Fix search vector_similarity_weight (#15108)

### What problem does this PR solve?

Fix search vector_similarity_weight

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

api/apps/services/dataset_api_service.py

@@ -938,6 +938,8 @@ async def search(dataset_id: str, tenant_id: str, req: dict):
     question = req.get("question", "")
     doc_ids = req.get("doc_ids", [])
     use_kg = req.get("use_kg", False)
+    similarity_threshold = float(req.get("similarity_threshold", 0.0))
+    vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
     top = max(1, min(int(req.get("top_k", 1024)), 2048))
     langs = req.get("cross_languages", [])
 
@@ -955,14 +957,31 @@ async def search(dataset_id: str, tenant_id: str, req: dict):
     local_doc_ids = list(doc_ids) if doc_ids else []
 
     meta_data_filter = {}
+    search_id = req.get("search_id", "")
+    search_config = {}
     chat_mdl = None
-    if req.get("search_id", ""):
-        search_detail = SearchService.get_detail(req.get("search_id", ""))
+    if search_id:
+        search_detail = SearchService.get_detail(search_id)
         if not search_detail:
-            logging.warning("search config not found: search_id=%s", req.get("search_id", ""))
+            logging.warning("search config not found: search_id=%s", search_id)
             return False, "Invalid search_id"
         search_config = search_detail.get("search_config", {})
         meta_data_filter = search_config.get("meta_data_filter", {})
+        similarity_threshold = float(search_config.get("similarity_threshold", similarity_threshold))
+        vector_similarity_weight = float(search_config.get("vector_similarity_weight", vector_similarity_weight))
+        top = max(1, min(int(search_config.get("top_k", top)), 2048))
+        use_kg = search_config.get("use_kg", use_kg)
+        langs = search_config.get("cross_languages", langs)
+        logging.debug(
+            "Dataset search loaded Search config: search_id=%s dataset_id=%s "
+            "vector_similarity_weight=%s full_text_weight=%s similarity_threshold=%s top_k=%s",
+            search_id,
+            dataset_id,
+            vector_similarity_weight,
+            1 - vector_similarity_weight,
+            similarity_threshold,
+            top,
+        )
         if meta_data_filter.get("method") in ["auto", "semi_auto"]:
             chat_id = search_config.get("chat_id", "")
             if chat_id:
@@ -1016,11 +1035,13 @@ async def search(dataset_id: str, tenant_id: str, req: dict):
             requester_tenant_id=tenant_id,
         )
         rerank_mdl = LLMBundle(kb.tenant_id, rerank_model_config)
-    elif req.get("rerank_id"):
-        rerank_model_config = get_model_config_by_type_and_name(kb.tenant_id, LLMType.RERANK.value, req["rerank_id"])
-        rerank_mdl = LLMBundle(kb.tenant_id, rerank_model_config)
+    else:
+        rerank_id = search_config.get("rerank_id") or req.get("rerank_id")
+        if rerank_id:
+            rerank_model_config = get_model_config_by_type_and_name(kb.tenant_id, LLMType.RERANK.value, rerank_id)
+            rerank_mdl = LLMBundle(kb.tenant_id, rerank_model_config)
 
-    if req.get("keyword", False):
+    if search_config.get("keyword", req.get("keyword", False)):
         default_chat_model_config = get_tenant_default_model_by_type(kb.tenant_id, LLMType.CHAT)
         chat_mdl = LLMBundle(kb.tenant_id, default_chat_model_config)
         _question += await keyword_extraction(chat_mdl, _question)
@@ -1033,12 +1054,13 @@ async def search(dataset_id: str, tenant_id: str, req: dict):
         [dataset_id],
         page,
         size,
-        float(req.get("similarity_threshold", 0.0)),
-        float(req.get("vector_similarity_weight", 0.3)),
+        similarity_threshold,
+        vector_similarity_weight,
         doc_ids=local_doc_ids,
         top=top,
         rerank_mdl=rerank_mdl,
         rank_feature=labels,
+        trace_id=search_id,
     )
 
     if use_kg:
@@ -1291,6 +1313,8 @@ async def search_datasets(tenant_id: str, req: dict):
     question = req.get("question", "")
     doc_ids = req.get("doc_ids", [])
     use_kg = req.get("use_kg", False)
+    similarity_threshold = float(req.get("similarity_threshold", 0.0))
+    vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
     top = max(1, min(int(req.get("top_k", 1024)), 2048))
     langs = req.get("cross_languages", [])
 
@@ -1321,14 +1345,31 @@ async def search_datasets(tenant_id: str, req: dict):
     local_doc_ids = list(doc_ids) if doc_ids else []
 
     meta_data_filter = {}
+    search_id = req.get("search_id", "")
+    search_config = {}
     chat_mdl = None
-    if req.get("search_id", ""):
-        search_detail = SearchService.get_detail(req.get("search_id", ""))
+    if search_id:
+        search_detail = SearchService.get_detail(search_id)
         if not search_detail:
-            logging.warning("search config not found: search_id=%s", req.get("search_id", ""))
+            logging.warning("search config not found: search_id=%s", search_id)
             return False, "Invalid search_id"
         search_config = search_detail.get("search_config", {})
         meta_data_filter = search_config.get("meta_data_filter", {})
+        similarity_threshold = float(search_config.get("similarity_threshold", similarity_threshold))
+        vector_similarity_weight = float(search_config.get("vector_similarity_weight", vector_similarity_weight))
+        top = max(1, min(int(search_config.get("top_k", top)), 2048))
+        use_kg = search_config.get("use_kg", use_kg)
+        langs = search_config.get("cross_languages", langs)
+        logging.debug(
+            "Dataset search loaded Search config: search_id=%s dataset_ids=%s "
+            "vector_similarity_weight=%s full_text_weight=%s similarity_threshold=%s top_k=%s",
+            search_id,
+            kb_ids,
+            vector_similarity_weight,
+            1 - vector_similarity_weight,
+            similarity_threshold,
+            top,
+        )
         if meta_data_filter.get("method") in ["auto", "semi_auto"]:
             chat_id = search_config.get("chat_id", "")
             if chat_id:
@@ -1384,11 +1425,13 @@ async def search_datasets(tenant_id: str, req: dict):
             requester_tenant_id=tenant_id,
         )
         rerank_mdl = LLMBundle(kb.tenant_id, rerank_model_config)
-    elif req.get("rerank_id"):
-        rerank_model_config = get_model_config_by_type_and_name(kb.tenant_id, LLMType.RERANK.value, req["rerank_id"])
-        rerank_mdl = LLMBundle(kb.tenant_id, rerank_model_config)
+    else:
+        rerank_id = search_config.get("rerank_id") or req.get("rerank_id")
+        if rerank_id:
+            rerank_model_config = get_model_config_by_type_and_name(kb.tenant_id, LLMType.RERANK.value, rerank_id)
+            rerank_mdl = LLMBundle(kb.tenant_id, rerank_model_config)
 
-    if req.get("keyword", False):
+    if search_config.get("keyword", req.get("keyword", False)):
         default_chat_model_config = get_tenant_default_model_by_type(kb.tenant_id, LLMType.CHAT)
         chat_mdl = LLMBundle(kb.tenant_id, default_chat_model_config)
         _question += await keyword_extraction(chat_mdl, _question)
@@ -1401,12 +1444,13 @@ async def search_datasets(tenant_id: str, req: dict):
         kb_ids,
         page,
         size,
-        float(req.get("similarity_threshold", 0.0)),
-        float(req.get("vector_similarity_weight", 0.3)),
+        similarity_threshold,
+        vector_similarity_weight,
         doc_ids=local_doc_ids,
         top=top,
         rerank_mdl=rerank_mdl,
         rank_feature=labels,
+        trace_id=search_id,
     )
 
     if use_kg: