a3f61fcc0f
- DB: segments 用户输入文本（Alembic 0002） - Chat: 阶段检测/阶段提示/回复限制，编排与访谈/画像 prompts 调整 - Memoir: 忠实度检查 agent，叙事与分类等链路更新 - Core: agent 日志、Alembic 启动、LangChain/日志/配置等 - Story: time_hints；Memory 检索与相关测试 - Expo: 助手头像、会话页与消息拆分、实时会话与文案/i18n - Docs/scripts/tests: 迁移脚本、LLM JSON/记忆检索文档、新增单测
96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
"""DeepSeek / OpenAI-compatible LLM adapter — implements LLMProvider port."""
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from collections.abc import AsyncIterator
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from langchain_openai import ChatOpenAI
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class DeepSeekLLMProvider:
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"""LangChain-based LLM adapter for DeepSeek and OpenAI-compatible APIs.
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`langchain_llm` 供 Agent / 任务同步调用；若需 JSON object 模式，请用
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`app.core.langchain_llm.bind_json_object_mode` 或 `invoke_json_object` /
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`ainvoke_json_object`（见 `api/docs/llm-json-mode.md`），勿使用已废弃的
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`bind(model_kwargs={"response_format": ...})`。
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"""
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def __init__(
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self,
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api_key: str,
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base_url: str = "https://api.deepseek.com",
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model: str = "deepseek-chat",
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temperature: float = 0.7,
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):
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self._default_model = model
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self._default_temperature = temperature
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kwargs: dict = {
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"temperature": temperature,
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"model": model,
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"api_key": api_key,
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}
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if base_url:
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cleaned = base_url.rstrip("/")
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for suffix in ("/v1/chat/completions", "/v1"):
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if cleaned.endswith(suffix):
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cleaned = cleaned[: -len(suffix)]
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kwargs["base_url"] = cleaned
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self._llm = ChatOpenAI(**kwargs)
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@property
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def langchain_llm(self) -> ChatOpenAI:
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"""Expose underlying ChatOpenAI for LangChain agent interop (Phase 2 will remove)."""
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return self._llm
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async def complete(
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self,
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messages: list[dict],
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*,
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temperature: float | None = None,
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model: str | None = None,
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) -> str:
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llm = self._get_llm(temperature, model)
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lc_messages = _to_langchain_messages(messages)
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result = await llm.ainvoke(lc_messages)
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return str(result.content)
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async def stream(
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self,
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messages: list[dict],
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*,
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temperature: float | None = None,
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model: str | None = None,
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) -> AsyncIterator[str]:
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llm = self._get_llm(temperature, model)
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lc_messages = _to_langchain_messages(messages)
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async for chunk in llm.astream(lc_messages):
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if chunk.content:
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yield str(chunk.content)
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def _get_llm(self, temperature: float | None, model: str | None):
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if temperature is None and model is None:
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return self._llm
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kwargs: dict = {}
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if temperature is not None:
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kwargs["temperature"] = temperature
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if model is not None:
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kwargs["model"] = model
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return self._llm.bind(**kwargs) if kwargs else self._llm
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def _to_langchain_messages(messages: list[dict]) -> list:
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from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
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mapping = {
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"system": SystemMessage,
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"human": HumanMessage,
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"user": HumanMessage,
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"ai": AIMessage,
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"assistant": AIMessage,
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}
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result = []
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for msg in messages:
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cls = mapping.get(msg.get("role", ""), HumanMessage)
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result.append(cls(content=msg.get("content", "")))
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return result
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