feat(retriever): 添加自动适配不同 embedding 配置的功能

支持自动检测 collection 的 embedding 配置并动态创建对应的 embedding 服务
新增从向量维度推断配置的功能，兼容旧的 collection

backend/app/api/v1/endpoints/agent_tasks.py

@@ -644,10 +644,12 @@ async def _initialize_tools(
         collection_name = f"project_{project_id}" if project_id else "default_project"
 
         # 创建 CodeRetriever（用于搜索）
+        # 🔥 传递 api_key，用于自动适配 collection 的 embedding 配置
         retriever = CodeRetriever(
             collection_name=collection_name,
             embedding_service=embedding_service,
             persist_directory=settings.VECTOR_DB_PATH,
+            api_key=embedding_api_key,  # 🔥 传递 api_key 以支持自动切换 embedding
         )
 
         logger.info(f"✅ RAG 系统初始化成功: collection={collection_name}")

backend/app/services/rag/embeddings.py

@@ -498,19 +498,21 @@ class EmbeddingService:
         self.cache_enabled = cache_enabled
         self._cache: Dict[str, List[float]] = {}
 
-        # 确定提供商
+        # 确定提供商（保存原始值用于属性访问）
-        provider = provider or getattr(settings, 'EMBEDDING_PROVIDER', 'openai')
+        self.provider = provider or getattr(settings, 'EMBEDDING_PROVIDER', 'openai')
-        model = model or getattr(settings, 'EMBEDDING_MODEL', 'text-embedding-3-small')
+        self.model = model or getattr(settings, 'EMBEDDING_MODEL', 'text-embedding-3-small')
+        self.api_key = api_key
+        self.base_url = base_url
 
         # 创建提供商实例
         self._provider = self._create_provider(
-            provider=provider,
+            provider=self.provider,
-            model=model,
+            model=self.model,
             api_key=api_key,
             base_url=base_url,
         )
 
-        logger.info(f"Embedding service initialized with {provider}/{model}")
+        logger.info(f"Embedding service initialized with {self.provider}/{self.model}")
     
     def _create_provider(
         self,

backend/app/services/rag/indexer.py

@@ -114,9 +114,11 @@ class ChromaVectorStore(VectorStore):
         self,
         collection_name: str,
         persist_directory: Optional[str] = None,
+        embedding_config: Optional[Dict[str, Any]] = None,  # 🔥 新增：embedding 配置
     ):
         self.collection_name = collection_name
         self.persist_directory = persist_directory
+        self.embedding_config = embedding_config or {}  # 🔥 存储 embedding 配置
         self._client = None
         self._collection = None
 
@@ -136,9 +138,19 @@ class ChromaVectorStore(VectorStore):
                     settings=Settings(anonymized_telemetry=False),
                 )
 
+            # 🔥 构建 collection 元数据，包含 embedding 配置
+            collection_metadata = {"hnsw:space": "cosine"}
+            if self.embedding_config:
+                # 在元数据中记录 embedding 配置
+                collection_metadata["embedding_provider"] = self.embedding_config.get("provider", "openai")
+                collection_metadata["embedding_model"] = self.embedding_config.get("model", "text-embedding-3-small")
+                collection_metadata["embedding_dimension"] = self.embedding_config.get("dimension", 1536)
+                if self.embedding_config.get("base_url"):
+                    collection_metadata["embedding_base_url"] = self.embedding_config.get("base_url")
+
             self._collection = self._client.get_or_create_collection(
                 name=self.collection_name,
-                metadata={"hnsw:space": "cosine"},
+                metadata=collection_metadata,
             )
 
             logger.info(f"Chroma collection '{self.collection_name}' initialized")
@@ -146,6 +158,24 @@ class ChromaVectorStore(VectorStore):
         except ImportError:
             raise ImportError("chromadb is required. Install with: pip install chromadb")
 
+    def get_embedding_config(self) -> Dict[str, Any]:
+        """
+        🔥 获取 collection 的 embedding 配置
+
+        Returns:
+            包含 provider, model, dimension, base_url 的字典
+        """
+        if not self._collection:
+            return {}
+
+        metadata = self._collection.metadata or {}
+        return {
+            "provider": metadata.get("embedding_provider"),
+            "model": metadata.get("embedding_model"),
+            "dimension": metadata.get("embedding_dimension"),
+            "base_url": metadata.get("embedding_base_url"),
+        }
+    
     async def add_documents(
         self,
         ids: List[str],
@@ -335,6 +365,14 @@ class CodeIndexer:
         self.embedding_service = embedding_service or EmbeddingService()
         self.splitter = splitter or CodeSplitter()
 
+        # 🔥 从 embedding_service 获取配置，用于存储到 collection 元数据
+        embedding_config = {
+            "provider": getattr(self.embedding_service, 'provider', 'openai'),
+            "model": getattr(self.embedding_service, 'model', 'text-embedding-3-small'),
+            "dimension": getattr(self.embedding_service, 'dimension', 1536),
+            "base_url": getattr(self.embedding_service, 'base_url', None),
+        }
+
         # 创建向量存储
         if vector_store:
             self.vector_store = vector_store
@@ -343,6 +381,7 @@ class CodeIndexer:
                 self.vector_store = ChromaVectorStore(
                     collection_name=collection_name,
                     persist_directory=persist_directory,
+                    embedding_config=embedding_config,  # 🔥 传递 embedding 配置
                 )
             except ImportError:
                 logger.warning("Chroma not available, using in-memory store")

backend/app/services/rag/retriever.py

@@ -4,6 +4,7 @@
 """
 
 import re
+import asyncio
 import logging
 from typing import List, Dict, Any, Optional
 from dataclasses import dataclass, field
@@ -75,6 +76,10 @@ class CodeRetriever:
     """
     代码检索器
     支持语义检索、关键字检索和混合检索
+
+    🔥 自动兼容不同维度的向量：
+    - 查询时自动检测 collection 的 embedding 配置
+    - 动态创建对应的 embedding 服务
     """
 
     def __init__(
@@ -83,18 +88,22 @@ class CodeRetriever:
         embedding_service: Optional[EmbeddingService] = None,
         vector_store: Optional[VectorStore] = None,
         persist_directory: Optional[str] = None,
+        api_key: Optional[str] = None,  # 🔥 新增：用于动态创建 embedding 服务
     ):
         """
         初始化检索器
 
         Args:
             collection_name: 向量集合名称
-            embedding_service: 嵌入服务
+            embedding_service: 嵌入服务（可选，会根据 collection 配置自动创建）
             vector_store: 向量存储
             persist_directory: 持久化目录
+            api_key: API Key（用于动态创建 embedding 服务）
         """
         self.collection_name = collection_name
-        self.embedding_service = embedding_service or EmbeddingService()
+        self._provided_embedding_service = embedding_service  # 用户提供的 embedding 服务
+        self.embedding_service = embedding_service  # 实际使用的 embedding 服务
+        self._api_key = api_key
 
         # 创建向量存储
         if vector_store:
@@ -112,10 +121,120 @@ class CodeRetriever:
         self._initialized = False
 
     async def initialize(self):
-        """初始化检索器"""
+        """初始化检索器，自动检测并适配 collection 的 embedding 配置"""
-        if not self._initialized:
+        if self._initialized:
-            await self.vector_store.initialize()
+            return
-            self._initialized = True
+
+        await self.vector_store.initialize()
+
+        # 🔥 自动检测 collection 的 embedding 配置
+        if hasattr(self.vector_store, 'get_embedding_config'):
+            stored_config = self.vector_store.get_embedding_config()
+            stored_provider = stored_config.get("provider")
+            stored_model = stored_config.get("model")
+            stored_dimension = stored_config.get("dimension")
+            stored_base_url = stored_config.get("base_url")
+
+            # 🔥 如果没有存储的配置（旧的 collection），尝试通过维度推断
+            if not stored_provider or not stored_model:
+                inferred = await self._infer_embedding_config_from_dimension()
+                if inferred:
+                    stored_provider = inferred.get("provider")
+                    stored_model = inferred.get("model")
+                    stored_dimension = inferred.get("dimension")
+                    logger.info(f"📊 从向量维度推断 embedding 配置: {stored_provider}/{stored_model}")
+
+            if stored_provider and stored_model:
+                # 检查是否需要使用不同的 embedding 服务
+                current_provider = getattr(self.embedding_service, 'provider', None) if self.embedding_service else None
+                current_model = getattr(self.embedding_service, 'model', None) if self.embedding_service else None
+
+                if current_provider != stored_provider or current_model != stored_model:
+                    logger.info(
+                        f"🔄 Collection 使用的 embedding 配置与当前不同: "
+                        f"{stored_provider}/{stored_model} (维度: {stored_dimension}) vs "
+                        f"{current_provider}/{current_model}"
+                    )
+                    logger.info(f"🔄 自动切换到 collection 的 embedding 配置")
+
+                    # 动态创建对应的 embedding 服务
+                    api_key = self._api_key
+                    if not api_key and self._provided_embedding_service:
+                        api_key = getattr(self._provided_embedding_service, 'api_key', None)
+
+                    self.embedding_service = EmbeddingService(
+                        provider=stored_provider,
+                        model=stored_model,
+                        api_key=api_key,
+                        base_url=stored_base_url,
+                    )
+                    logger.info(f"✅ 已切换到: {stored_provider}/{stored_model}")
+
+        # 如果仍然没有 embedding 服务，创建默认的
+        if not self.embedding_service:
+            self.embedding_service = EmbeddingService()
+
+        self._initialized = True
+
+    async def _infer_embedding_config_from_dimension(self) -> Optional[Dict[str, Any]]:
+        """
+        🔥 从向量维度推断 embedding 配置（用于处理旧的 collection）
+
+        Returns:
+            推断的 embedding 配置，如果无法推断则返回 None
+        """
+        try:
+            # 获取一个样本向量来检查维度
+            if hasattr(self.vector_store, '_collection') and self.vector_store._collection:
+                count = await self.vector_store.get_count()
+                if count > 0:
+                    sample = await asyncio.to_thread(
+                        self.vector_store._collection.peek,
+                        limit=1
+                    )
+                    embeddings = sample.get("embeddings")
+                    if embeddings is not None and len(embeddings) > 0:
+                        dim = len(embeddings[0])
+
+                        # 🔥 根据维度推断模型（优先选择常用模型）
+                        dimension_mapping = {
+                            # OpenAI 系列
+                            1536: {"provider": "openai", "model": "text-embedding-3-small", "dimension": 1536},
+                            3072: {"provider": "openai", "model": "text-embedding-3-large", "dimension": 3072},
+
+                            # HuggingFace 系列
+                            1024: {"provider": "huggingface", "model": "BAAI/bge-m3", "dimension": 1024},
+                            384: {"provider": "huggingface", "model": "sentence-transformers/all-MiniLM-L6-v2", "dimension": 384},
+
+                            # Ollama 系列
+                            768: {"provider": "ollama", "model": "nomic-embed-text", "dimension": 768},
+
+                            # Jina 系列
+                            512: {"provider": "jina", "model": "jina-embeddings-v2-small-en", "dimension": 512},
+
+                            # Cohere 系列
+                            # 1024 已被 HuggingFace 占用，Cohere 维度相同时会默认使用 HuggingFace
+                        }
+
+                        inferred = dimension_mapping.get(dim)
+                        if inferred:
+                            logger.info(f"📊 检测到向量维度 {dim}，推断为: {inferred['provider']}/{inferred['model']}")
+                        return inferred
+        except Exception as e:
+            logger.warning(f"无法推断 embedding 配置: {e}")
+
+        return None
+
+    def get_collection_embedding_config(self) -> Dict[str, Any]:
+        """
+        获取 collection 存储的 embedding 配置
+
+        Returns:
+            包含 provider, model, dimension, base_url 的字典
+        """
+        if hasattr(self.vector_store, 'get_embedding_config'):
+            return self.vector_store.get_embedding_config()
+        return {}
     
     async def retrieve(
         self,