tool_example_to_messages() — langchain Function Reference

Architecture documentation for the tool_example_to_messages() function in function_calling.py from the langchain codebase.

Function python CoreAbstractions MessageSchema

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CoreAbstractions→ MessageSchema→ tool_example_to_messages() — langchain Function Reference

Dependency Diagram

graph TD
  5698c317_1512_4bd9_d73a_5eabccbd039a["tool_example_to_messages()"]
  344b2838_87a8_d5dc_b550_fdb443ff6c4e["function_calling.py"]
  5698c317_1512_4bd9_d73a_5eabccbd039a -->|defined in| 344b2838_87a8_d5dc_b550_fdb443ff6c4e
  style 5698c317_1512_4bd9_d73a_5eabccbd039a fill:#6366f1,stroke:#818cf8,color:#fff

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Source Code

libs/core/langchain_core/utils/function_calling.py lines 604–705

def tool_example_to_messages(
    input: str,
    tool_calls: list[BaseModel],
    tool_outputs: list[str] | None = None,
    *,
    ai_response: str | None = None,
) -> list[BaseMessage]:
    """Convert an example into a list of messages that can be fed into an LLM.

    This code is an adapter that converts a single example to a list of messages
    that can be fed into a chat model.

    The list of messages per example by default corresponds to:

    1. `HumanMessage`: contains the content from which content should be extracted.
    2. `AIMessage`: contains the extracted information from the model
    3. `ToolMessage`: contains confirmation to the model that the model requested a
        tool correctly.

    If `ai_response` is specified, there will be a final `AIMessage` with that
    response.

    The `ToolMessage` is required because some chat models are hyper-optimized for
    agents rather than for an extraction use case.

    Args:
        input: The user input
        tool_calls: Tool calls represented as Pydantic BaseModels
        tool_outputs: Tool call outputs.

            Does not need to be provided.

            If not provided, a placeholder value will be inserted.
        ai_response: If provided, content for a final `AIMessage`.

    Returns:
        A list of messages

    Examples:
        ```python
        from typing import Optional
        from pydantic import BaseModel, Field
        from langchain_openai import ChatOpenAI


        class Person(BaseModel):
            '''Information about a person.'''

            name: str | None = Field(..., description="The name of the person")
            hair_color: str | None = Field(
                ..., description="The color of the person's hair if known"
            )
            height_in_meters: str | None = Field(..., description="Height in METERS")


        examples = [
            (
                "The ocean is vast and blue. It's more than 20,000 feet deep.",
                Person(name=None, height_in_meters=None, hair_color=None),
            ),
            (
                "Fiona traveled far from France to Spain.",
                Person(name="Fiona", height_in_meters=None, hair_color=None),
            ),
        ]


        messages = []

        for txt, tool_call in examples:
            messages.extend(tool_example_to_messages(txt, [tool_call]))
        ```
    """
    messages: list[BaseMessage] = [HumanMessage(content=input)]

    openai_tool_calls = [
        {
            "id": str(uuid.uuid4()),
            "type": "function",
            "function": {
                # The name of the function right now corresponds to the name