feat: add timing logs for dense and sparse retrieval processes and adjust top K results in sparse retriever

astrbot/core/knowledge_base/retrieval/manager.py

@@ -3,6 +3,8 @@
 协调稠密检索、稀疏检索和 Rerank,提供统一的检索接口
 """
 
+import time
+
 from dataclasses import dataclass
 from typing import List
 
@@ -104,25 +106,40 @@ class RetrievalManager:
         kb_ids = new_kb_ids
 
         # 1. 稠密检索
+        time_start = time.time()
         dense_results = await self._dense_retrieve(
             query=query,
             kb_ids=kb_ids,
             kb_options=kb_options,
         )
+        time_end = time.time()
+        logger.debug(
+            f"Dense retrieval across {len(kb_ids)} bases took {time_end - time_start:.2f}s and returned {len(dense_results)} results."
+        )
 
         # 2. 稀疏检索
+        time_start = time.time()
         sparse_results = await self.sparse_retriever.retrieve(
             query=query,
             kb_ids=kb_ids,
             kb_options=kb_options,
         )
+        time_end = time.time()
+        logger.debug(
+            f"Sparse retrieval across {len(kb_ids)} bases took {time_end - time_start:.2f}s and returned {len(sparse_results)} results."
+        )
 
         # 3. 结果融合
+        time_start = time.time()
         fused_results = await self.rank_fusion.fuse(
             dense_results=dense_results,
             sparse_results=sparse_results,
             top_k=top_k_fusion,
         )
+        time_end = time.time()
+        logger.debug(
+            f"Rank fusion took {time_end - time_start:.2f}s and returned {len(fused_results)} results."
+        )
 
         # 4. 转换为 RetrievalResult (获取元数据)
         retrieval_results = []

astrbot/core/knowledge_base/retrieval/sparse_retriever.py

@@ -68,6 +68,7 @@ class SparseRetriever:
             List[SparseResult]: 检索结果列表
         """
         # 1. 获取所有相关块
+        top_k_sparse = 0
         chunks = []
         for kb_id in kb_ids:
             vec_db: FaissVecDB = kb_options.get(kb_id, {}).get("vec_db")
@@ -88,6 +89,7 @@ class SparseRetriever:
                 for doc, chunk_md in zip(result, chunk_mds)
             ]
             chunks.extend(result)
+            top_k_sparse += kb_options.get(kb_id, {}).get("top_k_sparse", 50)
 
         if not chunks:
             return []
@@ -127,4 +129,4 @@ class SparseRetriever:
 
         results.sort(key=lambda x: x.score, reverse=True)
         # return results[: len(results) // len(kb_ids)]
-        return results
+        return results[:top_k_sparse]