重构：添加模型服务模块，支持嵌入和重排服务的自动降级
新增功能： - 创建 app/model_services 模块，提供统一的模型服务获取接口 - 实现 BaseServiceProvider 基类和 FallbackServiceChain 降级链 - 实现 get_embedding_service()：优先本地 llama.cpp，降级到智谱 API - 实现 get_rerank_service()：优先本地 llama.cpp，降级到智谱 API - 支持单例管理，确保全局只有一个服务实例 修改内容： - 更新 app/config.py，添加智谱 API 相关配置 - 修改 rag_core/vector_store.py：支持接受外部传入的 embeddings - 修改 rag_core/retriever_factory.py：支持接受外部传入的 embeddings - 修改 app/agent/rag_initializer.py：使用 get_embedding_service() - 修改 app/rag/pipeline.py：使用 get_rerank_service() - 修改 app/memory/mem0_client.py：智能判断可用服务配置 mem0 - 修改 rag_indexer/index_builder.py：支持使用新服务，保持向后兼容 - 修改 rag_indexer/config.py：添加智谱配置 环境变量： - ZHIPUAI_API_KEY：智谱 API 密钥（必选） - ZHIPU_EMBEDDING_MODEL：可选，默认 embedding-3 - ZHIPU_RERANK_MODEL：可选，默认 rerank-2 - ZHIPU_API_BASE：可选，默认 https://open.bigmodel.cn/api/paas/v4
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backend/app/model_services/rerank_services.py
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backend/app/model_services/rerank_services.py
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"""
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重排模型服务模块
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本模块提供统一的重排模型服务获取接口，支持自动降级：
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1. 优先使用本地 llama.cpp 重排服务
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2. 本地服务不可用时，自动降级到智谱 API 重排服务
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主要功能：
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- LocalLlamaCppRerankProvider：本地 llama.cpp 重排服务提供者
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- ZhipuRerankProvider：智谱 API 重排服务提供者
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- get_rerank_service()：获取重排服务的统一接口
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"""
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import logging
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from typing import List
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import requests
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from langchain_core.documents import Document
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from .base import (
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BaseServiceProvider,
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FallbackServiceChain,
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SingletonServiceManager
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)
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from ..config import (
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LLAMACPP_RERANKER_URL,
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LLAMACPP_API_KEY,
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ZHIPUAI_API_KEY,
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ZHIPU_RERANK_MODEL,
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ZHIPU_API_BASE
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)
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logger = logging.getLogger(__name__)
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class BaseReranker:
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"""
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重排器基类，定义统一的接口
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"""
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def compress_documents(self, documents: List[Document], query: str, top_n: int = 5) -> List[Document]:
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"""
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对文档进行重排序
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Args:
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documents: 待排序的文档列表
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query: 查询字符串
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top_n: 返回前 N 个结果
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Returns:
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排序后的文档列表
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"""
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raise NotImplementedError
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class LocalLlamaCppReranker(BaseReranker):
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"""
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使用远程 llama.cpp 服务对检索结果重排序
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"""
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def __init__(self, base_url: str, api_key: str, model: str = "bge-reranker-v2-m3", timeout: int = 60):
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self.base_url = base_url
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self.api_key = api_key
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self.model = model
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self.timeout = timeout
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self.endpoint = f"{self.base_url}/rerank"
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def compress_documents(self, documents: List[Document], query: str, top_n: int = 5) -> List[Document]:
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"""
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对文档进行重排序
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"""
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if not documents:
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return []
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# 准备请求体
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payload = {
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"model": self.model,
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"query": query,
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"documents": [doc.page_content for doc in documents],
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"top_n": top_n
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}
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}"
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}
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try:
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response = requests.post(self.endpoint, json=payload, headers=headers, timeout=self.timeout)
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response.raise_for_status()
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results = response.json()
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# 解析返回结果
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sorted_indices = [item["index"] for item in results["results"]]
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sorted_docs = [documents[idx] for idx in sorted_indices]
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return sorted_docs
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except Exception as e:
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logger.warning(f"远程重排序过程出错，返回原始前 {top_n} 个结果: {e}")
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return documents[:top_n]
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class ZhipuReranker(BaseReranker):
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"""
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使用智谱 API 对检索结果重排序
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"""
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def __init__(self, model: str | None = None):
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self.model = model or ZHIPU_RERANK_MODEL
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self.api_key = ZHIPUAI_API_KEY
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def compress_documents(self, documents: List[Document], query: str, top_n: int = 5) -> List[Document]:
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"""
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对文档进行重排序
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"""
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if not documents:
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return []
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try:
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from zhipuai import ZhipuAI
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client = ZhipuAI(api_key=self.api_key)
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response = client.rerank.create(
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model=self.model,
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query=query,
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documents=[doc.page_content for doc in documents],
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top_n=top_n
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)
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sorted_indices = [item.index for item in response.results]
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sorted_docs = [documents[idx] for idx in sorted_indices]
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return sorted_docs
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except Exception as e:
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logger.warning(f"智谱重排序过程出错，返回原始前 {top_n} 个结果: {e}")
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return documents[:top_n]
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class LocalLlamaCppRerankProvider(BaseServiceProvider[BaseReranker]):
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"""
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本地 llama.cpp 重排服务提供者
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"""
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def __init__(self, model: str = "bge-reranker-v2-m3"):
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super().__init__("local_llamacpp_rerank")
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self._model = model
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def is_available(self) -> bool:
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"""
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检查本地 llama.cpp 重排服务是否可用
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"""
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if not LLAMACPP_RERANKER_URL:
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logger.warning("LLAMACPP_RERANKER_URL 未配置")
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return False
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try:
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# 测试重排服务
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test_docs = [Document(page_content="test document 1"), Document(page_content="test document 2")]
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reranker = LocalLlamaCppReranker(
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base_url=LLAMACPP_RERANKER_URL,
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api_key=LLAMACPP_API_KEY,
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model=self._model
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)
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result = reranker.compress_documents(test_docs, "test query", top_n=1)
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logger.info(f"本地 llama.cpp 重排服务可用")
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return True
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except Exception as e:
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logger.warning(f"本地 llama.cpp 重排服务不可用: {e}")
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return False
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def get_service(self) -> BaseReranker:
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"""
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获取本地 llama.cpp 重排服务
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"""
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if self._service_instance is None:
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self._service_instance = LocalLlamaCppReranker(
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base_url=LLAMACPP_RERANKER_URL,
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api_key=LLAMACPP_API_KEY,
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model=self._model
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)
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return self._service_instance
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class ZhipuRerankProvider(BaseServiceProvider[BaseReranker]):
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"""
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智谱 API 重排服务提供者
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"""
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def __init__(self, model: str | None = None):
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super().__init__("zhipu_rerank")
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self._model = model or ZHIPU_RERANK_MODEL
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def is_available(self) -> bool:
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"""
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检查智谱 API 重排服务是否可用
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"""
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if not ZHIPUAI_API_KEY:
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logger.warning("ZHIPUAI_API_KEY 未配置")
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return False
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try:
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# 测试重排服务
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test_docs = [Document(page_content="test document 1"), Document(page_content="test document 2")]
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reranker = ZhipuReranker(model=self._model)
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result = reranker.compress_documents(test_docs, "test query", top_n=1)
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logger.info(f"智谱重排服务可用")
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return True
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except ImportError:
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logger.warning("zhipuai 库未安装")
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return False
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except Exception as e:
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logger.warning(f"智谱重排服务不可用: {e}")
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return False
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def get_service(self) -> BaseReranker:
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"""
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获取智谱 API 重排服务
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"""
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if self._service_instance is None:
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self._service_instance = ZhipuReranker(model=self._model)
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return self._service_instance
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def get_rerank_service() -> BaseReranker:
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"""
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获取重排服务（带自动降级）
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Returns:
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BaseReranker: 重排服务实例
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"""
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def _create_chain():
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primary = LocalLlamaCppRerankProvider()
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fallback = ZhipuRerankProvider()
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return FallbackServiceChain(primary, [fallback])
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chain = SingletonServiceManager.get_or_create("rerank_service_chain", _create_chain)
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return chain.get_available_service()
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