feat(vector_db): Implement Vector Database Service with multiple providers

api/routes/config.py

@@ -40,7 +40,7 @@ async def set_config(
     if key == "AI_EMBED_DIM" and str(original_value) != value_to_save:
         try:
             service = VectorDBService()
-            service.clear_all_data()
+            await service.clear_all_data()
         except Exception as exc:
             raise HTTPException(status_code=500, detail=f"Failed to clear vector database: {exc}")
 

api/routes/search.py

@@ -9,7 +9,7 @@ router = APIRouter(prefix="/api/search", tags=["search"])
 async def search_files_by_vector(q: str, top_k: int):
     embedding = await get_text_embedding(q)
     vector_db = VectorDBService()
-    results = vector_db.search_vectors("vector_collection", embedding, top_k)
+    results = await vector_db.search_vectors("vector_collection", embedding, top_k)
     items = [
         SearchResultItem(id=res["id"], path=res["entity"]["path"], score=res["distance"])
         for res in results[0]
@@ -18,7 +18,7 @@ async def search_files_by_vector(q: str, top_k: int):
 
 async def search_files_by_name(q: str, top_k: int):
     vector_db = VectorDBService()
-    results = vector_db.search_by_path("vector_collection", q, top_k)
+    results = await vector_db.search_by_path("vector_collection", q, top_k)
     items = [
         SearchResultItem(id=idx, path=res["entity"]["path"], score=res["distance"])
         for idx, res in enumerate(results[0])
@@ -38,4 +38,4 @@ async def search_files(
     elif mode == "filename":
         return await search_files_by_name(q, top_k)
     else:
-        return {"items": [], "query": q, "error": "Invalid search mode"}
+        return {"items": [], "query": q, "error": "Invalid search mode"}

api/routes/vector_db.py

@@ -1,19 +1,100 @@
+from typing import Any, Dict
+
 from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel, Field
+
 from services.auth import get_current_active_user
 from models.database import UserAccount
-from services.vector_db import VectorDBService
+from services.vector_db import (
+    VectorDBService,
+    VectorDBConfigManager,
+    list_providers,
+    get_provider_entry,
+)
+from services.vector_db.providers import get_provider_class
 from api.response import success
 
 router = APIRouter(prefix="/api/vector-db", tags=["vector-db"])
 
 
+class VectorDBConfigPayload(BaseModel):
+    type: str = Field(..., description="向量数据库提供者类型")
+    config: Dict[str, Any] = Field(default_factory=dict, description="提供者配置参数")
+
+
 @router.post("/clear-all", summary="清空向量数据库")
 async def clear_vector_db(user: UserAccount = Depends(get_current_active_user)):
     if user.username != 'admin':
         raise HTTPException(status_code=403, detail="仅管理员可操作")
     try:
         service = VectorDBService()
-        service.clear_all_data()
+        await service.clear_all_data()
         return success(msg="向量数据库已清空")
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/stats", summary="获取向量数据库统计")
+async def get_vector_db_stats(user: UserAccount = Depends(get_current_active_user)):
+    if user.username != 'admin':
+        raise HTTPException(status_code=403, detail="仅管理员可操作")
+    try:
+        service = VectorDBService()
+        data = await service.get_all_stats()
+        return success(data=data)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/providers", summary="列出可用向量数据库提供者")
+async def list_vector_providers(user: UserAccount = Depends(get_current_active_user)):
+    if user.username != 'admin':
+        raise HTTPException(status_code=403, detail="仅管理员可操作")
+    return success(list_providers())
+
+
+@router.get("/config", summary="获取当前向量数据库配置")
+async def get_vector_db_config(user: UserAccount = Depends(get_current_active_user)):
+    if user.username != 'admin':
+        raise HTTPException(status_code=403, detail="仅管理员可操作")
+    service = VectorDBService()
+    data = await service.current_provider()
+    return success(data)
+
+
+@router.post("/config", summary="更新向量数据库配置")
+async def update_vector_db_config(payload: VectorDBConfigPayload, user: UserAccount = Depends(get_current_active_user)):
+    if user.username != 'admin':
+        raise HTTPException(status_code=403, detail="仅管理员可操作")
+
+    entry = get_provider_entry(payload.type)
+    if not entry:
+        raise HTTPException(status_code=400, detail=f"未知的向量数据库类型: {payload.type}")
+    if not entry.get("enabled", True):
+        raise HTTPException(status_code=400, detail="该向量数据库类型暂不可用")
+
+    provider_cls = get_provider_class(payload.type)
+    if not provider_cls:
+        raise HTTPException(status_code=400, detail=f"未找到类型 {payload.type} 对应的实现")
+
+    # 先尝试建立连接，确保配置有效
+    test_provider = provider_cls(payload.config)
+    try:
+        await test_provider.initialize()
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+    finally:
+        client = getattr(test_provider, "client", None)
+        close_fn = getattr(client, "close", None)
+        if callable(close_fn):
+            try:
+                close_fn()
+            except Exception:
+                pass
+
+    await VectorDBConfigManager.save_config(payload.type, payload.config)
+    service = VectorDBService()
+    await service.reload()
+    config_data = await service.current_provider()
+    stats = await service.get_all_stats()
+    return success({"config": config_data, "stats": stats})

pyproject.toml

@@ -64,6 +64,7 @@ dependencies = [
     "python-multipart==0.0.20",
     "pytz==2025.2",
     "pyyaml==6.0.2",
+    "qdrant-client==1.15.1",
     "rawpy==0.25.1",
     "rich==14.1.0",
     "rich-toolkit==0.15.0",

services/processors/vector_index.py

@@ -34,7 +34,7 @@ class VectorIndexProcessor:
         vector_db = VectorDBService()
         collection_name = "vector_collection"
         if action == "destroy":
-            vector_db.delete_vector(collection_name, path)
+            await vector_db.delete_vector(collection_name, path)
             await LogService.info(
                 "processor:vector_index",
                 f"Destroyed {index_type} index for {path}",
@@ -43,8 +43,8 @@ class VectorIndexProcessor:
             return Response(content=f"文件 {path} 的 {index_type} 索引已销毁", media_type="text/plain")
 
         if index_type == 'simple':
-            vector_db.ensure_collection(collection_name, vector=False)
-            vector_db.upsert_vector(collection_name, {'path': path})
+            await vector_db.ensure_collection(collection_name, vector=False)
+            await vector_db.upsert_vector(collection_name, {'path': path})
             await LogService.info(
                 "processor:vector_index",
                 f"Created simple index for {path}",
@@ -80,8 +80,8 @@ class VectorIndexProcessor:
         if vector_dim <= 0:
             vector_dim = DEFAULT_VECTOR_DIMENSION
 
-        vector_db.ensure_collection(collection_name, vector=True, dim=vector_dim)
-        vector_db.upsert_vector(
+        await vector_db.ensure_collection(collection_name, vector=True, dim=vector_dim)
+        await vector_db.upsert_vector(
             collection_name, {'path': path, 'embedding': embedding})
         
         await LogService.info(

services/vector_db.py

@@ -1,92 +0,0 @@
-from pymilvus import CollectionSchema, DataType, FieldSchema, MilvusClient
-
-
-DEFAULT_VECTOR_DIMENSION = 4096
-
-
-class VectorDBService:
-    _instance = None
-
-    def __new__(cls, *args, **kwargs):
-        if not cls._instance:
-            cls._instance = super(VectorDBService, cls).__new__(cls)
-        return cls._instance
-
-    def __init__(self):
-        if not hasattr(self, 'client'):
-            self.client = MilvusClient("data/db/milvus.db")
-
-    def ensure_collection(self, collection_name, vector: bool = True, dim: int = DEFAULT_VECTOR_DIMENSION):
-        if self.client.has_collection(collection_name):
-            return
-        if vector:
-            try:
-                vector_dim = int(dim)
-            except (TypeError, ValueError):
-                vector_dim = DEFAULT_VECTOR_DIMENSION
-            if vector_dim <= 0:
-                vector_dim = DEFAULT_VECTOR_DIMENSION
-            fields = [
-                FieldSchema(name="path", dtype=DataType.VARCHAR,
-                            max_length=512, is_primary=True, auto_id=False),
-                FieldSchema(name="embedding",
-                            dtype=DataType.FLOAT_VECTOR, dim=vector_dim)
-            ]
-            schema = CollectionSchema(
-                fields, description="Image vector collection")
-            self.client.create_collection(collection_name, schema=schema)
-            index_params = MilvusClient.prepare_index_params()
-            index_params.add_index(
-                field_name="embedding",
-                index_type="IVF_FLAT",
-                index_name="vector_index",
-                metric_type="COSINE",
-                params={
-                    "nlist": 64,
-                }
-            )
-            self.client.create_index(
-                collection_name,
-                index_params=index_params
-            )
-        else:
-            fields = [
-                FieldSchema(name="path", dtype=DataType.VARCHAR,
-                            max_length=512, is_primary=True, auto_id=False),
-            ]
-            schema = CollectionSchema(fields, description="Simple file index")
-            self.client.create_collection(collection_name, schema=schema)
-
-    def upsert_vector(self, collection_name, data):
-        self.client.upsert(collection_name, data)
-
-    def delete_vector(self, collection_name, path: str):
-        self.client.delete(collection_name, ids=[path])
-
-    def search_vectors(self, collection_name, query_embedding, top_k=5):
-        search_params = {"metric_type": "COSINE"}
-        results = self.client.search(
-            collection_name,
-            data=[query_embedding],
-            anns_field="embedding",
-            search_params=search_params,
-            limit=top_k,
-            output_fields=["path"]
-        )
-        print(results)
-        return results
-
-    def search_by_path(self, collection_name, query_path, top_k=20):
-        results = self.client.query(
-            collection_name,
-            filter=f"path like '%{query_path}%'",
-            limit=top_k,
-            output_fields=["path"]
-        )
-        return [[{'id': r['path'], 'distance': 1.0, 'entity': {'path': r['path']}} for r in results]]
-
-    def clear_all_data(self):
-        """清空所有集合的内容"""
-        collections = self.client.list_collections()
-        for collection_name in collections:
-            self.client.drop_collection(collection_name)

services/vector_db/__init__.py

@@ -0,0 +1,11 @@
+from .service import VectorDBService, DEFAULT_VECTOR_DIMENSION
+from .providers import list_providers, get_provider_entry
+from .config_manager import VectorDBConfigManager
+
+__all__ = [
+    "VectorDBService",
+    "DEFAULT_VECTOR_DIMENSION",
+    "list_providers",
+    "get_provider_entry",
+    "VectorDBConfigManager",
+]

services/vector_db/config_manager.py

@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+import json
+from typing import Any, Dict, Tuple
+
+from services.config import ConfigCenter
+
+
+class VectorDBConfigManager:
+    TYPE_KEY = "VECTOR_DB_TYPE"
+    CONFIG_KEY = "VECTOR_DB_CONFIG"
+    DEFAULT_TYPE = "milvus_lite"
+
+    @classmethod
+    async def load_config(cls) -> Tuple[str, Dict[str, Any]]:
+        raw_type = await ConfigCenter.get(cls.TYPE_KEY, cls.DEFAULT_TYPE)
+        provider_type = str(raw_type or cls.DEFAULT_TYPE)
+
+        raw_config = await ConfigCenter.get(cls.CONFIG_KEY)
+        config_dict: Dict[str, Any] = {}
+        if isinstance(raw_config, str) and raw_config:
+            try:
+                config_dict = json.loads(raw_config)
+            except json.JSONDecodeError:
+                config_dict = {}
+        elif isinstance(raw_config, dict):
+            config_dict = raw_config
+        return provider_type, config_dict
+
+    @classmethod
+    async def save_config(cls, provider_type: str, config: Dict[str, Any]) -> None:
+        await ConfigCenter.set(cls.TYPE_KEY, provider_type)
+        await ConfigCenter.set(cls.CONFIG_KEY, json.dumps(config or {}))
+
+    @classmethod
+    async def get_type(cls) -> str:
+        provider_type, _ = await cls.load_config()
+        return provider_type
+
+    @classmethod
+    async def get_config(cls) -> Dict[str, Any]:
+        _, config = await cls.load_config()
+        return config

services/vector_db/providers/__init__.py

@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+from typing import Dict, List, Type
+
+from .base import BaseVectorProvider
+from .milvus_lite import MilvusLiteProvider
+from .milvus_server import MilvusServerProvider
+from .qdrant import QdrantProvider
+
+_PROVIDER_REGISTRY: Dict[str, Dict[str, object]] = {
+    MilvusLiteProvider.type: {
+        "class": MilvusLiteProvider,
+        "label": MilvusLiteProvider.label,
+        "description": MilvusLiteProvider.description,
+        "enabled": MilvusLiteProvider.enabled,
+        "config_schema": MilvusLiteProvider.config_schema,
+    },
+    MilvusServerProvider.type: {
+        "class": MilvusServerProvider,
+        "label": MilvusServerProvider.label,
+        "description": MilvusServerProvider.description,
+        "enabled": MilvusServerProvider.enabled,
+        "config_schema": MilvusServerProvider.config_schema,
+    },
+    QdrantProvider.type: {
+        "class": QdrantProvider,
+        "label": QdrantProvider.label,
+        "description": QdrantProvider.description,
+        "enabled": QdrantProvider.enabled,
+        "config_schema": QdrantProvider.config_schema,
+    },
+}
+
+
+def list_providers() -> List[Dict[str, object]]:
+    return [
+        {
+            "type": type_key,
+            "label": meta["label"],
+            "description": meta.get("description"),
+            "enabled": meta.get("enabled", True),
+            "config_schema": meta.get("config_schema", []),
+        }
+        for type_key, meta in _PROVIDER_REGISTRY.items()
+    ]
+
+
+def get_provider_entry(provider_type: str) -> Dict[str, object] | None:
+    return _PROVIDER_REGISTRY.get(provider_type)
+
+
+def get_provider_class(provider_type: str) -> Type[BaseVectorProvider] | None:
+    entry = get_provider_entry(provider_type)
+    if not entry:
+        return None
+    return entry.get("class")  # type: ignore[return-value]

services/vector_db/providers/base.py

@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List
+
+
+class BaseVectorProvider:
+    """向量数据库提供者基础类，所有实际实现需继承该类"""
+
+    type: str = ""
+    label: str = ""
+    description: str | None = None
+    enabled: bool = True
+    config_schema: List[Dict[str, Any]] = []
+
+    def __init__(self, config: Dict[str, Any] | None = None):
+        self.config = config or {}
+
+    async def initialize(self) -> None:
+        """执行初始化逻辑，例如建立连接"""
+        raise NotImplementedError
+
+    def ensure_collection(self, collection_name: str, vector: bool, dim: int) -> None:
+        raise NotImplementedError
+
+    def upsert_vector(self, collection_name: str, data: Dict[str, Any]) -> None:
+        raise NotImplementedError
+
+    def delete_vector(self, collection_name: str, path: str) -> None:
+        raise NotImplementedError
+
+    def search_vectors(self, collection_name: str, query_embedding, top_k: int):
+        raise NotImplementedError
+
+    def search_by_path(self, collection_name: str, query_path: str, top_k: int):
+        raise NotImplementedError
+
+    def get_all_stats(self) -> Dict[str, Any]:
+        raise NotImplementedError
+
+    def clear_all_data(self) -> None:
+        raise NotImplementedError

services/vector_db/providers/milvus_lite.py

@@ -0,0 +1,196 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from pymilvus import CollectionSchema, DataType, FieldSchema, MilvusClient
+
+from .base import BaseVectorProvider
+
+
+class MilvusLiteProvider(BaseVectorProvider):
+    type = "milvus_lite"
+    label = "Milvus Lite"
+    description = "Embedded Milvus Lite (local file storage)."
+    enabled = True
+    config_schema: List[Dict[str, Any]] = [
+        {
+            "key": "db_path",
+            "label": "Database file path",
+            "type": "text",
+            "default": "data/db/milvus.db",
+            "required": False,
+        }
+    ]
+
+    def __init__(self, config: Dict[str, Any] | None = None):
+        super().__init__(config)
+        self.db_path = Path(self.config.get("db_path") or "data/db/milvus.db")
+        self.client: MilvusClient | None = None
+
+    async def initialize(self) -> None:
+        try:
+            self.client = MilvusClient(str(self.db_path))
+        except Exception as exc:  # pragma: no cover - depends on local environment
+            raise RuntimeError(f"Failed to open Milvus Lite at {self.db_path}: {exc}") from exc
+
+    def _get_client(self) -> MilvusClient:
+        if not self.client:
+            raise RuntimeError("Milvus Lite client is not initialized")
+        return self.client
+
+    @staticmethod
+    def _to_int(value: Any) -> int:
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return 0
+
+    def ensure_collection(self, collection_name: str, vector: bool, dim: int) -> None:
+        client = self._get_client()
+        if client.has_collection(collection_name):
+            return
+        if vector:
+            vector_dim = dim if isinstance(dim, int) and dim > 0 else 0
+            if vector_dim <= 0:
+                vector_dim = 4096
+            fields = [
+                FieldSchema(name="path", dtype=DataType.VARCHAR, max_length=512, is_primary=True, auto_id=False),
+                FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=vector_dim),
+            ]
+            schema = CollectionSchema(fields, description="Image vector collection")
+            client.create_collection(collection_name, schema=schema)
+            index_params = MilvusClient.prepare_index_params()
+            index_params.add_index(
+                field_name="embedding",
+                index_type="IVF_FLAT",
+                index_name="vector_index",
+                metric_type="COSINE",
+                params={"nlist": 64},
+            )
+            client.create_index(collection_name, index_params=index_params)
+        else:
+            fields = [
+                FieldSchema(name="path", dtype=DataType.VARCHAR, max_length=512, is_primary=True, auto_id=False),
+            ]
+            schema = CollectionSchema(fields, description="Simple file index")
+            client.create_collection(collection_name, schema=schema)
+
+    def upsert_vector(self, collection_name: str, data: Dict[str, Any]) -> None:
+        self._get_client().upsert(collection_name, data)
+
+    def delete_vector(self, collection_name: str, path: str) -> None:
+        self._get_client().delete(collection_name, ids=[path])
+
+    def search_vectors(self, collection_name: str, query_embedding, top_k: int):
+        search_params = {"metric_type": "COSINE"}
+        return self._get_client().search(
+            collection_name,
+            data=[query_embedding],
+            anns_field="embedding",
+            search_params=search_params,
+            limit=top_k,
+            output_fields=["path"],
+        )
+
+    def search_by_path(self, collection_name: str, query_path: str, top_k: int):
+        filter_expr = f"path like '%{query_path}%'" if query_path else "path like '%%'"
+        results = self._get_client().query(
+            collection_name,
+            filter=filter_expr,
+            limit=top_k,
+            output_fields=["path"],
+        )
+        return [[{"id": r["path"], "distance": 1.0, "entity": {"path": r["path"]}} for r in results]]
+
+    def get_all_stats(self) -> Dict[str, Any]:
+        client = self._get_client()
+        try:
+            collection_names = client.list_collections()
+        except Exception as exc:
+            raise RuntimeError(f"Failed to list collections: {exc}") from exc
+
+        collections: List[Dict[str, Any]] = []
+        total_vectors = 0
+        total_estimated_memory = 0
+
+        for name in collection_names:
+            try:
+                stats = client.get_collection_stats(name) or {}
+            except Exception:
+                stats = {}
+            row_count = self._to_int(stats.get("row_count"))
+            total_vectors += row_count
+
+            dimension: Optional[int] = None
+            is_vector_collection = False
+            try:
+                description = client.describe_collection(name)
+            except Exception:
+                description = None
+
+            if description:
+                for field in description.get("fields", []):
+                    if field.get("type") == DataType.FLOAT_VECTOR:
+                        params = field.get("params") or {}
+                        dimension = self._to_int(params.get("dim")) or 4096
+                        is_vector_collection = True
+                        break
+
+            estimated_memory = 0
+            if is_vector_collection and dimension:
+                estimated_memory = row_count * dimension * 4
+                total_estimated_memory += estimated_memory
+
+            indexes: List[Dict[str, Any]] = []
+            try:
+                index_names = client.list_indexes(name) or []
+            except Exception:
+                index_names = []
+
+            for index_name in index_names:
+                try:
+                    detail = client.describe_index(name, index_name) or {}
+                except Exception:
+                    detail = {}
+                indexes.append(
+                    {
+                        "index_name": index_name,
+                        "index_type": detail.get("index_type"),
+                        "metric_type": detail.get("metric_type"),
+                        "indexed_rows": self._to_int(detail.get("indexed_rows")),
+                        "pending_index_rows": self._to_int(detail.get("pending_index_rows")),
+                        "state": detail.get("state"),
+                    }
+                )
+
+            collections.append(
+                {
+                    "name": name,
+                    "row_count": row_count,
+                    "dimension": dimension if is_vector_collection else None,
+                    "estimated_memory_bytes": estimated_memory,
+                    "is_vector_collection": is_vector_collection,
+                    "indexes": indexes,
+                }
+            )
+
+        db_file_size = None
+        try:
+            if self.db_path.exists():
+                db_file_size = self.db_path.stat().st_size
+        except OSError:
+            db_file_size = None
+
+        return {
+            "collections": collections,
+            "collection_count": len(collections),
+            "total_vectors": total_vectors,
+            "estimated_total_memory_bytes": total_estimated_memory,
+            "db_file_size_bytes": db_file_size,
+        }
+
+    def clear_all_data(self) -> None:
+        client = self._get_client()
+        for collection_name in client.list_collections():
+            client.drop_collection(collection_name)

services/vector_db/providers/milvus_server.py

@@ -0,0 +1,197 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+from pymilvus import CollectionSchema, DataType, FieldSchema, MilvusClient
+
+from .base import BaseVectorProvider
+
+
+class MilvusServerProvider(BaseVectorProvider):
+    type = "milvus_server"
+    label = "Milvus Server"
+    description = "Remote Milvus instance accessed via URI."
+    enabled = True
+    config_schema: List[Dict[str, Any]] = [
+        {
+            "key": "uri",
+            "label": "Server URI",
+            "type": "text",
+            "required": True,
+            "placeholder": "http://localhost:19530",
+        },
+        {
+            "key": "token",
+            "label": "Token",
+            "type": "password",
+            "required": False,
+            "placeholder": "user:password",
+        },
+    ]
+
+    def __init__(self, config: Dict[str, Any] | None = None):
+        super().__init__(config)
+        self.client: MilvusClient | None = None
+
+    async def initialize(self) -> None:
+        uri = self.config.get("uri")
+        if not uri:
+            raise RuntimeError("Milvus Server URI is required")
+        try:
+            self.client = MilvusClient(uri=uri, token=self.config.get("token"))
+        except Exception as exc:  # pragma: no cover - depends on remote availability
+            raise RuntimeError(f"Failed to connect to Milvus Server {uri}: {exc}") from exc
+
+    def _get_client(self) -> MilvusClient:
+        if not self.client:
+            raise RuntimeError("Milvus Server client is not initialized")
+        return self.client
+
+    @staticmethod
+    def _to_int(value: Any) -> int:
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return 0
+
+    def ensure_collection(self, collection_name: str, vector: bool, dim: int) -> None:
+        client = self._get_client()
+        if client.has_collection(collection_name):
+            return
+        if vector:
+            vector_dim = dim if isinstance(dim, int) and dim > 0 else 0
+            if vector_dim <= 0:
+                vector_dim = 4096
+            fields = [
+                FieldSchema(name="path", dtype=DataType.VARCHAR, max_length=512, is_primary=True, auto_id=False),
+                FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=vector_dim),
+            ]
+            schema = CollectionSchema(fields, description="Image vector collection")
+            client.create_collection(collection_name, schema=schema)
+            index_params = MilvusClient.prepare_index_params()
+            index_params.add_index(
+                field_name="embedding",
+                index_type="IVF_FLAT",
+                index_name="vector_index",
+                metric_type="COSINE",
+                params={"nlist": 64},
+            )
+            client.create_index(collection_name, index_params=index_params)
+        else:
+            fields = [
+                FieldSchema(name="path", dtype=DataType.VARCHAR, max_length=512, is_primary=True, auto_id=False),
+            ]
+            schema = CollectionSchema(fields, description="Simple file index")
+            client.create_collection(collection_name, schema=schema)
+
+    def upsert_vector(self, collection_name: str, data: Dict[str, Any]) -> None:
+        self._get_client().upsert(collection_name, data)
+
+    def delete_vector(self, collection_name: str, path: str) -> None:
+        self._get_client().delete(collection_name, ids=[path])
+
+    def search_vectors(self, collection_name: str, query_embedding, top_k: int):
+        search_params = {"metric_type": "COSINE"}
+        return self._get_client().search(
+            collection_name,
+            data=[query_embedding],
+            anns_field="embedding",
+            search_params=search_params,
+            limit=top_k,
+            output_fields=["path"],
+        )
+
+    def search_by_path(self, collection_name: str, query_path: str, top_k: int):
+        filter_expr = f"path like '%{query_path}%'" if query_path else "path like '%%'"
+        results = self._get_client().query(
+            collection_name,
+            filter=filter_expr,
+            limit=top_k,
+            output_fields=["path"],
+        )
+        return [[{"id": r["path"], "distance": 1.0, "entity": {"path": r["path"]}} for r in results]]
+
+    def get_all_stats(self) -> Dict[str, Any]:
+        client = self._get_client()
+        try:
+            collection_names = client.list_collections()
+        except Exception as exc:
+            raise RuntimeError(f"Failed to list collections: {exc}") from exc
+
+        collections: List[Dict[str, Any]] = []
+        total_vectors = 0
+        total_estimated_memory = 0
+
+        for name in collection_names:
+            try:
+                stats = client.get_collection_stats(name) or {}
+            except Exception:
+                stats = {}
+            row_count = self._to_int(stats.get("row_count"))
+            total_vectors += row_count
+
+            dimension: Optional[int] = None
+            is_vector_collection = False
+            try:
+                description = client.describe_collection(name)
+            except Exception:
+                description = None
+
+            if description:
+                for field in description.get("fields", []):
+                    if field.get("type") == DataType.FLOAT_VECTOR:
+                        params = field.get("params") or {}
+                        dimension = self._to_int(params.get("dim")) or 4096
+                        is_vector_collection = True
+                        break
+
+            estimated_memory = 0
+            if is_vector_collection and dimension:
+                estimated_memory = row_count * dimension * 4
+                total_estimated_memory += estimated_memory
+
+            indexes: List[Dict[str, Any]] = []
+            try:
+                index_names = client.list_indexes(name) or []
+            except Exception:
+                index_names = []
+
+            for index_name in index_names:
+                try:
+                    detail = client.describe_index(name, index_name) or {}
+                except Exception:
+                    detail = {}
+                indexes.append(
+                    {
+                        "index_name": index_name,
+                        "index_type": detail.get("index_type"),
+                        "metric_type": detail.get("metric_type"),
+                        "indexed_rows": self._to_int(detail.get("indexed_rows")),
+                        "pending_index_rows": self._to_int(detail.get("pending_index_rows")),
+                        "state": detail.get("state"),
+                    }
+                )
+
+            collections.append(
+                {
+                    "name": name,
+                    "row_count": row_count,
+                    "dimension": dimension if is_vector_collection else None,
+                    "estimated_memory_bytes": estimated_memory,
+                    "is_vector_collection": is_vector_collection,
+                    "indexes": indexes,
+                }
+            )
+
+        return {
+            "collections": collections,
+            "collection_count": len(collections),
+            "total_vectors": total_vectors,
+            "estimated_total_memory_bytes": total_estimated_memory,
+            "db_file_size_bytes": None,
+        }
+
+    def clear_all_data(self) -> None:
+        client = self._get_client()
+        for collection_name in client.list_collections():
+            client.drop_collection(collection_name)

services/vector_db/providers/qdrant.py

@@ -0,0 +1,237 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Sequence
+from uuid import NAMESPACE_URL, uuid5
+
+from qdrant_client import QdrantClient
+from qdrant_client.http import models as qmodels
+
+from .base import BaseVectorProvider
+
+
+class QdrantProvider(BaseVectorProvider):
+    type = "qdrant"
+    label = "Qdrant"
+    description = "Qdrant vector database (HTTP API)."
+    enabled = True
+    config_schema: List[Dict[str, Any]] = [
+        {
+            "key": "url",
+            "label": "Server URL",
+            "type": "text",
+            "required": True,
+            "placeholder": "http://localhost:6333",
+        },
+        {
+            "key": "api_key",
+            "label": "API Key",
+            "type": "password",
+            "required": False,
+        },
+    ]
+
+    def __init__(self, config: Dict[str, Any] | None = None):
+        super().__init__(config)
+        self.client: Optional[QdrantClient] = None
+
+    async def initialize(self) -> None:
+        url = (self.config.get("url") or "").strip()
+        if not url:
+            raise RuntimeError("Qdrant URL is required")
+
+        api_key = (self.config.get("api_key") or None) or None
+        try:
+            client = QdrantClient(url=url, api_key=api_key)
+            # 简单连通性校验
+            client.get_collections()
+            self.client = client
+        except Exception as exc:  # pragma: no cover - 依赖外部服务
+            raise RuntimeError(f"Failed to connect to Qdrant at {url}: {exc}") from exc
+
+    def _get_client(self) -> QdrantClient:
+        if not self.client:
+            raise RuntimeError("Qdrant client is not initialized")
+        return self.client
+
+    @staticmethod
+    def _vector_params(vector: bool, dim: int) -> qmodels.VectorParams:
+        size = dim if vector and isinstance(dim, int) and dim > 0 else 1
+        return qmodels.VectorParams(size=size, distance=qmodels.Distance.COSINE)
+
+    def ensure_collection(self, collection_name: str, vector: bool, dim: int) -> None:
+        client = self._get_client()
+        try:
+            if client.collection_exists(collection_name):
+                return
+        except Exception as exc:  # pragma: no cover - 依赖外部服务
+            raise RuntimeError(f"Failed to check Qdrant collection '{collection_name}': {exc}") from exc
+
+        vectors_config = self._vector_params(vector, dim)
+        try:
+            client.create_collection(collection_name=collection_name, vectors_config=vectors_config)
+        except Exception as exc:  # pragma: no cover
+            if "already exists" in str(exc).lower():
+                return
+            raise RuntimeError(f"Failed to create Qdrant collection '{collection_name}': {exc}") from exc
+
+    @staticmethod
+    def _point_id(path: str) -> str:
+        return str(uuid5(NAMESPACE_URL, path))
+
+    def _prepare_point(self, data: Dict[str, Any]) -> qmodels.PointStruct:
+        path = data.get("path")
+        if not path:
+            raise ValueError("Qdrant upsert requires 'path' in data")
+
+        embedding = data.get("embedding")
+        if embedding is None:
+            vector = [0.0]
+        else:
+            vector = [float(x) for x in embedding]
+
+        payload = {"path": path}
+        return qmodels.PointStruct(id=self._point_id(path), vector=vector, payload=payload)
+
+    def upsert_vector(self, collection_name: str, data: Dict[str, Any]) -> None:
+        client = self._get_client()
+        point = self._prepare_point(data)
+        client.upsert(collection_name=collection_name, wait=True, points=[point])
+
+    def delete_vector(self, collection_name: str, path: str) -> None:
+        client = self._get_client()
+        selector = qmodels.PointIdsList(points=[self._point_id(path)])
+        client.delete(collection_name=collection_name, points_selector=selector, wait=True)
+
+    def _format_search_results(self, points: Sequence[qmodels.ScoredPoint]):
+        return [
+            {
+                "id": point.id,
+                "distance": point.score,
+                "entity": {"path": (point.payload or {}).get("path")},
+            }
+            for point in points
+        ]
+
+    def search_vectors(self, collection_name: str, query_embedding, top_k: int):
+        client = self._get_client()
+        vector = [float(x) for x in query_embedding]
+        points = client.search(
+            collection_name=collection_name,
+            query_vector=vector,
+            limit=top_k,
+            with_payload=True,
+        )
+        return [self._format_search_results(points)]
+
+    def search_by_path(self, collection_name: str, query_path: str, top_k: int):
+        client = self._get_client()
+        results: List[Dict[str, Any]] = []
+        offset: Optional[str | int] = None
+        remaining = max(top_k, 1)
+
+        while len(results) < top_k:
+            batch_size = min(max(remaining * 2, 10), 200)
+            records, next_offset = client.scroll(
+                collection_name=collection_name,
+                limit=batch_size,
+                offset=offset,
+                with_payload=True,
+            )
+            if not records:
+                break
+
+            for record in records:
+                path = (record.payload or {}).get("path")
+                if query_path and path:
+                    if query_path not in path:
+                        continue
+                results.append({"id": record.id, "distance": 1.0, "entity": {"path": path}})
+                if len(results) >= top_k:
+                    break
+
+            if next_offset is None or len(results) >= top_k:
+                break
+            offset = next_offset
+            remaining = top_k - len(results)
+
+        return [results]
+
+    def _extract_vector_config(self, vectors) -> Optional[qmodels.VectorParams]:
+        if isinstance(vectors, qmodels.VectorParams):
+            return vectors
+        if isinstance(vectors, dict):
+            for value in vectors.values():
+                if isinstance(value, qmodels.VectorParams):
+                    return value
+        return None
+
+    def get_all_stats(self) -> Dict[str, Any]:
+        client = self._get_client()
+        try:
+            response = client.get_collections()
+        except Exception as exc:  # pragma: no cover
+            raise RuntimeError(f"Failed to list Qdrant collections: {exc}") from exc
+
+        collections: List[Dict[str, Any]] = []
+        total_vectors = 0
+        total_estimated_memory = 0
+
+        for description in response.collections or []:
+            name = description.name
+            try:
+                info = client.get_collection(name)
+            except Exception:
+                continue
+
+            row_count = int(info.points_count or 0)
+            total_vectors += row_count
+
+            vector_params = self._extract_vector_config(info.config.params.vectors if info.config and info.config.params else None)
+            dimension = int(vector_params.size) if vector_params and vector_params.size else None
+            estimated_memory = row_count * dimension * 4 if dimension else 0
+            total_estimated_memory += estimated_memory
+            distance = str(vector_params.distance) if vector_params and vector_params.distance else None
+
+            indexed_rows = int(info.indexed_vectors_count or 0)
+            pending_rows = max(row_count - indexed_rows, 0)
+
+            collections.append(
+                {
+                    "name": name,
+                    "row_count": row_count,
+                    "dimension": dimension,
+                    "estimated_memory_bytes": estimated_memory,
+                    "is_vector_collection": dimension is not None and dimension > 1,
+                    "indexes": [
+                        {
+                            "index_name": "hnsw",
+                            "index_type": "HNSW",
+                            "metric_type": distance,
+                            "indexed_rows": indexed_rows,
+                            "pending_index_rows": pending_rows,
+                            "state": info.status,
+                        }
+                    ],
+                }
+            )
+
+        return {
+            "collections": collections,
+            "collection_count": len(collections),
+            "total_vectors": total_vectors,
+            "estimated_total_memory_bytes": total_estimated_memory,
+            "db_file_size_bytes": None,
+        }
+
+    def clear_all_data(self) -> None:
+        client = self._get_client()
+        try:
+            response = client.get_collections()
+        except Exception as exc:  # pragma: no cover
+            raise RuntimeError(f"Failed to list Qdrant collections: {exc}") from exc
+
+        for description in response.collections or []:
+            try:
+                client.delete_collection(description.name)
+            except Exception:
+                continue

services/vector_db/service.py

@@ -0,0 +1,99 @@
+from __future__ import annotations
+
+import asyncio
+from typing import Any, Dict, Optional
+
+from .config_manager import VectorDBConfigManager
+from .providers import get_provider_class, get_provider_entry
+from .providers.base import BaseVectorProvider
+
+DEFAULT_VECTOR_DIMENSION = 4096
+
+
+class VectorDBService:
+    _instance: "VectorDBService" | None = None
+
+    def __new__(cls, *args, **kwargs):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self):
+        if not hasattr(self, "_provider"):
+            self._provider: Optional[BaseVectorProvider] = None
+            self._provider_type: Optional[str] = None
+            self._provider_config: Dict[str, Any] | None = None
+            self._lock = asyncio.Lock()
+
+    async def _ensure_provider(self) -> BaseVectorProvider:
+        if self._provider is None:
+            await self.reload()
+        assert self._provider is not None  # for type checker
+        return self._provider
+
+    async def reload(self) -> BaseVectorProvider:
+        async with self._lock:
+            provider_type, provider_config = await VectorDBConfigManager.load_config()
+            normalized_config = dict(provider_config or {})
+            if (
+                self._provider
+                and self._provider_type == provider_type
+                and self._provider_config == normalized_config
+            ):
+                return self._provider
+
+            entry = get_provider_entry(provider_type)
+            if not entry:
+                raise RuntimeError(f"Unknown vector database provider: {provider_type}")
+            if not entry.get("enabled", True):
+                raise RuntimeError(f"Vector database provider '{provider_type}' is disabled")
+
+            provider_cls = get_provider_class(provider_type)
+            if not provider_cls:
+                raise RuntimeError(f"Provider class not found for '{provider_type}'")
+
+            provider = provider_cls(provider_config)
+            await provider.initialize()
+
+            self._provider = provider
+            self._provider_type = provider_type
+            self._provider_config = normalized_config
+            return provider
+
+    async def ensure_collection(self, collection_name: str, vector: bool = True, dim: int = DEFAULT_VECTOR_DIMENSION) -> None:
+        provider = await self._ensure_provider()
+        provider.ensure_collection(collection_name, vector, dim)
+
+    async def upsert_vector(self, collection_name: str, data: Dict[str, Any]) -> None:
+        provider = await self._ensure_provider()
+        provider.upsert_vector(collection_name, data)
+
+    async def delete_vector(self, collection_name: str, path: str) -> None:
+        provider = await self._ensure_provider()
+        provider.delete_vector(collection_name, path)
+
+    async def search_vectors(self, collection_name: str, query_embedding, top_k: int = 5):
+        provider = await self._ensure_provider()
+        return provider.search_vectors(collection_name, query_embedding, top_k)
+
+    async def search_by_path(self, collection_name: str, query_path: str, top_k: int = 20):
+        provider = await self._ensure_provider()
+        return provider.search_by_path(collection_name, query_path, top_k)
+
+    async def get_all_stats(self) -> Dict[str, Any]:
+        provider = await self._ensure_provider()
+        return provider.get_all_stats()
+
+    async def clear_all_data(self) -> None:
+        provider = await self._ensure_provider()
+        provider.clear_all_data()
+
+    async def current_provider(self) -> Dict[str, Any]:
+        provider_type, provider_config = await VectorDBConfigManager.load_config()
+        entry = get_provider_entry(provider_type) or {}
+        return {
+            "type": provider_type,
+            "config": provider_config,
+            "label": entry.get("label"),
+            "enabled": entry.get("enabled", True),
+        }

uv.lock

@@ -415,6 +415,7 @@ dependencies = [
     { name = "python-multipart" },
     { name = "pytz" },
     { name = "pyyaml" },
+    { name = "qdrant-client" },
     { name = "rawpy" },
     { name = "rich" },
     { name = "rich-toolkit" },
@@ -505,6 +506,7 @@ requires-dist = [
     { name = "python-multipart", specifier = "==0.0.20" },
     { name = "pytz", specifier = "==2025.2" },
     { name = "pyyaml", specifier = "==6.0.2" },
+    { name = "qdrant-client", specifier = "==1.15.1" },
     { name = "rawpy", specifier = "==0.25.1" },
     { name = "rich", specifier = "==14.1.0" },
     { name = "rich-toolkit", specifier = "==0.15.0" },
@@ -604,6 +606,28 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
 ]
 
+[[package]]
+name = "h2"
+version = "4.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "hpack" },
+    { name = "hyperframe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1d/17/afa56379f94ad0fe8defd37d6eb3f89a25404ffc71d4d848893d270325fc/h2-4.3.0.tar.gz", hash = "sha256:6c59efe4323fa18b47a632221a1888bd7fde6249819beda254aeca909f221bf1", size = 2152026, upload-time = "2025-08-23T18:12:19.778Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/69/b2/119f6e6dcbd96f9069ce9a2665e0146588dc9f88f29549711853645e736a/h2-4.3.0-py3-none-any.whl", hash = "sha256:c438f029a25f7945c69e0ccf0fb951dc3f73a5f6412981daee861431b70e2bdd", size = 61779, upload-time = "2025-08-23T18:12:17.779Z" },
+]
+
+[[package]]
+name = "hpack"
+version = "4.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2c/48/71de9ed269fdae9c8057e5a4c0aa7402e8bb16f2c6e90b3aa53327b113f8/hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca", size = 51276, upload-time = "2025-01-22T21:44:58.347Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/07/c6/80c95b1b2b94682a72cbdbfb85b81ae2daffa4291fbfa1b1464502ede10d/hpack-4.1.0-py3-none-any.whl", hash = "sha256:157ac792668d995c657d93111f46b4535ed114f0c9c8d672271bbec7eae1b496", size = 34357, upload-time = "2025-01-22T21:44:56.92Z" },
+]
+
 [[package]]
 name = "httpcore"
 version = "1.0.9"
@@ -647,6 +671,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" },
 ]
 
+[package.optional-dependencies]
+http2 = [
+    { name = "h2" },
+]
+
+[[package]]
+name = "hyperframe"
+version = "6.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/02/e7/94f8232d4a74cc99514c13a9f995811485a6903d48e5d952771ef6322e30/hyperframe-6.1.0.tar.gz", hash = "sha256:f630908a00854a7adeabd6382b43923a4c4cd4b821fcb527e6ab9e15382a3b08", size = 26566, upload-time = "2025-01-22T21:41:49.302Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/48/30/47d0bf6072f7252e6521f3447ccfa40b421b6824517f82854703d0f5a98b/hyperframe-6.1.0-py3-none-any.whl", hash = "sha256:b03380493a519fce58ea5af42e4a42317bf9bd425596f7a0835ffce80f1a42e5", size = 13007, upload-time = "2025-01-22T21:41:47.295Z" },
+]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -950,6 +988,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/89/c7/5572fa4a3f45740eaab6ae86fcdf7195b55beac1371ac8c619d880cfe948/pillow-11.3.0-cp314-cp314t-win_arm64.whl", hash = "sha256:79ea0d14d3ebad43ec77ad5272e6ff9bba5b679ef73375ea760261207fa8e0aa", size = 2512835, upload-time = "2025-07-01T09:15:50.399Z" },
 ]
 
+[[package]]
+name = "portalocker"
+version = "3.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pywin32", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/77/65b857a69ed876e1951e88aaba60f5ce6120c33703f7cb61a3c894b8c1b6/portalocker-3.2.0.tar.gz", hash = "sha256:1f3002956a54a8c3730586c5c77bf18fae4149e07eaf1c29fc3faf4d5a3f89ac", size = 95644, upload-time = "2025-06-14T13:20:40.03Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4b/a6/38c8e2f318bf67d338f4d629e93b0b4b9af331f455f0390ea8ce4a099b26/portalocker-3.2.0-py3-none-any.whl", hash = "sha256:3cdc5f565312224bc570c49337bd21428bba0ef363bbcf58b9ef4a9f11779968", size = 22424, upload-time = "2025-06-14T13:20:38.083Z" },
+]
+
 [[package]]
 name = "propcache"
 version = "0.3.2"
@@ -1161,6 +1211,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00", size = 509225, upload-time = "2025-03-25T02:24:58.468Z" },
 ]
 
+[[package]]
+name = "pywin32"
+version = "311"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a5/be/3fd5de0979fcb3994bfee0d65ed8ca9506a8a1260651b86174f6a86f52b3/pywin32-311-cp313-cp313-win32.whl", hash = "sha256:f95ba5a847cba10dd8c4d8fefa9f2a6cf283b8b88ed6178fa8a6c1ab16054d0d", size = 8705700, upload-time = "2025-07-14T20:13:26.471Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/28/e0a1909523c6890208295a29e05c2adb2126364e289826c0a8bc7297bd5c/pywin32-311-cp313-cp313-win_amd64.whl", hash = "sha256:718a38f7e5b058e76aee1c56ddd06908116d35147e133427e59a3983f703a20d", size = 9494700, upload-time = "2025-07-14T20:13:28.243Z" },
+    { url = "https://files.pythonhosted.org/packages/04/bf/90339ac0f55726dce7d794e6d79a18a91265bdf3aa70b6b9ca52f35e022a/pywin32-311-cp313-cp313-win_arm64.whl", hash = "sha256:7b4075d959648406202d92a2310cb990fea19b535c7f4a78d3f5e10b926eeb8a", size = 8709318, upload-time = "2025-07-14T20:13:30.348Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/31/097f2e132c4f16d99a22bfb777e0fd88bd8e1c634304e102f313af69ace5/pywin32-311-cp314-cp314-win32.whl", hash = "sha256:b7a2c10b93f8986666d0c803ee19b5990885872a7de910fc460f9b0c2fbf92ee", size = 8840714, upload-time = "2025-07-14T20:13:32.449Z" },
+    { url = "https://files.pythonhosted.org/packages/90/4b/07c77d8ba0e01349358082713400435347df8426208171ce297da32c313d/pywin32-311-cp314-cp314-win_amd64.whl", hash = "sha256:3aca44c046bd2ed8c90de9cb8427f581c479e594e99b5c0bb19b29c10fd6cb87", size = 9656800, upload-time = "2025-07-14T20:13:34.312Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/d2/21af5c535501a7233e734b8af901574572da66fcc254cb35d0609c9080dd/pywin32-311-cp314-cp314-win_arm64.whl", hash = "sha256:a508e2d9025764a8270f93111a970e1d0fbfc33f4153b388bb649b7eec4f9b42", size = 8932540, upload-time = "2025-07-14T20:13:36.379Z" },
+]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.2"
@@ -1178,6 +1241,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446, upload-time = "2024-08-06T20:33:04.33Z" },
 ]
 
+[[package]]
+name = "qdrant-client"
+version = "1.15.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "grpcio" },
+    { name = "httpx", extra = ["http2"] },
+    { name = "numpy" },
+    { name = "portalocker" },
+    { name = "protobuf" },
+    { name = "pydantic" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/79/8b/76c7d325e11d97cb8eb5e261c3759e9ed6664735afbf32fdded5b580690c/qdrant_client-1.15.1.tar.gz", hash = "sha256:631f1f3caebfad0fd0c1fba98f41be81d9962b7bf3ca653bed3b727c0e0cbe0e", size = 295297, upload-time = "2025-07-31T19:35:19.627Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ef/33/d8df6a2b214ffbe4138db9a1efe3248f67dc3c671f82308bea1582ecbbb7/qdrant_client-1.15.1-py3-none-any.whl", hash = "sha256:2b975099b378382f6ca1cfb43f0d59e541be6e16a5892f282a4b8de7eff5cb63", size = 337331, upload-time = "2025-07-31T19:35:17.539Z" },
+]
+
 [[package]]
 name = "rawpy"
 version = "0.25.1"

web/src/api/vectorDB.ts

@@ -1,5 +1,65 @@
 import client from './client';
 
+export interface VectorDBIndexInfo {
+  index_name: string;
+  index_type?: string;
+  metric_type?: string;
+  indexed_rows: number;
+  pending_index_rows: number;
+  state?: string;
+}
+
+export interface VectorDBCollectionStats {
+  name: string;
+  row_count: number;
+  dimension: number | null;
+  estimated_memory_bytes: number;
+  is_vector_collection: boolean;
+  indexes: VectorDBIndexInfo[];
+}
+
+export interface VectorDBStats {
+  collections: VectorDBCollectionStats[];
+  collection_count: number;
+  total_vectors: number;
+  estimated_total_memory_bytes: number;
+  db_file_size_bytes: number | null;
+}
+
+export interface VectorDBProviderField {
+  key: string;
+  label: string;
+  type: 'text' | 'password';
+  required?: boolean;
+  default?: string;
+  placeholder?: string;
+}
+
+export interface VectorDBProviderMeta {
+  type: string;
+  label: string;
+  description?: string;
+  enabled: boolean;
+  config_schema: VectorDBProviderField[];
+}
+
+export interface VectorDBCurrentConfig {
+  type: string;
+  config: Record<string, string>;
+  label?: string;
+  enabled?: boolean;
+}
+
+export interface UpdateVectorDBConfigResponse {
+  config: VectorDBCurrentConfig;
+  stats: VectorDBStats;
+}
+
 export const vectorDBApi = {
+  getProviders: () => client<VectorDBProviderMeta[]>('/vector-db/providers', { method: 'GET' }),
+  getConfig: () => client<VectorDBCurrentConfig>('/vector-db/config', { method: 'GET' }),
+  getStats: () => client<VectorDBStats>('/vector-db/stats', { method: 'GET' }),
+  updateConfig: (payload: { type: string; config: Record<string, string> }) =>
+    client<UpdateVectorDBConfigResponse>('/vector-db/config', { method: 'POST', json: payload }),
   clearAll: () => client('/vector-db/clear-all', { method: 'POST' }),
-};
+};

web/src/i18n/locales/en.ts

@@ -205,6 +205,32 @@ export const en = {
   'Embedding Dimension': 'Embedding Dimension',
   'Vector Database': 'Vector Database',
   'Vector Database Settings': 'Vector Database Settings',
+  'Current Statistics': 'Current Statistics',
+  'Collections': 'Collections',
+  'Vectors': 'Vectors',
+  'Database Size': 'Database Size',
+  'Estimated Memory': 'Estimated Memory',
+  'No collections': 'No collections',
+  'Dimension': 'Dimension',
+  'Non-vector collection': 'Non-vector collection',
+  'Estimated memory': 'Estimated memory',
+  'Indexes': 'Indexes',
+  'Unnamed index': 'Unnamed index',
+  'Indexed rows': 'Indexed rows',
+  'Pending rows': 'Pending rows',
+  'Estimated memory is calculated as vectors x dimension x 4 bytes (float32).': 'Estimated memory is calculated as vectors x dimension x 4 bytes (float32).',
+  'Database Provider': 'Database Provider',
+  'Please select a provider': 'Please select a provider',
+  'Coming soon': 'Coming soon',
+  'This provider is not available yet': 'This provider is not available yet',
+  'Database file path': 'Database file path',
+  'Server URI': 'Server URI',
+  'Token': 'Token',
+  'Server URL': 'Server URL',
+  'API Key': 'API Key',
+  'Embedded Milvus Lite (local file storage).': 'Embedded Milvus Lite (local file storage).',
+  'Remote Milvus instance accessed via URI.': 'Remote Milvus instance accessed via URI.',
+  'Qdrant vector database (HTTP API).': 'Qdrant vector database (HTTP API).',
   'Database Type': 'Database Type',
   'Confirm embedding dimension change': 'Confirm embedding dimension change',
   'Changing the embedding dimension will clear the vector database automatically. You will need to rebuild indexes afterwards. Continue?': 'Changing the embedding dimension will clear the vector database automatically. You will need to rebuild indexes afterwards. Continue?',

web/src/i18n/locales/zh.ts

@@ -207,6 +207,32 @@ export const zh = {
   'Embedding Dimension': '向量维度',
   'Vector Database': '向量数据库',
   'Vector Database Settings': '向量数据库设置',
+  'Current Statistics': '当前统计',
+  'Collections': '集合',
+  'Vectors': '向量',
+  'Database Size': '数据库大小',
+  'Estimated Memory': '估算内存',
+  'No collections': '暂无集合',
+  'Dimension': '维度',
+  'Non-vector collection': '非向量集合',
+  'Estimated memory': '估算内存',
+  'Indexes': '索引',
+  'Unnamed index': '未命名索引',
+  'Indexed rows': '已索引行数',
+  'Pending rows': '待索引行数',
+  'Estimated memory is calculated as vectors x dimension x 4 bytes (float32).': '估算内存 = 向量数量 x 维度 x 4 字节（float32）。',
+  'Database Provider': '数据库提供者',
+  'Please select a provider': '请选择提供者',
+  'Coming soon': '敬请期待',
+  'This provider is not available yet': '该提供者暂不可用',
+  'Database file path': '数据库文件路径',
+  'Server URI': '服务器 URI',
+  'Token': '令牌',
+  'Server URL': '服务器地址',
+  'API Key': 'API Key',
+  'Embedded Milvus Lite (local file storage).': '嵌入式 Milvus Lite，本地文件存储。',
+  'Remote Milvus instance accessed via URI.': '通过 URI 访问的远程 Milvus 实例。',
+  'Qdrant vector database (HTTP API).': 'Qdrant 向量数据库（HTTP API）。',
   'Database Type': '数据库类型',
   'Confirm embedding dimension change': '确认修改向量维度',
   'Changing the embedding dimension will clear the vector database automatically. You will need to rebuild indexes afterwards. Continue?': '修改向量维度会自动清空向量数据库，之后需要重建索引，是否继续？',

web/src/pages/SystemSettingsPage/SystemSettingsPage.tsx

@@ -1,8 +1,8 @@
-import { Form, Input, Button, message, Tabs, Space, Card, Select, Modal, Radio, InputNumber } from 'antd';
-import { useEffect, useState } from 'react';
+import { Form, Input, Button, message, Tabs, Space, Card, Select, Modal, Radio, InputNumber, Spin, Empty, Alert } from 'antd';
+import { useEffect, useState, useCallback } from 'react';
 import PageCard from '../../components/PageCard';
 import { getAllConfig, setConfig } from '../../api/config';
-import { vectorDBApi } from '../../api/vectorDB';
+import { vectorDBApi, type VectorDBStats, type VectorDBProviderMeta, type VectorDBCurrentConfig } from '../../api/vectorDB';
 import { AppstoreOutlined, RobotOutlined, DatabaseOutlined, SkinOutlined } from '@ant-design/icons';
 import { useTheme } from '../../contexts/ThemeContext';
 import '../../styles/settings-tabs.css';
@@ -32,6 +32,20 @@ const EMBED_CONFIG_KEYS = [
 
 const ALL_AI_KEYS = [...VISION_CONFIG_KEYS, ...EMBED_CONFIG_KEYS, { key: EMBED_DIM_KEY, default: DEFAULT_EMBED_DIMENSION }];
 
+const formatBytes = (bytes?: number | null) => {
+  if (bytes === null || bytes === undefined) return '-';
+  if (bytes === 0) return '0 B';
+  const units = ['B', 'KB', 'MB', 'GB', 'TB'];
+  let value = bytes;
+  let unitIndex = 0;
+  while (value >= 1024 && unitIndex < units.length - 1) {
+    value /= 1024;
+    unitIndex += 1;
+  }
+  const precision = value >= 10 || unitIndex === 0 ? 0 : 1;
+  return `${value.toFixed(precision)} ${units[unitIndex]}`;
+};
+
 // Theme related config keys
 const THEME_KEYS = {
   MODE: 'THEME_MODE',
@@ -42,9 +56,19 @@ const THEME_KEYS = {
 };
 
 export default function SystemSettingsPage() {
+  const [vectorConfigForm] = Form.useForm();
   const [loading, setLoading] = useState(false);
   const [config, setConfigState] = useState<Record<string, string> | null>(null);
   const [activeTab, setActiveTab] = useState('appearance');
+  const [vectorStats, setVectorStats] = useState<VectorDBStats | null>(null);
+  const [vectorStatsLoading, setVectorStatsLoading] = useState(false);
+  const [vectorStatsError, setVectorStatsError] = useState<string | null>(null);
+  const [vectorProviders, setVectorProviders] = useState<VectorDBProviderMeta[]>([]);
+  const [vectorConfig, setVectorConfig] = useState<VectorDBCurrentConfig | null>(null);
+  const [vectorConfigLoading, setVectorConfigLoading] = useState(false);
+  const [vectorConfigSaving, setVectorConfigSaving] = useState(false);
+  const [vectorMetaError, setVectorMetaError] = useState<string | null>(null);
+  const [selectedProviderType, setSelectedProviderType] = useState<string | null>(null);
   const { refreshTheme, previewTheme } = useTheme();
   const { t } = useI18n();
 
@@ -52,6 +76,72 @@ export default function SystemSettingsPage() {
     getAllConfig().then((data) => setConfigState(data as Record<string, string>));
   }, []);
 
+  const fetchVectorStats = useCallback(async () => {
+    setVectorStatsLoading(true);
+    setVectorStatsError(null);
+    try {
+      const data = await vectorDBApi.getStats();
+      setVectorStats(data);
+    } catch (e: any) {
+      const msg = e?.message || t('Load failed');
+      setVectorStatsError(msg);
+      message.error(msg);
+    } finally {
+      setVectorStatsLoading(false);
+    }
+  }, [t]);
+
+  const buildProviderConfigValues = useCallback((provider: VectorDBProviderMeta | undefined, existing?: Record<string, string>) => {
+    if (!provider) return {};
+    const values: Record<string, string> = {};
+    const schema = provider.config_schema || [];
+    schema.forEach((field) => {
+      const current = existing && existing[field.key] !== undefined && existing[field.key] !== null
+        ? String(existing[field.key])
+        : undefined;
+      if (current !== undefined) {
+        values[field.key] = current;
+      } else if (field.default !== undefined && field.default !== null) {
+        values[field.key] = String(field.default);
+      } else {
+        values[field.key] = '';
+      }
+    });
+    return values;
+  }, []);
+
+  const fetchVectorMeta = useCallback(async () => {
+    setVectorConfigLoading(true);
+    setVectorMetaError(null);
+    try {
+      const [providers, current] = await Promise.all([
+        vectorDBApi.getProviders(),
+        vectorDBApi.getConfig(),
+      ]);
+      setVectorProviders(providers);
+      setVectorConfig(current);
+
+      const enabled = providers.filter((item) => item.enabled);
+      let nextType: string | null = current?.type ?? null;
+      if (nextType && !providers.some((item) => item.type === nextType)) {
+        nextType = null;
+      }
+      if (!nextType) {
+        nextType = enabled[0]?.type ?? providers[0]?.type ?? null;
+      }
+      setSelectedProviderType(nextType);
+      const provider = providers.find((item) => item.type === nextType);
+      const configValues = buildProviderConfigValues(provider, nextType === current?.type ? current?.config : undefined);
+      vectorConfigForm.setFieldsValue({ type: nextType || undefined, config: configValues });
+    } catch (e: any) {
+      const msg = e?.message || t('Load failed');
+      setVectorMetaError(msg);
+      message.error(msg);
+    } finally {
+      setVectorConfigLoading(false);
+    }
+  }, [buildProviderConfigValues, message, t, vectorConfigForm]);
+
   const handleSave = async (values: any) => {
     setLoading(true);
     try {
@@ -70,6 +160,40 @@ export default function SystemSettingsPage() {
     setLoading(false);
   };
 
+  const handleProviderChange = useCallback((value: string) => {
+    setSelectedProviderType(value);
+    const provider = vectorProviders.find((item) => item.type === value);
+    const existing = value === vectorConfig?.type ? vectorConfig?.config : undefined;
+    const configValues = buildProviderConfigValues(provider, existing);
+    vectorConfigForm.setFieldsValue({ type: value, config: configValues });
+  }, [vectorProviders, vectorConfig, buildProviderConfigValues, vectorConfigForm]);
+
+  const handleVectorConfigSave = useCallback(async (values: { type: string; config?: Record<string, string> }) => {
+    if (!values?.type) {
+      return;
+    }
+    setVectorConfigSaving(true);
+    try {
+      const configPayload = Object.fromEntries(
+        Object.entries(values.config || {}).filter(([, val]) => val !== undefined && val !== null && String(val).trim() !== '')
+          .map(([key, val]) => [key, String(val)])
+      );
+      const response = await vectorDBApi.updateConfig({ type: values.type, config: configPayload });
+      setVectorConfig(response.config);
+      setVectorStats(response.stats);
+      setVectorStatsError(null);
+      setSelectedProviderType(response.config.type);
+      const provider = vectorProviders.find((item) => item.type === response.config.type);
+      const mergedValues = buildProviderConfigValues(provider, response.config.config);
+      vectorConfigForm.setFieldsValue({ type: response.config.type, config: mergedValues });
+      message.success(t('Saved successfully'));
+    } catch (e: any) {
+      message.error(e?.message || t('Save failed'));
+    } finally {
+      setVectorConfigSaving(false);
+    }
+  }, [buildProviderConfigValues, message, t, vectorConfigForm, vectorProviders]);
+
   // 离开“外观设置”时，恢复后端持久化配置（取消未保存的预览）
   useEffect(() => {
     if (activeTab !== 'appearance') {
@@ -77,6 +201,27 @@ export default function SystemSettingsPage() {
     }
   }, [activeTab]);
 
+  useEffect(() => {
+    if (activeTab === 'vector-db') {
+      if (!vectorProviders.length && !vectorConfigLoading) {
+        fetchVectorMeta();
+      }
+      if (!vectorStats && !vectorStatsLoading) {
+        fetchVectorStats();
+      }
+    }
+  }, [
+    activeTab,
+    fetchVectorMeta,
+    fetchVectorStats,
+    vectorProviders.length,
+    vectorConfigLoading,
+    vectorStats,
+    vectorStatsLoading,
+  ]);
+
+  const selectedProvider = vectorProviders.find((item) => item.type === selectedProviderType || (!selectedProviderType && item.enabled));
+
   if (!config) {
     return <PageCard title={t('System Settings')}><div>{t('Loading...')}</div></PageCard>;
   }
@@ -275,41 +420,187 @@ export default function SystemSettingsPage() {
               ),
               children: (
                 <Card title={t('Vector Database Settings')} style={{ marginTop: 24 }}>
-                  <Form layout="vertical">
-                    <Form.Item label={t('Database Type')}>
-                      <Select
-                        size="large"
-                        value={'Milvus Lite'}
-                        disabled
-                        options={[{ value: 'Milvus Lite', label: 'Milvus Lite' }]}
-                      />
-                    </Form.Item>
-                    <Form.Item>
-                      <Button
-                        danger
-                        block
-                        onClick={() => {
-                          Modal.confirm({
-                            title: t('Confirm clear vector database?'),
-                            content: t('This will delete all collections irreversibly.'),
-                            okText: t('Confirm Clear'),
-                            okType: 'danger',
-                            cancelText: t('Cancel'),
-                            onOk: async () => {
-                              try {
-                                await vectorDBApi.clearAll();
-                                message.success(t('Vector database cleared'));
-                              } catch (e: any) {
-                                message.error(e.message || t('Clear failed'));
-                              }
-                            },
-                          });
-                        }}
+                  <Space direction="vertical" size={24} style={{ width: '100%' }}>
+                    <Space direction="vertical" size={16} style={{ width: '100%' }}>
+                      <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', flexWrap: 'wrap', gap: 12 }}>
+                        <strong>{t('Current Statistics')}</strong>
+                        <Button onClick={() => { fetchVectorMeta(); fetchVectorStats(); }} loading={vectorStatsLoading || vectorConfigLoading} disabled={(vectorStatsLoading || vectorConfigLoading) && !vectorStats}>
+                          {t('Refresh')}
+                        </Button>
+                      </div>
+                      {vectorMetaError ? (
+                        <Alert type="error" showIcon message={vectorMetaError} />
+                      ) : null}
+                      {vectorStatsLoading && !vectorStats ? (
+                        <Spin />
+                      ) : vectorStats ? (
+                        <Space direction="vertical" size={16} style={{ width: '100%' }}>
+                          <div style={{ display: 'flex', flexWrap: 'wrap', gap: 24 }}>
+                            <div>
+                              <div style={{ color: '#888' }}>{t('Collections')}</div>
+                              <div style={{ fontSize: 20, fontWeight: 600 }}>{vectorStats.collection_count}</div>
+                            </div>
+                            <div>
+                              <div style={{ color: '#888' }}>{t('Vectors')}</div>
+                              <div style={{ fontSize: 20, fontWeight: 600 }}>{vectorStats.total_vectors}</div>
+                            </div>
+                            <div>
+                              <div style={{ color: '#888' }}>{t('Database Size')}</div>
+                              <div style={{ fontSize: 20, fontWeight: 600 }}>{formatBytes(vectorStats.db_file_size_bytes)}</div>
+                            </div>
+                            <div>
+                              <div style={{ color: '#888' }}>{t('Estimated Memory')}</div>
+                              <div style={{ fontSize: 20, fontWeight: 600 }}>{formatBytes(vectorStats.estimated_total_memory_bytes)}</div>
+                            </div>
+                          </div>
+                          {vectorStats.collections.length ? (
+                            <Space direction="vertical" style={{ width: '100%' }} size={16}>
+                              {vectorStats.collections.map((collection) => (
+                                <div key={collection.name} style={{ border: '1px solid #f0f0f0', borderRadius: 8, padding: 16 }}>
+                                  <Space direction="vertical" size={12} style={{ width: '100%' }}>
+                                    <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', flexWrap: 'wrap', gap: 12 }}>
+                                      <strong>{collection.name}</strong>
+                                      <span style={{ color: '#888' }}>
+                                        {collection.is_vector_collection && collection.dimension
+                                          ? `${t('Dimension')}: ${collection.dimension}`
+                                          : t('Non-vector collection')}
+                                      </span>
+                                    </div>
+                                    <div>{t('Vectors')}: {collection.row_count}</div>
+                                    {collection.is_vector_collection ? (
+                                      <div>{t('Estimated memory')}: {formatBytes(collection.estimated_memory_bytes)}</div>
+                                    ) : null}
+                                    {collection.indexes.length ? (
+                                      <Space direction="vertical" size={4} style={{ width: '100%' }}>
+                                        <span>{t('Indexes')}:</span>
+                                        <ul style={{ paddingLeft: 20, margin: 0 }}>
+                                          {collection.indexes.map((index) => (
+                                            <li key={`${collection.name}-${index.index_name || 'default'}`}>
+                                              <span>{index.index_name || t('Unnamed index')}</span>
+                                              <span>{' · '}{index.index_type || '-'}</span>
+                                              <span>{' · '}{index.metric_type || '-'}</span>
+                                              <span>{' · '}{t('Indexed rows')}: {index.indexed_rows}</span>
+                                              <span>{' · '}{t('Pending rows')}: {index.pending_index_rows}</span>
+                                              <span>{' · '}{t('Status')}: {index.state || '-'}</span>
+                                            </li>
+                                          ))}
+                                        </ul>
+                                      </Space>
+                                    ) : null}
+                                  </Space>
+                                </div>
+                              ))}
+                            </Space>
+                          ) : (
+                            <Empty description={t('No collections')} />
+                          )}
+                          <div style={{ color: '#888' }}>
+                            {t('Estimated memory is calculated as vectors x dimension x 4 bytes (float32).')}
+                          </div>
+                        </Space>
+                      ) : vectorStatsError ? (
+                        <div style={{ color: '#ff4d4f' }}>{vectorStatsError}</div>
+                      ) : (
+                        <Empty description={t('No collections')} />
+                      )}
+                    </Space>
+                    {vectorConfigLoading && !vectorProviders.length ? (
+                      <Spin />
+                    ) : (
+                      <Form
+                        layout="vertical"
+                        form={vectorConfigForm}
+                        onFinish={handleVectorConfigSave}
+                        initialValues={{ type: selectedProviderType || undefined, config: {} }}
                       >
-                        {t('Clear Vector DB')}
-                      </Button>
-                    </Form.Item>
-                  </Form>
+                        <Form.Item
+                          name="type"
+                          label={t('Database Provider')}
+                          rules={[{ required: true, message: t('Please select a provider') }]}
+                        >
+                          <Select
+                            size="large"
+                            options={vectorProviders.map((provider) => ({
+                              value: provider.type,
+                              label: provider.enabled ? provider.label : `${provider.label} (${t('Coming soon')})`,
+                              disabled: !provider.enabled,
+                            }))}
+                            onChange={handleProviderChange}
+                            loading={vectorConfigLoading && !vectorProviders.length}
+                          />
+                        </Form.Item>
+                        {selectedProvider?.description ? (
+                          <Alert
+                            type="info"
+                            showIcon
+                            message={t(selectedProvider.description)}
+                            style={{ marginBottom: 16 }}
+                          />
+                        ) : null}
+                        {selectedProvider?.config_schema?.map((field) => (
+                          <Form.Item
+                            key={field.key}
+                            name={['config', field.key]}
+                            label={t(field.label)}
+                            rules={field.required ? [{ required: true, message: t('Please input {label}', { label: t(field.label) }) }] : []}
+                          >
+                            {field.type === 'password' ? (
+                              <Input.Password size="large" placeholder={field.placeholder ? t(field.placeholder) : undefined} />
+                            ) : (
+                              <Input size="large" placeholder={field.placeholder ? t(field.placeholder) : undefined} />
+                            )}
+                          </Form.Item>
+                        ))}
+                        {selectedProvider && !selectedProvider.enabled ? (
+                          <Alert
+                            type="warning"
+                            showIcon
+                            message={t('This provider is not available yet')}
+                            style={{ marginBottom: 16 }}
+                          />
+                        ) : null}
+                        <Form.Item>
+                          <Space direction="vertical" style={{ width: '100%' }}>
+                            <Button
+                              type="primary"
+                              htmlType="submit"
+                              loading={vectorConfigSaving}
+                              block
+                              disabled={!selectedProvider?.enabled}
+                            >
+                              {t('Save')}
+                            </Button>
+                            <Button
+                              danger
+                              htmlType="button"
+                              block
+                              onClick={() => {
+                                Modal.confirm({
+                                  title: t('Confirm clear vector database?'),
+                                  content: t('This will delete all collections irreversibly.'),
+                                  okText: t('Confirm Clear'),
+                                  okType: 'danger',
+                                  cancelText: t('Cancel'),
+                                  onOk: async () => {
+                                    try {
+                                      await vectorDBApi.clearAll();
+                                      message.success(t('Vector database cleared'));
+                                      await fetchVectorStats();
+                                      await fetchVectorMeta();
+                                    } catch (e: any) {
+                                      message.error(e.message || t('Clear failed'));
+                                    }
+                                  },
+                                });
+                              }}
+                            >
+                              {t('Clear Vector DB')}
+                            </Button>
+                          </Space>
+                        </Form.Item>
+                      </Form>
+                    )}
+                  </Space>
                 </Card>
               ),
             },