Function●Since v0.1

tool_example_to_messages

tool_example_to_messages(
  input: str,
  tool_calls: list[BaseModel],
  tool_outputs: list[

Parameters

Name	Type	Description
`input`*	`str`	The user input
`tool_calls`*	`list[BaseModel]`	Tool calls represented as Pydantic BaseModels
`tool_outputs`	`list[str] \| None`	Default:`None`
`ai_response`	`str \| None`	Default:`None`

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:

HumanMessage: contains the content from which content should be extracted.
AIMessage: contains the extracted information from the model
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.