life-echo/api/app/adapters/llm/deepseek.py

"""DeepSeek / OpenAI-compatible LLM adapter — implements LLMProvider port."""

from collections.abc import AsyncIterator

from langchain_openai import ChatOpenAI

from app.core.llm_telemetry import langchain_invoke_span, observe_astream


class DeepSeekLLMProvider:
    """LangChain-based LLM adapter for DeepSeek and OpenAI-compatible APIs.

    `langchain_llm` 供 Agent / 任务同步调用；若需 JSON object 模式，请用
    `app.core.langchain_llm.bind_json_object_mode` 或 `invoke_json_object` /
    `ainvoke_json_object`（见 `api/docs/llm-json-mode.md`），勿使用已废弃的
    `bind(model_kwargs={"response_format": ...})`。
    """

    def __init__(
        self,
        api_key: str,
        base_url: str = "https://api.deepseek.com",
        model: str = "deepseek-v4-flash",
        temperature: float = 0.7,
        *,
        extra_body: dict | None = None,
    ):
        self._default_model = model
        self._default_temperature = temperature
        kwargs: dict = {
            "temperature": temperature,
            "model": model,
            "api_key": api_key,
        }
        if extra_body:
            kwargs["extra_body"] = extra_body
        if base_url:
            cleaned = base_url.rstrip("/")
            for suffix in ("/v1/chat/completions", "/v1"):
                if cleaned.endswith(suffix):
                    cleaned = cleaned[: -len(suffix)]
            kwargs["base_url"] = cleaned

        self._llm = ChatOpenAI(**kwargs)

    @property
    def langchain_llm(self) -> ChatOpenAI:
        """Expose underlying ChatOpenAI for LangChain agent interop (Phase 2 will remove)."""
        return self._llm

    async def complete(
        self,
        messages: list[dict],
        *,
        temperature: float | None = None,
        model: str | None = None,
        max_tokens: int | None = None,
    ) -> str:
        llm = self._get_llm(temperature, model, max_tokens)
        lc_messages = _to_langchain_messages(messages)
        resolved_model = model or self._default_model
        with langchain_invoke_span(
            agent="deepseek.complete",
            provider="deepseek",
            model=resolved_model,
            call_type="chat",
        ) as tel:
            result = await llm.ainvoke(lc_messages)
            tel["response"] = result
        return str(result.content)

    async def stream(
        self,
        messages: list[dict],
        *,
        temperature: float | None = None,
        model: str | None = None,
        max_tokens: int | None = None,
    ) -> AsyncIterator[str]:
        llm = self._get_llm(temperature, model, max_tokens)
        lc_messages = _to_langchain_messages(messages)
        resolved_model = model or self._default_model
        async for chunk in observe_astream(
            llm,
            lc_messages,
            agent="deepseek.stream",
            provider="deepseek",
            model=resolved_model,
        ):
            if chunk.content:
                yield str(chunk.content)

    def _get_llm(
        self,
        temperature: float | None,
        model: str | None,
        max_tokens: int | None = None,
    ):
        if temperature is None and model is None and max_tokens is None:
            return self._llm
        kwargs: dict = {}
        if temperature is not None:
            kwargs["temperature"] = temperature
        if model is not None:
            kwargs["model"] = model
        if max_tokens is not None:
            kwargs["max_tokens"] = max_tokens
        return self._llm.bind(**kwargs) if kwargs else self._llm


def _to_langchain_messages(messages: list[dict]) -> list:
    from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

    mapping = {
        "system": SystemMessage,
        "human": HumanMessage,
        "user": HumanMessage,
        "ai": AIMessage,
        "assistant": AIMessage,
    }
    result = []
    for msg in messages:
        cls = mapping.get(msg.get("role", ""), HumanMessage)
        result.append(cls(content=msg.get("content", "")))
    return result