Agent plans tasks by referring to its own prompt. (#9315)

### What problem does this PR solve?

Fixes the issue in the analyze_task execution flow where the Lead Agent
was not utilizing its own sys_prompt during task analysis, resulting in
incorrect or incomplete task planning.
https://github.com/infiniflow/ragflow/issues/9294
### Type of change

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

agent/component/agent_with_tools.py

@@ -165,7 +165,7 @@ class Agent(LLM, ToolBase):
         _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
         use_tools = []
         ans = ""
-        for delta_ans, tk in self._react_with_tools_streamly(msg, use_tools):
+        for delta_ans, tk in self._react_with_tools_streamly(prompt, msg, use_tools):
             ans += delta_ans
 
         if ans.find("**ERROR**") >= 0:
@@ -185,7 +185,7 @@ class Agent(LLM, ToolBase):
         _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
         answer_without_toolcall = ""
         use_tools = []
-        for delta_ans,_ in self._react_with_tools_streamly(msg, use_tools):
+        for delta_ans,_ in self._react_with_tools_streamly(prompt, msg, use_tools):
             if delta_ans.find("**ERROR**") >= 0:
                 if self.get_exception_default_value():
                     self.set_output("content", self.get_exception_default_value())
@@ -208,7 +208,7 @@ class Agent(LLM, ToolBase):
                                                   ]):
             yield delta_ans
 
-    def _react_with_tools_streamly(self, history: list[dict], use_tools):
+    def _react_with_tools_streamly(self, prompt, history: list[dict], use_tools):
         token_count = 0
         tool_metas = self.tool_meta
         hist = deepcopy(history)
@@ -221,7 +221,7 @@ class Agent(LLM, ToolBase):
 
         def use_tool(name, args):
             nonlocal hist, use_tools, token_count,last_calling,user_request
-            print(f"{last_calling=} == {name=}", )
+            logging.info(f"{last_calling=} == {name=}")
             # Summarize of function calling
             #if all([
             #    isinstance(self.toolcall_session.get_tool_obj(name), Agent),
@@ -275,7 +275,7 @@ class Agent(LLM, ToolBase):
             else:
                 hist.append({"role": "user", "content": content})
 
-        task_desc = analyze_task(self.chat_mdl, user_request, tool_metas)
+        task_desc = analyze_task(self.chat_mdl, prompt, user_request, tool_metas)
         self.callback("analyze_task", {}, task_desc)
         for _ in range(self._param.max_rounds + 1):
             response, tk = next_step(self.chat_mdl, hist, tool_metas, task_desc)

rag/prompts/analyze_task_user.md

@@ -4,6 +4,9 @@ Task: {{ task }}
 
 Context: {{ context }}
 
+**Agent Prompt**
+{{ agent_prompt }}
+
 **Analysis Requirements:**
 1. Is it just a small talk? (If yes, no further plan or analysis is needed)
 2. What is the core objective of the task?

rag/prompts/prompts.py

@@ -335,13 +335,13 @@ def form_history(history, limit=-6):
     return context
 
 
-def analyze_task(chat_mdl, task_name, tools_description: list[dict]):
+def analyze_task(chat_mdl, prompt, task_name, tools_description: list[dict]):
     tools_desc = tool_schema(tools_description)
     context = ""
 
     template = PROMPT_JINJA_ENV.from_string(ANALYZE_TASK_USER)
-
-    kwd = chat_mdl.chat(ANALYZE_TASK_SYSTEM,[{"role": "user", "content": template.render(task=task_name, context=context, tools_desc=tools_desc)}], {})
+    context = template.render(task=task_name, context=context, agent_prompt=prompt, tools_desc=tools_desc)
+    kwd = chat_mdl.chat(ANALYZE_TASK_SYSTEM,[{"role": "user", "content": context}], {})
     if isinstance(kwd, tuple):
         kwd = kwd[0]
     kwd = re.sub(r"^.*</think>", "", kwd, flags=re.DOTALL)