feat(rag): all APIs ok

pkg/vector/vdb.py

@@ -6,7 +6,7 @@ import numpy as np
 
 class VectorDatabase(abc.ABC):
     @abc.abstractmethod
-    def add_embeddings(
+    async def add_embeddings(
         self,
         collection: str,
         ids: list[str],
@@ -18,16 +18,20 @@ class VectorDatabase(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def search(self, collection: str, query_embedding: np.ndarray, k: int = 5) -> Dict[str, Any]:
+    async def search(self, collection: str, query_embedding: np.ndarray, k: int = 5) -> Dict[str, Any]:
         """在指定 collection 中检索最相似的向量。"""
         pass
 
     @abc.abstractmethod
-    def delete_by_metadata(self, collection: str, where: Dict[str, Any]) -> None:
-        """根据元数据删除指定 collection 中的向量。"""
+    async def delete_by_file_id(self, collection: str, file_id: str) -> None:
+        """根据 file_id 删除指定 collection 中的向量。"""
         pass
 
     @abc.abstractmethod
-    def get_or_create_collection(self, collection: str):
+    async def get_or_create_collection(self, collection: str):
         """获取或创建 collection。"""
         pass
+
+    @abc.abstractmethod
+    async def delete_collection(self, collection: str):
+        pass

pkg/vector/vdbs/chroma.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
-import chromadb
+import asyncio
 from typing import Any
 from chromadb import PersistentClient
 from pkg.vector.vdb import VectorDatabase
 from pkg.core import app
+import chromadb
 
 
 class ChromaVectorDatabase(VectorDatabase):
@@ -12,26 +13,29 @@ class ChromaVectorDatabase(VectorDatabase):
         self.client = PersistentClient(path=base_path)
         self._collections = {}
 
-    def get_or_create_collection(self, collection: str) -> chromadb.Collection:
+    async def get_or_create_collection(self, collection: str) -> chromadb.Collection:
         if collection not in self._collections:
-            self._collections[collection] = self.client.get_or_create_collection(name=collection)
+            self._collections[collection] = await asyncio.to_thread(
+                self.client.get_or_create_collection, name=collection
+            )
             self.ap.logger.info(f"Chroma collection '{collection}' accessed/created.")
         return self._collections[collection]
 
-    def add_embeddings(
+    async def add_embeddings(
         self,
         collection: str,
         ids: list[str],
         embeddings_list: list[list[float]],
         metadatas: list[dict[str, Any]],
     ) -> None:
-        col = self.get_or_create_collection(collection)
-        col.add(embeddings=embeddings_list, ids=ids, metadatas=metadatas)
+        col = await self.get_or_create_collection(collection)
+        await asyncio.to_thread(col.add, embeddings=embeddings_list, ids=ids, metadatas=metadatas)
         self.ap.logger.info(f"Added {len(ids)} embeddings to Chroma collection '{collection}'.")
 
-    def search(self, collection: str, query_embedding: list[float], k: int = 5) -> dict[str, Any]:
-        col = self.get_or_create_collection(collection)
-        results = col.query(
+    async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> dict[str, Any]:
+        col = await self.get_or_create_collection(collection)
+        results = await asyncio.to_thread(
+            col.query,
             query_embeddings=query_embedding,
             n_results=k,
             include=['metadatas', 'distances', 'documents'],
@@ -39,7 +43,13 @@ class ChromaVectorDatabase(VectorDatabase):
         self.ap.logger.info(f"Chroma search in '{collection}' returned {len(results.get('ids', [[]])[0])} results.")
         return results
 
-    def delete_by_metadata(self, collection: str, where: dict[str, Any]) -> None:
-        col = self.get_or_create_collection(collection)
-        col.delete(where=where)
-        self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' with filter: {where}")
+    async def delete_by_file_id(self, collection: str, file_id: str) -> None:
+        col = await self.get_or_create_collection(collection)
+        await asyncio.to_thread(col.delete, where={'file_id': file_id})
+        self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' with file_id: {file_id}")
+
+    async def delete_collection(self, collection: str):
+        if collection in self._collections:
+            del self._collections[collection]
+        await asyncio.to_thread(self.client.delete_collection, name=collection)
+        self.ap.logger.info(f"Chroma collection '{collection}' deleted.")