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

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### 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)
This commit is contained in:
TeslaZY
2025-08-11 17:05:06 +08:00
committed by GitHub
parent b9c4954c2f
commit 476c56868d
3 changed files with 11 additions and 8 deletions
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10
agent/component/agent_with_tools.py
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@ -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)