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https://github.com/hwchase17/langchain.git
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26e0a00c4c
Largely: - Remove explicit `"Default is x"` since new refs show default inferred from sig - Inline code (useful for eventual parsing) - Fix code block rendering (indentations)
94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
"""Output parsers for Anthropic tool calls."""
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from __future__ import annotations
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from typing import Any, cast
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from langchain_core.messages import AIMessage, ToolCall
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from langchain_core.messages.tool import tool_call
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from langchain_core.output_parsers import BaseGenerationOutputParser
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from langchain_core.outputs import ChatGeneration, Generation
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from pydantic import BaseModel, ConfigDict
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class ToolsOutputParser(BaseGenerationOutputParser):
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"""Output parser for tool calls."""
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first_tool_only: bool = False
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"""Whether to return only the first tool call."""
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args_only: bool = False
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"""Whether to return only the arguments of the tool calls."""
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pydantic_schemas: list[type[BaseModel]] | None = None
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"""Pydantic schemas to parse tool calls into."""
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model_config = ConfigDict(
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extra="forbid",
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)
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def parse_result(self, result: list[Generation], *, partial: bool = False) -> Any:
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"""Parse a list of candidate model Generations into a specific format.
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Args:
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result: A list of `Generation` to be parsed. The Generations are assumed
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to be different candidate outputs for a single model input.
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partial: (Not used) Whether the result is a partial result. If `True`, the
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parser may return a partial result, which may not be complete or valid.
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Returns:
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Structured output.
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"""
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if not result or not isinstance(result[0], ChatGeneration):
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return None if self.first_tool_only else []
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message = cast("AIMessage", result[0].message)
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tool_calls: list = [
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dict(tc) for tc in _extract_tool_calls_from_message(message)
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]
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if isinstance(message.content, list):
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# Map tool call id to index
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id_to_index = {
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block["id"]: i
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for i, block in enumerate(message.content)
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if isinstance(block, dict) and block["type"] == "tool_use"
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}
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tool_calls = [{**tc, "index": id_to_index[tc["id"]]} for tc in tool_calls]
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if self.pydantic_schemas:
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tool_calls = [self._pydantic_parse(tc) for tc in tool_calls]
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elif self.args_only:
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tool_calls = [tc["args"] for tc in tool_calls]
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else:
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pass
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if self.first_tool_only:
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return tool_calls[0] if tool_calls else None
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return list(tool_calls)
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def _pydantic_parse(self, tool_call: dict) -> BaseModel:
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cls_ = {schema.__name__: schema for schema in self.pydantic_schemas or []}[
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tool_call["name"]
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]
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return cls_(**tool_call["args"])
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def _extract_tool_calls_from_message(message: AIMessage) -> list[ToolCall]:
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"""Extract tool calls from a list of content blocks."""
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if message.tool_calls:
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return message.tool_calls
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return extract_tool_calls(message.content)
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def extract_tool_calls(content: str | list[str | dict]) -> list[ToolCall]:
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"""Extract tool calls from a list of content blocks."""
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if isinstance(content, list):
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tool_calls = []
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for block in content:
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if isinstance(block, str):
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continue
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if block["type"] != "tool_use":
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continue
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tool_calls.append(
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tool_call(name=block["name"], args=block["input"], id=block["id"]),
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)
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return tool_calls
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return []
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