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/restful_apis/search_api.py

@@ -16,6 +16,7 @@
 
 import json
 import logging
+from numbers import Real
 
 from quart import Response, request
 from api.db.services.dialog_service import async_ask
@@ -31,6 +32,12 @@ from common.constants import RetCode, StatusEnum
 from api.utils.api_utils import get_data_error_result, get_json_result, get_request_json, server_error_response, validate_request
 
 
+def _full_text_weight(vector_similarity_weight):
+    if isinstance(vector_similarity_weight, Real):
+        return 1 - vector_similarity_weight
+    return None
+
+
 @manager.route("/searches", methods=["POST"])  # noqa: F821
 @login_required
 @validate_request("name")
@@ -158,6 +165,16 @@ async def update(search_id):
         if not isinstance(new_config, dict):
             return get_data_error_result(message="search_config must be a JSON object")
         req["search_config"] = {**current_config, **new_config}
+        logging.debug(
+            "Search update weight: search_id=%s user_id=%s "
+            "incoming_vector_similarity_weight=%s stored_vector_similarity_weight=%s "
+            "stored_full_text_weight=%s",
+            search_id,
+            current_user.id,
+            new_config.get("vector_similarity_weight"),
+            req["search_config"].get("vector_similarity_weight"),
+            _full_text_weight(req["search_config"].get("vector_similarity_weight", 0.3)),
+        )
 
         for field in ("search_id", "tenant_id", "created_by", "update_time", "id"):
             req.pop(field, None)
@@ -209,6 +226,14 @@ async def completion(search_id):
         return get_data_error_result(message=f"Cannot find search {search_id}")
 
     search_config = search_app.get("search_config", {})
+    logging.debug(
+        "Search completion loaded weight: search_id=%s user_id=%s "
+        "stored_vector_similarity_weight=%s stored_full_text_weight=%s",
+        search_id,
+        uid,
+        search_config.get("vector_similarity_weight", 0.3),
+        _full_text_weight(search_config.get("vector_similarity_weight", 0.3)),
+    )
     kb_ids = search_config.get("kb_ids") or req.get("kb_ids") or []
     if not kb_ids:
         return get_data_error_result(message="`kb_ids` is required.")
@@ -216,7 +241,7 @@ async def completion(search_id):
     async def stream():
         nonlocal req, uid, kb_ids, search_config
         try:
-            async for ans in async_ask(req["question"], kb_ids, uid, search_config=search_config):
+            async for ans in async_ask(req["question"], kb_ids, uid, search_config=search_config, search_id=search_id):
                 yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
         except Exception as ex:
             yield "data:" + json.dumps(

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:

api/db/services/dialog_service.py

@@ -1525,7 +1525,7 @@ async def _stream_with_think_delta(stream_iter, min_tokens: int = 16):
         yield ("marker", "</think>", state)
 
 
-async def async_ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
+async def async_ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}, search_id=None):
     doc_ids = search_config.get("doc_ids", [])
     rerank_mdl = None
     kb_ids = search_config.get("kb_ids", kb_ids)
@@ -1561,6 +1561,21 @@ async def async_ask(question, kb_ids, tenant_id, chat_llm_name=None, search_conf
             metas_loader=lambda: DocMetadataService.get_flatted_meta_by_kbs(kb_ids),
         )
 
+    vector_similarity_weight = search_config.get("vector_similarity_weight", 0.3)
+    try:
+        full_text_weight = 1 - vector_similarity_weight
+    except TypeError:
+        full_text_weight = None
+    logger.debug(
+        "Search async_ask retrieval weight: search_id=%s tenant_id=%s kb_count=%s "
+        "vector_similarity_weight=%s full_text_weight=%s",
+        search_id,
+        tenant_id,
+        len(kb_ids),
+        vector_similarity_weight,
+        full_text_weight,
+    )
+
     kbinfos = await retriever.retrieval(
         question=question,
         embd_mdl=embd_mdl,
@@ -1569,12 +1584,13 @@ async def async_ask(question, kb_ids, tenant_id, chat_llm_name=None, search_conf
         page=1,
         page_size=12,
         similarity_threshold=search_config.get("similarity_threshold", 0.1),
-        vector_similarity_weight=search_config.get("vector_similarity_weight", 0.3),
+        vector_similarity_weight=vector_similarity_weight,
         top=search_config.get("top_k", 1024),
         doc_ids=doc_ids,
         aggs=True,
         rerank_mdl=rerank_mdl,
         rank_feature=label_question(question, kbs),
+        trace_id=search_id,
     )
     if include_reference_metadata:
         logging.debug(

rag/nlp/search.py

@@ -575,6 +575,7 @@ class Dealer:
             rerank_mdl=None,
             highlight=False,
             rank_feature: dict | None = {PAGERANK_FLD: 10},
+            trace_id=None,
     ):
         ranks = {"total": 0, "chunks": [], "doc_aggs": {}}
         if not question:
@@ -614,12 +615,24 @@ class Dealer:
             ranks["doc_aggs"] = []
             return ranks
 
+        term_similarity_weight = 1 - vector_similarity_weight
+        logging.debug(
+            "[Search] retrieval weights: trace_id=%s kb_count=%s similarity_threshold=%s "
+            "vector_similarity_weight=%s full_text_weight=%s rerank_enabled=%s",
+            trace_id,
+            len(kb_ids),
+            similarity_threshold,
+            vector_similarity_weight,
+            term_similarity_weight,
+            bool(rerank_mdl),
+        )
+
         if rerank_mdl and sres.total > 0:
             sim, tsim, vsim = self.rerank_by_model(
                 rerank_mdl,
                 sres,
                 question,
-                1 - vector_similarity_weight,
+                term_similarity_weight,
                 vector_similarity_weight,
                 rank_feature=rank_feature,
             )
@@ -636,7 +649,7 @@ class Dealer:
                 sim, tsim, vsim = self.rerank(
                     sres,
                     question,
-                    1 - vector_similarity_weight,
+                    term_similarity_weight,
                     vector_similarity_weight,
                     rank_feature=rank_feature,
                 )
@@ -650,7 +663,7 @@ class Dealer:
                     sres,
                     question,
                     knn_scores,
-                    1 - vector_similarity_weight,
+                    term_similarity_weight,
                     vector_similarity_weight,
                     rank_feature=rank_feature,
                 )

web/src/pages/next-search/search-setting.tsx

@@ -132,9 +132,7 @@ const SearchSetting: React.FC<SearchSettingProps> = ({
       search_config: {
         kb_ids: search_config?.kb_ids || [],
         vector_similarity_weight:
-          (search_config?.vector_similarity_weight
-            ? 1 - search_config?.vector_similarity_weight
-            : 0.3) || 0.3,
+          search_config?.vector_similarity_weight ?? 0.3,
         web_search: search_config?.web_search || false,
         doc_ids: [],
         similarity_threshold: search_config?.similarity_threshold || 0.2,
@@ -309,7 +307,7 @@ const SearchSetting: React.FC<SearchSettingProps> = ({
           ...other_config,
           reference_metadata: normalizedReferenceMetadata,
           chat_id: llm_setting.llm_id,
-          vector_similarity_weight: 1 - vector_similarity_weight,
+          vector_similarity_weight,
           rerank_id: use_rerank ? rerank_id : '',
           llm_setting: { ...llmSetting },
         },