Initial commit

api/routes/search.py

@@ -0,0 +1,41 @@
+from fastapi import APIRouter, Depends, Query
+from schemas.fs import SearchResultItem
+from services.auth import get_current_active_user, User
+from services.ai import get_text_embedding
+from services.vector_db import VectorDBService
+
+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)
+    items = [
+        SearchResultItem(id=res["id"], path=res["entity"]["path"], score=res["distance"])
+        for res in results[0]
+    ]
+    return {"items": items, "query": q}
+
+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)
+    items = [
+        SearchResultItem(id=idx, path=res["entity"]["path"], score=res["distance"])
+        for idx, res in enumerate(results[0])
+    ]
+    return {"items": items, "query": q}
+
+
+@router.get("")
+async def search_files(
+    q: str = Query(..., description="搜索查询"),
+    top_k: int = Query(10, description="返回结果数量"),
+    mode: str = Query("vector", description="搜索模式: 'vector' 或 'filename'"),
+    user: User = Depends(get_current_active_user),
+):
+    if mode == "vector":
+        return await search_files_by_vector(q, top_k)
+    elif mode == "filename":
+        return await search_files_by_name(q, top_k)
+    else:
+        return {"items": [], "query": q, "error": "Invalid search mode"}