Refactor:Improve VoyageRerank not texts handling (#9539)

### What problem does this PR solve?

Improve VoyageRerank not texts handling

### Type of change

- [x] Refactoring

agent/canvas.py

@@ -484,7 +484,7 @@ class Canvas:
             threads.append(exe.submit(FileService.parse, file["name"], FileService.get_blob(file["created_by"], file["id"]), True, file["created_by"]))
         return [th.result() for th in threads]
 
-    def tool_use_callback(self, agent_id: str, func_name: str, params: dict, result: Any):
+    def tool_use_callback(self, agent_id: str, func_name: str, params: dict, result: Any, elapsed_time=None):
         agent_ids = agent_id.split("-->")
         agent_name = self.get_component_name(agent_ids[0])
         path = agent_name if len(agent_ids) < 2 else agent_name+"-->"+"-->".join(agent_ids[1:])
@@ -493,16 +493,16 @@ class Canvas:
             if bin:
                 obj = json.loads(bin.encode("utf-8"))
                 if obj[-1]["component_id"] == agent_ids[0]:
-                    obj[-1]["trace"].append({"path": path, "tool_name": func_name, "arguments": params, "result": result})
+                    obj[-1]["trace"].append({"path": path, "tool_name": func_name, "arguments": params, "result": result, "elapsed_time": elapsed_time})
                 else:
                     obj.append({
                     "component_id": agent_ids[0],
-                    "trace": [{"path": path, "tool_name": func_name, "arguments": params, "result": result}]
+                    "trace": [{"path": path, "tool_name": func_name, "arguments": params, "result": result, "elapsed_time": elapsed_time}]
                 })
             else:
                 obj = [{
                     "component_id": agent_ids[0],
-                    "trace": [{"path": path, "tool_name": func_name, "arguments": params, "result": result}]
+                    "trace": [{"path": path, "tool_name": func_name, "arguments": params, "result": result, "elapsed_time": elapsed_time}]
                 }]
             REDIS_CONN.set_obj(f"{self.task_id}-{self.message_id}-logs", obj, 60*10)
         except Exception as e:

agent/component/agent_with_tools.py

@@ -22,7 +22,7 @@ from functools import partial
 from typing import Any
 
 import json_repair
-
+from timeit import default_timer as timer
 from agent.tools.base import LLMToolPluginCallSession, ToolParamBase, ToolBase, ToolMeta
 from api.db.services.llm_service import LLMBundle
 from api.db.services.tenant_llm_service import TenantLLMService
@@ -215,8 +215,9 @@ class Agent(LLM, ToolBase):
         hist = deepcopy(history)
         last_calling = ""
         if len(hist) > 3:
+            st = timer()
             user_request = full_question(messages=history, chat_mdl=self.chat_mdl)
-            self.callback("Multi-turn conversation optimization", {}, user_request)
+            self.callback("Multi-turn conversation optimization", {}, user_request, elapsed_time=timer()-st)
         else:
             user_request = history[-1]["content"]
 
@@ -263,12 +264,13 @@ class Agent(LLM, ToolBase):
             if not need2cite or cited:
                 return
 
+            st = timer()
             txt = ""
             for delta_ans in self._gen_citations(entire_txt):
                 yield delta_ans, 0
                 txt += delta_ans
 
-            self.callback("gen_citations", {}, txt)
+            self.callback("gen_citations", {}, txt, elapsed_time=timer()-st)
 
         def append_user_content(hist, content):
             if hist[-1]["role"] == "user":
@@ -276,8 +278,9 @@ class Agent(LLM, ToolBase):
             else:
                 hist.append({"role": "user", "content": content})
 
+        st = timer()
         task_desc = analyze_task(self.chat_mdl, prompt, user_request, tool_metas)
-        self.callback("analyze_task", {}, task_desc)
+        self.callback("analyze_task", {}, task_desc, elapsed_time=timer()-st)
         for _ in range(self._param.max_rounds + 1):
             response, tk = next_step(self.chat_mdl, hist, tool_metas, task_desc)
             # self.callback("next_step", {}, str(response)[:256]+"...")
@@ -303,9 +306,10 @@ class Agent(LLM, ToolBase):
 
                         thr.append(executor.submit(use_tool, name, args))
 
+                    st = timer()
                     reflection = reflect(self.chat_mdl, hist, [th.result() for th in thr])
                     append_user_content(hist, reflection)
-                    self.callback("reflection", {}, str(reflection))
+                    self.callback("reflection", {}, str(reflection), elapsed_time=timer()-st)
 
             except Exception as e:
                 logging.exception(msg=f"Wrong JSON argument format in LLM ReAct response: {e}")

agent/tools/base.py

@@ -24,6 +24,7 @@ from api.utils import hash_str2int
 from rag.llm.chat_model import ToolCallSession
 from rag.prompts.prompts import kb_prompt
 from rag.utils.mcp_tool_call_conn import MCPToolCallSession
+from timeit import default_timer as timer
 
 
 class ToolParameter(TypedDict):
@@ -49,12 +50,13 @@ class LLMToolPluginCallSession(ToolCallSession):
 
     def tool_call(self, name: str, arguments: dict[str, Any]) -> Any:
         assert name in self.tools_map, f"LLM tool {name} does not exist"
+        st = timer()
         if isinstance(self.tools_map[name], MCPToolCallSession):
             resp = self.tools_map[name].tool_call(name, arguments, 60)
         else:
             resp = self.tools_map[name].invoke(**arguments)
 
-        self.callback(name, arguments, resp)
+        self.callback(name, arguments, resp, elapsed_time=timer()-st)
         return resp
 
     def get_tool_obj(self, name):

agent/tools/exesql.py

@@ -79,6 +79,17 @@ class ExeSQL(ToolBase, ABC):
 
     @timeout(os.environ.get("COMPONENT_EXEC_TIMEOUT", 60))
     def _invoke(self, **kwargs):
+
+        def convert_decimals(obj):
+            from decimal import Decimal
+            if isinstance(obj, Decimal):
+                return float(obj)  # 或 str(obj)
+            elif isinstance(obj, dict):
+                return {k: convert_decimals(v) for k, v in obj.items()}
+            elif isinstance(obj, list):
+                return [convert_decimals(item) for item in obj]
+            return obj
+
         sql = kwargs.get("sql")
         if not sql:
             raise Exception("SQL for `ExeSQL` MUST not be empty.")
@@ -122,7 +133,11 @@ class ExeSQL(ToolBase, ABC):
                 single_res = pd.DataFrame([i for i in cursor.fetchmany(self._param.max_records)])
                 single_res.columns = [i[0] for i in cursor.description]
 
-            sql_res.append(single_res.to_dict(orient='records'))
+            for col in single_res.columns:
+                if pd.api.types.is_datetime64_any_dtype(single_res[col]):
+                    single_res[col] = single_res[col].dt.strftime('%Y-%m-%d')
+
+            sql_res.append(convert_decimals(single_res.to_dict(orient='records')))
             formalized_content.append(single_res.to_markdown(index=False, floatfmt=".6f"))
 
         self.set_output("json", sql_res)
@@ -130,4 +145,4 @@ class ExeSQL(ToolBase, ABC):
         return self.output("formalized_content")
 
     def thoughts(self) -> str:
-        return "Query sent—waiting for the data."
+        return "Query sent—waiting for the data."

agent/tools/retrieval.py

@@ -86,10 +86,16 @@ class Retrieval(ToolBase, ABC):
                 kb_ids.append(id)
                 continue
             kb_nm = self._canvas.get_variable_value(id)
-            e, kb = KnowledgebaseService.get_by_name(kb_nm, self._canvas._tenant_id)
-            if not e:
-                raise Exception(f"Dataset({kb_nm}) does not exist.")
-            kb_ids.append(kb.id)
+            # if kb_nm is a list
+            kb_nm_list = kb_nm if isinstance(kb_nm, list) else [kb_nm]
+            for nm_or_id in kb_nm_list:
+                e, kb = KnowledgebaseService.get_by_name(nm_or_id,
+                                                         self._canvas._tenant_id)
+                if not e:
+                    e, kb = KnowledgebaseService.get_by_id(nm_or_id)
+                    if not e:
+                        raise Exception(f"Dataset({nm_or_id}) does not exist.")
+                kb_ids.append(kb.id)
 
         filtered_kb_ids: list[str] = list(set([kb_id for kb_id in kb_ids if kb_id]))
 

api/apps/conversation_app.py

@@ -29,6 +29,7 @@ from api.db.services.conversation_service import ConversationService, structure_
 from api.db.services.dialog_service import DialogService, ask, chat
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import LLMBundle
+from api.db.services.search_service import SearchService
 from api.db.services.tenant_llm_service import TenantLLMService
 from api.db.services.user_service import TenantService, UserTenantService
 from api.utils.api_utils import get_data_error_result, get_json_result, server_error_response, validate_request
@@ -344,10 +345,18 @@ def ask_about():
     req = request.json
     uid = current_user.id
 
+    search_id = req.get("search_id", "")
+    search_app = None
+    search_config = {}
+    if search_id:
+        search_app = SearchService.get_detail(search_id)
+    if search_app:
+        search_config = search_app.get("search_config", {})
+
     def stream():
         nonlocal req, uid
         try:
-            for ans in ask(req["question"], req["kb_ids"], uid):
+            for ans in ask(req["question"], req["kb_ids"], uid, search_config=search_config):
                 yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
         except Exception as e:
             yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
@@ -366,15 +375,68 @@ def ask_about():
 @validate_request("question", "kb_ids")
 def mindmap():
     req = request.json
+
+    search_id = req.get("search_id", "")
+    search_app = None
+    search_config = {}
+    if search_id:
+        search_app = SearchService.get_detail(search_id)
+    if search_app:
+        search_config = search_app.get("search_config", {})
+
     kb_ids = req["kb_ids"]
+    if search_config.get("kb_ids", []):
+        kb_ids = search_config.get("kb_ids", [])
     e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
     if not e:
         return get_data_error_result(message="Knowledgebase not found!")
 
-    embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
-    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+    chat_id = ""
+    similarity_threshold = 0.3,
+    vector_similarity_weight = 0.3,
+    top = 1024,
+    doc_ids = []
+    rerank_id = ""
+    rerank_mdl = None
+
+    if search_config:
+        if search_config.get("chat_id", ""):
+            chat_id = search_config.get("chat_id", "")
+        if search_config.get("similarity_threshold", 0.2):
+            similarity_threshold = search_config.get("similarity_threshold", 0.2)
+        if search_config.get("vector_similarity_weight", 0.3):
+            vector_similarity_weight = search_config.get("vector_similarity_weight", 0.3)
+        if search_config.get("top_k", 1024):
+            top = search_config.get("top_k", 1024)
+        if search_config.get("doc_ids", []):
+            doc_ids = search_config.get("doc_ids", [])
+        if search_config.get("rerank_id", ""):
+            rerank_id = search_config.get("rerank_id", "")
+
+    tenant_id = kb.tenant_id
+    if search_app and search_app.get("tenant_id", ""):
+        tenant_id = search_app.get("tenant_id", "")
+
+    embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=chat_id)
+    if rerank_id:
+        rerank_mdl = LLMBundle(tenant_id, LLMType.RERANK, rerank_id)
     question = req["question"]
-    ranks = settings.retrievaler.retrieval(question, embd_mdl, kb.tenant_id, kb_ids, 1, 12, 0.3, 0.3, aggs=False, rank_feature=label_question(question, [kb]))
+    ranks = settings.retrievaler.retrieval(
+        question=question,
+        embd_mdl=embd_mdl,
+        tenant_ids=tenant_id,
+        kb_ids=kb_ids,
+        page=1,
+        page_size=12,
+        similarity_threshold=similarity_threshold,
+        vector_similarity_weight=vector_similarity_weight,
+        top=top,
+        doc_ids=doc_ids,
+        aggs=False,
+        rerank_mdl=rerank_mdl,
+        rank_feature=label_question(question, [kb]),
+    )
     mindmap = MindMapExtractor(chat_mdl)
     mind_map = trio.run(mindmap, [c["content_with_weight"] for c in ranks["chunks"]])
     mind_map = mind_map.output
@@ -388,8 +450,19 @@ def mindmap():
 @validate_request("question")
 def related_questions():
     req = request.json
+
+    search_id = req.get("search_id", "")
+    search_config = {}
+    if search_id:
+        if search_app := SearchService.get_detail(search_id):
+            search_config = search_app.get("search_config", {})
+
     question = req["question"]
-    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+
+    chat_id = search_config.get("chat_id", "")
+    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT, chat_id)
+
+    gen_conf = search_config.get("llm_setting", {"temperature": 0.9})
     prompt = load_prompt("related_question")
     ans = chat_mdl.chat(
         prompt,
@@ -402,6 +475,6 @@ Related search terms:
     """,
             }
         ],
-        {"temperature": 0.9},
+        gen_conf,
     )
     return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])

api/apps/sdk/session.py

@@ -902,10 +902,16 @@ def ask_about_embedded():
     req = request.json
     uid = objs[0].tenant_id
 
+    search_id = req.get("search_id", "")
+    search_config = {}
+    if search_id:
+        if search_app := SearchService.get_detail(search_id):
+            search_config = search_app.get("search_config", {})
+
     def stream():
         nonlocal req, uid
         try:
-            for ans in ask(req["question"], req["kb_ids"], uid):
+            for ans in ask(req["question"], req["kb_ids"], uid, search_config):
                 yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
         except Exception as e:
             yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
@@ -1021,8 +1027,19 @@ def related_questions_embedded():
     tenant_id = objs[0].tenant_id
     if not tenant_id:
         return get_error_data_result(message="permission denined.")
+
+    search_id = req.get("search_id", "")
+    search_config = {}
+    if search_id:
+        if search_app := SearchService.get_detail(search_id):
+            search_config = search_app.get("search_config", {})
+
     question = req["question"]
-    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
+
+    chat_id = search_config.get("chat_id", "")
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_id)
+
+    gen_conf = search_config.get("llm_setting", {"temperature": 0.9})
     prompt = load_prompt("related_question")
     ans = chat_mdl.chat(
         prompt,
@@ -1035,7 +1052,7 @@ Related search terms:
     """,
             }
         ],
-        {"temperature": 0.9},
+        gen_conf,
     )
     return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])
 
@@ -1083,15 +1100,62 @@ def mindmap():
 
     tenant_id = objs[0].tenant_id
     req = request.json
+
+    search_id = req.get("search_id", "")
+    search_config = {}
+    if search_id:
+        if search_app := SearchService.get_detail(search_id):
+            search_config = search_app.get("search_config", {})
+
     kb_ids = req["kb_ids"]
+    if search_config.get("kb_ids", []):
+        kb_ids = search_config.get("kb_ids", [])
     e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
     if not e:
         return get_error_data_result(message="Knowledgebase not found!")
 
-    embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
-    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
+    chat_id = ""
+    similarity_threshold = 0.3,
+    vector_similarity_weight = 0.3,
+    top = 1024,
+    doc_ids = []
+    rerank_id = ""
+    rerank_mdl = None
+
+    if search_config:
+        if search_config.get("chat_id", ""):
+            chat_id = search_config.get("chat_id", "")
+        if search_config.get("similarity_threshold", 0.2):
+            similarity_threshold = search_config.get("similarity_threshold", 0.2)
+        if search_config.get("vector_similarity_weight", 0.3):
+            vector_similarity_weight = search_config.get("vector_similarity_weight", 0.3)
+        if search_config.get("top_k", 1024):
+            top = search_config.get("top_k", 1024)
+        if search_config.get("doc_ids", []):
+            doc_ids = search_config.get("doc_ids", [])
+        if search_config.get("rerank_id", ""):
+            rerank_id = search_config.get("rerank_id", "")
+
+    embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=chat_id)
+    if rerank_id:
+        rerank_mdl = LLMBundle(tenant_id, LLMType.RERANK, rerank_id)
     question = req["question"]
-    ranks = settings.retrievaler.retrieval(question, embd_mdl, kb.tenant_id, kb_ids, 1, 12, 0.3, 0.3, aggs=False, rank_feature=label_question(question, [kb]))
+    ranks = settings.retrievaler.retrieval(
+        question=question,
+        embd_mdl=embd_mdl,
+        tenant_ids=tenant_id,
+        kb_ids=kb_ids,
+        page=1,
+        page_size=12,
+        similarity_threshold=similarity_threshold,
+        vector_similarity_weight=vector_similarity_weight,
+        top=top,
+        doc_ids=doc_ids,
+        aggs=False,
+        rerank_mdl=rerank_mdl,
+        rank_feature=label_question(question, [kb]),
+    )
     mindmap = MindMapExtractor(chat_mdl)
     mind_map = trio.run(mindmap, [c["content_with_weight"] for c in ranks["chunks"]])
     mind_map = mind_map.output

api/db/db_models.py

@@ -872,7 +872,7 @@ class Search(DataBaseModel):
         default={
             "kb_ids": [],
             "doc_ids": [],
-            "similarity_threshold": 0.0,
+            "similarity_threshold": 0.2,
             "vector_similarity_weight": 0.3,
             "use_kg": False,
             # rerank settings

api/db/services/dialog_service.py

@@ -40,7 +40,7 @@ from rag.app.resume import forbidden_select_fields4resume
 from rag.app.tag import label_question
 from rag.nlp.search import index_name
 from rag.prompts import chunks_format, citation_prompt, cross_languages, full_question, kb_prompt, keyword_extraction, message_fit_in
-from rag.prompts.prompts import gen_meta_filter
+from rag.prompts.prompts import gen_meta_filter, PROMPT_JINJA_ENV, ASK_SUMMARY
 from rag.utils import num_tokens_from_string, rmSpace
 from rag.utils.tavily_conn import Tavily
 
@@ -687,7 +687,30 @@ def tts(tts_mdl, text):
     return binascii.hexlify(bin).decode("utf-8")
 
 
-def ask(question, kb_ids, tenant_id, chat_llm_name=None):
+def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
+    similarity_threshold = 0.1,
+    vector_similarity_weight = 0.3,
+    top = 1024,
+    doc_ids = []
+    rerank_id = ""
+    rerank_mdl = None
+
+    if search_config:
+        if search_config.get("kb_ids", []):
+            kb_ids = search_config.get("kb_ids", [])
+        if search_config.get("chat_id", ""):
+            chat_llm_name = search_config.get("chat_id", "")
+        if search_config.get("similarity_threshold", 0.1):
+            similarity_threshold = search_config.get("similarity_threshold", 0.1)
+        if search_config.get("vector_similarity_weight", 0.3):
+            vector_similarity_weight = search_config.get("vector_similarity_weight", 0.3)
+        if search_config.get("top_k", 1024):
+            top = search_config.get("top_k", 1024)
+        if search_config.get("doc_ids", []):
+            doc_ids = search_config.get("doc_ids", [])
+        if search_config.get("rerank_id", ""):
+            rerank_id = search_config.get("rerank_id", "")
+
     kbs = KnowledgebaseService.get_by_ids(kb_ids)
     embedding_list = list(set([kb.embd_id for kb in kbs]))
 
@@ -696,30 +719,34 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None):
 
     embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embedding_list[0])
     chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_llm_name)
+    if rerank_id:
+        rerank_mdl = LLMBundle(tenant_id, LLMType.RERANK, rerank_id)
     max_tokens = chat_mdl.max_length
     tenant_ids = list(set([kb.tenant_id for kb in kbs]))
-    kbinfos = retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids, 1, 12, 0.1, 0.3, aggs=False, rank_feature=label_question(question, kbs))
+
+    kbinfos = retriever.retrieval(
+        question = question,
+        embd_mdl=embd_mdl,
+        tenant_ids=tenant_ids,
+        kb_ids=kb_ids,
+        page=1,
+        page_size=12,
+        similarity_threshold=similarity_threshold,
+        vector_similarity_weight=vector_similarity_weight,
+        top=top,
+        doc_ids=doc_ids,
+        aggs=False,
+        rerank_mdl=rerank_mdl,
+        rank_feature=label_question(question, kbs)
+    )
+
     knowledges = kb_prompt(kbinfos, max_tokens)
-    prompt = """
-    Role: You're a smart assistant. Your name is Miss R.
-    Task: Summarize the information from knowledge bases and answer user's question.
-    Requirements and restriction:
-      - DO NOT make things up, especially for numbers.
-      - If the information from knowledge is irrelevant with user's question, JUST SAY: Sorry, no relevant information provided.
-      - Answer with markdown format text.
-      - Answer in language of user's question.
-      - DO NOT make things up, especially for numbers.
+    sys_prompt = PROMPT_JINJA_ENV.from_string(ASK_SUMMARY).render(knowledge="\n".join(knowledges))
 
-    ### Information from knowledge bases
-    %s
-
-    The above is information from knowledge bases.
-
-    """ % "\n".join(knowledges)
     msg = [{"role": "user", "content": question}]
 
     def decorate_answer(answer):
-        nonlocal knowledges, kbinfos, prompt
+        nonlocal knowledges, kbinfos, sys_prompt
         answer, idx = retriever.insert_citations(answer, [ck["content_ltks"] for ck in kbinfos["chunks"]], [ck["vector"] for ck in kbinfos["chunks"]], embd_mdl, tkweight=0.7, vtweight=0.3)
         idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
         recall_docs = [d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
@@ -737,7 +764,7 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None):
         return {"answer": answer, "reference": refs}
 
     answer = ""
-    for ans in chat_mdl.chat_streamly(prompt, msg, {"temperature": 0.1}):
+    for ans in chat_mdl.chat_streamly(sys_prompt, msg, {"temperature": 0.1}):
         answer = ans
         yield {"answer": answer, "reference": {}}
     yield decorate_answer(answer)

api/ragflow_server.py

@@ -59,11 +59,14 @@ def update_progress():
             if redis_lock.acquire():
                 DocumentService.update_progress()
                 redis_lock.release()
-            stop_event.wait(6)
         except Exception:
             logging.exception("update_progress exception")
         finally:
-            redis_lock.release()
+            try:
+                redis_lock.release()
+            except Exception:
+                logging.exception("update_progress exception")
+            stop_event.wait(6)
 
 def signal_handler(sig, frame):
     logging.info("Received interrupt signal, shutting down...")

rag/llm/rerank_model.py

@@ -482,9 +482,10 @@ class VoyageRerank(Base):
         self.model_name = model_name
 
     def similarity(self, query: str, texts: list):
-        rank = np.zeros(len(texts), dtype=float)
         if not texts:
-            return rank, 0
+            return np.array([]), 0
+        rank = np.zeros(len(texts), dtype=float)
+
         res = self.client.rerank(query=query, documents=texts, model=self.model_name, top_k=len(texts))
         try:
             for r in res.results:

rag/nlp/__init__.py

@@ -611,10 +611,6 @@ def naive_merge_with_images(texts, images, chunk_token_num=128, delimiter="\n。
                 if re.match(f"^{dels}$", sub_sec):
                     continue
                 add_chunk(sub_sec, image)
-        
-        for img in images:
-            if isinstance(img, Image.Image):
-                img.close()
 
     return cks, result_images
 

rag/prompts/ask_summary.md

@@ -0,0 +1,14 @@
+Role: You're a smart assistant. Your name is Miss R.
+Task: Summarize the information from knowledge bases and answer user's question.
+Requirements and restriction:
+  - DO NOT make things up, especially for numbers.
+  - If the information from knowledge is irrelevant with user's question, JUST SAY: Sorry, no relevant information provided.
+  - Answer with markdown format text.
+  - Answer in language of user's question.
+  - DO NOT make things up, especially for numbers.
+
+### Information from knowledge bases
+
+{{ knowledge }}
+
+The above is information from knowledge bases.

rag/prompts/prompts.py

@@ -150,6 +150,7 @@ REFLECT = load_prompt("reflect")
 SUMMARY4MEMORY = load_prompt("summary4memory")
 RANK_MEMORY = load_prompt("rank_memory")
 META_FILTER = load_prompt("meta_filter")
+ASK_SUMMARY = load_prompt("ask_summary")
 
 PROMPT_JINJA_ENV = jinja2.Environment(autoescape=False, trim_blocks=True, lstrip_blocks=True)
 

web/src/components/large-model-form-field.tsx

@@ -38,9 +38,12 @@ export const LargeModelFilterFormSchema = {
   llm_filter: z.string().optional(),
 };
 
-type LargeModelFormFieldProps = Pick<NextInnerLLMSelectProps, 'showTTSModel'>;
+type LargeModelFormFieldProps = Pick<
+  NextInnerLLMSelectProps,
+  'showSpeech2TextModel'
+>;
 export function LargeModelFormField({
-  showTTSModel,
+  showSpeech2TextModel: showTTSModel,
 }: LargeModelFormFieldProps) {
   const form = useFormContext();
   const { t } = useTranslation();
@@ -91,7 +94,7 @@ export function LargeModelFormField({
                 <NextLLMSelect
                   {...field}
                   filter={filter}
-                  showTTSModel={showTTSModel}
+                  showSpeech2TextModel={showTTSModel}
                 />
               </FormControl>
             </section>

web/src/components/llm-select/next.tsx

@@ -13,18 +13,18 @@ export interface NextInnerLLMSelectProps {
   onChange?: (value: string) => void;
   disabled?: boolean;
   filter?: string;
-  showTTSModel?: boolean;
+  showSpeech2TextModel?: boolean;
 }
 
 const NextInnerLLMSelect = forwardRef<
   React.ElementRef<typeof SelectPrimitive.Trigger>,
   NextInnerLLMSelectProps
->(({ value, disabled, filter, showTTSModel = false }, ref) => {
+>(({ value, disabled, filter, showSpeech2TextModel = false }, ref) => {
   const [isPopoverOpen, setIsPopoverOpen] = useState(false);
 
   const ttsModel = useMemo(() => {
-    return showTTSModel ? [LlmModelType.TTS] : [];
-  }, [showTTSModel]);
+    return showSpeech2TextModel ? [LlmModelType.Speech2text] : [];
+  }, [showSpeech2TextModel]);
 
   const modelTypes = useMemo(() => {
     if (filter === LlmModelType.Chat) {

web/src/components/message-item/index.less

@@ -24,7 +24,7 @@
   .messageText {
     .chunkText();
     .messageTextBase();
-    background-color: #e6f4ff;
+    // background-color: #e6f4ff;
     word-break: break-word;
   }
   .messageTextDark {

web/src/components/message-item/index.tsx

@@ -9,6 +9,7 @@ import {
   useFetchDocumentThumbnailsByIds,
 } from '@/hooks/document-hooks';
 import { IRegenerateMessage, IRemoveMessageById } from '@/hooks/logic-hooks';
+import { cn } from '@/lib/utils';
 import { IMessage } from '@/pages/chat/interface';
 import MarkdownContent from '@/pages/chat/markdown-content';
 import { Avatar, Flex, Space } from 'antd';
@@ -129,13 +130,14 @@ const MessageItem = ({
               {/* <b>{isAssistant ? '' : nickname}</b> */}
             </Space>
             <div
-              className={
+              className={cn(
                 isAssistant
                   ? theme === 'dark'
                     ? styles.messageTextDark
                     : styles.messageText
-                  : styles.messageUserText
-              }
+                  : styles.messageUserText,
+                { '!bg-bg-card': !isAssistant },
+              )}
             >
               <MarkdownContent
                 loading={loading}

web/src/hooks/logic-hooks.ts

@@ -369,22 +369,28 @@ export const useScrollToBottom = (
     return () => container.removeEventListener('scroll', handleScroll);
   }, [containerRef, checkIfUserAtBottom]);
 
+  // Imperative scroll function
+  const scrollToBottom = useCallback(() => {
+    if (containerRef?.current) {
+      const container = containerRef.current;
+      container.scrollTo({
+        top: container.scrollHeight - container.clientHeight,
+        behavior: 'smooth',
+      });
+    }
+  }, [containerRef]);
+
   useEffect(() => {
     if (!messages) return;
     if (!containerRef?.current) return;
     requestAnimationFrame(() => {
       setTimeout(() => {
         if (isAtBottomRef.current) {
-          ref.current?.scrollIntoView({ behavior: 'smooth' });
+          scrollToBottom();
         }
-      }, 30);
+      }, 100);
     });
-  }, [messages, containerRef]);
-
-  // Imperative scroll function
-  const scrollToBottom = useCallback(() => {
-    ref.current?.scrollIntoView({ behavior: 'smooth' });
-  }, []);
+  }, [messages, containerRef, scrollToBottom]);
 
   return { scrollRef: ref, isAtBottom, scrollToBottom };
 };

web/src/interfaces/request/chat.ts

@@ -7,4 +7,5 @@ export interface IFeedbackRequestBody {
 export interface IAskRequestBody {
   question: string;
   kb_ids: string[];
+  search_id?: string;
 }

web/src/layouts/next-header.tsx

@@ -1,6 +1,5 @@
 import { RAGFlowAvatar } from '@/components/ragflow-avatar';
 import { useTheme } from '@/components/theme-provider';
-import { Badge } from '@/components/ui/badge';
 import { Button } from '@/components/ui/button';
 import {
   DropdownMenu,
@@ -163,9 +162,10 @@ export function Header() {
             className="size-8 cursor-pointer"
             onClick={navigateToProfile}
           ></RAGFlowAvatar>
-          <Badge className="h-5 w-8 absolute font-normal p-0 justify-center -right-8 -top-2 text-bg-base bg-gradient-to-l from-[#42D7E7] to-[#478AF5]">
+          {/* Temporarily hidden */}
+          {/* <Badge className="h-5 w-8 absolute font-normal p-0 justify-center -right-8 -top-2 text-bg-base bg-gradient-to-l from-[#42D7E7] to-[#478AF5]">
             Pro
-          </Badge>
+          </Badge> */}
         </div>
       </div>
     </section>

web/src/locales/en.ts

@@ -5,6 +5,7 @@ export default {
       deleteModalTitle: 'Are you sure to delete this item?',
       ok: 'Yes',
       cancel: 'No',
+      no: 'No',
       total: 'Total',
       rename: 'Rename',
       name: 'Name',
@@ -575,6 +576,8 @@ This auto-tagging feature enhances retrieval by adding another layer of domain-s
         automatic: 'Automatic',
         manual: 'Manual',
       },
+      cancel: 'Cancel',
+      chatSetting: 'Chat setting',
     },
     setting: {
       profile: 'Profile',
@@ -1437,6 +1440,8 @@ This delimiter is used to split the input text into several text pieces echo of
       showQueryMindmap: 'Show Query Mindmap',
       embedApp: 'Embed App',
       relatedSearch: 'Related Search',
+      okText: 'Save',
+      cancelText: 'Cancel',
     },
   },
 };

web/src/locales/zh.ts

@@ -569,6 +569,8 @@ General：实体和关系提取提示来自 GitHub - microsoft/graphrag：基于
         automatic: '自动',
         manual: '手动',
       },
+      cancel: '取消',
+      chatSetting: '聊天设置',
     },
     setting: {
       profile: '概要',
@@ -1341,6 +1343,8 @@ General：实体和关系提取提示来自 GitHub - microsoft/graphrag：基于
       showQueryMindmap: '显示查询思维导图',
       embedApp: '嵌入网站',
       relatedSearch: '相关搜索',
+      okText: '保存',
+      cancelText: '返回',
     },
   },
 };

web/src/pages/agent/canvas/node/dropdown/next-step-dropdown.tsx

@@ -242,7 +242,7 @@ export function InnerNextStepDropdown({
         }}
         onClick={(e) => e.stopPropagation()}
       >
-        <div className="w-[300px] font-semibold bg-white border border-border rounded-md shadow-lg">
+        <div className="w-[300px] font-semibold bg-bg-base border border-border rounded-md shadow-lg">
           <div className="px-3 py-2 border-b border-border">
             <div className="text-sm font-medium">Next Step</div>
           </div>

web/src/pages/agent/form/agent-form/index.tsx

@@ -128,7 +128,7 @@ function AgentForm({ node }: INextOperatorForm) {
       <FormWrapper>
         <FormContainer>
           {isSubAgent && <DescriptionField></DescriptionField>}
-          <LargeModelFormField showTTSModel></LargeModelFormField>
+          <LargeModelFormField showSpeech2TextModel></LargeModelFormField>
           {findLlmByUuid(llmId)?.model_type === LlmModelType.Image2text && (
             <QueryVariable
               name="visual_files_var"

web/src/pages/agent/log-sheet/tool-timeline-item.tsx

@@ -158,8 +158,9 @@ const ToolTimelineItem = ({
                           </span>
                         )}
                         <span className="text-text-secondary text-xs">
-                          {/* 0:00
-                          {x.data.elapsed_time?.toString().slice(0, 6)} */}
+                          {/* 0:00*/}
+                          {tool.elapsed_time?.toString().slice(0, 6) || ''}
+                          {tool.elapsed_time ? 's' : ''}
                         </span>
                         <span
                           className={cn(

web/src/pages/agent/log-sheet/workflow-timeline.tsx

@@ -153,6 +153,22 @@ export const WorkFlowTimeline = ({
     }, []);
   }, [currentEventListWithoutMessage, sendLoading]);
 
+  const getElapsedTime = (nodeId: string) => {
+    if (nodeId === 'begin') {
+      return '';
+    }
+    const data = currentEventListWithoutMessage?.find((x) => {
+      return (
+        x.data.component_id === nodeId &&
+        x.event === MessageEventType.NodeFinished
+      );
+    });
+    if (!data || data?.data.elapsed_time < 0.000001) {
+      return '';
+    }
+    return data?.data.elapsed_time || '';
+  };
+
   const hasTrace = useCallback(
     (componentId: string) => {
       if (Array.isArray(traceData)) {
@@ -272,7 +288,10 @@ export const WorkFlowTimeline = ({
                                 nodeLabel)}
                           </span>
                           <span className="text-text-secondary text-xs">
-                            {x.data.elapsed_time?.toString().slice(0, 6)}
+                            {getElapsedTime(x.data.component_id)
+                              .toString()
+                              .slice(0, 6)}
+                            {getElapsedTime(x.data.component_id) ? 's' : ''}
                           </span>
                           <span
                             className={cn(

web/src/pages/next-chats/chat/app-settings/chat-settings.tsx

@@ -1,4 +1,4 @@
-import { ButtonLoading } from '@/components/ui/button';
+import { Button, ButtonLoading } from '@/components/ui/button';
 import { Form } from '@/components/ui/form';
 import { Separator } from '@/components/ui/separator';
 import { useFetchDialog, useSetDialog } from '@/hooks/use-chat-request';
@@ -11,6 +11,7 @@ import { zodResolver } from '@hookform/resolvers/zod';
 import { X } from 'lucide-react';
 import { useEffect } from 'react';
 import { useForm } from 'react-hook-form';
+import { useTranslation } from 'react-i18next';
 import { useParams } from 'umi';
 import { z } from 'zod';
 import { DatasetMetadata } from '../../constants';
@@ -25,6 +26,7 @@ export function ChatSettings({ switchSettingVisible }: ChatSettingsProps) {
   const { data } = useFetchDialog();
   const { setDialog, loading } = useSetDialog();
   const { id } = useParams();
+  const { t } = useTranslation();
 
   type FormSchemaType = z.infer<typeof formSchema>;
 
@@ -89,25 +91,26 @@ export function ChatSettings({ switchSettingVisible }: ChatSettingsProps) {
   return (
     <section className="p-5  w-[440px] border-l">
       <div className="flex justify-between items-center text-base pb-2">
-        Chat Settings
+        {t('chat.chatSetting')}
         <X className="size-4 cursor-pointer" onClick={switchSettingVisible} />
       </div>
       <Form {...form}>
         <form onSubmit={form.handleSubmit(onSubmit, onInvalid)}>
-          <section className="space-y-6 overflow-auto max-h-[85vh] pr-4">
+          <section className="space-y-6 overflow-auto max-h-[82vh] pr-4">
             <ChatBasicSetting></ChatBasicSetting>
             <Separator />
             <ChatPromptEngine></ChatPromptEngine>
             <Separator />
             <ChatModelSettings></ChatModelSettings>
           </section>
-          <ButtonLoading
-            className="w-full my-4"
-            type="submit"
-            loading={loading}
-          >
-            Update
-          </ButtonLoading>
+          <div className="space-x-5 text-right">
+            <Button variant={'outline'} onClick={switchSettingVisible}>
+              {t('chat.cancel')}
+            </Button>
+            <ButtonLoading className=" my-4" type="submit" loading={loading}>
+              {t('common.save')}
+            </ButtonLoading>
+          </div>
         </form>
       </Form>
     </section>

web/src/pages/next-chats/chat/chat-box/single-chat-box.tsx

@@ -23,7 +23,7 @@ interface IProps {
 export function SingleChatBox({ controller }: IProps) {
   const {
     value,
-    // scrollRef,
+    scrollRef,
     messageContainerRef,
     sendLoading,
     derivedMessages,
@@ -47,7 +47,7 @@ export function SingleChatBox({ controller }: IProps) {
   return (
     <section className="flex flex-col p-5 h-full">
       <div ref={messageContainerRef} className="flex-1 overflow-auto min-h-0">
-        <div className="w-full">
+        <div className="w-full pr-5">
           {derivedMessages?.map((message, i) => {
             return (
               <MessageItem
@@ -77,7 +77,7 @@ export function SingleChatBox({ controller }: IProps) {
             );
           })}
         </div>
-        {/* <div ref={scrollRef} /> */}
+        <div ref={scrollRef} />
       </div>
       <NextMessageInput
         disabled={disabled}

web/src/pages/next-chats/chat/index.tsx

@@ -100,7 +100,7 @@ export default function Chat() {
           {t('common.embedIntoSite')}
         </Button>
       </PageHeader>
-      <div className="flex flex-1 min-h-0">
+      <div className="flex flex-1 min-h-0 pb-9">
         <Sessions
           hasSingleChatBox={hasSingleChatBox}
           handleConversationCardClick={handleConversationCardClick}

web/src/pages/next-chats/chat/sessions.tsx

@@ -11,6 +11,7 @@ import {
 import { cn } from '@/lib/utils';
 import { PanelLeftClose, PanelRightClose, Plus } from 'lucide-react';
 import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
 import { useHandleClickConversationCard } from '../hooks/use-click-card';
 import { useSelectDerivedConversationList } from '../hooks/use-select-conversation-list';
 import { ConversationDropdown } from './conversation-dropdown';
@@ -24,6 +25,7 @@ export function Sessions({
   handleConversationCardClick,
   switchSettingVisible,
 }: SessionProps) {
+  const { t } = useTranslation();
   const {
     list: conversationList,
     addTemporaryConversation,
@@ -102,8 +104,9 @@ export function Sessions({
           className="w-full"
           onClick={switchSettingVisible}
           disabled={!hasSingleChatBox}
+          variant={'outline'}
         >
-          Chat Settings
+          {t('chat.chatSetting')}
         </Button>
       </div>
     </section>

web/src/pages/next-search/embed-app-modal.tsx

@@ -1,4 +1,3 @@
-import { useFetchTokenListBeforeOtherStep } from '@/components/embed-dialog/use-show-embed-dialog';
 import HightLightMarkdown from '@/components/highlight-markdown';
 import { Modal } from '@/components/ui/modal/modal';
 import { RAGFlowSelect } from '@/components/ui/select';
@@ -9,7 +8,7 @@ import {
 } from '@/constants/common';
 import { useTranslate } from '@/hooks/common-hooks';
 import { message } from 'antd';
-import { useCallback, useEffect, useMemo, useState } from 'react';
+import { useCallback, useMemo, useState } from 'react';
 
 type IEmbedAppModalProps = {
   open: any;
@@ -18,17 +17,13 @@ type IEmbedAppModalProps = {
   from: string;
   setOpen: (e: any) => void;
   tenantId: string;
+  beta?: string;
 };
 
 const EmbedAppModal = (props: IEmbedAppModalProps) => {
   const { t } = useTranslate('search');
-  const { open, setOpen, token = '', from, url, tenantId } = props;
-  const { beta, handleOperate } = useFetchTokenListBeforeOtherStep();
-  useEffect(() => {
-    if (open && !beta) {
-      handleOperate();
-    }
-  }, [handleOperate, open, beta]);
+  const { open, setOpen, token = '', from, url, tenantId, beta = '' } = props;
+
   const [hideAvatar, setHideAvatar] = useState(false);
   const [locale, setLocale] = useState('');
 

web/src/pages/next-search/hooks.ts

@@ -234,7 +234,10 @@ export const useTestRetrieval = (
     setSelectedDocumentIds,
   };
 };
-export const useFetchRelatedQuestions = (tenantId?: string) => {
+export const useFetchRelatedQuestions = (
+  tenantId?: string,
+  searchId?: string,
+) => {
   const [searchParams] = useSearchParams();
   const shared_id = searchParams.get('shared_id');
   const retrievalTestFunc = shared_id
@@ -251,6 +254,7 @@ export const useFetchRelatedQuestions = (tenantId?: string) => {
       const { data } = await retrievalTestFunc({
         question,
         tenant_id: tenantId,
+        search_id: searchId,
       });
 
       return data?.data ?? [];
@@ -260,7 +264,12 @@ export const useFetchRelatedQuestions = (tenantId?: string) => {
   return { data, loading, fetchRelatedQuestions: mutateAsync };
 };
 
-export const useSendQuestion = (kbIds: string[], tenantId?: string) => {
+export const useSendQuestion = (
+  kbIds: string[],
+  tenantId?: string,
+  searchId: string = '',
+  related_search: boolean = false,
+) => {
   const { sharedId } = useGetSharedSearchParams();
   const { send, answer, done, stopOutputMessage } = useSendMessageWithSse(
     sharedId ? api.askShare : api.ask,
@@ -271,7 +280,7 @@ export const useSendQuestion = (kbIds: string[], tenantId?: string) => {
   const [sendingLoading, setSendingLoading] = useState(false);
   const [currentAnswer, setCurrentAnswer] = useState({} as IAnswer);
   const { fetchRelatedQuestions, data: relatedQuestions } =
-    useFetchRelatedQuestions(tenantId);
+    useFetchRelatedQuestions(tenantId, searchId);
   const [searchStr, setSearchStr] = useState<string>('');
   const [isFirstRender, setIsFirstRender] = useState(true);
   const [selectedDocumentIds, setSelectedDocumentIds] = useState<string[]>([]);
@@ -286,7 +295,7 @@ export const useSendQuestion = (kbIds: string[], tenantId?: string) => {
       setIsFirstRender(false);
       setCurrentAnswer({} as IAnswer);
       setSendingLoading(true);
-      send({ kb_ids: kbIds, question: q, tenantId });
+      send({ kb_ids: kbIds, question: q, tenantId, search_id: searchId });
       testChunk({
         kb_id: kbIds,
         highlight: true,
@@ -295,7 +304,9 @@ export const useSendQuestion = (kbIds: string[], tenantId?: string) => {
         size: pagination.pageSize,
       });
 
-      fetchRelatedQuestions(q);
+      if (related_search) {
+        fetchRelatedQuestions(q);
+      }
     },
     [
       send,
@@ -305,6 +316,8 @@ export const useSendQuestion = (kbIds: string[], tenantId?: string) => {
       setPagination,
       pagination.pageSize,
       tenantId,
+      searchId,
+      related_search,
     ],
   );
 
@@ -408,7 +421,12 @@ export const useSearching = ({
     isSearchStrEmpty,
     setSearchStr,
     stopOutputMessage,
-  } = useSendQuestion(searchData.search_config.kb_ids, tenantId as string);
+  } = useSendQuestion(
+    searchData.search_config.kb_ids,
+    tenantId as string,
+    searchData.id,
+    searchData.search_config.related_search,
+  );
 
   const handleSearchStrChange = useCallback(
     (value: string) => {
@@ -435,15 +453,20 @@ export const useSearching = ({
     showMindMapModal,
     mindMapLoading,
     mindMap,
-  } = useShowMindMapDrawer(searchData.search_config.kb_ids, searchStr);
+  } = useShowMindMapDrawer(
+    searchData.search_config.kb_ids,
+    searchStr,
+    searchData.id,
+  );
   const { chunks, total } = useSelectTestingResult();
 
   const handleSearch = useCallback(
     (value: string) => {
       sendQuestion(value);
       setSearchStr?.(value);
+      hideMindMapModal();
     },
-    [setSearchStr, sendQuestion],
+    [setSearchStr, sendQuestion, hideMindMapModal],
   );
 
   const { pagination, setPagination } = useGetPaginationWithRouter();

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

@@ -1,3 +1,4 @@
+import { useFetchTokenListBeforeOtherStep } from '@/components/embed-dialog/use-show-embed-dialog';
 import { PageHeader } from '@/components/page-header';
 import {
   Breadcrumb,
@@ -10,7 +11,10 @@ import {
 import { Button } from '@/components/ui/button';
 import { SharedFrom } from '@/constants/chat';
 import { useNavigatePage } from '@/hooks/logic-hooks/navigate-hooks';
-import { useFetchTenantInfo } from '@/hooks/user-setting-hooks';
+import {
+  useFetchTenantInfo,
+  useFetchUserInfo,
+} from '@/hooks/user-setting-hooks';
 import { Send, Settings } from 'lucide-react';
 import { useEffect, useState } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -29,11 +33,13 @@ export default function SearchPage() {
   const { navigateToSearchList } = useNavigatePage();
   const [isSearching, setIsSearching] = useState(false);
   const { data: SearchData } = useFetchSearchDetail();
+  const { beta, handleOperate } = useFetchTokenListBeforeOtherStep();
 
   const [openSetting, setOpenSetting] = useState(false);
   const [openEmbed, setOpenEmbed] = useState(false);
   const [searchText, setSearchText] = useState('');
   const { data: tenantInfo } = useFetchTenantInfo();
+  const { data: userInfo } = useFetchUserInfo();
   const tenantId = tenantInfo.tenant_id;
   const { t } = useTranslation();
   const { openSetting: checkOpenSetting } = useCheckSettings(
@@ -75,6 +81,7 @@ export default function SearchPage() {
                 isSearching={isSearching}
                 searchText={searchText}
                 setSearchText={setSearchText}
+                userInfo={userInfo}
               />
             </div>
           )}
@@ -105,6 +112,7 @@ export default function SearchPage() {
             token={SearchData?.id as string}
             from={SharedFrom.Search}
             tenantId={tenantId}
+            beta={beta}
           />
         }
         {
@@ -121,7 +129,14 @@ export default function SearchPage() {
       <div className="absolute right-5 top-12 ">
         <Button
           className="bg-text-primary  text-bg-base border-b-[#00BEB4] border-b-2"
-          onClick={() => setOpenEmbed(!openEmbed)}
+          onClick={() => {
+            handleOperate().then((res) => {
+              console.log(res, 'res');
+              if (res) {
+                setOpenEmbed(!openEmbed);
+              }
+            });
+          }}
         >
           <Send />
           <div>{t('search.embedApp')}</div>

web/src/pages/next-search/markdown-content/index.tsx

@@ -4,7 +4,7 @@ import { IReference, IReferenceChunk } from '@/interfaces/database/chat';
 import { getExtension } from '@/utils/document-util';
 import { InfoCircleOutlined } from '@ant-design/icons';
 import DOMPurify from 'dompurify';
-import { useCallback, useEffect, useMemo } from 'react';
+import { memo, useCallback, useEffect, useMemo } from 'react';
 import Markdown from 'react-markdown';
 import reactStringReplace from 'react-string-replace';
 import SyntaxHighlighter from 'react-syntax-highlighter';
@@ -82,18 +82,18 @@ const MarkdownContent = ({
     (
       documentId: string,
       chunk: IReferenceChunk,
-      isPdf: boolean,
-      documentUrl?: string,
+      // isPdf: boolean,
+      // documentUrl?: string,
     ) =>
       () => {
-        if (!isPdf) {
-          if (!documentUrl) {
-            return;
-          }
-          window.open(documentUrl, '_blank');
-        } else {
-          clickDocumentButton?.(documentId, chunk);
-        }
+        // if (!isPdf) {
+        //   if (!documentUrl) {
+        //     return;
+        //   }
+        //   window.open(documentUrl, '_blank');
+        // } else {
+        clickDocumentButton?.(documentId, chunk);
+        // }
       },
     [clickDocumentButton],
   );
@@ -144,7 +144,6 @@ const MarkdownContent = ({
   const getPopoverContent = useCallback(
     (chunkIndex: number) => {
       const {
-        documentUrl,
         fileThumbnail,
         fileExtension,
         imageId,
@@ -198,8 +197,8 @@ const MarkdownContent = ({
                   onClick={handleDocumentButtonClick(
                     documentId,
                     chunkItem,
-                    fileExtension === 'pdf',
-                    documentUrl,
+                    // fileExtension === 'pdf',
+                    // documentUrl,
                   )}
                 >
                   {document?.doc_name}
@@ -218,8 +217,7 @@ const MarkdownContent = ({
       let replacedText = reactStringReplace(text, currentReg, (match, i) => {
         const chunkIndex = getChunkIndex(match);
 
-        const { documentUrl, fileExtension, imageId, chunkItem, documentId } =
-          getReferenceInfo(chunkIndex);
+        const { imageId, chunkItem, documentId } = getReferenceInfo(chunkIndex);
 
         const docType = chunkItem?.doc_type;
 
@@ -232,8 +230,8 @@ const MarkdownContent = ({
                 ? handleDocumentButtonClick(
                     documentId,
                     chunkItem,
-                    fileExtension === 'pdf',
-                    documentUrl,
+                    // fileExtension === 'pdf',
+                    // documentUrl,
                   )
                 : () => {}
             }
@@ -243,7 +241,9 @@ const MarkdownContent = ({
             <PopoverTrigger>
               <InfoCircleOutlined className={styles.referenceIcon} />
             </PopoverTrigger>
-            <PopoverContent>{getPopoverContent(chunkIndex)}</PopoverContent>
+            <PopoverContent className="!w-fit">
+              {getPopoverContent(chunkIndex)}
+            </PopoverContent>
           </Popover>
         );
       });
@@ -292,4 +292,4 @@ const MarkdownContent = ({
   );
 };
 
-export default MarkdownContent;
+export default memo(MarkdownContent);

web/src/pages/next-search/mindmap-drawer.tsx

@@ -27,7 +27,7 @@ const MindMapDrawer = ({ data, hideModal, visible, loading }: IProps) => {
         />
       </div>
       {loading && (
-        <div className="absolute top-48">
+        <div className=" rounded-lg p-4 w-full h-full">
           <Progress value={percent} className="h-1 flex-1 min-w-10" />
         </div>
       )}

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

@@ -1,5 +1,5 @@
 import { Input } from '@/components/originui/input';
-import { useFetchUserInfo } from '@/hooks/user-setting-hooks';
+import { IUserInfo } from '@/interfaces/database/user-setting';
 import { cn } from '@/lib/utils';
 import { Search } from 'lucide-react';
 import { Dispatch, SetStateAction } from 'react';
@@ -12,13 +12,15 @@ export default function SearchPage({
   setIsSearching,
   searchText,
   setSearchText,
+  userInfo,
 }: {
   isSearching: boolean;
   setIsSearching: Dispatch<SetStateAction<boolean>>;
   searchText: string;
   setSearchText: Dispatch<SetStateAction<string>>;
+  userInfo?: IUserInfo;
 }) {
-  const { data: userInfo } = useFetchUserInfo();
+  // const { data: userInfo } = useFetchUserInfo();
   const { t } = useTranslation();
   return (
     <section className="relative w-full flex transition-all justify-center items-center mt-32">
@@ -38,7 +40,11 @@ export default function SearchPage({
               <>
                 <p className="mb-4 transition-opacity">👋 Hi there</p>
                 <p className="mb-10 transition-opacity">
-                  {t('search.welcomeBack')}, {userInfo?.nickname}
+                  {userInfo && (
+                    <>
+                      {t('search.welcomeBack')}, {userInfo.nickname}
+                    </>
+                  )}
                 </p>
               </>
             )}

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

@@ -18,6 +18,7 @@ import {
 } from '@/components/ui/multi-select';
 import { RAGFlowSelect } from '@/components/ui/select';
 import { Switch } from '@/components/ui/switch';
+import { Textarea } from '@/components/ui/textarea';
 import { useFetchKnowledgeList } from '@/hooks/knowledge-hooks';
 import {
   useComposeLlmOptionsByModelTypes,
@@ -64,7 +65,7 @@ const SearchSettingFormSchema = z
     description: z.string().optional(),
     search_config: z.object({
       kb_ids: z.array(z.string()).min(1, 'At least one dataset is required'),
-      vector_similarity_weight: z.number().min(0).max(100),
+      vector_similarity_weight: z.number().min(0).max(1),
       web_search: z.boolean(),
       similarity_threshold: z.number(),
       use_kg: z.boolean(),
@@ -128,7 +129,7 @@ const SearchSetting: React.FC<SearchSettingProps> = ({
             : 0.3) || 0.3,
         web_search: search_config?.web_search || false,
         doc_ids: [],
-        similarity_threshold: 0.0,
+        similarity_threshold: search_config?.similarity_threshold || 0.2,
         use_kg: false,
         rerank_id: search_config?.rerank_id || '',
         use_rerank: search_config?.rerank_id ? true : false,
@@ -417,7 +418,7 @@ const SearchSetting: React.FC<SearchSettingProps> = ({
                 <FormItem>
                   <FormLabel>{t('search.description')}</FormLabel>
                   <FormControl>
-                    <Input
+                    <Textarea
                       placeholder="You are an intelligent assistant."
                       {...field}
                       onFocus={() => {
@@ -466,7 +467,41 @@ const SearchSetting: React.FC<SearchSettingProps> = ({
                 </FormItem>
               )}
             />
-
+            <FormField
+              control={formMethods.control}
+              name="search_config.similarity_threshold"
+              render={({ field }) => (
+                <FormItem>
+                  <FormLabel>Similarity Threshold</FormLabel>
+                  <div
+                    className={cn(
+                      'flex items-center gap-4 justify-between',
+                      className,
+                    )}
+                  >
+                    <FormControl>
+                      <SingleFormSlider
+                        {...field}
+                        max={1}
+                        min={0}
+                        step={0.01}
+                      ></SingleFormSlider>
+                    </FormControl>
+                    <FormControl>
+                      <Input
+                        type={'number'}
+                        className="h-7 w-20 bg-bg-card"
+                        max={1}
+                        min={0}
+                        step={0.01}
+                        {...field}
+                      ></Input>
+                    </FormControl>
+                  </div>
+                  <FormMessage />
+                </FormItem>
+              )}
+            />
             {/* Keyword Similarity Weight */}
             <FormField
               control={formMethods.control}
@@ -474,7 +509,7 @@ const SearchSetting: React.FC<SearchSettingProps> = ({
               render={({ field }) => (
                 <FormItem>
                   <FormLabel>
-                    <span className="text-destructive mr-1"> *</span>Keyword
+                    <span className="text-destructive mr-1"> *</span>Vector
                     Similarity Weight
                   </FormLabel>
                   <div
@@ -608,7 +643,7 @@ const SearchSetting: React.FC<SearchSettingProps> = ({
             )}
 
             {/* Feature Controls */}
-            <FormField
+            {/* <FormField
               control={formMethods.control}
               name="search_config.web_search"
               render={({ field }) => (
@@ -622,7 +657,7 @@ const SearchSetting: React.FC<SearchSettingProps> = ({
                   <FormLabel>{t('search.enableWebSearch')}</FormLabel>
                 </FormItem>
               )}
-            />
+            /> */}
 
             <FormField
               control={formMethods.control}
@@ -666,9 +701,9 @@ const SearchSetting: React.FC<SearchSettingProps> = ({
                   setOpen(false);
                 }}
               >
-                {t('modal.cancelText')}
+                {t('search.cancelText')}
               </Button>
-              <Button type="submit">{t('modal.okText')}</Button>
+              <Button type="submit">{t('search.okText')}</Button>
             </div>
           </form>
         </Form>

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

@@ -276,7 +276,7 @@ export default function SearchingView({
         </div>
 
         {mindMapVisible && (
-          <div className="flex-1 h-[88dvh] z-30 ml-8 mt-5">
+          <div className="flex-1 h-[88dvh] z-30 ml-32 mt-5">
             <MindMapDrawer
               visible={mindMapVisible}
               hideModal={hideMindMapModal}

web/src/pages/next-search/share/index.tsx

@@ -1,26 +1,28 @@
 import { RAGFlowAvatar } from '@/components/ragflow-avatar';
 import i18n from '@/locales/config';
-import { useEffect } from 'react';
+import { useEffect, useState } from 'react';
 import {
   ISearchAppDetailProps,
   useFetchSearchDetail,
 } from '../../next-searches/hooks';
 import { useGetSharedSearchParams, useSearching } from '../hooks';
 import '../index.less';
-import SearchingView from '../search-view';
-export default function SearchingPage() {
+import SearchHome from '../search-home';
+import SearchingPage from '../searching';
+export default function ShareSeachPage() {
   const { tenantId, locale, visibleAvatar } = useGetSharedSearchParams();
   const {
     data: searchData = {
       search_config: { kb_ids: [] },
     } as unknown as ISearchAppDetailProps,
   } = useFetchSearchDetail(tenantId as string);
+  const [isSearching, setIsSearching] = useState(false);
+  const [searchText, setSearchText] = useState('');
   const searchingParam = useSearching({
     data: searchData,
   });
 
   useEffect(() => {
-    console.log('locale', locale, i18n.language);
     if (locale && i18n.language !== locale) {
       i18n.changeLanguage(locale);
     }
@@ -28,15 +30,36 @@ export default function SearchingPage() {
   return (
     <>
       {visibleAvatar && (
-        <div className="flex justify-start items-center gap-1 mx-6 mt-6 text-text-primary">
+        <div className="flex justify-start items-center gap-2 mx-6 mt-6 text-text-primary">
           <RAGFlowAvatar
+            className="size-6"
             avatar={searchData.avatar}
             name={searchData.name}
           ></RAGFlowAvatar>
           <div>{searchData.name}</div>
         </div>
       )}
-      <SearchingView {...searchingParam} searchData={searchData} />;
+      {/* <SearchingView {...searchingParam} searchData={searchData} />; */}
+      {!isSearching && (
+        <div className="animate-fade-in-down">
+          <SearchHome
+            setIsSearching={setIsSearching}
+            isSearching={isSearching}
+            searchText={searchText}
+            setSearchText={setSearchText}
+          />
+        </div>
+      )}
+      {isSearching && (
+        <div className="animate-fade-in-up">
+          <SearchingPage
+            setIsSearching={setIsSearching}
+            searchText={searchText}
+            setSearchText={setSearchText}
+            data={searchData as ISearchAppDetailProps}
+          />
+        </div>
+      )}
     </>
   );
 }

web/src/pages/next-searches/hooks.ts

@@ -6,7 +6,6 @@ import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
 import { useCallback, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import { useParams, useSearchParams } from 'umi';
-
 interface CreateSearchProps {
   name: string;
   description?: string;
@@ -122,40 +121,6 @@ interface DeleteSearchResponse {
   message: string;
 }
 
-export const useDeleteSearch = () => {
-  const { t } = useTranslation();
-
-  const {
-    data,
-    isError,
-    mutateAsync: deleteSearchMutation,
-  } = useMutation<DeleteSearchResponse, Error, DeleteSearchProps>({
-    mutationKey: ['deleteSearch'],
-    mutationFn: async (props) => {
-      const response = await searchService.deleteSearch(props);
-      if (response.code !== 0) {
-        throw new Error(response.message || 'Failed to delete search');
-      }
-      return response;
-    },
-    onSuccess: () => {
-      message.success(t('message.deleted'));
-    },
-    onError: (error) => {
-      message.error(t('message.error', { error: error.message }));
-    },
-  });
-
-  const deleteSearch = useCallback(
-    (props: DeleteSearchProps) => {
-      return deleteSearchMutation(props);
-    },
-    [deleteSearchMutation],
-  );
-
-  return { data, isError, deleteSearch };
-};
-
 export interface IllmSettingProps {
   llm_id: string;
   parameter: string;
@@ -237,6 +202,42 @@ export const useFetchSearchDetail = (tenantId?: string) => {
   return { data: data?.data, isLoading, isError };
 };
 
+export const useDeleteSearch = () => {
+  const { t } = useTranslation();
+  const queryClient = useQueryClient();
+  const {
+    data,
+    isError,
+    mutateAsync: deleteSearchMutation,
+  } = useMutation<DeleteSearchResponse, Error, DeleteSearchProps>({
+    mutationKey: ['deleteSearch'],
+    mutationFn: async (props) => {
+      const { data: response } = await searchService.deleteSearch(props);
+      if (response.code !== 0) {
+        throw new Error(response.message || 'Failed to delete search');
+      }
+
+      queryClient.invalidateQueries({ queryKey: ['searchList'] });
+      return response;
+    },
+    onSuccess: () => {
+      message.success(t('message.deleted'));
+    },
+    onError: (error) => {
+      message.error(t('message.error', { error: error.message }));
+    },
+  });
+
+  const deleteSearch = useCallback(
+    (props: DeleteSearchProps) => {
+      return deleteSearchMutation(props);
+    },
+    [deleteSearchMutation],
+  );
+
+  return { data, isError, deleteSearch };
+};
+
 export type IUpdateSearchProps = Omit<ISearchAppDetailProps, 'id'> & {
   search_id: string;
 };

web/src/pages/search/hooks.ts

@@ -217,7 +217,11 @@ export const useTestRetrieval = (
   };
 };
 
-export const useShowMindMapDrawer = (kbIds: string[], question: string) => {
+export const useShowMindMapDrawer = (
+  kbIds: string[],
+  question: string,
+  searchId = '',
+) => {
   const { visible, showModal, hideModal } = useSetModalState();
   const ref = useRef<any>();
 
@@ -228,7 +232,7 @@ export const useShowMindMapDrawer = (kbIds: string[], question: string) => {
   } = useSearchFetchMindMap();
 
   const handleShowModal = useCallback(() => {
-    const searchParams = { question: trim(question), kb_ids: kbIds };
+    const searchParams = { question: trim(question), kb_ids: kbIds, searchId };
     if (
       !isEmpty(searchParams.question) &&
       !isEqual(searchParams, ref.current)
@@ -237,7 +241,7 @@ export const useShowMindMapDrawer = (kbIds: string[], question: string) => {
       fetchMindMap(searchParams);
     }
     showModal();
-  }, [fetchMindMap, showModal, question, kbIds]);
+  }, [fetchMindMap, showModal, question, kbIds, searchId]);
 
   return {
     mindMap,