_convert_chunk_to_message_chunk() — langchain Function Reference

Source Code

libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py lines 248–301

def _convert_chunk_to_message_chunk(
    chunk: Mapping[str, Any], default_class: type[BaseMessageChunk]
) -> BaseMessageChunk:
    choice = chunk["choices"][0]
    _dict = choice["delta"]
    role = cast(str, _dict.get("role"))
    content = cast(str, _dict.get("content") or "")
    additional_kwargs: dict = {}
    tool_call_chunks: list[ToolCallChunk] = []
    if _dict.get("function_call"):
        function_call = dict(_dict["function_call"])
        if "name" in function_call and function_call["name"] is None:
            function_call["name"] = ""
        additional_kwargs["function_call"] = function_call
    if raw_tool_calls := _dict.get("tool_calls"):
        additional_kwargs["tool_calls"] = raw_tool_calls
        for rtc in raw_tool_calls:
            with contextlib.suppress(KeyError):
                tool_call_chunks.append(
                    create_tool_call_chunk(
                        name=rtc["function"].get("name"),
                        args=rtc["function"].get("arguments"),
                        id=rtc.get("id"),
                        index=rtc.get("index"),
                    )
                )
    if role == "user" or default_class == HumanMessageChunk:
        return HumanMessageChunk(content=content)
    if role == "assistant" or default_class == AIMessageChunk:
        if usage := chunk.get("usage"):
            input_tokens = usage.get("prompt_tokens", 0)
            output_tokens = usage.get("completion_tokens", 0)
            usage_metadata = {
                "input_tokens": input_tokens,
                "output_tokens": output_tokens,
                "total_tokens": usage.get("total_tokens", input_tokens + output_tokens),
            }
        else:
            usage_metadata = None
        return AIMessageChunk(
            content=content,
            additional_kwargs=additional_kwargs,
            tool_call_chunks=tool_call_chunks,
            usage_metadata=usage_metadata,  # type: ignore[arg-type]
        )
    if role == "system" or default_class == SystemMessageChunk:
        return SystemMessageChunk(content=content)
    if role == "function" or default_class == FunctionMessageChunk:
        return FunctionMessageChunk(content=content, name=_dict["name"])
    if role == "tool" or default_class == ToolMessageChunk:
        return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
    if role or default_class == ChatMessageChunk:
        return ChatMessageChunk(content=content, role=role)
    return default_class(content=content)  # type: ignore[call-arg]