146 lines
4.4 KiB
Python
146 lines
4.4 KiB
Python
"""
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LLM 服务 - 支持多种 LLM 提供商
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OpenAI / Claude / Ollama / DeepSeek / 任意 OpenAI 兼容接口
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"""
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from abc import ABC, abstractmethod
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from typing import AsyncIterator
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from langchain_core.messages import BaseMessage, AIMessage
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from langchain_openai import ChatOpenAI
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from langchain_anthropic import ChatAnthropic
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from langchain_ollama import ChatOllama
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from app.config import settings
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import httpx
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import os
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os.makedirs(settings.DATA_DIR, exist_ok=True)
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os.makedirs(settings.CHROMA_PERSIST_DIR, exist_ok=True)
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class LLMService(ABC):
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@abstractmethod
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async def invoke(self, messages: list[BaseMessage]) -> AIMessage:
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raise NotImplementedError
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@abstractmethod
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async def stream(self, messages: list[BaseMessage]) -> AsyncIterator[str]:
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raise NotImplementedError
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@abstractmethod
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def get_model_name(self) -> str:
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raise NotImplementedError
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class OpenAICompatibleService(LLMService):
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"""
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OpenAI 兼容接口
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支持 OpenAI、DeepSeek、硅基流动、任意 OpenAI API 兼容服务
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"""
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def __init__(
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self,
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api_key: str | None = None,
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model: str | None = None,
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base_url: str | None = None,
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):
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self.api_key = api_key or settings.OPENAI_API_KEY
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self.model = model or settings.OPENAI_MODEL
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self.base_url = base_url or settings.OPENAI_BASE_URL
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self._llm = ChatOpenAI(
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api_key=self.api_key,
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model=self.model,
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base_url=self.base_url,
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timeout=httpx.Timeout(60.0, connect=10.0),
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)
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async def invoke(self, messages: list[BaseMessage]) -> AIMessage:
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return await self._llm.ainvoke(messages)
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async def stream(self, messages: list[BaseMessage]) -> AsyncIterator[str]:
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async for chunk in self._llm.astream(messages):
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if chunk.content:
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yield chunk.content
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def get_model_name(self) -> str:
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return self.model
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class ClaudeService(LLMService):
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def __init__(
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self,
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api_key: str | None = None,
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model: str | None = None,
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max_tokens: int = 8192,
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):
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self.api_key = api_key or settings.ANTHROPIC_API_KEY
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self.model = model or settings.CLAUDE_MODEL
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self._llm = ChatAnthropic(
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api_key=self.api_key,
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model=self.model,
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max_tokens=max_tokens,
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timeout=httpx.Timeout(60.0, connect=10.0),
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)
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async def invoke(self, messages: list[BaseMessage]) -> AIMessage:
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return await self._llm.ainvoke(messages)
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async def stream(self, messages: list[BaseMessage]) -> AsyncIterator[str]:
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async for chunk in self._llm.astream(messages):
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if chunk.content:
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yield chunk.content
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def get_model_name(self) -> str:
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return self.model
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class OllamaService(LLMService):
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def __init__(
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self,
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base_url: str | None = None,
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model: str | None = None,
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):
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self.base_url = base_url or settings.OLLAMA_BASE_URL
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self.model = model or settings.OLLAMA_MODEL
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self._llm = ChatOllama(
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base_url=self.base_url,
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model=self.model,
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timeout=httpx.Timeout(120.0, connect=10.0),
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)
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async def invoke(self, messages: list[BaseMessage]) -> AIMessage:
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return await self._llm.ainvoke(messages)
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async def stream(self, messages: list[BaseMessage]) -> AsyncIterator[str]:
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async for chunk in self._llm.astream(messages):
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if chunk.content:
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yield chunk.content
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def get_model_name(self) -> str:
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return self.model
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# 单例缓存
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_llm_instance: LLMService | None = None
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def get_llm() -> LLMService:
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"""根据配置获取 LLM 实例"""
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global _llm_instance
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if _llm_instance is None:
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provider = settings.LLM_PROVIDER
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if provider == "openai":
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_llm_instance = OpenAICompatibleService()
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elif provider == "deepseek":
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_llm_instance = OpenAICompatibleService(
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base_url="https://api.deepseek.com/v1",
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model="deepseek-chat",
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)
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elif provider == "custom":
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_llm_instance = OpenAICompatibleService()
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elif provider == "claude":
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_llm_instance = ClaudeService()
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elif provider == "ollama":
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_llm_instance = OllamaService()
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else:
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raise ValueError(f"Unknown LLM provider: {provider}")
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return _llm_instance
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