feat: enhance embedding dimension detection with Qwen support and expand RAG index rebuild triggers.

backend/app/services/ci_service.py

@@ -208,14 +208,23 @@ class CIService:
         try:
             context_results = await retriever.retrieve(query, top_k=5)
         except Exception as e:
-            # Check for Chroma dimension mismatch
+            err_msg = str(e).lower()
-            if "dimension" in str(e).lower():
+            # Dimension mismatch, 404 (model not found), or 401 (auth issue usually model related)
-                logger.warning(f"Dimension mismatch detected for project {project.id}. Rebuilding index...")
+            # indicator of need for re-index with current settings
+            should_rebuild = any(x in err_msg for x in ["dimension", "404", "401", "400", "invalid_model"])
+            
+            if should_rebuild:
+                logger.warning(f"Embedding/RAG error for project {project.id}: {e}. Triggering full rebuild...")
+                # Rebuild using current correct configuration
                 await self._ensure_indexed(project, repo, branch, force_rebuild=True)
-                # Retry once
+                # Retry retrieval
+                try:
                     context_results = await retriever.retrieve(query, top_k=5)
+                except Exception as retry_e:
+                    logger.error(f"Retry retrieval failed: {retry_e}")
+                    context_results = []
             else:
-                logger.error(f"Retrieval error: {e}")
+                logger.error(f"Retrieval error (no rebuild): {e}")
                 context_results = []
 
         repo_context = "\n".join([r.to_context_string() for r in context_results])

backend/app/services/rag/retriever.py

@@ -198,26 +198,19 @@ class CodeRetriever:
                     if embeddings is not None and len(embeddings) > 0:
                         dim = len(embeddings[0])
 
-                        # 🔥 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},
                             3072: {"provider": "openai", "model": "text-embedding-3-large", "dimension": 3072},
 
+                            # Qwen (DashScope) 系列
+                            # 1536: {"provider": "qwen", "model": "text-embedding-v2", "dimension": 1536},
+                            1024: {"provider": "qwen", "model": "text-embedding-v4", "dimension": 1024},
+
                             # HuggingFace 系列
-                            1024: {"provider": "huggingface", "model": "BAAI/bge-m3", "dimension": 1024},
+                            # 1024 已被 Qwen 占用，优先选用 Qwen 如果配置了
+                            # 1024: {"provider": "huggingface", "model": "BAAI/bge-m3", "dimension": 1024},
                             384: {"provider": "huggingface", "model": "sentence-transformers/all-MiniLM-L6-v2", "dimension": 384},
 
                             # Ollama 系列
@@ -227,6 +220,10 @@ class CodeRetriever:
                             512: {"provider": "jina", "model": "jina-embeddings-v2-small-en", "dimension": 512},
                         }
                         
+                        # Special case: If EMBEDDING_PROVIDER is 'qwen', use qwen for 1536 too
+                        if settings.EMBEDDING_PROVIDER == "qwen" and dim == 1536:
+                             return {"provider": "qwen", "model": "text-embedding-v2", "dimension": 1536}
+
                         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']}")