/src/frontend/.gitignore @@ -22,5 +22,5 @@ npm-debug.log* yarn-debug.log* yarn-error.log* /test-results/ -/playwright-report/ +/playwright-report/*/ /playwright/.cache/ diff --git a/src/frontend/harFiles/langflow.har b/src/frontend/harFiles/langflow.har index 2f73c3515..edac6806e 100644 --- a/src/frontend/harFiles/langflow.har +++ b/src/frontend/harFiles/langflow.har @@ -161,7 +161,7 @@ "content": { "size": -1, "mimeType": "application/json", - "text": "{\"chains\":{\"ConversationChain\":{\"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: {input}\\nAI:\",\"template_format\":\"f-string\",\"validate_template\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"input_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"input\",\"password\":false,\"name\":\"input_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"llm_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"llm_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"output_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"response\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"return_final_only\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_final_only\",\"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},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationChain\"},\"description\":\"Chain to have a conversation and load context from memory.\",\"base_classes\":[\"Chain\",\"LLMChain\",\"ConversationChain\",\"function\"],\"display_name\":\"ConversationChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"ConversationalRetrievalChain\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Callbacks\",\"list\":false},\"condense_question_llm\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"condense_question_llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"condense_question_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"chat_history\",\"question\"],\"output_parser\":null,\"partial_variables\":{},\"template\":\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\\n\\nChat History:\\n{chat_history}\\nFollow Up Input: {question}\\nStandalone question:\",\"template_format\":\"f-string\",\"validate_template\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"condense_question_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"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},\"combine_docs_chain_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"combine_docs_chain_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"return_source_documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return source documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationalRetrievalChain\"},\"description\":\"Convenience method to load chain from LLM and retriever.\",\"base_classes\":[\"Chain\",\"BaseConversationalRetrievalChain\",\"ConversationalRetrievalChain\",\"function\"],\"display_name\":\"ConversationalRetrievalChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/chat_vector_db\",\"beta\":false,\"error\":null},\"LLMChain\":{\"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,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLMOutputParser\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"llm_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"llm_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"output_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"return_final_only\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_final_only\",\"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},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"LLMChain\"},\"description\":\"Chain to run queries against LLMs.\",\"base_classes\":[\"Chain\",\"LLMChain\",\"function\"],\"display_name\":\"LLMChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/foundational/llm_chain\",\"beta\":false,\"error\":null},\"LLMCheckerChain\":{\"template\":{\"check_assertions_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"assertions\"],\"output_parser\":null,\"partial_variables\":{},\"template\":\"Here is a bullet point list of assertions:\\n{assertions}\\nFor each assertion, determine whether it is true or false. If it is false, explain why.\\n\\n\",\"template_format\":\"f-string\",\"validate_template\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"check_assertions_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"create_draft_answer_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"question\"],\"output_parser\":null,\"partial_variables\":{},\"template\":\"{question}\\n\\n\",\"template_format\":\"f-string\",\"validate_template\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"create_draft_answer_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"list_assertions_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"statement\"],\"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\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"list_assertions_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"revised_answer_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"checked_assertions\",\"question\"],\"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\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"revised_answer_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"_type\":\"LLMCheckerChain\"},\"description\":\"\",\"base_classes\":[\"Chain\",\"LLMCheckerChain\",\"function\"],\"display_name\":\"LLMCheckerChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\",\"beta\":false,\"error\":null},\"LLMMathChain\":{\"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},\"llm_chain\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"LLMChain\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"question\"],\"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\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"input_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"question\",\"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\":\"code\",\"list\":false},\"output_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"answer\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"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,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"LLMMathChain\"},\"description\":\"Chain that interprets a prompt and executes python code to do math.\",\"base_classes\":[\"Chain\",\"LLMMathChain\",\"function\"],\"display_name\":\"LLMMathChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_math\",\"beta\":false,\"error\":null},\"RetrievalQA\":{\"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\":\"code\",\"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\":[\"Chain\",\"BaseRetrievalQA\",\"RetrievalQA\",\"function\"],\"display_name\":\"RetrievalQA\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/vector_db_qa\",\"beta\":false,\"error\":null},\"RetrievalQAWithSourcesChain\":{\"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},\"answer_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"answer\",\"password\":false,\"name\":\"answer_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_docs_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"docs\",\"password\":false,\"name\":\"input_docs_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"max_tokens_limit\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":3375,\"password\":false,\"name\":\"max_tokens_limit\",\"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\":\"code\",\"list\":false},\"question_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"question\",\"password\":false,\"name\":\"question_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"reduce_k_below_max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"reduce_k_below_max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"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},\"sources_answer_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"sources\",\"password\":false,\"name\":\"sources_answer_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"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\":\"RetrievalQAWithSourcesChain\"},\"description\":\"Question-answering with sources over an index.\",\"base_classes\":[\"Chain\",\"RetrievalQAWithSourcesChain\",\"BaseQAWithSourcesChain\",\"function\"],\"display_name\":\"RetrievalQAWithSourcesChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SQLDatabaseChain\":{\"template\":{\"db\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"SQLDatabase\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"_type\":\"SQLDatabaseChain\"},\"description\":\"\",\"base_classes\":[\"Chain\",\"SQLDatabaseChain\",\"function\"],\"display_name\":\"SQLDatabaseChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"CombineDocsChain\":{\"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},\"SeriesCharacterChain\":{\"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},\"character\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"character\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"series\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"series\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_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\",\"function\"],\"display_name\":\"SeriesCharacterChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"MidJourneyPromptChain\":{\"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},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"_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\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"TimeTravelGuideChain\":{\"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},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"_type\":\"TimeTravelGuideChain\"},\"description\":\"Time travel guide chain.\",\"base_classes\":[\"LLMChain\",\"BaseCustomChain\",\"TimeTravelGuideChain\",\"Chain\",\"ConversationChain\"],\"display_name\":\"TimeTravelGuideChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"PromptRunner\":{\"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 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 = 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 \\\"inputs\\\": {\\\"field_type\\\": \\\"code\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLLM,\\n prompt: PromptTemplate,\\n ) -> Document:\\n chain = prompt | llm\\n # The input is an empty dict because the prompt is already filled\\n result = chain.invoke({})\\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\",\"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\":{\"llm\":null,\"prompt\":null},\"output_types\":[\"PromptRunner\"],\"documentation\":\"\",\"beta\":true,\"error\":null}},\"agents\":{\"ZeroShotAgent\":{\"template\":{\"callback_manager\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callback_manager\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseCallbackManager\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"AgentOutputParser\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseTool\",\"list\":true},\"format_instructions\":{\"required\":false,\"placeholder\":\"\",\"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\",\"password\":false,\"name\":\"format_instructions\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"Answer the following questions as best you can. You have access to the following tools:\",\"password\":false,\"name\":\"prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"suffix\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"Begin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\"password\":false,\"name\":\"suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"ZeroShotAgent\"},\"description\":\"Construct an agent from an LLM and tools.\",\"base_classes\":[\"ZeroShotAgent\",\"BaseSingleActionAgent\",\"Agent\",\"function\"],\"display_name\":\"ZeroShotAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent\",\"beta\":false,\"error\":null},\"JsonAgent\":{\"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},\"toolkit\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseToolkit\",\"list\":false},\"_type\":\"json_agent\"},\"description\":\"Construct a json agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"JsonAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/openapi\",\"beta\":false,\"error\":null},\"CSVAgent\":{\"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\":\"\",\"suffixes\":[\".csv\"],\"password\":false,\"name\":\"path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"csv\"],\"file_path\":null},\"_type\":\"csv_agent\"},\"description\":\"Construct a CSV agent from a CSV and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"CSVAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/csv\",\"beta\":false,\"error\":null},\"AgentInitializer\":{\"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},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true},\"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\",\"openai-functions\",\"openai-multi-functions\"],\"name\":\"agent\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"initialize_agent\"},\"description\":\"Construct a zero shot agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\",\"function\"],\"display_name\":\"AgentInitializer\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":false,\"error\":null},\"VectorStoreAgent\":{\"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},\"vectorstoreinfo\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectorstoreinfo\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"VectorStoreInfo\",\"list\":false},\"_type\":\"vectorstore_agent\"},\"description\":\"Construct an agent from a Vector Store.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"VectorStoreAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"VectorStoreRouterAgent\":{\"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},\"vectorstoreroutertoolkit\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectorstoreroutertoolkit\",\"display_name\":\"Vector Store Router Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"VectorStoreRouterToolkit\",\"list\":false},\"_type\":\"vectorstorerouter_agent\"},\"description\":\"Construct an agent from a Vector Store Router.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"VectorStoreRouterAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SQLAgent\":{\"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},\"database_uri\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"database_uri\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"sql_agent\"},\"description\":\"Construct an SQL agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"SQLAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"OpenAIConversationalAgent\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional\\nfrom langchain.prompts import SystemMessagePromptTemplate\\nfrom langchain.tools import Tool\\nfrom langchain.schema.memory import BaseMemory\\nfrom langchain.chat_models import ChatOpenAI\\n\\nfrom langchain.agents.agent import AgentExecutor\\nfrom langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\\nfrom langchain.memory.token_buffer import ConversationTokenBufferMemory\\nfrom langchain.prompts.chat import MessagesPlaceholder\\nfrom langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import (\\n _get_default_system_message,\\n)\\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-3.5-turbo-0613\\\",\\n \\\"gpt-3.5-turbo-16k-0613\\\",\\n \\\"gpt-4-0613\\\",\\n \\\"gpt-4-32k-0613\\\",\\n ]\\n return {\\n \\\"tools\\\": {\\\"is_list\\\": True, \\\"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 openai_api_base: str,\\n tools: Tool,\\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 openai_api_key=openai_api_key,\\n openai_api_base=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, tools=tools, 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 )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"max_token_limit\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":2000,\"password\":false,\"name\":\"max_token_limit\",\"display_name\":\"Max Token Limit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"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},\"model_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-0613\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo-16k-0613\",\"gpt-4-0613\",\"gpt-4-32k-0613\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"openai_api_base\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"openai_api_base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_key\",\"display_name\":\"openai_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"system_message\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"system_message\",\"display_name\":\"System Message\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"SystemMessagePromptTemplate\",\"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\":\"Conversational Agent that can use OpenAI's function calling API\",\"base_classes\":[\"AgentExecutor\",\"Chain\"],\"display_name\":\"OpenAI Conversational Agent\",\"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\"],\"documentation\":\"\",\"beta\":true,\"error\":null}},\"prompts\":{\"ChatMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"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\",\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"role\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"role\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"ChatMessagePromptTemplate\"},\"description\":\"Chat message prompt template.\",\"base_classes\":[\"ChatMessagePromptTemplate\",\"BaseStringMessagePromptTemplate\",\"BaseMessagePromptTemplate\"],\"display_name\":\"ChatMessagePromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates\",\"beta\":false,\"error\":null},\"ChatPromptTemplate\":{\"template\":{\"messages\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMessagePromptTemplate\",\"list\":true},\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"_type\":\"ChatPromptTemplate\"},\"description\":\"A prompt template for chat models.\",\"base_classes\":[\"BasePromptTemplate\",\"ChatPromptTemplate\",\"BaseChatPromptTemplate\"],\"display_name\":\"ChatPromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/how_to/prompts\",\"beta\":false,\"error\":null},\"HumanMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"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\",\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"_type\":\"HumanMessagePromptTemplate\"},\"description\":\"Human message prompt template. This is a message that is sent to the user.\",\"base_classes\":[\"HumanMessagePromptTemplate\",\"BaseStringMessagePromptTemplate\",\"BaseMessagePromptTemplate\"],\"display_name\":\"HumanMessagePromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/how_to/prompts\",\"beta\":false,\"error\":null},\"PromptTemplate\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\"},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"BasePromptTemplate\",\"StringPromptTemplate\",\"PromptTemplate\"],\"display_name\":\"PromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"beta\":false,\"error\":null},\"SystemMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"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\",\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"_type\":\"SystemMessagePromptTemplate\"},\"description\":\"System message prompt template.\",\"base_classes\":[\"SystemMessagePromptTemplate\",\"BaseStringMessagePromptTemplate\",\"BaseMessagePromptTemplate\"],\"display_name\":\"SystemMessagePromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/how_to/prompts\",\"beta\":false,\"error\":null}},\"llms\":{\"Anthropic\":{\"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},\"count_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"count_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Callable[[str], 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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(\\n query, use_autoprompt=use_autoprompt, num_results=search_num_results\\n )\\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 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Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"batch_size\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":64,\"password\":false,\"name\":\"batch_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"collection_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"content_payload_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"page_content\",\"password\":false,\"name\":\"content_payload_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"distance_func\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Cosine\",\"password\":false,\"name\":\"distance_func\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"force_recreate\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"force_recreate\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6334,\"password\":false,\"name\":\"grpc_port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"https\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"https\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"location\":{\"required\":false,\"placeholder\":\":memory:\",\"show\":true,\"multiline\":false,\"value\":\":memory:\",\"password\":false,\"name\":\"location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadata_payload_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"metadata\",\"password\":false,\"name\":\"metadata_payload_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":true},\"on_disk\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"on_disk\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"on_disk_payload\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"on_disk_payload\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"path\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6333,\"password\":false,\"name\":\"port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"prefer_grpc\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"prefer_grpc\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"prefix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"replication_factor\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"replication_factor\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"shard_number\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"shard_number\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"vector_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"vector_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"write_consistency_factor\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"write_consistency_factor\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"_type\":\"Qdrant\"},\"description\":\"Construct Qdrant wrapper from a list of texts.\",\"base_classes\":[\"VectorStore\",\"Qdrant\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Qdrant\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant\",\"beta\":false,\"error\":null},\"SupabaseVectorStore\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"supabase.client.Client\",\"list\":false},\"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},\"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\":\"code\",\"list\":true},\"query_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"query_name\",\"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\":\"code\",\"list\":false},\"supabase_service_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"supabase_service_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"supabase_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"supabase_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"table_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"table_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"SupabaseVectorStore\"},\"description\":\"Return VectorStore initialized from texts and embeddings.\",\"base_classes\":[\"VectorStore\",\"SupabaseVectorStore\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Supabase\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase\",\"beta\":false,\"error\":null},\"Weaviate\":{\"template\":{\"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},\"client_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"client_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":true},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"weaviate_url\":{\"required\":true,\"placeholder\":\"http://localhost:8080\",\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"password\":false,\"name\":\"weaviate_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"Weaviate\"},\"description\":\"Construct Weaviate wrapper from raw documents.\",\"base_classes\":[\"VectorStore\",\"Weaviate\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Weaviate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/weaviate\",\"beta\":false,\"error\":null},\"Vectara\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional, Union\\nfrom langflow import CustomComponent\\n\\nfrom langchain.vectorstores import Vectara\\nfrom langchain.schema import Document\\nfrom langchain.vectorstores.base import VectorStore\\nfrom langchain.schema import BaseRetriever\\nfrom langchain.embeddings.base import Embeddings\\n\\n\\nclass VectaraComponent(CustomComponent):\\n display_name: str = \\\"Vectara\\\"\\n description: str = \\\"Implementation of Vector Store using Vectara\\\"\\n documentation = (\\n \\\"https://python.langchain.com/docs/integrations/vectorstores/vectara\\\"\\n )\\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 \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n }\\n\\n def build(\\n self,\\n vectara_customer_id: str,\\n vectara_corpus_id: str,\\n vectara_api_key: str,\\n embedding: Optional[Embeddings] = None,\\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 and embedding 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 embedding=embedding,\\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 )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"embedding\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"vectara_api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"vectara_api_key\",\"display_name\":\"Vectara API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"vectara_corpus_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectara_corpus_id\",\"display_name\":\"Vectara Corpus ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"vectara_customer_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectara_customer_id\",\"display_name\":\"Vectara Customer ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Implementation of Vector Store using Vectara\",\"base_classes\":[\"VectorStore\",\"BaseRetriever\"],\"display_name\":\"Vectara\",\"custom_fields\":{\"documents\":null,\"embedding\":null,\"vectara_api_key\":null,\"vectara_corpus_id\":null,\"vectara_customer_id\":null},\"output_types\":[\"Vectara\"],\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/vectara\",\"beta\":true,\"error\":null},\"Chroma (1)\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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 (Custom Component)\\\"\\n description: str = \\\"Implementation of Vector Store using Chroma\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/chroma\\\"\\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 \\\"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\\n 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(\\n persist_directory=persist_directory, client_settings=chroma_settings\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chroma_server_cors_allow_origins\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chroma_server_host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chroma_server_ssl_enabled\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"collection_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"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\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"persist\":{\"required\":true,\"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\",\"display_name\":\"Persist Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Implementation of Vector Store using Chroma\",\"base_classes\":[\"VectorStore\",\"BaseRetriever\"],\"display_name\":\"Chroma (Custom Component)\",\"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\"],\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/chroma\",\"beta\":true,\"error\":null}},\"documentloaders\":{\"AZLyricsLoader\":{\"template\":{\"metadata\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"web_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web 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`Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredPowerPointLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_powerpoint\",\"beta\":false,\"error\":null},\"UnstructuredWordDocumentLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".docx\",\".doc\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"docx\",\"doc\"],\"file_path\":null},\"metadata\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"_type\":\"UnstructuredWordDocumentLoader\"},\"description\":\"Load `Microsof Word` file using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredWordDocumentLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_word\",\"beta\":false,\"error\":null},\"WebBaseLoader\":{\"template\":{\"metadata\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"web_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"WebBaseLoader\"},\"description\":\"Load HTML pages using `urllib` and parse them with `BeautifulSoup'.\",\"base_classes\":[\"Document\"],\"display_name\":\"WebBaseLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base\",\"beta\":false,\"error\":null}},\"textsplitters\":{\"CharacterTextSplitter\":{\"template\":{\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"chunk_overlap\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":200,\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"separator\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\\\\n\",\"password\":false,\"name\":\"separator\",\"display_name\":\"Separator\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"CharacterTextSplitter\"},\"description\":\"Splitting text that looks at characters.\",\"base_classes\":[\"Document\"],\"display_name\":\"CharacterTextSplitter\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter\",\"beta\":false,\"error\":null},\"RecursiveCharacterTextSplitter\":{\"template\":{\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"chunk_overlap\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":200,\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"separator_type\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Text\",\"password\":false,\"options\":[\"Text\",\"cpp\",\"go\",\"html\",\"java\",\"js\",\"latex\",\"markdown\",\"php\",\"proto\",\"python\",\"rst\",\"ruby\",\"rust\",\"scala\",\"sol\",\"swift\"],\"name\":\"separator_type\",\"display_name\":\"Separator Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"separators\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\\\\n\",\"password\":false,\"name\":\"separators\",\"display_name\":\"Separator\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"RecursiveCharacterTextSplitter\"},\"description\":\"Splitting text by recursively look at characters.\",\"base_classes\":[\"Document\"],\"display_name\":\"RecursiveCharacterTextSplitter\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter\",\"beta\":false,\"error\":null}},\"utilities\":{\"BingSearchAPIWrapper\":{\"template\":{\"bing_search_url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"bing_search_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"bing_subscription_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"bing_subscription_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"_type\":\"BingSearchAPIWrapper\"},\"description\":\"Wrapper for Bing Search API.\",\"base_classes\":[\"BingSearchAPIWrapper\"],\"display_name\":\"BingSearchAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"GoogleSearchAPIWrapper\":{\"template\":{\"google_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"google_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"google_cse_id\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"google_cse_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_engine\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"search_engine\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"siterestrict\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"siterestrict\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"GoogleSearchAPIWrapper\"},\"description\":\"Wrapper for Google Search API.\",\"base_classes\":[\"GoogleSearchAPIWrapper\"],\"display_name\":\"GoogleSearchAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"GoogleSerperAPIWrapper\":{\"template\":{\"aiosession\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ClientSession\",\"list\":false},\"gl\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"us\",\"password\":false,\"name\":\"gl\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"hl\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"hl\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"result_key_for_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{\\n \\\"news\\\": \\\"news\\\",\\n \\\"places\\\": \\\"places\\\",\\n \\\"images\\\": \\\"images\\\",\\n \\\"search\\\": \\\"organic\\\"\\n}\",\"password\":true,\"name\":\"result_key_for_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"serper_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"serper_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"tbs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tbs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"type\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"search\",\"password\":false,\"options\":[\"news\",\"search\",\"places\",\"images\"],\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"GoogleSerperAPIWrapper\"},\"description\":\"Wrapper around the Serper.dev Google Search API.\",\"base_classes\":[\"GoogleSerperAPIWrapper\"],\"display_name\":\"GoogleSerperAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SearxSearchWrapper\":{\"template\":{\"aiosession\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"categories\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"categories\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"engines\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"engines\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"{'Authorization': 'Bearer '}\",\"password\":false,\"name\":\"headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"params\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"params\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"query_suffix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"query_suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"searx_host\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"searx_host\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"unsecure\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"unsecure\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"SearxSearchWrapper\"},\"description\":\"Wrapper for Searx API.\",\"base_classes\":[\"SearxSearchWrapper\"],\"display_name\":\"SearxSearchWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SerpAPIWrapper\":{\"template\":{\"aiosession\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ClientSession\",\"list\":false},\"params\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"{\\n \\\"engine\\\": \\\"google\\\",\\n \\\"google_domain\\\": \\\"google.com\\\",\\n \\\"gl\\\": \\\"us\\\",\\n \\\"hl\\\": \\\"en\\\"\\n}\",\"password\":false,\"name\":\"params\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"search_engine\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"search_engine\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"serpapi_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"serpapi_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"SerpAPIWrapper\"},\"description\":\"Wrapper around SerpAPI.\",\"base_classes\":[\"SerpAPIWrapper\"],\"display_name\":\"SerpAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"WikipediaAPIWrapper\":{\"template\":{\"doc_content_chars_max\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":4000,\"password\":false,\"name\":\"doc_content_chars_max\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"lang\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"lang\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"load_all_available_meta\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"load_all_available_meta\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"top_k_results\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":3,\"password\":false,\"name\":\"top_k_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"wiki_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wiki_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"_type\":\"WikipediaAPIWrapper\"},\"description\":\"Wrapper around WikipediaAPI.\",\"base_classes\":[\"WikipediaAPIWrapper\"],\"display_name\":\"WikipediaAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"WolframAlphaAPIWrapper\":{\"template\":{\"wolfram_alpha_appid\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wolfram_alpha_appid\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"wolfram_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wolfram_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"_type\":\"WolframAlphaAPIWrapper\"},\"description\":\"Wrapper for Wolfram Alpha.\",\"base_classes\":[\"WolframAlphaAPIWrapper\"],\"display_name\":\"WolframAlphaAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"GetRequest\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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 = 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 \\\"field_type\\\": \\\"code\\\",\\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(\\n self, session: requests.Session, url: str, headers: Optional[dict], timeout: int\\n ) -> 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\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"type\":\"code\",\"list\":false},\"timeout\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"The timeout to use for the request.\",\"type\":\"int\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to\",\"type\":\"str\",\"list\":true}},\"description\":\"Make a GET request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"GET Request\",\"custom_fields\":{\"headers\":null,\"timeout\":null,\"url\":null},\"output_types\":[\"GetRequest\"],\"documentation\":\"https://docs.langflow.org/components/utilities#get-request\",\"beta\":true,\"error\":null},\"PostRequest\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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 = 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\\\": \\\"code\\\",\\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(\\n isinstance(doc, Document) for doc in document\\n ):\\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 = [\\n self.post_document(session, doc, url, headers) for doc in documents\\n ]\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"document\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"type\":\"code\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"type\":\"str\",\"list\":false}},\"description\":\"Make a POST request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"POST Request\",\"custom_fields\":{\"document\":null,\"headers\":null,\"url\":null},\"output_types\":[\"PostRequest\"],\"documentation\":\"https://docs.langflow.org/components/utilities#post-request\",\"beta\":true,\"error\":null},\"UpdateRequest\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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 = 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\\\": \\\"code\\\",\\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(\\n url, headers=headers, data=document.page_content\\n )\\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(\\n isinstance(doc, Document) for doc in document\\n ):\\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 = [\\n self.update_document(session, doc, url, headers, method)\\n for doc in documents\\n ]\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"document\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"type\":\"code\",\"list\":false},\"method\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"PATCH\",\"password\":false,\"options\":[\"PATCH\",\"PUT\"],\"name\":\"method\",\"display_name\":\"Method\",\"advanced\":false,\"dynamic\":false,\"info\":\"The HTTP method to use.\",\"type\":\"str\",\"list\":true},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"type\":\"str\",\"list\":false}},\"description\":\"Make a PATCH request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"Update Request\",\"custom_fields\":{\"document\":null,\"headers\":null,\"method\":null,\"url\":null},\"output_types\":[\"UpdateRequest\"],\"documentation\":\"https://docs.langflow.org/components/utilities#update-request\",\"beta\":true,\"error\":null},\"JSONDocumentBuilder\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"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 langflow import CustomComponent\\nfrom langchain.schema import Document\\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 = (\\n \\\"https://docs.langflow.org/components/utilities#json-document-builder\\\"\\n )\\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(\\n page_content=orjson_dumps({key: doc.page_content}, indent_2=False)\\n )\\n for doc in document\\n ]\\n elif isinstance(document, Document):\\n documents = Document(\\n page_content=orjson_dumps({key: document.page_content}, indent_2=False)\\n )\\n else:\\n raise TypeError(\\n f\\\"Expected Document or list of Documents, got {type(document)}\\\"\\n )\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"document\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"key\",\"display_name\":\"Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Build a Document containing a JSON object using a key and another Document page content.\",\"base_classes\":[\"Document\"],\"display_name\":\"JSON Document Builder\",\"custom_fields\":{\"document\":null,\"key\":null},\"output_types\":[\"JSONDocumentBuilder\"],\"documentation\":\"https://docs.langflow.org/components/utilities#json-document-builder\",\"beta\":true,\"error\":null}},\"output_parsers\":{\"ResponseSchema\":{\"template\":{\"description\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"string\",\"password\":false,\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"ResponseSchema\"},\"description\":\"A schema for a response from a structured output parser.\",\"base_classes\":[\"ResponseSchema\"],\"display_name\":\"ResponseSchema\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"beta\":false,\"error\":null},\"StructuredOutputParser\":{\"template\":{\"response_schemas\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"response_schemas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ResponseSchema\",\"list\":true},\"_type\":\"StructuredOutputParser\"},\"description\":\"\",\"base_classes\":[\"StructuredOutputParser\",\"BaseOutputParser\",\"BaseLLMOutputParser\"],\"display_name\":\"StructuredOutputParser\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"beta\":false,\"error\":null}},\"retrievers\":{\"MultiQueryRetriever\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{\"input_variables\":[\"question\"],\"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\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"list\":false},\"parser_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"lines\",\"password\":false,\"name\":\"parser_key\",\"display_name\":\"Parser Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"MultiQueryRetriever\"},\"description\":\"Initialize from llm using default template.\",\"base_classes\":[\"BaseRetriever\",\"MultiQueryRetriever\"],\"display_name\":\"MultiQueryRetriever\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\",\"beta\":false,\"error\":null}},\"custom_components\":{\"CustomComponent\":{\"template\":{\"code\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.chains import LLMChain\\nfrom langchain import PromptTemplate\\nfrom langchain.schema import Document\\n\\nimport requests\\n\\nclass YourComponent(CustomComponent):\\n display_name: str = \\\"Custom Component\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n\\n def build_config(self):\\n return { \\\"url\\\": { \\\"multiline\\\": True, \\\"required\\\": True } }\\n\\n def build(self, url: str, llm: BaseLLM, prompt: PromptTemplate) -> Document:\\n response = requests.get(url)\\n chain = LLMChain(llm=llm, prompt=prompt)\\n result = chain.run(response.text[:300])\\n return Document(page_content=str(result))\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\"},\"description\":\"Create any custom component you want!\",\"base_classes\":[],\"display_name\":\"Custom Component\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":true,\"error\":null}}}" + "text": "{\"chains\":{\"ConversationChain\":{\"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\"],\"input_types\":{},\"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: {input}\\nAI:\",\"template_format\":\"f-string\",\"validate_template\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"input_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"input\",\"password\":false,\"name\":\"input_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"llm_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"llm_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"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\":\"response\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"return_final_only\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_final_only\",\"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},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationChain\"},\"description\":\"Chain to have a conversation and load context from memory.\",\"base_classes\":[\"LLMChain\",\"Chain\",\"ConversationChain\",\"function\"],\"display_name\":\"ConversationChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"ConversationalRetrievalChain\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Callbacks\",\"list\":false},\"condense_question_llm\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"condense_question_llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"condense_question_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"chat_history\",\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"template\":\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\\n\\nChat History:\\n{chat_history}\\nFollow Up Input: {question}\\nStandalone question:\",\"template_format\":\"f-string\",\"validate_template\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"condense_question_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"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},\"combine_docs_chain_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"combine_docs_chain_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"return_source_documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return source documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationalRetrievalChain\"},\"description\":\"Convenience method to load chain from LLM and retriever.\",\"base_classes\":[\"BaseConversationalRetrievalChain\",\"Chain\",\"ConversationalRetrievalChain\",\"function\"],\"display_name\":\"ConversationalRetrievalChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/chat_vector_db\",\"beta\":false,\"error\":null},\"LLMChain\":{\"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,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLMOutputParser\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"llm_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"llm_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"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\":\"text\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"return_final_only\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"return_final_only\",\"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},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"LLMChain\"},\"description\":\"Chain to run queries against LLMs.\",\"base_classes\":[\"LLMChain\",\"Chain\",\"function\"],\"display_name\":\"LLMChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/foundational/llm_chain\",\"beta\":false,\"error\":null},\"LLMCheckerChain\":{\"template\":{\"check_assertions_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"assertions\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"template\":\"Here is a bullet point list of assertions:\\n{assertions}\\nFor each assertion, determine whether it is true or false. If it is false, explain why.\\n\\n\",\"template_format\":\"f-string\",\"validate_template\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"check_assertions_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"create_draft_answer_prompt\":{\"required\":false,\"placeholder\":\"\",\"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\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"create_draft_answer_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"list_assertions_prompt\":{\"required\":false,\"placeholder\":\"\",\"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\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"list_assertions_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"revised_answer_prompt\":{\"required\":false,\"placeholder\":\"\",\"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\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"revised_answer_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"_type\":\"LLMCheckerChain\"},\"description\":\"\",\"base_classes\":[\"Chain\",\"LLMCheckerChain\",\"function\"],\"display_name\":\"LLMCheckerChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\",\"beta\":false,\"error\":null},\"LLMMathChain\":{\"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},\"llm_chain\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm_chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"LLMChain\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"prompt\":{\"required\":false,\"placeholder\":\"\",\"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\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"input_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"question\",\"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\":\"answer\",\"password\":false,\"name\":\"output_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"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,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"LLMMathChain\"},\"description\":\"Chain that interprets a prompt and executes python code to do math.\",\"base_classes\":[\"Chain\",\"LLMMathChain\",\"function\"],\"display_name\":\"LLMMathChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_math\",\"beta\":false,\"error\":null},\"RetrievalQA\":{\"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\":[\"Chain\",\"RetrievalQA\",\"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},\"RetrievalQAWithSourcesChain\":{\"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},\"answer_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"answer\",\"password\":false,\"name\":\"answer_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_docs_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"docs\",\"password\":false,\"name\":\"input_docs_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"max_tokens_limit\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":3375,\"password\":false,\"name\":\"max_tokens_limit\",\"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},\"question_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"question\",\"password\":false,\"name\":\"question_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"reduce_k_below_max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"reduce_k_below_max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"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},\"sources_answer_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"sources\",\"password\":false,\"name\":\"sources_answer_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"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\":\"RetrievalQAWithSourcesChain\"},\"description\":\"Question-answering with sources over an index.\",\"base_classes\":[\"Chain\",\"BaseQAWithSourcesChain\",\"RetrievalQAWithSourcesChain\",\"function\"],\"display_name\":\"RetrievalQAWithSourcesChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SQLDatabaseChain\":{\"template\":{\"db\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"SQLDatabase\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"_type\":\"SQLDatabaseChain\"},\"description\":\"\",\"base_classes\":[\"Chain\",\"SQLDatabaseChain\",\"function\"],\"display_name\":\"SQLDatabaseChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"CombineDocsChain\":{\"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},\"SeriesCharacterChain\":{\"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},\"character\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"character\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"series\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"series\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_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\",\"function\"],\"display_name\":\"SeriesCharacterChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"MidJourneyPromptChain\":{\"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},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"_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\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"TimeTravelGuideChain\":{\"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},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"_type\":\"TimeTravelGuideChain\"},\"description\":\"Time travel guide chain.\",\"base_classes\":[\"LLMChain\",\"BaseCustomChain\",\"TimeTravelGuideChain\",\"Chain\",\"ConversationChain\"],\"display_name\":\"TimeTravelGuideChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"PromptRunner\":{\"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 = 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(\\n self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}\\n ) -> 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}},\"agents\":{\"ZeroShotAgent\":{\"template\":{\"callback_manager\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callback_manager\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseCallbackManager\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"AgentOutputParser\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseTool\",\"list\":true},\"format_instructions\":{\"required\":false,\"placeholder\":\"\",\"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\",\"password\":false,\"name\":\"format_instructions\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"Answer the following questions as best you can. You have access to the following tools:\",\"password\":false,\"name\":\"prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"suffix\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"Begin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\"password\":false,\"name\":\"suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"ZeroShotAgent\"},\"description\":\"Construct an agent from an LLM and tools.\",\"base_classes\":[\"Agent\",\"BaseSingleActionAgent\",\"ZeroShotAgent\",\"function\"],\"display_name\":\"ZeroShotAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent\",\"beta\":false,\"error\":null},\"JsonAgent\":{\"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},\"toolkit\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseToolkit\",\"list\":false},\"_type\":\"json_agent\"},\"description\":\"Construct a json agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"JsonAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/openapi\",\"beta\":false,\"error\":null},\"CSVAgent\":{\"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\":\"\",\"suffixes\":[\".csv\"],\"password\":false,\"name\":\"path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"csv\"],\"file_path\":null},\"_type\":\"csv_agent\"},\"description\":\"Construct a CSV agent from a CSV and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"CSVAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/csv\",\"beta\":false,\"error\":null},\"AgentInitializer\":{\"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},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true},\"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\",\"openai-functions\",\"openai-multi-functions\"],\"name\":\"agent\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"initialize_agent\"},\"description\":\"Construct a zero shot agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\",\"function\"],\"display_name\":\"AgentInitializer\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":false,\"error\":null},\"VectorStoreAgent\":{\"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},\"vectorstoreinfo\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectorstoreinfo\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"VectorStoreInfo\",\"list\":false},\"_type\":\"vectorstore_agent\"},\"description\":\"Construct an agent from a Vector Store.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"VectorStoreAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"VectorStoreRouterAgent\":{\"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},\"vectorstoreroutertoolkit\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectorstoreroutertoolkit\",\"display_name\":\"Vector Store Router Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"VectorStoreRouterToolkit\",\"list\":false},\"_type\":\"vectorstorerouter_agent\"},\"description\":\"Construct an agent from a Vector Store Router.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"VectorStoreRouterAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SQLAgent\":{\"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},\"database_uri\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"database_uri\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"sql_agent\"},\"description\":\"Construct an SQL agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"SQLAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"OpenAIConversationalAgent\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional\\nfrom langchain.prompts import SystemMessagePromptTemplate\\nfrom langchain.tools import Tool\\nfrom langchain.schema.memory import BaseMemory\\nfrom langchain.chat_models import ChatOpenAI\\n\\nfrom langchain.agents.agent import AgentExecutor\\nfrom langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\\nfrom langchain.memory.token_buffer import ConversationTokenBufferMemory\\nfrom langchain.prompts.chat import MessagesPlaceholder\\nfrom langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import (\\n _get_default_system_message,\\n)\\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-3.5-turbo-0613\\\",\\n \\\"gpt-3.5-turbo-16k-0613\\\",\\n \\\"gpt-4-0613\\\",\\n \\\"gpt-4-32k-0613\\\",\\n ]\\n return {\\n \\\"tools\\\": {\\\"is_list\\\": True, \\\"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: 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 openai_api_key=openai_api_key,\\n openai_api_base=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, tools=tools, 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\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"max_token_limit\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":2000,\"password\":false,\"name\":\"max_token_limit\",\"display_name\":\"Max Token Limit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"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},\"model_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-0613\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo-16k-0613\",\"gpt-4-0613\",\"gpt-4-32k-0613\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"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\":\"\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_key\",\"display_name\":\"openai_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"system_message\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"system_message\",\"display_name\":\"System Message\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"SystemMessagePromptTemplate\",\"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\":\"Conversational Agent that can use OpenAI's function calling API\",\"base_classes\":[\"Chain\",\"AgentExecutor\"],\"display_name\":\"OpenAI Conversational Agent\",\"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\"],\"documentation\":\"\",\"beta\":true,\"error\":null}},\"prompts\":{\"ChatMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"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\",\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"role\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"role\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"ChatMessagePromptTemplate\"},\"description\":\"Chat message prompt template.\",\"base_classes\":[\"BaseStringMessagePromptTemplate\",\"ChatMessagePromptTemplate\",\"BaseMessagePromptTemplate\"],\"display_name\":\"ChatMessagePromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates\",\"beta\":false,\"error\":null},\"ChatPromptTemplate\":{\"template\":{\"messages\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"messages\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseMessagePromptTemplate\",\"list\":true},\"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},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"ChatPromptTemplate\"},\"description\":\"A prompt template for chat models.\",\"base_classes\":[\"BaseChatPromptTemplate\",\"BasePromptTemplate\",\"ChatPromptTemplate\"],\"display_name\":\"ChatPromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/how_to/prompts\",\"beta\":false,\"error\":null},\"HumanMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"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\",\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"_type\":\"HumanMessagePromptTemplate\"},\"description\":\"Human message prompt template. This is a message sent from the user.\",\"base_classes\":[\"BaseStringMessagePromptTemplate\",\"HumanMessagePromptTemplate\",\"BaseMessagePromptTemplate\"],\"display_name\":\"HumanMessagePromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/how_to/prompts\",\"beta\":false,\"error\":null},\"PromptTemplate\":{\"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},\"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},\"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\":true,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\"},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"display_name\":\"PromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"beta\":false,\"error\":null},\"SystemMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"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\",\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"_type\":\"SystemMessagePromptTemplate\"},\"description\":\"System message prompt template.\",\"base_classes\":[\"BaseStringMessagePromptTemplate\",\"BaseMessagePromptTemplate\",\"SystemMessagePromptTemplate\"],\"display_name\":\"SystemMessagePromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/how_to/prompts\",\"beta\":false,\"error\":null}},\"llms\":{\"Anthropic\":{\"template\":{\"anthropic_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"anthropic_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"SecretStr\",\"list\":false},\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"count_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"count_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Callable[[str], int]\",\"list\":false},\"AI_PROMPT\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"AI_PROMPT\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"HUMAN_PROMPT\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"HUMAN_PROMPT\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"anthropic_api_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"anthropic_api_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"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_request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"max_tokens_to_sample\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":256,\"password\":false,\"name\":\"max_tokens_to_sample\",\"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\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"claude-2\",\"password\":false,\"name\":\"model\",\"advanced\":false,\"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},\"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,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"top_k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"top_k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"top_p\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"top_p\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"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\":\"Anthropic\"},\"description\":\"Anthropic large language models.\",\"base_classes\":[\"_AnthropicCommon\",\"BaseLanguageModel\",\"Anthropic\",\"BaseLLM\",\"LLM\"],\"display_name\":\"Anthropic\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"Cohere\":{\"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},\"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},\"cohere_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"cohere_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"frequency_penalty\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":0.0,\"password\":false,\"name\":\"frequency_penalty\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":0,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":256,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"p\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"p\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"presence_penalty\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":0.0,\"password\":false,\"name\":\"presence_penalty\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"stop\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"stop\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"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\":0.75,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"truncate\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"truncate\",\"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\":\"Cohere\"},\"description\":\"Cohere large language 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models.\",\"base_classes\":[\"_VertexAICommon\",\"VertexAI\",\"BaseLanguageModel\",\"BaseLLM\",\"_VertexAIBase\"],\"display_name\":\"VertexAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/google_vertex_ai_palm\",\"beta\":false,\"error\":null},\"ChatAnthropic\":{\"template\":{\"anthropic_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"anthropic_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"SecretStr\",\"list\":false},\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"count_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"count_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Callable[[str], int]\",\"list\":false},\"AI_PROMPT\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"AI_PROMPT\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"HUMAN_PROMPT\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"HUMAN_PROMPT\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"anthropic_api_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"anthropic_api_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"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_request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"default_request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"max_tokens_to_sample\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":256,\"password\":false,\"name\":\"max_tokens_to_sample\",\"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\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"claude-2\",\"password\":false,\"name\":\"model\",\"advanced\":false,\"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},\"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,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"top_k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"top_k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"top_p\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"top_p\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"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\":\"ChatAnthropic\"},\"description\":\"`Anthropic` chat large language models.\",\"base_classes\":[\"_AnthropicCommon\",\"BaseLanguageModel\",\"BaseChatModel\",\"ChatAnthropic\",\"BaseLLM\"],\"display_name\":\"ChatAnthropic\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\",\"beta\":false,\"error\":null},\"ChatOpenAI\":{\"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},\"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-0613\",\"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\":[\"BaseLanguageModel\",\"BaseChatModel\",\"ChatOpenAI\",\"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},\"ChatVertexAI\":{\"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},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ForwardRef(\\\"'_LanguageModel'\\\")\",\"list\":false},\"credentials\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"json\"],\"file_path\":null},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"location\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"password\":false,\"name\":\"location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"max_output_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":128,\"password\":false,\"name\":\"max_output_tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"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},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"password\":false,\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"project\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_parallelism\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"request_parallelism\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"stop\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"stop\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"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\":0.0,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"top_k\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":40,\"password\":false,\"name\":\"top_k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"top_p\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.95,\"password\":false,\"name\":\"top_p\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatVertexAI\"},\"description\":\"`Vertex AI` Chat large language models API.\",\"base_classes\":[\"_VertexAICommon\",\"BaseChatModel\",\"BaseLanguageModel\",\"_VertexAIBase\",\"ChatVertexAI\",\"BaseLLM\"],\"display_name\":\"ChatVertexAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm\",\"beta\":false,\"error\":null},\"HuggingFaceEndpoints\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"endpoint_url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"huggingfacehub_api_token\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"task\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true}},\"description\":\"LLM model from Hugging Face Inference API.\",\"base_classes\":[\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"Hugging Face Inference API\",\"custom_fields\":{\"endpoint_url\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null,\"task\":null},\"output_types\":[\"HuggingFaceEndpoints\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"BaiduQianfanChatEndpoints\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom langchain.llms.base import BaseLLM\\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=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\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"endpoint\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"type\":\"str\",\"list\":false},\"model\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"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\",\"type\":\"str\",\"list\":true},\"penalty_score\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1.0,\"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\",\"type\":\"float\",\"list\":false},\"qianfan_ak\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"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\",\"type\":\"str\",\"list\":false},\"qianfan_sk\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"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\",\"type\":\"str\",\"list\":false},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.95,\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"type\":\"float\",\"list\":false},\"top_p\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.8,\"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\",\"type\":\"float\",\"list\":false}},\"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\",\"custom_fields\":{\"endpoint\":null,\"model\":null,\"penalty_score\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"temperature\":null,\"top_p\":null},\"output_types\":[\"BaiduQianfanChatEndpoints\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"BaiduQianfanLLMEndpoints\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"endpoint\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"type\":\"str\",\"list\":false},\"model\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"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\",\"type\":\"str\",\"list\":true},\"penalty_score\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1.0,\"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\",\"type\":\"float\",\"list\":false},\"qianfan_ak\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"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\",\"type\":\"str\",\"list\":false},\"qianfan_sk\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"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\",\"type\":\"str\",\"list\":false},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.95,\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"type\":\"float\",\"list\":false},\"top_p\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.8,\"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\",\"type\":\"float\",\"list\":false}},\"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\",\"custom_fields\":{\"endpoint\":null,\"model\":null,\"penalty_score\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"temperature\":null,\"top_p\":null},\"output_types\":[\"BaiduQianfanLLMEndpoints\"],\"documentation\":\"\",\"beta\":true,\"error\":null}},\"memories\":{\"ConversationBufferMemory\":{\"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},\"ConversationBufferWindowMemory\":{\"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},\"k\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"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\":\"ConversationBufferWindowMemory\"},\"description\":\"Buffer for storing conversation memory inside a limited size window.\",\"base_classes\":[\"BaseMemory\",\"ConversationBufferWindowMemory\",\"BaseChatMemory\"],\"display_name\":\"ConversationBufferWindowMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer_window\",\"beta\":false,\"error\":null},\"ConversationEntityMemory\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"entity_extraction_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"entity_store\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_store\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseEntityStore\",\"list\":false},\"entity_summarization_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_summarization_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"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},\"chat_history_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"history\",\"password\":false,\"name\":\"chat_history_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"entity_cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"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},\"k\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"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\":\"ConversationEntityMemory\"},\"description\":\"Entity extractor & summarizer memory.\",\"base_classes\":[\"BaseMemory\",\"ConversationEntityMemory\",\"BaseChatMemory\"],\"display_name\":\"ConversationEntityMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/entity_memory_with_sqlite\",\"beta\":false,\"error\":null},\"ConversationKGMemory\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"entity_extraction_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"kg\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"kg\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"NetworkxEntityGraph\",\"list\":false},\"knowledge_extraction_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"knowledge_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"summary_message_cls\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"summary_message_cls\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Type[langchain.schema.messages.BaseMessage]\",\"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},\"k\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"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\":\"ConversationKGMemory\"},\"description\":\"Knowledge graph conversation 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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(\\n query, use_autoprompt=use_autoprompt, num_results=search_num_results\\n )\\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\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"metaphor_api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"metaphor_api_key\",\"display_name\":\"Metaphor API 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Pinecone wrapper from raw documents.\",\"base_classes\":[\"Pinecone\",\"VectorStore\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Pinecone\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone\",\"beta\":false,\"error\":null},\"Qdrant\":{\"template\":{\"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},\"hnsw_config\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"hnsw_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.HnswConfigDiff\",\"list\":false},\"init_from\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"init_from\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.InitFrom\",\"list\":false},\"optimizers_config\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"optimizers_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.OptimizersConfigDiff\",\"list\":false},\"quantization_config\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"quantization_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.QuantizationConfig\",\"list\":false},\"wal_config\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wal_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.WalConfigDiff\",\"list\":false},\"api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"batch_size\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":64,\"password\":false,\"name\":\"batch_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"collection_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"content_payload_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"page_content\",\"password\":false,\"name\":\"content_payload_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"distance_func\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Cosine\",\"password\":false,\"name\":\"distance_func\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"force_recreate\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"force_recreate\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6334,\"password\":false,\"name\":\"grpc_port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"https\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"https\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"location\":{\"required\":false,\"placeholder\":\":memory:\",\"show\":true,\"multiline\":false,\"value\":\":memory:\",\"password\":false,\"name\":\"location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadata_payload_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"metadata\",\"password\":false,\"name\":\"metadata_payload_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"on_disk\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"on_disk\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"on_disk_payload\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"on_disk_payload\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"path\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6333,\"password\":false,\"name\":\"port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"prefer_grpc\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"prefer_grpc\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"prefix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"replication_factor\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"replication_factor\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"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},\"shard_number\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"shard_number\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"vector_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"vector_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"write_consistency_factor\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"write_consistency_factor\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"_type\":\"Qdrant\"},\"description\":\"Construct Qdrant wrapper from a list of texts.\",\"base_classes\":[\"VectorStore\",\"Qdrant\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Qdrant\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant\",\"beta\":false,\"error\":null},\"SupabaseVectorStore\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"supabase.client.Client\",\"list\":false},\"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},\"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},\"query_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"query_name\",\"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},\"supabase_service_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"supabase_service_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"supabase_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"supabase_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"table_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"table_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"SupabaseVectorStore\"},\"description\":\"Return VectorStore initialized from texts and embeddings.\",\"base_classes\":[\"SupabaseVectorStore\",\"VectorStore\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Supabase\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase\",\"beta\":false,\"error\":null},\"Weaviate\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"weaviate.Client\",\"list\":false},\"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},\"relevance_score_fn\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"relevance_score_fn\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Callable[[float], float]\",\"list\":false},\"batch_size\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"batch_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"by_text\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"by_text\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"client_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"index_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"index_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"text_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"text\",\"password\":true,\"name\":\"text_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"weaviate_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"weaviate_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"weaviate_url\":{\"required\":true,\"placeholder\":\"http://localhost:8080\",\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"password\":false,\"name\":\"weaviate_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"Weaviate\"},\"description\":\"Construct Weaviate wrapper from raw documents.\",\"base_classes\":[\"Weaviate\",\"VectorStore\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Weaviate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/weaviate\",\"beta\":false,\"error\":null},\"Vectara\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional, Union\\nfrom langflow import CustomComponent\\n\\nfrom langchain.vectorstores import Vectara\\nfrom langchain.schema import Document\\nfrom langchain.vectorstores.base import VectorStore\\nfrom langchain.schema import BaseRetriever\\n\\n\\nclass VectaraComponent(CustomComponent):\\n display_name: str = \\\"Vectara\\\"\\n description: str = \\\"Implementation of Vector Store using Vectara\\\"\\n documentation = (\\n \\\"https://python.langchain.com/docs/integrations/vectorstores/vectara\\\"\\n )\\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\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"vectara_api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"vectara_api_key\",\"display_name\":\"Vectara API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"vectara_corpus_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectara_corpus_id\",\"display_name\":\"Vectara Corpus ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"vectara_customer_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectara_customer_id\",\"display_name\":\"Vectara Customer ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Implementation of Vector Store using Vectara\",\"base_classes\":[\"VectorStore\",\"BaseRetriever\"],\"display_name\":\"Vectara\",\"custom_fields\":{\"documents\":null,\"vectara_api_key\":null,\"vectara_corpus_id\":null,\"vectara_customer_id\":null},\"output_types\":[\"Vectara\"],\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/vectara\",\"beta\":true,\"error\":null},\"Chroma (1)\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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 (Custom Component)\\\"\\n description: str = \\\"Implementation of Vector Store using Chroma\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/chroma\\\"\\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 \\\"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\\n 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(\\n persist_directory=persist_directory, client_settings=chroma_settings\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chroma_server_cors_allow_origins\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chroma_server_host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chroma_server_ssl_enabled\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"collection_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"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\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"persist\":{\"required\":true,\"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\",\"display_name\":\"Persist Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Implementation of Vector Store using Chroma\",\"base_classes\":[\"VectorStore\",\"BaseRetriever\"],\"display_name\":\"Chroma (Custom Component)\",\"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\"],\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/chroma\",\"beta\":true,\"error\":null}},\"documentloaders\":{\"AZLyricsLoader\":{\"template\":{\"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},\"web_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web 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webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"AZLyricsLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/azlyrics\",\"beta\":false,\"error\":null},\"AirbyteJSONLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".json\"],\"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},\"BSHTMLLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".html\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"html\"],\"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\":\"BSHTMLLoader\"},\"description\":\"Load `HTML` files and parse them with `beautiful soup`.\",\"base_classes\":[\"Document\"],\"display_name\":\"BSHTMLLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"beta\":false,\"error\":null},\"CSVLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".csv\"],\"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\":{},\"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},\"CoNLLULoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".csv\"],\"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\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CoNLLULoader\"},\"description\":\"Load `CoNLL-U` files.\",\"base_classes\":[\"Document\"],\"display_name\":\"CoNLLULoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/conll-u\",\"beta\":false,\"error\":null},\"CollegeConfidentialLoader\":{\"template\":{\"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},\"web_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"CollegeConfidentialLoader\"},\"description\":\"Load `College Confidential` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"CollegeConfidentialLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/college_confidential\",\"beta\":false,\"error\":null},\"DirectoryLoader\":{\"template\":{\"glob\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"**/*.txt\",\"password\":false,\"name\":\"glob\",\"display_name\":\"glob\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"load_hidden\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"False\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load hidden files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"max_concurrency\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"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},\"path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"recursive\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"True\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"silent_errors\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"False\",\"password\":false,\"name\":\"silent_errors\",\"display_name\":\"Silent errors\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"use_multithreading\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"True\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"DirectoryLoader\"},\"description\":\"Load from a directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"DirectoryLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/file_directory\",\"beta\":false,\"error\":null},\"EverNoteLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".xml\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"xml\"],\"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\":\"EverNoteLoader\"},\"description\":\"Load from `EverNote`.\",\"base_classes\":[\"Document\"],\"display_name\":\"EverNoteLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/evernote\",\"beta\":false,\"error\":null},\"FacebookChatLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".json\"],\"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\":\"FacebookChatLoader\"},\"description\":\"Load `Facebook Chat` messages directory 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URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"file_filter\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_filter\",\"display_name\":\"File extensions (comma-separated)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"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},\"repo_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"repo_path\",\"display_name\":\"Path to repository\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"GitLoader\"},\"description\":\"Load `Git` repository 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directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"ReadTheDocsLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/readthedocs_documentation\",\"beta\":false,\"error\":null},\"SRTLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".srt\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"srt\"],\"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\":\"SRTLoader\"},\"description\":\"Load `.srt` (subtitle) files.\",\"base_classes\":[\"Document\"],\"display_name\":\"SRTLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/subtitle\",\"beta\":false,\"error\":null},\"SlackDirectoryLoader\":{\"template\":{\"zip_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".zip\"],\"password\":false,\"name\":\"zip_path\",\"display_name\":\"Path to zip file\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"zip\"],\"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},\"workspace_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"workspace_url\",\"display_name\":\"Workspace URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"SlackDirectoryLoader\"},\"description\":\"Load from a `Slack` directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"SlackDirectoryLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/slack\",\"beta\":false,\"error\":null},\"TextLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".txt\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"txt\"],\"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\":\"TextLoader\"},\"description\":\"Load text file.\",\"base_classes\":[\"Document\"],\"display_name\":\"TextLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/\",\"beta\":false,\"error\":null},\"UnstructuredEmailLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".eml\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"eml\"],\"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\":\"UnstructuredEmailLoader\"},\"description\":\"Load email files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredEmailLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/email\",\"beta\":false,\"error\":null},\"UnstructuredHTMLLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".html\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"html\"],\"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\":\"UnstructuredHTMLLoader\"},\"description\":\"Load `HTML` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredHTMLLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"beta\":false,\"error\":null},\"UnstructuredMarkdownLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".md\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"md\"],\"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\":\"UnstructuredMarkdownLoader\"},\"description\":\"Load `Markdown` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredMarkdownLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/markdown\",\"beta\":false,\"error\":null},\"UnstructuredPowerPointLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".pptx\",\".ppt\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"pptx\",\"ppt\"],\"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\":\"UnstructuredPowerPointLoader\"},\"description\":\"Load `Microsoft PowerPoint` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredPowerPointLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_powerpoint\",\"beta\":false,\"error\":null},\"UnstructuredWordDocumentLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".docx\",\".doc\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"docx\",\"doc\"],\"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\":\"UnstructuredWordDocumentLoader\"},\"description\":\"Load `Microsoft Word` file using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredWordDocumentLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_word\",\"beta\":false,\"error\":null},\"WebBaseLoader\":{\"template\":{\"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},\"web_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"WebBaseLoader\"},\"description\":\"Load HTML pages using `urllib` and parse them with `BeautifulSoup'.\",\"base_classes\":[\"Document\"],\"display_name\":\"WebBaseLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base\",\"beta\":false,\"error\":null}},\"textsplitters\":{\"CharacterTextSplitter\":{\"template\":{\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"chunk_overlap\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":200,\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"separator\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\\\\n\",\"password\":false,\"name\":\"separator\",\"display_name\":\"Separator\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"CharacterTextSplitter\"},\"description\":\"Splitting text that looks at characters.\",\"base_classes\":[\"Document\"],\"display_name\":\"CharacterTextSplitter\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter\",\"beta\":false,\"error\":null},\"LanguageRecursiveTextSplitter\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chunk_overlap\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":200,\"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\":1000,\"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},\"separator_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Python\",\"password\":false,\"options\":[\"cpp\",\"go\",\"java\",\"kotlin\",\"js\",\"ts\",\"php\",\"proto\",\"python\",\"rst\",\"ruby\",\"rust\",\"scala\",\"swift\",\"markdown\",\"latex\",\"html\",\"sol\",\"csharp\"],\"name\":\"separator_type\",\"display_name\":\"Separator Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of separator to use.\",\"type\":\"str\",\"list\":true}},\"description\":\"Split text into chunks of a specified length based on language.\",\"base_classes\":[\"Document\"],\"display_name\":\"Language Recursive Text Splitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separator_type\":null},\"output_types\":[\"LanguageRecursiveTextSplitter\"],\"documentation\":\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\",\"beta\":true,\"error\":null},\"RecursiveCharacterTextSplitter\":{\"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\":200,\"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\":1000,\"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}},\"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}},\"utilities\":{\"BingSearchAPIWrapper\":{\"template\":{\"bing_search_url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"bing_search_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"bing_subscription_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"bing_subscription_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"_type\":\"BingSearchAPIWrapper\"},\"description\":\"Wrapper for Bing Search API.\",\"base_classes\":[\"BingSearchAPIWrapper\"],\"display_name\":\"BingSearchAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"GoogleSearchAPIWrapper\":{\"template\":{\"google_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"google_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"google_cse_id\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"google_cse_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_engine\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"search_engine\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"siterestrict\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"siterestrict\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"GoogleSearchAPIWrapper\"},\"description\":\"Wrapper for Google Search API.\",\"base_classes\":[\"GoogleSearchAPIWrapper\"],\"display_name\":\"GoogleSearchAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"GoogleSerperAPIWrapper\":{\"template\":{\"aiosession\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ClientSession\",\"list\":false},\"gl\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"us\",\"password\":false,\"name\":\"gl\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"hl\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"hl\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"result_key_for_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{\\n \\\"news\\\": \\\"news\\\",\\n \\\"places\\\": \\\"places\\\",\\n \\\"images\\\": \\\"images\\\",\\n \\\"search\\\": \\\"organic\\\"\\n}\",\"password\":true,\"name\":\"result_key_for_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"serper_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"serper_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"tbs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tbs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"type\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"search\",\"password\":false,\"options\":[\"news\",\"search\",\"places\",\"images\"],\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"GoogleSerperAPIWrapper\"},\"description\":\"Wrapper around the Serper.dev Google Search API.\",\"base_classes\":[\"GoogleSerperAPIWrapper\"],\"display_name\":\"GoogleSerperAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SearxSearchWrapper\":{\"template\":{\"aiosession\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"categories\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"categories\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"engines\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"engines\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"password\":false,\"name\":\"headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"params\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"params\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"query_suffix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"query_suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"searx_host\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"searx_host\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"unsecure\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"unsecure\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"SearxSearchWrapper\"},\"description\":\"Wrapper for Searx API.\",\"base_classes\":[\"SearxSearchWrapper\"],\"display_name\":\"SearxSearchWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SerpAPIWrapper\":{\"template\":{\"aiosession\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ClientSession\",\"list\":false},\"params\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"{\\n \\\"engine\\\": \\\"google\\\",\\n \\\"google_domain\\\": \\\"google.com\\\",\\n \\\"gl\\\": \\\"us\\\",\\n \\\"hl\\\": \\\"en\\\"\\n}\",\"password\":false,\"name\":\"params\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"search_engine\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"search_engine\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"serpapi_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"serpapi_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"SerpAPIWrapper\"},\"description\":\"Wrapper around SerpAPI.\",\"base_classes\":[\"SerpAPIWrapper\"],\"display_name\":\"SerpAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"WikipediaAPIWrapper\":{\"template\":{\"doc_content_chars_max\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":4000,\"password\":false,\"name\":\"doc_content_chars_max\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"lang\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"lang\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"load_all_available_meta\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"load_all_available_meta\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"top_k_results\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":3,\"password\":false,\"name\":\"top_k_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"wiki_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wiki_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"_type\":\"WikipediaAPIWrapper\"},\"description\":\"Wrapper around WikipediaAPI.\",\"base_classes\":[\"WikipediaAPIWrapper\"],\"display_name\":\"WikipediaAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"WolframAlphaAPIWrapper\":{\"template\":{\"wolfram_alpha_appid\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wolfram_alpha_appid\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"wolfram_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wolfram_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"_type\":\"WolframAlphaAPIWrapper\"},\"description\":\"Wrapper for Wolfram Alpha.\",\"base_classes\":[\"WolframAlphaAPIWrapper\"],\"display_name\":\"WolframAlphaAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"GetRequest\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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 = 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(\\n self, session: requests.Session, url: str, headers: Optional[dict], timeout: int\\n ) -> 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\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"type\":\"dict\",\"list\":false},\"timeout\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"The timeout to use for the request.\",\"type\":\"int\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to\",\"type\":\"str\",\"list\":true}},\"description\":\"Make a GET request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"GET Request\",\"custom_fields\":{\"headers\":null,\"timeout\":null,\"url\":null},\"output_types\":[\"GetRequest\"],\"documentation\":\"https://docs.langflow.org/components/utilities#get-request\",\"beta\":true,\"error\":null},\"PostRequest\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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 = 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(\\n isinstance(doc, Document) for doc in document\\n ):\\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 = [\\n self.post_document(session, doc, url, headers) for doc in documents\\n ]\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"document\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"type\":\"dict\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"type\":\"str\",\"list\":false}},\"description\":\"Make a POST request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"POST Request\",\"custom_fields\":{\"document\":null,\"headers\":null,\"url\":null},\"output_types\":[\"PostRequest\"],\"documentation\":\"https://docs.langflow.org/components/utilities#post-request\",\"beta\":true,\"error\":null},\"UpdateRequest\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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 = 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(\\n url, headers=headers, data=document.page_content\\n )\\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(\\n isinstance(doc, Document) for doc in document\\n ):\\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 = [\\n self.update_document(session, doc, url, headers, method)\\n for doc in documents\\n ]\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"document\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"type\":\"NestedDict\",\"list\":false},\"method\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"PATCH\",\"password\":false,\"options\":[\"PATCH\",\"PUT\"],\"name\":\"method\",\"display_name\":\"Method\",\"advanced\":false,\"dynamic\":false,\"info\":\"The HTTP method to use.\",\"type\":\"str\",\"list\":true},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"type\":\"str\",\"list\":false}},\"description\":\"Make a PATCH request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"Update Request\",\"custom_fields\":{\"document\":null,\"headers\":null,\"method\":null,\"url\":null},\"output_types\":[\"UpdateRequest\"],\"documentation\":\"https://docs.langflow.org/components/utilities#update-request\",\"beta\":true,\"error\":null},\"JSONDocumentBuilder\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"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 langflow import CustomComponent\\nfrom langchain.schema import Document\\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 = (\\n \\\"https://docs.langflow.org/components/utilities#json-document-builder\\\"\\n )\\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(\\n page_content=orjson_dumps({key: doc.page_content}, indent_2=False)\\n )\\n for doc in document\\n ]\\n elif isinstance(document, Document):\\n documents = Document(\\n page_content=orjson_dumps({key: document.page_content}, indent_2=False)\\n )\\n else:\\n raise TypeError(\\n f\\\"Expected Document or list of Documents, got {type(document)}\\\"\\n )\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"document\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"key\",\"display_name\":\"Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Build a Document containing a JSON object using a key and another Document page content.\",\"base_classes\":[\"Document\"],\"display_name\":\"JSON Document Builder\",\"custom_fields\":{\"document\":null,\"key\":null},\"output_types\":[\"JSONDocumentBuilder\"],\"documentation\":\"https://docs.langflow.org/components/utilities#json-document-builder\",\"beta\":true,\"error\":null}},\"output_parsers\":{\"ResponseSchema\":{\"template\":{\"description\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"string\",\"password\":false,\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"ResponseSchema\"},\"description\":\"A schema for a response from a structured output parser.\",\"base_classes\":[\"ResponseSchema\"],\"display_name\":\"ResponseSchema\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"beta\":false,\"error\":null},\"StructuredOutputParser\":{\"template\":{\"response_schemas\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"response_schemas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ResponseSchema\",\"list\":true},\"_type\":\"StructuredOutputParser\"},\"description\":\"\",\"base_classes\":[\"BaseLLMOutputParser\",\"BaseOutputParser\",\"StructuredOutputParser\"],\"display_name\":\"StructuredOutputParser\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"beta\":false,\"error\":null}},\"retrievers\":{\"MultiQueryRetriever\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"prompt\":{\"required\":false,\"placeholder\":\"\",\"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\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"list\":false},\"parser_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"lines\",\"password\":false,\"name\":\"parser_key\",\"display_name\":\"Parser Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"MultiQueryRetriever\"},\"description\":\"Initialize from llm using default template.\",\"base_classes\":[\"MultiQueryRetriever\",\"BaseRetriever\"],\"display_name\":\"MultiQueryRetriever\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\",\"beta\":false,\"error\":null},\"MetalRetriever\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"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(\\n self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None\\n ) -> 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\",\"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\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"client_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"client_id\",\"display_name\":\"Client ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"index_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"params\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false}},\"description\":\"Retriever that uses the Metal API.\",\"base_classes\":[\"BaseRetriever\"],\"display_name\":\"Metal Retriever\",\"custom_fields\":{\"api_key\":null,\"client_id\":null,\"index_id\":null,\"params\":null},\"output_types\":[\"MetalRetriever\"],\"documentation\":\"\",\"beta\":true,\"error\":null}},\"custom_components\":{\"CustomComponent\":{\"template\":{\"code\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nfrom langflow.field_typing import (\\n Tool,\\n PromptTemplate,\\n Chain,\\n BaseChatMemory,\\n BaseLLM,\\n BaseLoader,\\n BaseMemory,\\n BaseOutputParser,\\n BaseRetriever,\\n VectorStore,\\n Embeddings,\\n TextSplitter,\\n Document,\\n AgentExecutor,\\n NestedDict,\\n Data,\\n)\\n\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Custom Component\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n\\n def build_config(self):\\n return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n def build(self, param: Data) -> Data:\\n return param\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\"},\"description\":null,\"base_classes\":[],\"display_name\":\"Custom Component\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":true,\"error\":null}}}" }, "headersSize": -1, "bodySize": -1, diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index 2a02d8c14..c6a0d8feb 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -59,7 +59,7 @@ "react-tabs": "^6.0.2", "react-tooltip": "^5.21.1", "react18-json-view": "^0.2.3", - "reactflow": "^11.8.3", + "reactflow": "^11.9.2", "rehype-mathjax": "^4.0.3", "remark-gfm": "^3.0.1", "remark-math": "^5.1.1", @@ -219,27 +219,27 @@ } }, "node_modules/@babel/compat-data": { - "version": "7.22.20", - "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.22.20.tgz", - "integrity": "sha512-BQYjKbpXjoXwFW5jGqiizJQQT/aC7pFm9Ok1OWssonuguICi264lbgMzRp2ZMmRSlfkX6DsWDDcsrctK8Rwfiw==", + "version": "7.23.2", + "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.23.2.tgz", + "integrity": "sha512-0S9TQMmDHlqAZ2ITT95irXKfxN9bncq8ZCoJhun3nHL/lLUxd2NKBJYoNGWH7S0hz6fRQwWlAWn/ILM0C70KZQ==", "engines": { "node": ">=6.9.0" } }, "node_modules/@babel/core": { - "version": "7.23.0", - "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.23.0.tgz", - "integrity": "sha512-97z/ju/Jy1rZmDxybphrBuI+jtJjFVoz7Mr9yUQVVVi+DNZE333uFQeMOqcCIy1x3WYBIbWftUSLmbNXNT7qFQ==", + "version": "7.23.2", + "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.23.2.tgz", + "integrity": "sha512-n7s51eWdaWZ3vGT2tD4T7J6eJs3QoBXydv7vkUM06Bf1cbVD2Kc2UrkzhiQwobfV7NwOnQXYL7UBJ5VPU+RGoQ==", "dependencies": { "@ampproject/remapping": "^2.2.0", "@babel/code-frame": "^7.22.13", "@babel/generator": "^7.23.0", "@babel/helper-compilation-targets": "^7.22.15", "@babel/helper-module-transforms": "^7.23.0", - "@babel/helpers": "^7.23.0", + "@babel/helpers": "^7.23.2", "@babel/parser": "^7.23.0", "@babel/template": "^7.22.15", - "@babel/traverse": "^7.23.0", + "@babel/traverse": "^7.23.2", "@babel/types": "^7.23.0", "convert-source-map": "^2.0.0", "debug": "^4.1.0", @@ -425,12 +425,12 @@ } }, "node_modules/@babel/helpers": { - "version": "7.23.1", - "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.23.1.tgz", - "integrity": "sha512-chNpneuK18yW5Oxsr+t553UZzzAs3aZnFm4bxhebsNTeshrC95yA7l5yl7GBAG+JG1rF0F7zzD2EixK9mWSDoA==", + "version": "7.23.2", + "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.23.2.tgz", + "integrity": "sha512-lzchcp8SjTSVe/fPmLwtWVBFC7+Tbn8LGHDVfDp9JGxpAY5opSaEFgt8UQvrnECWOTdji2mOWMz1rOhkHscmGQ==", "dependencies": { "@babel/template": "^7.22.15", - "@babel/traverse": "^7.23.0", + "@babel/traverse": "^7.23.2", "@babel/types": "^7.23.0" }, "engines": { @@ -526,9 +526,9 @@ } }, "node_modules/@babel/runtime": { - "version": "7.23.1", - "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.23.1.tgz", - "integrity": "sha512-hC2v6p8ZSI/W0HUzh3V8C5g+NwSKzKPtJwSpTjwl0o297GP9+ZLQSkdvHz46CM3LqyoXxq+5G9komY+eSqSO0g==", + "version": "7.23.2", + "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.23.2.tgz", + "integrity": "sha512-mM8eg4yl5D6i3lu2QKPuPH4FArvJ8KhTofbE7jwMUv9KX5mBvwPAqnV3MlyBNqdp9RyRKP6Yck8TrfYrPvX3bg==", "dependencies": { "regenerator-runtime": "^0.14.0" }, @@ -550,9 +550,9 @@ } }, "node_modules/@babel/traverse": { - "version": "7.23.0", - "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.23.0.tgz", - "integrity": "sha512-t/QaEvyIoIkwzpiZ7aoSKK8kObQYeF7T2v+dazAYCb8SXtp58zEVkWW7zAnju8FNKNdr4ScAOEDmMItbyOmEYw==", + "version": "7.23.2", + "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.23.2.tgz", + "integrity": "sha512-azpe59SQ48qG6nu2CzcMLbxUudtN+dOM9kDbUqGq3HXUJRlo7i8fvPoxQUzYgLZ4cMVmuZgm8vvBpNeRhd6XSw==", "dependencies": { "@babel/code-frame": "^7.22.13", "@babel/generator": "^7.23.0", @@ -1075,9 +1075,9 @@ } }, "node_modules/@floating-ui/utils": { - "version": "0.1.4", - "resolved": "https://registry.npmjs.org/@floating-ui/utils/-/utils-0.1.4.tgz", - "integrity": "sha512-qprfWkn82Iw821mcKofJ5Pk9wgioHicxcQMxx+5zt5GSKoqdWvgG5AxVmpmUUjzTLPVSH5auBrhI93Deayn/DA==" + "version": "0.1.6", + "resolved": "https://registry.npmjs.org/@floating-ui/utils/-/utils-0.1.6.tgz", + "integrity": "sha512-OfX7E2oUDYxtBvsuS4e/jSn4Q9Qb6DzgeYtsAdkPZ47znpoNsMgZw0+tVijiv3uGNR6dgNlty6r9rzIzHjtd/A==" }, "node_modules/@headlessui/react": { "version": "1.7.17", @@ -1137,9 +1137,9 @@ "integrity": "sha512-eF2rxCRulEKXHTRiDrDy6erMYWqNw4LPdQ8UQA4huuxaQsVeRPFl2oM8oDGxMFhJUWZf9McpLtJasDDZb/Bpeg==" }, "node_modules/@jridgewell/trace-mapping": { - "version": "0.3.19", - "resolved": 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--git a/src/frontend/package.json b/src/frontend/package.json index 1c64b6333..0be0c9260 100644 --- a/src/frontend/package.json +++ b/src/frontend/package.json @@ -54,7 +54,7 @@ "react-tabs": "^6.0.2", "react-tooltip": "^5.21.1", "react18-json-view": "^0.2.3", - "reactflow": "^11.8.3", + "reactflow": "^11.9.2", "rehype-mathjax": "^4.0.3", "remark-gfm": "^3.0.1", "remark-math": "^5.1.1", diff --git a/src/frontend/playwright-report/index.html b/src/frontend/playwright-report/index.html new file mode 100644 index 000000000..d65e584e4 --- /dev/null +++ b/src/frontend/playwright-report/index.html @@ -0,0 +1,18 @@ + + + + + + Document + + + + + \ No newline at end of file diff --git a/src/frontend/playwright.config.ts b/src/frontend/playwright.config.ts index 6af6cb8d7..8f7490b9e 100644 --- a/src/frontend/playwright.config.ts +++ b/src/frontend/playwright.config.ts @@ -20,11 +20,13 @@ export default defineConfig({ /* Opt out of parallel tests on CI. */ workers: process.env.CI ? 1 : undefined, /* Reporter to use. See https://playwright.dev/docs/test-reporters */ - reporter: "html", + reporter: [ + ["html", { open: "never", outputFolder: "playwright-report/test-results" }], + ], /* Shared settings for all the projects below. See https://playwright.dev/docs/api/class-testoptions. */ use: { /* Base URL to use in actions like `await page.goto('/')`. */ - // baseURL: 'http://127.0.0.1:3000', + // baseURL: "http://127.0.0.1:3000", /* Collect trace when retrying the failed test. See https://playwright.dev/docs/trace-viewer */ trace: "on-first-retry", @@ -69,9 +71,16 @@ export default defineConfig({ ], /* Run your local dev server before starting the tests */ - // webServer: { - // command: 'npm run start', - // url: 'http://127.0.0.1:3000', - // reuseExistingServer: !process.env.CI, - // }, + // webServer: [ + // { + // command: "npm run backend", + // reuseExistingServer: !process.env.CI, + // timeout: 120 * 1000, + // }, + // { + // command: "npm run start", + // url: "http://127.0.0.1:3000", + // reuseExistingServer: !process.env.CI, + // }, + // ], }); diff --git a/src/frontend/run-tests.sh b/src/frontend/run-tests.sh new file mode 100755 index 000000000..76e2fd756 --- /dev/null +++ b/src/frontend/run-tests.sh @@ -0,0 +1,78 @@ +#!/bin/bash + +# Default value for the --ui flag +ui=false + +# Parse command-line arguments +while [[ $# -gt 0 ]]; do + key="$1" + case $key in + --ui) + ui=true + shift + ;; + *) + echo "Unknown option: $key" + exit 1 + ;; + esac + shift +done + +# Function to forcibly terminate a process by port +terminate_process_by_port() { + port="$1" + echo "Terminating process on port: $port" + fuser -k -n tcp "$port" # Forcefully terminate processes using the specified port + echo "Process terminated." +} + +# Trap signals to ensure cleanup on script termination +trap 'terminate_process_by_port 7860; terminate_process_by_port 3000' EXIT + +# install playwright if there is not installed yet +npx playwright install + +# Navigate to the project root directory (where the Makefile is located) +cd ../../ + +# Start the frontend using 'make frontend' in the background +make frontend & + +# Give some time for the frontend to start (adjust sleep duration as needed) +sleep 10 + +# Navigate to the test directory +cd src/frontend + +# Run frontend only Playwright tests with or without UI based on the --ui flag +if [ "$ui" = true ]; then + PLAYWRIGHT_HTML_REPORT=playwright-report/onlyFront npx playwright test tests/onlyFront --ui --project=chromium +else + PLAYWRIGHT_HTML_REPORT=playwright-report/onlyFront npx playwright test tests/onlyFront --project=chromium +fi + +# Navigate back to the project root directory +cd ../../ + +# Start the backend using 'make backend' in the background +make backend & + +# Give some time for the backend to start (adjust sleep duration as needed) +sleep 25 + +# Navigate back to the test directory +cd src/frontend + +# Run Playwright tests with or without UI based on the --ui flag +if [ "$ui" = true ]; then + PLAYWRIGHT_HTML_REPORT=playwright-report/e2e npx playwright test tests/end-to-end --ui --project=chromium +else + PLAYWRIGHT_HTML_REPORT=playwright-report/e2e npx playwright test tests/end-to-end --project=chromium +fi + +npx playwright show-report + +# After the tests are finished, you can add cleanup or teardown logic here if needed + +# The trap will automatically terminate processes by port on script exit diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx index 6968d6472..78881d959 100644 --- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx @@ -32,6 +32,7 @@ import { convertValuesToNumbers, hasDuplicateKeys, isValidConnection, + scapedJSONStringfy, } from "../../../../utils/reactflowUtils"; import { nodeColors, @@ -53,14 +54,16 @@ export default function ParameterComponent({ required = false, optionalHandle = null, info = "", + proxy, showNode, + index = "", }: ParameterComponentType): JSX.Element { const ref = useRef(null); const refHtml = useRef(null); const infoHtml = useRef(null); const updateNodeInternals = useUpdateNodeInternals(); const [position, setPosition] = useState(0); - const { setTabsState, tabId, save, flows } = useContext(TabsContext); + const { setTabsState, tabId, flows } = useContext(TabsContext); const flow = flows.find((flow) => flow.id === tabId)?.data?.nodes ?? null; @@ -80,8 +83,9 @@ export default function ParameterComponent({ const { reactFlowInstance, setFilterEdge } = useContext(typesContext); let disabled = - reactFlowInstance?.getEdges().some((edge) => edge.targetHandle === id) ?? - false; + reactFlowInstance + ?.getEdges() + .some((edge) => edge.targetHandle === scapedJSONStringfy(id)) ?? false; const { data: myData } = useContext(typesContext); @@ -112,7 +116,6 @@ export default function ParameterComponent({ const [errorDuplicateKey, setErrorDuplicateKey] = useState(false); useEffect(() => { - if (name === "openai_api_base") console.log(info); // @ts-ignore infoHtml.current = (
@@ -136,7 +139,7 @@ export default function ParameterComponent({ nodeIconsLucide[item.family] ?? nodeIconsLucide["unknown"]; return ( - <> +
{index === 0 && ( {left @@ -183,7 +186,7 @@ export default function ParameterComponent({ - +
); }); } else { @@ -204,33 +207,46 @@ export default function ParameterComponent({ type === "code" || type === "prompt" || type === "file" || - type === "int") && + type === "int" || + type === "dict" || + type === "NestedDict") && !optionalHandle ? ( <> ) : ( - - - isValidConnection(connection, reactFlowInstance!) - } - className={classNames( - left ? "my-12 -ml-0.5 " : " my-12 -mr-0.5 ", - "h-3 w-3 rounded-full border-2 bg-background" - )} - style={{ - borderColor: color, - top: position, - }} - > - + ) ) : (
- {title} + {proxy ? ( + {proxy.id}}> + {title} + + ) : ( + title + )} {required ? " *" : ""}
{info !== "" && ( @@ -285,7 +307,11 @@ export default function ParameterComponent({ isValidConnection(connection, reactFlowInstance!) } @@ -326,9 +352,11 @@ export default function ParameterComponent({ disabled={disabled} value={data.node.template[name].value ?? ""} onChange={handleOnNewValue} + id={"textarea-" + index} /> ) : ( { @@ -368,6 +397,11 @@ export default function ParameterComponent({ ) : left === true && type === "code" ? (
{ data.node = nodeClass; @@ -376,6 +410,7 @@ export default function ParameterComponent({ disabled={disabled} value={data.node?.template[name].value ?? ""} onChange={handleOnNewValue} + id={"code-input-" + index} />
) : left === true && type === "file" ? ( @@ -388,7 +423,6 @@ export default function ParameterComponent({ suffixes={data.node?.template[name].suffixes} onFileChange={(filePath: string) => { data.node!.template[name].file_path = filePath; - save(); }} >
@@ -398,11 +432,13 @@ export default function ParameterComponent({ disabled={disabled} value={data.node?.template[name].value ?? ""} onChange={handleOnNewValue} + id={"int-input-" + index} />
) : left === true && type === "prompt" ? (
{ data.node = nodeClass; @@ -416,6 +452,7 @@ export default function ParameterComponent({ onChange={(e) => { handleOnNewValue(e); }} + id={"prompt-input-" + index} />
) : left === true && type === "NestedDict" ? ( @@ -424,7 +461,8 @@ export default function ParameterComponent({ disabled={disabled} editNode={false} value={ - data.node!.template[name].value.toString() === "{}" + !data.node!.template[name].value || + data.node!.template[name].value?.toString() === "{}" ? { yourkey: "value", } diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx index 524b54ead..a58f7a92c 100644 --- a/src/frontend/src/CustomNodes/GenericNode/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx @@ -4,23 +4,33 @@ import { NodeToolbar, useUpdateNodeInternals } from "reactflow"; import ShadTooltip from "../../components/ShadTooltipComponent"; import Tooltip from "../../components/TooltipComponent"; import IconComponent from "../../components/genericIconComponent"; +import InputComponent from "../../components/inputComponent"; +import { Textarea } from "../../components/ui/textarea"; import { useSSE } from "../../contexts/SSEContext"; import { TabsContext } from "../../contexts/tabsContext"; import { typesContext } from "../../contexts/typesContext"; import NodeToolbarComponent from "../../pages/FlowPage/components/nodeToolbarComponent"; import { validationStatusType } from "../../types/components"; import { NodeDataType } from "../../types/flow"; -import { cleanEdges } from "../../utils/reactflowUtils"; +import { + cleanEdges, + handleKeyDown, + scapedJSONStringfy, +} from "../../utils/reactflowUtils"; import { nodeColors, nodeIconsLucide } from "../../utils/styleUtils"; import { classNames, toTitleCase } from "../../utils/utils"; import ParameterComponent from "./components/parameterComponent"; export default function GenericNode({ data: olddata, + xPos, + yPos, selected, }: { data: NodeDataType; selected: boolean; + xPos: number; + yPos: number; }): JSX.Element { const [data, setData] = useState(olddata); const { updateFlow, flows, tabId } = useContext(TabsContext); @@ -28,6 +38,12 @@ export default function GenericNode({ const { types, deleteNode, reactFlowInstance, setFilterEdge, getFilterEdge } = useContext(typesContext); const name = nodeIconsLucide[data.type] ? data.type : types[data.type]; + const [inputName, setInputName] = useState(true); + const [nodeName, setNodeName] = useState(data.node!.display_name); + const [inputDescription, setInputDescription] = useState(false); + const [nodeDescription, setNodeDescription] = useState( + data.node?.description! + ); const [validationStatus, setValidationStatus] = useState(null); const [showNode, setShowNode] = useState(true); @@ -111,6 +127,7 @@ export default function GenericNode({ <> {showNode && (
- -
- {data.node?.display_name} + {data.node?.flow && inputName ? ( +
+ { + setInputName(false); + if (nodeName.trim() !== "") { + setNodeName(nodeName); + data.node!.display_name = nodeName; + } else { + setNodeName(data.node!.display_name); + } + }} + value={nodeName} + onChange={setNodeName} + password={false} + blurOnEnter={true} + />
- + ) : ( + +
setInputName(true)} + > + {data.node?.display_name} +
+
+ )}
)}
@@ -178,16 +219,15 @@ export default function GenericNode({ data.node!.template[templateField].show && !data.node!.template[templateField].advanced && ( ) )} - {data.node?.description !== "" && showNode && ( -
+ {data.node?.description !== "" && + showNode && + data.node?.flow && + inputDescription ? ( +