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https://github.com/infiniflow/ragflow.git
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Feat: enhance webhook response to include status and success fields and simplify ReAct agent (#12091)
### What problem does this PR solve? change： enhance webhook response to include status and success fields and simplify ReAct agent ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
buua436
@ -29,8 +29,8 @@ from api.db.services.llm_service import LLMBundle
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from api.db.services.tenant_llm_service import TenantLLMService
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from api.db.services.mcp_server_service import MCPServerService
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from common.connection_utils import timeout
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from rag.prompts.generator import next_step_async, COMPLETE_TASK, analyze_task_async, \
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citation_prompt, reflect_async, kb_prompt, citation_plus, full_question, message_fit_in, structured_output_prompt
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from rag.prompts.generator import next_step_async, COMPLETE_TASK, \
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citation_prompt, kb_prompt, citation_plus, full_question, message_fit_in, structured_output_prompt
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from common.mcp_tool_call_conn import MCPToolCallSession, mcp_tool_metadata_to_openai_tool
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from agent.component.llm import LLMParam, LLM
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@ -246,7 +246,7 @@ class Agent(LLM, ToolBase):
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_, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
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answer_without_toolcall = ""
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use_tools = []
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async for delta_ans, _ in self._react_with_tools_streamly_async(prompt, msg, use_tools, user_defined_prompt):
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async for delta_ans, _ in self._react_with_tools_streamly_async_simple(prompt, msg, use_tools, user_defined_prompt):
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if self.check_if_canceled("Agent streaming"):
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return
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@ -264,7 +264,7 @@ class Agent(LLM, ToolBase):
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if use_tools:
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self.set_output("use_tools", use_tools)
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async def _react_with_tools_streamly_async(self, prompt, history: list[dict], use_tools, user_defined_prompt={}, schema_prompt: str = ""):
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async def _react_with_tools_streamly_async_simple(self, prompt, history: list[dict], use_tools, user_defined_prompt={}, schema_prompt: str = ""):
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token_count = 0
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tool_metas = self.tool_meta
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hist = deepcopy(history)
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@ -276,6 +276,28 @@ class Agent(LLM, ToolBase):
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else:
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user_request = history[-1]["content"]
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def build_task_desc(prompt: str, user_request: str, tool_metas: list[dict], user_defined_prompt: dict | None = None) -> str:
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"""Build a minimal task_desc by concatenating prompt, query, and tool schemas."""
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user_defined_prompt = user_defined_prompt or {}
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tools_json = json.dumps(tool_metas, ensure_ascii=False, indent=2)
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task_desc = (
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"### Agent Prompt\n"
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f"{prompt}\n\n"
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"### User Request\n"
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f"{user_request}\n\n"
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"### Tools (schemas)\n"
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f"{tools_json}\n"
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)
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if user_defined_prompt:
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udp_json = json.dumps(user_defined_prompt, ensure_ascii=False, indent=2)
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task_desc += "\n### User Defined Prompts\n" + udp_json + "\n"
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return task_desc
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async def use_tool_async(name, args):
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nonlocal hist, use_tools, last_calling
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logging.info(f"{last_calling=} == {name=}")
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@ -286,9 +308,6 @@ class Agent(LLM, ToolBase):
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"arguments": args,
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"results": tool_response
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})
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# self.callback("add_memory", {}, "...")
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#self.add_memory(hist[-2]["content"], hist[-1]["content"], name, args, str(tool_response), user_defined_prompt)
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return name, tool_response
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async def complete():
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@ -326,6 +345,21 @@ class Agent(LLM, ToolBase):
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self.callback("gen_citations", {}, txt, elapsed_time=timer()-st)
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def build_observation(tool_call_res: list[tuple]) -> str:
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"""
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Build a Observation from tool call results.
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No LLM involved.
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"""
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if not tool_call_res:
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return ""
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lines = ["Observation:"]
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for name, result in tool_call_res:
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lines.append(f"[{name} result]")
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lines.append(str(result))
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return "\n".join(lines)
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def append_user_content(hist, content):
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if hist[-1]["role"] == "user":
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hist[-1]["content"] += content
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@ -333,7 +367,7 @@ class Agent(LLM, ToolBase):
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hist.append({"role": "user", "content": content})
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st = timer()
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task_desc = await analyze_task_async(self.chat_mdl, prompt, user_request, tool_metas, user_defined_prompt)
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task_desc = build_task_desc(prompt, user_request, tool_metas, user_defined_prompt)
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self.callback("analyze_task", {}, task_desc, elapsed_time=timer()-st)
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for _ in range(self._param.max_rounds + 1):
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if self.check_if_canceled("Agent streaming"):
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@ -364,7 +398,7 @@ class Agent(LLM, ToolBase):
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results = await asyncio.gather(*tool_tasks) if tool_tasks else []
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st = timer()
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reflection = await reflect_async(self.chat_mdl, hist, results, user_defined_prompt)
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reflection = build_observation(results)
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append_user_content(hist, reflection)
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self.callback("reflection", {}, str(reflection), elapsed_time=timer()-st)
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@ -393,6 +427,135 @@ Respond immediately with your final comprehensive answer.
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async for txt, tkcnt in complete():
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yield txt, tkcnt
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# async def _react_with_tools_streamly_async(self, prompt, history: list[dict], use_tools, user_defined_prompt={}, schema_prompt: str = ""):
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# token_count = 0
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# tool_metas = self.tool_meta
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# hist = deepcopy(history)
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# last_calling = ""
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# if len(hist) > 3:
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# st = timer()
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# user_request = await full_question(messages=history, chat_mdl=self.chat_mdl)
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# self.callback("Multi-turn conversation optimization", {}, user_request, elapsed_time=timer()-st)
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# else:
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# user_request = history[-1]["content"]
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# async def use_tool_async(name, args):
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# nonlocal hist, use_tools, last_calling
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# logging.info(f"{last_calling=} == {name=}")
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# last_calling = name
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# tool_response = await self.toolcall_session.tool_call_async(name, args)
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# use_tools.append({
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# "name": name,
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# "arguments": args,
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# "results": tool_response
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# })
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# # self.callback("add_memory", {}, "...")
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# #self.add_memory(hist[-2]["content"], hist[-1]["content"], name, args, str(tool_response), user_defined_prompt)
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# return name, tool_response
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# async def complete():
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# nonlocal hist
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# need2cite = self._param.cite and self._canvas.get_reference()["chunks"] and self._id.find("-->") < 0
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# if schema_prompt:
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# need2cite = False
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# cited = False
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# if hist and hist[0]["role"] == "system":
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# if schema_prompt:
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# hist[0]["content"] += "\n" + schema_prompt
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# if need2cite and len(hist) < 7:
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# hist[0]["content"] += citation_prompt()
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# cited = True
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# yield "", token_count
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# _hist = hist
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# if len(hist) > 12:
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# _hist = [hist[0], hist[1], *hist[-10:]]
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# entire_txt = ""
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# async for delta_ans in self._generate_streamly(_hist):
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# if not need2cite or cited:
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# yield delta_ans, 0
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# entire_txt += delta_ans
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# if not need2cite or cited:
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# return
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# st = timer()
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# txt = ""
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# async for delta_ans in self._gen_citations_async(entire_txt):
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# if self.check_if_canceled("Agent streaming"):
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# return
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# yield delta_ans, 0
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# txt += delta_ans
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# self.callback("gen_citations", {}, txt, elapsed_time=timer()-st)
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# def append_user_content(hist, content):
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# if hist[-1]["role"] == "user":
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# hist[-1]["content"] += content
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# else:
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# hist.append({"role": "user", "content": content})
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# st = timer()
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# task_desc = await analyze_task_async(self.chat_mdl, prompt, user_request, tool_metas, user_defined_prompt)
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# self.callback("analyze_task", {}, task_desc, elapsed_time=timer()-st)
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# for _ in range(self._param.max_rounds + 1):
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# if self.check_if_canceled("Agent streaming"):
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# return
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# response, tk = await next_step_async(self.chat_mdl, hist, tool_metas, task_desc, user_defined_prompt)
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# # self.callback("next_step", {}, str(response)[:256]+"...")
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# token_count += tk or 0
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# hist.append({"role": "assistant", "content": response})
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# try:
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# functions = json_repair.loads(re.sub(r"```.*", "", response))
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# if not isinstance(functions, list):
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# raise TypeError(f"List should be returned, but `{functions}`")
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# for f in functions:
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# if not isinstance(f, dict):
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# raise TypeError(f"An object type should be returned, but `{f}`")
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# tool_tasks = []
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# for func in functions:
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# name = func["name"]
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# args = func["arguments"]
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# if name == COMPLETE_TASK:
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# append_user_content(hist, f"Respond with a formal answer. FORGET(DO NOT mention) about `{COMPLETE_TASK}`. The language for the response MUST be as the same as the first user request.\n")
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# async for txt, tkcnt in complete():
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# yield txt, tkcnt
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# return
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# tool_tasks.append(asyncio.create_task(use_tool_async(name, args)))
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# results = await asyncio.gather(*tool_tasks) if tool_tasks else []
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# st = timer()
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# reflection = await reflect_async(self.chat_mdl, hist, results, user_defined_prompt)
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# append_user_content(hist, reflection)
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# self.callback("reflection", {}, str(reflection), elapsed_time=timer()-st)
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# except Exception as e:
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# logging.exception(msg=f"Wrong JSON argument format in LLM ReAct response: {e}")
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# e = f"\nTool call error, please correct the input parameter of response format and call it again.\n *** Exception ***\n{e}"
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# append_user_content(hist, str(e))
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# logging.warning( f"Exceed max rounds: {self._param.max_rounds}")
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# final_instruction = f"""
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# {user_request}
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# IMPORTANT: You have reached the conversation limit. Based on ALL the information and research you have gathered so far, please provide a DIRECT and COMPREHENSIVE final answer to the original request.
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# Instructions:
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# 1. SYNTHESIZE all information collected during this conversation
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# 2. Provide a COMPLETE response using existing data - do not suggest additional research
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# 3. Structure your response as a FINAL DELIVERABLE, not a plan
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# 4. If information is incomplete, state what you found and provide the best analysis possible with available data
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# 5. DO NOT mention conversation limits or suggest further steps
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# 6. Focus on delivering VALUE with the information already gathered
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# Respond immediately with your final comprehensive answer.
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# """
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# if self.check_if_canceled("Agent final instruction"):
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# return
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# append_user_content(hist, final_instruction)
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# async for txt, tkcnt in complete():
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# yield txt, tkcnt
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async def _gen_citations_async(self, text):
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retrievals = self._canvas.get_reference()
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retrievals = {"chunks": list(retrievals["chunks"].values()), "doc_aggs": list(retrievals["doc_aggs"].values())}
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