feat: 支持可配置的混合检索融合权重

src/langbot/pkg/plugin/handler.py

@@ -531,6 +531,7 @@ async def vector_search(data: dict[str, Any]) -> handler.ActionResponse:
             filters = data.get('filters')
             search_type = data.get('search_type', 'vector')
             query_text = data.get('query_text', '')
+            vector_weight = data.get('vector_weight')
             try:
                 results = await self.ap.rag_runtime_service.vector_search(
                     collection_id,
@@ -539,6 +540,7 @@ async def vector_search(data: dict[str, Any]) -> handler.ActionResponse:
                     filters,
                     search_type,
                     query_text,
+                    vector_weight=vector_weight,
                 )
                 return handler.ActionResponse.success(data={'results': results})
             except Exception as e:

src/langbot/pkg/rag/service/runtime.py

@@ -41,6 +41,7 @@ async def vector_search(
         filters: dict[str, Any] | None = None,
         search_type: str = 'vector',
         query_text: str = '',
+        vector_weight: float | None = None,
     ) -> list[dict[str, Any]]:
         """Handle VECTOR_SEARCH action."""
         return await self.ap.vector_db_mgr.search(
@@ -50,6 +51,7 @@ async def vector_search(
             filter=filters,
             search_type=search_type,
             query_text=query_text,
+            vector_weight=vector_weight,
         )
 
     async def vector_delete(

src/langbot/pkg/vector/mgr.py

@@ -97,6 +97,7 @@ async def search(
         filter: dict | None = None,
         search_type: str = 'vector',
         query_text: str = '',
+        vector_weight: float | None = None,
     ) -> list[dict]:
         """Proxy: Search vectors.
 
@@ -111,6 +112,7 @@ async def search(
             search_type=search_type,
             query_text=query_text,
             filter=filter,
+            vector_weight=vector_weight,
         )
 
         if not results or 'ids' not in results or not results['ids']:

src/langbot/pkg/vector/vdb.py

@@ -53,6 +53,7 @@ async def search(
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> Dict[str, Any]:
         """Search for the most similar vectors in the specified collection.
 
@@ -70,6 +71,8 @@ async def search(
                     {"file_id": "abc"}
                     {"created_at": {"$gte": 1700000000}}
                     {"file_type": {"$in": ["pdf", "docx"]}}
+            vector_weight: Weight for vector search in hybrid mode (0.0–1.0).
+                ``None`` means use equal weights (backward compatible).
         """
         pass
 

src/langbot/pkg/vector/vdbs/chroma.py

@@ -52,13 +52,16 @@ async def search(
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> dict[str, Any]:
         col = await self.get_or_create_collection(collection)
 
         if search_type == SearchType.FULL_TEXT:
             return await self._full_text_search(col, collection, k, query_text, filter)
         elif search_type == SearchType.HYBRID:
-            return await self._hybrid_search(col, collection, query_embedding, k, query_text, filter)
+            return await self._hybrid_search(
+                col, collection, query_embedding, k, query_text, filter, vector_weight=vector_weight
+            )
 
         # Default: vector search
         return await self._vector_search(col, collection, query_embedding, k, filter)
@@ -127,6 +130,7 @@ async def _hybrid_search(
         k: int,
         query_text: str,
         filter: dict[str, Any] | None,
+        vector_weight: float | None = None,
     ) -> dict[str, Any]:
         # Fall back to pure vector search when no text is provided
         if not query_text:
@@ -144,7 +148,15 @@ async def _hybrid_search(
             return {'ids': [[]], 'metadatas': [[]], 'distances': [[]], 'documents': [[]]}
 
         # RRF fusion
-        fused = self._rrf_fuse([vector_ids, text_ids], k)
+        weights = None
+        if vector_weight is not None:
+            weights = [vector_weight, 1.0 - vector_weight]
+        self.ap.logger.info(
+            f"Chroma hybrid fusion config in '{collection}': "
+            f'vector_weight={vector_weight}, weights={weights or [1.0, 1.0]}, '
+            f'vector_hits={len(vector_ids)}, text_hits={len(text_ids)}'
+        )
+        fused = self._rrf_fuse([vector_ids, text_ids], k, weights=weights)
         if not fused:
             return {'ids': [[]], 'metadatas': [[]], 'distances': [[]], 'documents': [[]]}
 
@@ -197,16 +209,24 @@ async def _hybrid_search(
         }
 
     @staticmethod
-    def _rrf_fuse(result_lists: list[list[str]], k: int) -> list[tuple[str, float]]:
+    def _rrf_fuse(result_lists: list[list[str]], k: int, weights: list[float] | None = None) -> list[tuple[str, float]]:
         """Reciprocal Rank Fusion over multiple ranked ID lists.
 
         Returns a list of (doc_id, rrf_score) sorted by descending score,
         truncated to *k* entries.
+
+        Args:
+            result_lists: Ranked ID lists from different search methods.
+            k: Number of results to return.
+            weights: Per-list weights.  ``None`` means equal weight (1.0 each).
         """
+        if weights is None:
+            weights = [1.0] * len(result_lists)
         scores: dict[str, float] = {}
-        for ranked_ids in result_lists:
+        for list_idx, ranked_ids in enumerate(result_lists):
+            w = weights[list_idx]
             for rank, doc_id in enumerate(ranked_ids):
-                scores[doc_id] = scores.get(doc_id, 0.0) + 1.0 / (_RRF_K + rank + 1)
+                scores[doc_id] = scores.get(doc_id, 0.0) + w / (_RRF_K + rank + 1)
         sorted_results = sorted(scores.items(), key=lambda x: x[1], reverse=True)
         return sorted_results[:k]
 

src/langbot/pkg/vector/vdbs/milvus.py

@@ -255,6 +255,7 @@ async def search(
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> Dict[str, Any]:
         """Search for similar vectors in Milvus collection
 

src/langbot/pkg/vector/vdbs/pgvector_db.py

@@ -192,6 +192,7 @@ async def search(
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> Dict[str, Any]:
         """Search for similar vectors using cosine distance
 

src/langbot/pkg/vector/vdbs/qdrant.py

@@ -100,6 +100,7 @@ async def search(
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> dict[str, Any]:
         exists = await self.client.collection_exists(collection)
         if not exists:

src/langbot/pkg/vector/vdbs/seekdb.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import asyncio
+from decimal import Decimal
+import re
 from typing import Any, Dict, List
 
 
@@ -101,8 +103,28 @@ def __init__(self, ap: app.Application):
             }
         )
 
+    def _normalize_collection_name(self, collection: str) -> str:
+        """SeekDB only accepts [a-zA-Z0-9_], while LangBot uses UUID-like KB IDs."""
+        normalized = re.sub(r'[^A-Za-z0-9_]', '_', collection)
+        if normalized != collection:
+            self.ap.logger.info(f"Normalized SeekDB collection name: '{collection}' -> '{normalized}'")
+        return normalized
+
+    def _json_safe(self, value: Any) -> Any:
+        """Convert SeekDB result values into JSON-serializable Python primitives."""
+        if isinstance(value, Decimal):
+            return float(value)
+        if isinstance(value, dict):
+            return {k: self._json_safe(v) for k, v in value.items()}
+        if isinstance(value, list):
+            return [self._json_safe(v) for v in value]
+        if isinstance(value, tuple):
+            return [self._json_safe(v) for v in value]
+        return value
+
     async def _get_or_create_collection_internal(self, collection: str, vector_size: int = None) -> Any:
         """Internal method to get or create a collection with proper configuration."""
+        collection = self._normalize_collection_name(collection)
         if collection in self._collections:
             return self._collections[collection]
 
@@ -173,6 +195,7 @@ async def add_embeddings(
         if not embeddings_list:
             return
 
+        collection = self._normalize_collection_name(collection)
         # Ensure collection exists with correct dimension
         vector_size = len(embeddings_list[0])
         coll = await self._get_or_create_collection_internal(collection, vector_size)
@@ -194,6 +217,7 @@ async def search(
         search_type: str = 'vector',
         query_text: str = '',
         filter: Dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> Dict[str, Any]:
         """Search for the most similar vectors in the specified collection.
 
@@ -210,6 +234,7 @@ async def search(
         Returns:
             Dictionary with 'ids', 'metadatas', 'distances' keys
         """
+        collection = self._normalize_collection_name(collection)
         # Check if collection exists
         exists = await asyncio.to_thread(self.client.has_collection, collection)
         if not exists:
@@ -271,6 +296,17 @@ async def search(
                     query_cfg['where'] = filter
                     knn_cfg['where'] = filter
 
+                # Apply vector_weight via pyseekdb's native boost parameter
+                if vector_weight is not None:
+                    knn_cfg['boost'] = vector_weight
+                    query_cfg['boost'] = 1.0 - vector_weight
+                self.ap.logger.info(
+                    f"SeekDB hybrid fusion config in '{collection}': "
+                    f'vector_weight={vector_weight}, '
+                    f'knn_boost={knn_cfg.get("boost", 1.0)}, '
+                    f'query_boost={query_cfg.get("boost", 1.0)}'
+                )
+
                 results = await asyncio.to_thread(
                     coll.hybrid_search,
                     query=query_cfg,
@@ -279,13 +315,17 @@ async def search(
                     n_results=k,
                     include=['documents', 'metadatas'],
                 )
+                self.ap.logger.info(
+                    f"SeekDB hybrid search in '{collection}' returned {len(results.get('ids', [[]])[0])} results."
+                )
         else:
             # Default: vector search via query()
             query_kwargs = {'n_results': k, 'query_embeddings': query_embedding}
             if filter:
                 query_kwargs['where'] = filter
             results = await asyncio.to_thread(coll.query, **query_kwargs)
 
+        results = self._json_safe(results)
         self.ap.logger.info(
             f"SeekDB {search_type} search in '{collection}' returned {len(results.get('ids', [[]])[0])} results"
         )
@@ -299,6 +339,7 @@ async def delete_by_file_id(self, collection: str, file_id: str) -> None:
             collection: Collection name
             file_id: File ID to delete
         """
+        collection = self._normalize_collection_name(collection)
         # Check if collection exists
         exists = await asyncio.to_thread(self.client.has_collection, collection)
         if not exists:
@@ -325,6 +366,7 @@ async def delete_by_filter(self, collection: str, filter: Dict[str, Any]) -> int
             collection: Collection name
             filter: Chroma-style ``where`` filter dict
         """
+        collection = self._normalize_collection_name(collection)
         exists = await asyncio.to_thread(self.client.has_collection, collection)
         if not exists:
             self.ap.logger.warning(f"SeekDB collection '{collection}' not found for deletion")
@@ -347,6 +389,7 @@ async def list_by_filter(
         limit: int = 20,
         offset: int = 0,
     ) -> tuple[list[Dict[str, Any]], int]:
+        collection = self._normalize_collection_name(collection)
         exists = await asyncio.to_thread(self.client.has_collection, collection)
         if not exists:
             return [], 0
@@ -367,6 +410,7 @@ async def list_by_filter(
 
         results = await asyncio.to_thread(coll.get, **get_kwargs)
 
+        results = self._json_safe(results)
         ids = results.get('ids', [])
         metadatas = results.get('metadatas', []) or [None] * len(ids)
         documents = results.get('documents', []) or [None] * len(ids)
@@ -390,6 +434,7 @@ async def delete_collection(self, collection: str):
         Args:
             collection: Collection name
         """
+        collection = self._normalize_collection_name(collection)
         # Remove from cache
         if collection in self._collections:
             del self._collections[collection]