diff --git a/backend/app/services/ci_service.py b/backend/app/services/ci_service.py index bf9327b..80d4cf2 100644 --- a/backend/app/services/ci_service.py +++ b/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: diff --git a/backend/app/services/rag/embeddings.py b/backend/app/services/rag/embeddings.py index 9682f9c..f585ebe 100644 --- a/backend/app/services/rag/embeddings.py +++ b/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}") diff --git a/backend/app/services/rag/retriever.py b/backend/app/services/rag/retriever.py index d942b11..20cdf79 100644 --- a/backend/app/services/rag/retriever.py +++ b/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)