From 534ee129c00bc7df1f292cbd379aaba2e396389a Mon Sep 17 00:00:00 2001 From: root <953994191@qq.com> Date: Sun, 26 Apr 2026 12:00:57 +0800 Subject: [PATCH] =?UTF-8?q?refactor:=20=E5=B0=86=E7=94=9F=E6=88=90?= =?UTF-8?q?=E5=BC=8F=E5=A4=A7=E6=A8=A1=E5=9E=8B=E6=8F=90=E5=8F=96=E4=B8=BA?= =?UTF-8?q?=E6=9C=8D=E5=8A=A1=E5=B1=82=E6=9E=B6=E6=9E=84=EF=BC=8C=E7=A7=BB?= =?UTF-8?q?=E9=99=A4=20llm=5Ffactory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/app/agent/llm_factory.py | 57 ----- backend/app/agent/service.py | 11 +- backend/app/model_services/chat_services.py | 246 ++++++++++++++++++++ 3 files changed, 253 insertions(+), 61 deletions(-) delete mode 100644 backend/app/agent/llm_factory.py create mode 100644 backend/app/model_services/chat_services.py diff --git a/backend/app/agent/llm_factory.py b/backend/app/agent/llm_factory.py deleted file mode 100644 index 0dbe8ac..0000000 --- a/backend/app/agent/llm_factory.py +++ /dev/null @@ -1,57 +0,0 @@ -# app/llm_factory.py -import os -from ..config import ZHIPUAI_API_KEY, DEEPSEEK_API_KEY, VLLM_BASE_URL, LLAMACPP_API_KEY -from langchain_community.chat_models import ChatZhipuAI -from langchain_openai import ChatOpenAI -from pydantic import SecretStr - -class LLMFactory: - @staticmethod - def create_zhipu(): - api_key = ZHIPUAI_API_KEY - if not api_key: - raise ValueError("ZHIPUAI_API_KEY not set") - return ChatZhipuAI( - model="glm-4.7-flash", - api_key=api_key, - temperature=0.1, - max_tokens=4096, - timeout=120.0, - max_retries=3, - streaming=True, - ) - - @staticmethod - def create_deepseek(): - api_key = DEEPSEEK_API_KEY - if not api_key: - raise ValueError("DEEPSEEK_API_KEY not set") - return ChatOpenAI( - base_url="https://api.deepseek.com", - api_key=SecretStr(api_key), - model="deepseek-reasoner", - temperature=0.1, - max_tokens=4096, - timeout=60.0, - max_retries=2, - streaming=True, - ) - - @staticmethod - def create_local(): - base_url = VLLM_BASE_URL - return ChatOpenAI( - base_url=base_url, - api_key=SecretStr(LLAMACPP_API_KEY), - model="gemma-4-E4B-it", - timeout=60.0, - max_retries=2, - streaming=True, - ) - - # 模型创建器映射 - CREATORS = { - "zhipu": create_zhipu, - "local": create_local, - "deepseek": create_deepseek, - } \ No newline at end of file diff --git a/backend/app/agent/service.py b/backend/app/agent/service.py index 6af4c5f..8c0a5dc 100644 --- a/backend/app/agent/service.py +++ b/backend/app/agent/service.py @@ -8,7 +8,7 @@ import json # 本地模块 from ..graph.graph_builder import GraphBuilder, GraphContext from ..graph.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME -from .llm_factory import LLMFactory +from ..model_services.chat_services import get_all_chat_services, LocalVLLMChatProvider from .rag_initializer import init_rag_tool from ..logger import info, warning @@ -21,16 +21,19 @@ class AIAgentService: async def initialize(self): # 1. 初始化 RAG 工具（如果需要） - rag_tool = await init_rag_tool(LLMFactory.create_local) + def create_local_llm(): + provider = LocalVLLMChatProvider() + return provider.get_service() + rag_tool = await init_rag_tool(create_local_llm) if rag_tool: self.tools.append(rag_tool) self.tools_by_name[rag_tool.name] = rag_tool # 2. 构建各模型的 Graph - for name, creator in LLMFactory.CREATORS.items(): + chat_services = get_all_chat_services() + for name, llm in chat_services.items(): try: info(f"🔄 初始化模型 '{name}'...") - llm = creator() builder = GraphBuilder(llm, self.tools, self.tools_by_name).build() graph = builder.compile(checkpointer=self.checkpointer) self.graphs[name] = graph diff --git a/backend/app/model_services/chat_services.py b/backend/app/model_services/chat_services.py new file mode 100644 index 0000000..3bf0184 --- /dev/null +++ b/backend/app/model_services/chat_services.py @@ -0,0 +1,246 @@ +""" +生成式大模型服务模块 + +本模块提供统一的生成式大模型服务获取接口，支持多种模型： +1. Local VLLM 服务：本地 gemma-4-E4B-it 模型 +2. Zhipu AI：智谱 glm-4.7-flash 模型 +3. DeepSeek：deepseek-reasoner 模型 + +主要功能： +- LocalVLLMChatProvider：本地 VLLM 服务提供者 +- ZhipuChatProvider：智谱 API 服务提供者 +- DeepSeekChatProvider：DeepSeek API 服务提供者 +- get_chat_service()：获取默认服务（带自动降级） +- get_all_chat_services()：获取所有可用模型服务（用于多模型切换） +""" + +import logging +from typing import Dict, Callable +from langchain_core.language_models import BaseChatModel + +from .base import ( + BaseServiceProvider, + FallbackServiceChain, + SingletonServiceManager +) +from ..config import ( + VLLM_BASE_URL, + LLM_API_KEY, + ZHIPUAI_API_KEY, + DEEPSEEK_API_KEY +) + +logger = logging.getLogger(__name__) + + +class LocalVLLMChatProvider(BaseServiceProvider[BaseChatModel]): + """ + 本地 VLLM 生成式大模型服务提供者 + """ + + def __init__(self, model: str = "gemma-4-E4B-it"): + super().__init__("local_vllm_chat") + self._model = model + + def is_available(self) -> bool: + """ + 检查本地 VLLM 服务是否可用 + + Returns: + bool: 服务是否可用 + """ + if not VLLM_BASE_URL: + logger.warning("VLLM_BASE_URL 未配置") + return False + + try: + # 尝试创建一个简单的测试调用 + from langchain_openai import ChatOpenAI + from pydantic import SecretStr + + llm = ChatOpenAI( + base_url=VLLM_BASE_URL, + api_key=SecretStr(LLM_API_KEY) if LLM_API_KEY else SecretStr("dummy"), + model=self._model, + timeout=10.0, + max_retries=1, + ) + + # 简单的 ping 测试（不实际调用模型） + logger.info(f"本地 VLLM 服务配置正确，准备使用: {self._model}") + return True + except Exception as e: + logger.warning(f"本地 VLLM 服务不可用: {e}") + return False + + def get_service(self) -> BaseChatModel: + """ + 获取本地 VLLM 服务 + + Returns: + BaseChatModel: LangChain 兼容的 ChatModel 实例 + """ + if self._service_instance is None: + from langchain_openai import ChatOpenAI + from pydantic import SecretStr + + self._service_instance = ChatOpenAI( + base_url=VLLM_BASE_URL, + api_key=SecretStr(LLM_API_KEY) if LLM_API_KEY else SecretStr(""), + model=self._model, + timeout=60.0, + max_retries=2, + streaming=True, + ) + return self._service_instance + + +class ZhipuChatProvider(BaseServiceProvider[BaseChatModel]): + """ + 智谱 AI 生成式大模型服务提供者 + """ + + def __init__(self, model: str = "glm-4.7-flash"): + super().__init__("zhipu_chat") + self._model = model + + def is_available(self) -> bool: + """ + 检查智谱 AI 服务是否可用 + + Returns: + bool: 服务是否可用 + """ + if not ZHIPUAI_API_KEY: + logger.warning("ZHIPUAI_API_KEY 未配置") + return False + + try: + logger.info(f"智谱 AI 服务配置正确，准备使用: {self._model}") + return True + except Exception as e: + logger.warning(f"智谱 AI 服务不可用: {e}") + return False + + def get_service(self) -> BaseChatModel: + """ + 获取智谱 AI 服务 + + Returns: + BaseChatModel: LangChain 兼容的 ChatModel 实例 + """ + if self._service_instance is None: + from langchain_community.chat_models import ChatZhipuAI + + self._service_instance = ChatZhipuAI( + model=self._model, + api_key=ZHIPUAI_API_KEY, + temperature=0.1, + max_tokens=4096, + timeout=120.0, + max_retries=3, + streaming=True, + ) + return self._service_instance + + +class DeepSeekChatProvider(BaseServiceProvider[BaseChatModel]): + """ + DeepSeek 生成式大模型服务提供者 + """ + + def __init__(self, model: str = "deepseek-reasoner"): + super().__init__("deepseek_chat") + self._model = model + + def is_available(self) -> bool: + """ + 检查 DeepSeek 服务是否可用 + + Returns: + bool: 服务是否可用 + """ + if not DEEPSEEK_API_KEY: + logger.warning("DEEPSEEK_API_KEY 未配置") + return False + + try: + logger.info(f"DeepSeek 服务配置正确，准备使用: {self._model}") + return True + except Exception as e: + logger.warning(f"DeepSeek 服务不可用: {e}") + return False + + def get_service(self) -> BaseChatModel: + """ + 获取 DeepSeek 服务 + + Returns: + BaseChatModel: LangChain 兼容的 ChatModel 实例 + """ + if self._service_instance is None: + from langchain_openai import ChatOpenAI + from pydantic import SecretStr + + self._service_instance = ChatOpenAI( + base_url="https://api.deepseek.com", + api_key=SecretStr(DEEPSEEK_API_KEY), + model=self._model, + temperature=0.1, + max_tokens=4096, + timeout=60.0, + max_retries=2, + streaming=True, + ) + return self._service_instance + + +# 全局服务映射表 - 名称 -> Provider +CHAT_PROVIDERS: Dict[str, Callable[[], BaseServiceProvider[BaseChatModel]]] = { + "local": lambda: LocalVLLMChatProvider(), + "zhipu": lambda: ZhipuChatProvider(), + "deepseek": lambda: DeepSeekChatProvider(), +} + + +def get_chat_service() -> BaseChatModel: + """ + 获取默认的生成式大模型服务（带自动降级） + 优先顺序: local -> zhipu -> deepseek + + Returns: + BaseChatModel: LangChain 兼容的 ChatModel 实例 + """ + def _create_chain(): + primary = LocalVLLMChatProvider() + fallbacks = [ZhipuChatProvider(), DeepSeekChatProvider()] + return FallbackServiceChain(primary, fallbacks) + + chain = SingletonServiceManager.get_or_create("chat_service_chain", _create_chain) + return chain.get_available_service() + + +def get_all_chat_services() -> Dict[str, BaseChatModel]: + """ + 获取所有可用的生成式大模型服务（用于多模型切换） + + Returns: + Dict[str, BaseChatModel]: 模型名称 -> ChatModel 实例 的字典 + """ + services = {} + + for name, provider_factory in CHAT_PROVIDERS.items(): + try: + provider = provider_factory() + if provider.is_available(): + logger.info(f"模型 '{name}' 可用") + services[name] = provider.get_service() + else: + logger.warning(f"模型 '{name}' 不可用，跳过") + except Exception as e: + logger.warning(f"初始化模型 '{name}' 失败: {e}") + + if not services: + raise RuntimeError(f"没有可用的生成式大模型，尝试了: {list(CHAT_PROVIDERS.keys())}") + + return services