langchain_core.utils.function_calling.tool_example_to_messages¶

langchain_core.utils.function_calling.tool_example_to_messages(input: str, tool_calls: List[BaseModel], tool_outputs: Optional[List[str]] = None) → List[BaseMessage][source]¶

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 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.

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

Parameters

  • input (str) – string, the user input

  • tool_calls (List[BaseModel]) – List[BaseModel], a list of tool calls represented as Pydantic BaseModels

  • tool_outputs (Optional[List[str]]) – Optional[List[str]], a list of tool call outputs. Does not need to be provided. If not provided, a placeholder value will be inserted.

Returns

A list of messages

Return type

List[BaseMessage]

Examples

from typing import List, Optional
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI

class Person(BaseModel):
    '''Information about a person.'''
    name: Optional[str] = Field(..., description="The name of the person")
    hair_color: Optional[str] = Field(
        ..., description="The color of the peron's eyes if known"
    )
    height_in_meters: Optional[str] = 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])
    )