refactor: split virtual fs mapping and search modules

domain/virtual_fs/search/search_api.py

@@ -0,0 +1,26 @@
+from fastapi import APIRouter, Depends, Query
+
+from domain.auth.service import get_current_active_user
+from domain.auth.types import User
+from domain.virtual_fs.search.search_service import VirtualFSSearchService
+
+router = APIRouter(prefix="/api/fs/search", tags=["search"])
+
+
+@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'"),
+    page: int = Query(1, description="分页页码，仅在文件名搜索模式下生效"),
+    page_size: int = Query(10, description="分页大小，仅在文件名搜索模式下生效"),
+    user: User = Depends(get_current_active_user),
+):
+    if not q.strip():
+        return {"items": [], "query": q}
+
+    top_k = max(top_k, 1)
+    page = max(page, 1)
+    page_size = max(min(page_size, 100), 1)
+
+    return await VirtualFSSearchService.search(q, top_k, mode, page, page_size)

domain/virtual_fs/search/search_service.py

@@ -0,0 +1,118 @@
+from typing import Any, Dict, List, Tuple
+
+from domain.virtual_fs.types import SearchResultItem
+from domain.ai.inference import get_text_embedding
+from domain.ai.service import VectorDBService
+
+
+def _normalize_result(raw: Dict[str, Any], source: str, fallback_score: float = 0.0) -> SearchResultItem:
+    entity = dict(raw.get("entity") or {})
+    source_path = entity.get("source_path")
+    stored_path = entity.get("path")
+    path = source_path or stored_path or ""
+    chunk_id_value = entity.get("chunk_id")
+    chunk_id = str(chunk_id_value) if chunk_id_value is not None else None
+    snippet = entity.get("text") or entity.get("description") or entity.get("name")
+    mime = entity.get("mime")
+    start_offset = entity.get("start_offset")
+    end_offset = entity.get("end_offset")
+    raw_score = raw.get("distance")
+    score = float(raw_score) if raw_score is not None else fallback_score
+
+    metadata = {
+        "retrieval_source": source,
+        "raw_distance": raw_score,
+    }
+    if stored_path and stored_path != path:
+        metadata["stored_path"] = stored_path
+    vector_id = entity.get("vector_id")
+    if vector_id:
+        metadata["vector_id"] = vector_id
+
+    return SearchResultItem(
+        id=str(raw.get("id")),
+        path=path,
+        score=score,
+        chunk_id=chunk_id,
+        snippet=snippet,
+        mime=mime,
+        source_type=entity.get("type") or source,
+        start_offset=start_offset,
+        end_offset=end_offset,
+        metadata=metadata,
+    )
+
+
+async def _vector_search(query: str, top_k: int) -> List[SearchResultItem]:
+    vector_db = VectorDBService()
+    try:
+        embedding = await get_text_embedding(query)
+    except Exception:
+        embedding = None
+    if not embedding:
+        return []
+
+    try:
+        raw_results = await vector_db.search_vectors("vector_collection", embedding, max(top_k, 10))
+    except Exception:
+        return []
+
+    results: List[SearchResultItem] = []
+    for bucket in raw_results or []:
+        for record in bucket or []:
+            results.append(_normalize_result(record, "vector"))
+    return results
+
+
+async def _filename_search(query: str, page: int, page_size: int) -> Tuple[List[SearchResultItem], bool]:
+    vector_db = VectorDBService()
+    limit = max(page * page_size + 1, page_size * (page + 2))
+    limit = min(limit, 2000)
+    try:
+        raw_results = await vector_db.search_by_path("vector_collection", query, limit)
+    except Exception:
+        return [], False
+
+    records = raw_results[0] if raw_results else []
+    deduped: List[SearchResultItem] = []
+    seen_paths: set[str] = set()
+    for record in records or []:
+        item = _normalize_result(record, "filename", fallback_score=1.0)
+        stored_path = item.metadata.get("stored_path") if item.metadata else None
+        key = item.path or stored_path or ""
+        if key in seen_paths:
+            continue
+        seen_paths.add(key)
+        deduped.append(item)
+
+    start = max(page - 1, 0) * page_size
+    end = start + page_size
+    page_items = deduped[start:end]
+    for offset, item in enumerate(page_items):
+        if item.metadata is None:
+            item.metadata = {}
+        item.metadata.setdefault("retrieval_rank", start + offset)
+    has_more = len(deduped) > end
+    return page_items, has_more
+
+
+class VirtualFSSearchService:
+    @staticmethod
+    async def search(query: str, top_k: int, mode: str, page: int, page_size: int):
+        if mode == "vector":
+            items = (await _vector_search(query, top_k))[:top_k]
+            return {"items": items, "query": query, "mode": mode}
+        if mode == "filename":
+            items, has_more = await _filename_search(query, page, page_size)
+            return {
+                "items": items,
+                "query": query,
+                "mode": mode,
+                "pagination": {
+                    "page": page,
+                    "page_size": page_size,
+                    "has_more": has_more,
+                },
+            }
+        items = (await _vector_search(query, top_k))[:top_k]
+        return {"items": items, "query": query, "mode": mode}