ragflow/agent/component/agent_with_tools.py

#
#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#
import logging
import os
import re
from concurrent.futures import ThreadPoolExecutor
from copy import deepcopy
from functools import partial
from typing import Any

import json_repair

from agent.tools.base import LLMToolPluginCallSession, ToolParamBase, ToolBase, ToolMeta
from api.db.services.llm_service import LLMBundle, TenantLLMService
from api.db.services.mcp_server_service import MCPServerService
from api.utils.api_utils import timeout
from rag.prompts import message_fit_in
from rag.prompts.prompts import next_step, COMPLETE_TASK, analyze_task, \
    citation_prompt, reflect, rank_memories, kb_prompt, citation_plus, full_question
from rag.utils.mcp_tool_call_conn import MCPToolCallSession, mcp_tool_metadata_to_openai_tool
from agent.component.llm import LLMParam, LLM


class AgentParam(LLMParam, ToolParamBase):
    """
    Define the Agent component parameters.
    """

    def __init__(self):
        self.meta:ToolMeta = {
                "name": "agent",
                "description": "This is an agent for a specific task.",
                "parameters": {
                    "user_prompt": {
                        "type": "string",
                        "description": "This is the order you need to send to the agent.",
                        "default": "",
                        "required": True
                    },
                    "reasoning": {
                        "type": "string",
                        "description": (
                            "Supervisor's reasoning for choosing the this agent. "
                            "Explain why this agent is being invoked and what is expected of it."
                        ),
                        "required": True
                    },
                    "context": {
                        "type": "string",
                        "description": (
                                "All relevant background information, prior facts, decisions, "
                                "and state needed by the agent to solve the current query. "
                                "Should be as detailed and self-contained as possible."
                            ),
                        "required": True
                    },
                }
            }
        super().__init__()
        self.function_name = "agent"
        self.tools = []
        self.mcp = []
        self.max_rounds = 5
        self.description = ""


class Agent(LLM, ToolBase):
    component_name = "Agent"

    def __init__(self, canvas, id, param: LLMParam):
        LLM.__init__(self, canvas, id, param)
        self.tools = {}
        for cpn in self._param.tools:
            cpn = self._load_tool_obj(cpn)
            self.tools[cpn.get_meta()["function"]["name"]] = cpn

        self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), TenantLLMService.llm_id2llm_type(self._param.llm_id), self._param.llm_id,
                                  max_retries=self._param.max_retries,
                                  retry_interval=self._param.delay_after_error,
                                  max_rounds=self._param.max_rounds,
                                  verbose_tool_use=True
                                  )
        self.tool_meta = [v.get_meta() for _,v in self.tools.items()]

        for mcp in self._param.mcp:
            _, mcp_server = MCPServerService.get_by_id(mcp["mcp_id"])
            tool_call_session = MCPToolCallSession(mcp_server, mcp_server.variables)
            for tnm, meta in mcp["tools"].items():
                self.tool_meta.append(mcp_tool_metadata_to_openai_tool(meta))
                self.tools[tnm] = tool_call_session
        self.callback = partial(self._canvas.tool_use_callback, id)
        self.toolcall_session = LLMToolPluginCallSession(self.tools, self.callback)
        #self.chat_mdl.bind_tools(self.toolcall_session, self.tool_metas)

    def _load_tool_obj(self, cpn: dict) -> object:
        from agent.component import component_class
        param = component_class(cpn["component_name"] + "Param")()
        param.update(cpn["params"])
        try:
            param.check()
        except Exception as e:
            self.set_output("_ERROR", cpn["component_name"] + f" configuration error: {e}")
            raise
        cpn_id = f"{self._id}-->" + cpn.get("name", "").replace(" ", "_")
        return component_class(cpn["component_name"])(self._canvas, cpn_id, param)

    def get_meta(self) -> dict[str, Any]:
        self._param.function_name= self._id.split("-->")[-1]
        m = super().get_meta()
        if hasattr(self._param, "user_prompt") and self._param.user_prompt:
            m["function"]["parameters"]["properties"]["user_prompt"] = self._param.user_prompt
        return m

    def get_input_form(self) -> dict[str, dict]:
        res = {}
        for k, v in self.get_input_elements().items():
            res[k] = {
                "type": "line",
                "name": v["name"]
            }
        for cpn in self._param.tools:
            if not isinstance(cpn, LLM):
                continue
            res.update(cpn.get_input_form())
        return res

    @timeout(os.environ.get("COMPONENT_EXEC_TIMEOUT", 20*60))
    def _invoke(self, **kwargs):
        if kwargs.get("user_prompt"):
            usr_pmt = ""
            if kwargs.get("reasoning"):
                usr_pmt += "\nREASONING:\n{}\n".format(kwargs["reasoning"])
            if kwargs.get("context"):
                usr_pmt += "\nCONTEXT:\n{}\n".format(kwargs["context"])
            if usr_pmt:
                usr_pmt += "\nQUERY:\n{}\n".format(str(kwargs["user_prompt"]))
            else:
                usr_pmt = str(kwargs["user_prompt"])
            self._param.prompts = [{"role": "user", "content": usr_pmt}]

        if not self.tools:
            return LLM._invoke(self, **kwargs)

        prompt, msg = self._prepare_prompt_variables()

        downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else []
        ex = self.exception_handler()
        if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure and not (ex and ex["goto"]):
            self.set_output("content", partial(self.stream_output_with_tools, prompt, msg))
            return

        _, 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(prompt, msg, use_tools):
            ans += delta_ans

        if ans.find("**ERROR**") >= 0:
            logging.error(f"Agent._chat got error. response: {ans}")
            if self.get_exception_default_value():
                self.set_output("content", self.get_exception_default_value())
            else:
                self.set_output("_ERROR", ans)
            return

        self.set_output("content", ans)
        if use_tools:
            self.set_output("use_tools", use_tools)
        return ans

    def stream_output_with_tools(self, prompt, msg):
        _, 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(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())
                    yield self.get_exception_default_value()
                else:
                    self.set_output("_ERROR", delta_ans)
            answer_without_toolcall += delta_ans
            yield delta_ans

        self.set_output("content", answer_without_toolcall)
        if use_tools:
            self.set_output("use_tools", use_tools)

    def _gen_citations(self, text):
        retrievals = self._canvas.get_reference()
        retrievals = {"chunks": list(retrievals["chunks"].values()), "doc_aggs": list(retrievals["doc_aggs"].values())}
        formated_refer = kb_prompt(retrievals, self.chat_mdl.max_length, True)
        for delta_ans in self._generate_streamly([{"role": "system", "content": citation_plus("\n\n".join(formated_refer))},
                                                  {"role": "user", "content": text}
                                                  ]):
            yield delta_ans

    def _react_with_tools_streamly(self, prompt, history: list[dict], use_tools):
        token_count = 0
        tool_metas = self.tool_meta
        hist = deepcopy(history)
        last_calling = ""
        if len(hist) > 3:
            user_request = full_question(messages=history, chat_mdl=self.chat_mdl)
            self.callback("Multi-turn conversation optimization", {}, user_request)
        else:
            user_request = history[-1]["content"]

        def use_tool(name, args):
            nonlocal hist, use_tools, token_count,last_calling,user_request
            logging.info(f"{last_calling=} == {name=}")
            # Summarize of function calling
            #if all([
            #    isinstance(self.toolcall_session.get_tool_obj(name), Agent),
            #    last_calling,
            #    last_calling != name
            #]):
            #    self.toolcall_session.get_tool_obj(name).add2system_prompt(f"The chat history with other agents are as following: \n" + self.get_useful_memory(user_request, str(args["user_prompt"])))
            last_calling = name
            tool_response = self.toolcall_session.tool_call(name, args)
            use_tools.append({
                "name": name,
                "arguments": args,
                "results": tool_response
            })
            # self.callback("add_memory", {}, "...")
            #self.add_memory(hist[-2]["content"], hist[-1]["content"], name, args, str(tool_response))

            return name, tool_response

        def complete():
            nonlocal hist
            need2cite = self._canvas.get_reference()["chunks"] and self._id.find("-->") < 0
            cited = False
            if hist[0]["role"] == "system" and need2cite:
                if len(hist) < 7:
                    hist[0]["content"] += citation_prompt()
                    cited = True
            yield "", token_count

            _hist = hist
            if len(hist) > 12:
                _hist = [hist[0], hist[1], *hist[-10:]]
            entire_txt = ""
            for delta_ans in self._generate_streamly(_hist):
                if not need2cite or cited:
                    yield delta_ans, 0
                entire_txt += delta_ans
            if not need2cite or cited:
                return

            txt = ""
            for delta_ans in self._gen_citations(entire_txt):
                yield delta_ans, 0
                txt += delta_ans

            self.callback("gen_citations", {}, txt)

        def append_user_content(hist, content):
            if hist[-1]["role"] == "user":
                hist[-1]["content"] += content
            else:
                hist.append({"role": "user", "content": content})

        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)
            # self.callback("next_step", {}, str(response)[:256]+"...")
            token_count += tk
            hist.append({"role": "assistant", "content": response})
            try:
                functions = json_repair.loads(re.sub(r"```.*", "", response))
                if not isinstance(functions, list):
                    raise TypeError(f"List should be returned, but `{functions}`")
                for f in functions:
                    if not isinstance(f, dict):
                        raise TypeError(f"An object type should be returned, but `{f}`")
                with ThreadPoolExecutor(max_workers=5) as executor:
                    thr = []
                    for func in functions:
                        name = func["name"]
                        args = func["arguments"]
                        if name == COMPLETE_TASK:
                            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")
                            for txt, tkcnt in complete():
                                yield txt, tkcnt
                            return

                        thr.append(executor.submit(use_tool, name, args))

                    reflection = reflect(self.chat_mdl, hist, [th.result() for th in thr])
                    append_user_content(hist, reflection)
                    self.callback("reflection", {}, str(reflection))

            except Exception as e:
                logging.exception(msg=f"Wrong JSON argument format in LLM ReAct response: {e}")
                e = f"\nTool call error, please correct the input parameter of response format and call it again.\n *** Exception ***\n{e}"
                append_user_content(hist, str(e))

        logging.warning( f"Exceed max rounds: {self._param.max_rounds}")
        final_instruction = f"""
{user_request}
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.
Instructions:
1. SYNTHESIZE all information collected during this conversation
2. Provide a COMPLETE response using existing data - do not suggest additional research
3. Structure your response as a FINAL DELIVERABLE, not a plan
4. If information is incomplete, state what you found and provide the best analysis possible with available data
5. DO NOT mention conversation limits or suggest further steps
6. Focus on delivering VALUE with the information already gathered
Respond immediately with your final comprehensive answer.
        """
        append_user_content(hist, final_instruction)

        for txt, tkcnt in complete():
            yield txt, tkcnt

    def get_useful_memory(self, goal: str, sub_goal:str, topn=3) -> str:
        # self.callback("get_useful_memory", {"topn": 3}, "...")
        mems = self._canvas.get_memory()
        rank = rank_memories(self.chat_mdl, goal, sub_goal, [summ for (user, assist, summ) in mems])
        try:
            rank = json_repair.loads(re.sub(r"```.*", "", rank))[:topn]
            mems = [mems[r] for r in rank]
            return "\n\n".join([f"User: {u}\nAgent: {a}" for u, a,_ in mems])
        except Exception as e:
            logging.exception(e)

        return "Error occurred."