feat: Enhance embedding service configuration and add CI pipeline retry logic for dimension mismatches with optional index rebuild.

backend/app/services/ci_service.py

@@ -205,7 +205,19 @@ class CIService:
             await self._post_gitea_comment(repo, issue.get("number"), msg)
             return
 
-        context_results = await retriever.retrieve(query, top_k=5)
+        try:
+            context_results = await retriever.retrieve(query, top_k=5)
+        except Exception as e:
+            # Check for Chroma dimension mismatch
+            if "dimension" in str(e).lower():
+                logger.warning(f"Dimension mismatch detected for project {project.id}. Rebuilding index...")
+                await self._ensure_indexed(project, repo, branch, force_rebuild=True)
+                # Retry once
+                context_results = await retriever.retrieve(query, top_k=5)
+            else:
+                logger.error(f"Retrieval error: {e}")
+                context_results = []
+
         repo_context = "\n".join([r.to_context_string() for r in context_results])
         
         # 4. Build Prompt
@@ -281,7 +293,7 @@ class CIService:
             
         return project
 
-    async def _ensure_indexed(self, project: Project, repo: Dict, branch: str) -> Optional[str]:
+    async def _ensure_indexed(self, project: Project, repo: Dict, branch: str, force_rebuild: bool = False) -> Optional[str]:
         """
         Syncs the repository and ensures it is indexed.
         Returns the local path if successful.
@@ -295,15 +307,18 @@ class CIService:
             return None
         
         try:
-            # 2. Incremental Indexing
+            # 2. Incremental or Full Indexing
             indexer = CodeIndexer(
                 collection_name=f"ci_{project.id}", 
                 persist_directory=str(CI_VECTOR_DB_DIR / project.id)
             )
+            
+            update_mode = IndexUpdateMode.FULL if force_rebuild else IndexUpdateMode.INCREMENTAL
+            
             # Iterate over the generator to execute indexing
             async for progress in indexer.smart_index_directory(
                 directory=repo_path, 
-                update_mode=IndexUpdateMode.INCREMENTAL
+                update_mode=update_mode
             ):
                 # Log progress occasionally
                 if progress.total_files > 0 and progress.processed_files % 20 == 0:

backend/app/services/rag/embeddings.py

@@ -60,8 +60,18 @@ class OpenAIEmbedding(EmbeddingProvider):
         base_url: Optional[str] = None,
         model: str = "text-embedding-3-small",
     ):
-        self.api_key = api_key or settings.LLM_API_KEY
-        self.base_url = base_url or "https://api.openai.com/v1"
+        # 优先使用显性参数，其次使用 EMBEDDING_API_KEY，最后使用 LLM_API_KEY
+        self.api_key = (
+            api_key 
+            or getattr(settings, "EMBEDDING_API_KEY", None) 
+            or settings.LLM_API_KEY
+        )
+        # 优先使用显性参数，其次使用 EMBEDDING_BASE_URL，最后使用 OpenAI 默认地址
+        self.base_url = (
+            base_url 
+            or getattr(settings, "EMBEDDING_BASE_URL", None) 
+            or "https://api.openai.com/v1"
+        )
         self.model = model
         self._dimension = self.MODELS.get(model, 1536)
     
@@ -593,15 +603,15 @@ class EmbeddingService:
         # 确定提供商（保存原始值用于属性访问）
         self.provider = provider or getattr(settings, 'EMBEDDING_PROVIDER', 'openai')
         self.model = model or getattr(settings, 'EMBEDDING_MODEL', 'text-embedding-3-small')
-        self.api_key = api_key
-        self.base_url = base_url
+        self.api_key = api_key or getattr(settings, "EMBEDDING_API_KEY", None)
+        self.base_url = base_url or getattr(settings, "EMBEDDING_BASE_URL", None)
 
         # 创建提供商实例
         self._provider = self._create_provider(
             provider=self.provider,
             model=self.model,
-            api_key=api_key,
-            base_url=base_url,
+            api_key=self.api_key,
+            base_url=self.base_url,
         )
 
         logger.info(f"Embedding service initialized with {self.provider}/{self.model}")

backend/app/services/rag/retriever.py

@@ -13,6 +13,8 @@ from .embeddings import EmbeddingService
 from .indexer import VectorStore, ChromaVectorStore, InMemoryVectorStore
 from .splitter import CodeChunk, ChunkType
 
+from app.core.config import settings
+
 logger = logging.getLogger(__name__)
 
 
@@ -196,7 +198,19 @@ class CodeRetriever:
                     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）
+                        # 🔥 1. Check if the current provider supports this dimension
+                        current_provider_name = getattr(self.embedding_service, 'provider', settings.EMBEDDING_PROVIDER)
+                        current_model_name = getattr(self.embedding_service, 'model', settings.EMBEDDING_MODEL)
+                        
+                        # Use a temporary service to check dimension if needed, or just trust current settings if dimension matches
+                        if hasattr(self.embedding_service, 'dimension') and self.embedding_service.dimension == dim:
+                             return {
+                                 "provider": current_provider_name,
+                                 "model": current_model_name,
+                                 "dimension": dim
+                             }
+
+                        # 🔥 2. Fallback to hardcoded mapping
                         dimension_mapping = {
                             # OpenAI 系列
                             1536: {"provider": "openai", "model": "text-embedding-3-small", "dimension": 1536},
@@ -211,9 +225,6 @@ class CodeRetriever:
 
                             # 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)