create_retriever_tool() — langchain Function Reference

Architecture documentation for the create_retriever_tool() function in retriever.py from the langchain codebase.

Function python LangChainCore MessageInterface

Entity Profile

LangChainCore→ MessageInterface→ create_retriever_tool() — langchain Function Reference

Dependency Diagram

graph TD
  ca5cf150_7bcf_99b1_7bbd_5b6612450ce0["create_retriever_tool()"]
  bb53592c_609b_56c7_ded2_aeaa0c66b60b["retriever.py"]
  ca5cf150_7bcf_99b1_7bbd_5b6612450ce0 -->|defined in| bb53592c_609b_56c7_ded2_aeaa0c66b60b
  style ca5cf150_7bcf_99b1_7bbd_5b6612450ce0 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/tools/retriever.py lines 31–94

def create_retriever_tool(
    retriever: BaseRetriever,
    name: str,
    description: str,
    *,
    document_prompt: BasePromptTemplate | None = None,
    document_separator: str = "\n\n",
    response_format: Literal["content", "content_and_artifact"] = "content",
) -> StructuredTool:
    r"""Create a tool to do retrieval of documents.

    Args:
        retriever: The retriever to use for the retrieval
        name: The name for the tool.

            This will be passed to the language model, so should be unique and somewhat
            descriptive.
        description: The description for the tool.

            This will be passed to the language model, so should be descriptive.
        document_prompt: The prompt to use for the document.
        document_separator: The separator to use between documents.
        response_format: The tool response format.

            If `'content'` then the output of the tool is interpreted as the contents of
            a `ToolMessage`. If `'content_and_artifact'` then the output is expected to
            be a two-tuple corresponding to the `(content, artifact)` of a `ToolMessage`
            (artifact being a list of documents in this case).

    Returns:
        Tool class to pass to an agent.
    """
    document_prompt_ = document_prompt or PromptTemplate.from_template("{page_content}")

    def func(
        query: str, callbacks: Callbacks = None
    ) -> str | tuple[str, list[Document]]:
        docs = retriever.invoke(query, config={"callbacks": callbacks})
        content = document_separator.join(
            format_document(doc, document_prompt_) for doc in docs
        )
        if response_format == "content_and_artifact":
            return (content, docs)
        return content

    async def afunc(
        query: str, callbacks: Callbacks = None
    ) -> str | tuple[str, list[Document]]:
        docs = await retriever.ainvoke(query, config={"callbacks": callbacks})
        content = document_separator.join(
            [await aformat_document(doc, document_prompt_) for doc in docs]
        )
        if response_format == "content_and_artifact":
            return (content, docs)
        return content

    return StructuredTool(
        name=name,
        description=description,
        func=func,
        coroutine=afunc,
        args_schema=RetrieverInput,
        response_format=response_format,
    )