Feat: Support metadata auto filer for Search. (#9524)

### What problem does this PR solve?

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

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)

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
 
@@ -723,6 +723,7 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
         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 = question,
         embd_mdl=embd_mdl,
@@ -740,26 +741,12 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
     )
 
     knowledges = kb_prompt(kbinfos, max_tokens)
-    prompt = """
+    sys_prompt = PROMPT_JINJA_ENV.from_string(ASK_SUMMARY).render(knowledge="\n".join(knowledges))
-    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
-    %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]
@@ -777,7 +764,7 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
         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)

rag/nlp/__init__.py

@@ -612,10 +612,6 @@ def naive_merge_with_images(texts, images, chunk_token_num=128, delimiter="\n。
                     continue
                 add_chunk(sub_sec, image)
 
-        for img in images:
-            if isinstance(img, Image.Image):
-                img.close()
-
     return cks, result_images
 
 def docx_question_level(p, bull=-1):

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)