Update src/txagent/txagent.py

src/txagent/txagent.py

@@ -1,937 +1,943 @@
-import gradio as gr
-import os
-import sys
-import json
-import gc
-import numpy as np
-from vllm import LLM, SamplingParams
-from jinja2 import Template
-from typing import List
-import types
-from tooluniverse import ToolUniverse
-from gradio import ChatMessage
-from .toolrag import ToolRAGModel
-
-from .utils import NoRepeatSentenceProcessor, ReasoningTraceChecker, tool_result_format
-
-
-class TxAgent:
-    def __init__(self, model_name,
-                 rag_model_name,
-                 tool_files_dict=None,  # None leads to the default tool files in ToolUniverse
-                 enable_finish=True,
-                 enable_rag=True,
-                 enable_summary=False,
-                 init_rag_num=0,
-                 step_rag_num=10,
-                 summary_mode='step',
-                 summary_skip_last_k=0,
-                 summary_context_length=None,
-                 force_finish=True,
-                 avoid_repeat=True,
-                 seed=None,
-                 enable_checker=False,
-                 enable_chat=False,
-                 additional_default_tools=None,
-                 ):
-        self.model_name = model_name
-        self.tokenizer = None
-        self.terminators = None
-        self.rag_model_name = rag_model_name
-        self.tool_files_dict = tool_files_dict
-        self.model = None
-        self.rag_model = ToolRAGModel(rag_model_name)
-        self.tooluniverse = None
-        # self.tool_desc = None
-        self.prompt_multi_step = "You are a helpful assistant that will solve problems through detailed, step-by-step reasoning and actions based on your reasoning. Typically, your actions will use the provided functions. You have access to the following functions."
-        self.self_prompt = "Strictly follow the instruction."
-        self.chat_prompt = "You are helpful assistant to chat with the user."
-        self.enable_finish = enable_finish
-        self.enable_rag = enable_rag
-        self.enable_summary = enable_summary
-        self.summary_mode = summary_mode
-        self.summary_skip_last_k = summary_skip_last_k
-        self.summary_context_length = summary_context_length
-        self.init_rag_num = init_rag_num
-        self.step_rag_num = step_rag_num
-        self.force_finish = force_finish
-        self.avoid_repeat = avoid_repeat
-        self.seed = seed
-        self.enable_checker = enable_checker
-        self.additional_default_tools = additional_default_tools
-        self.print_self_values()
-
-    def init_model(self):
-        self.load_models()
-        self.load_tooluniverse()
-        self.load_tool_desc_embedding()
-
-    def print_self_values(self):
-        for attr, value in self.__dict__.items():
-            print(f"{attr}: {value}")
-
-    def load_models(self, model_name=None):
-        if model_name is not None:
-            if model_name == self.model_name:
-                return f"The model {model_name} is already loaded."
-            self.model_name = model_name
-
-        self.model = LLM(model=self.model_name)
-        self.chat_template = Template(self.model.get_tokenizer().chat_template)
-        self.tokenizer = self.model.get_tokenizer()
-
-        return f"Model {model_name} loaded successfully."
-
-    def load_tooluniverse(self):
-        self.tooluniverse = ToolUniverse(tool_files=self.tool_files_dict)
-        self.tooluniverse.load_tools()
-        special_tools = self.tooluniverse.prepare_tool_prompts(
-            self.tooluniverse.tool_category_dicts["special_tools"])
-        self.special_tools_name = [tool['name'] for tool in special_tools]
-
-    def load_tool_desc_embedding(self):
-        self.rag_model.load_tool_desc_embedding(self.tooluniverse)
-
-    def rag_infer(self, query, top_k=5):
-        return self.rag_model.rag_infer(query, top_k)
-
-    def initialize_tools_prompt(self, call_agent, call_agent_level, message):
-        picked_tools_prompt = []
-        picked_tools_prompt = self.add_special_tools(
-            picked_tools_prompt, call_agent=call_agent)
-        if call_agent:
-            call_agent_level += 1
-            if call_agent_level >= 2:
-                call_agent = False
-
-        if not call_agent:
-            picked_tools_prompt += self.tool_RAG(
-                message=message, rag_num=self.init_rag_num)
-        return picked_tools_prompt, call_agent_level
-
-    def initialize_conversation(self, message, conversation=None, history=None):
-        if conversation is None:
-            conversation = []
-
-        conversation = self.set_system_prompt(
-            conversation, self.prompt_multi_step)
-        if history is not None:
-            if len(history) == 0:
-                conversation = []
-                print("clear conversation successfully")
-            else:
-                for i in range(len(history)):
-                    if history[i]['role'] == 'user':
-                        if i-1 >= 0 and history[i-1]['role'] == 'assistant':
-                            conversation.append(
-                                {"role": "assistant", "content": history[i-1]['content']})
-                        conversation.append(
-                            {"role": "user", "content": history[i]['content']})
-                    if i == len(history)-1 and history[i]['role'] == 'assistant':
-                        conversation.append(
-                            {"role": "assistant", "content": history[i]['content']})
-
-        conversation.append({"role": "user", "content": message})
-
-        return conversation
-
-    def tool_RAG(self, message=None,
-                 picked_tool_names=None,
-                 existing_tools_prompt=[],
-                 rag_num=5,
-                 return_call_result=False):
-        extra_factor = 30  # Factor to retrieve more than rag_num
-        if picked_tool_names is None:
-            assert picked_tool_names is not None or message is not None
-            picked_tool_names = self.rag_infer(
-                message, top_k=rag_num*extra_factor)
-
-        picked_tool_names_no_special = []
-        for tool in picked_tool_names:
-            if tool not in self.special_tools_name:
-                picked_tool_names_no_special.append(tool)
-        picked_tool_names_no_special = picked_tool_names_no_special[:rag_num]
-        picked_tool_names = picked_tool_names_no_special[:rag_num]
-
-        picked_tools = self.tooluniverse.get_tool_by_name(picked_tool_names)
-        picked_tools_prompt = self.tooluniverse.prepare_tool_prompts(
-            picked_tools)
-        if return_call_result:
-            return picked_tools_prompt, picked_tool_names
-        return picked_tools_prompt
-
-    def add_special_tools(self, tools, call_agent=False):
-        if self.enable_finish:
-            tools.append(self.tooluniverse.get_one_tool_by_one_name(
-                'Finish', return_prompt=True))
-            print("Finish tool is added")
-        if call_agent:
-            tools.append(self.tooluniverse.get_one_tool_by_one_name(
-                'CallAgent', return_prompt=True))
-            print("CallAgent tool is added")
-        else:
-            if self.enable_rag:
-                tools.append(self.tooluniverse.get_one_tool_by_one_name(
-                    'Tool_RAG', return_prompt=True))
-                print("Tool_RAG tool is added")
-
-            if self.additional_default_tools is not None:
-                for each_tool_name in self.additional_default_tools:
-                    tool_prompt = self.tooluniverse.get_one_tool_by_one_name(
-                        each_tool_name, return_prompt=True)
-                    if tool_prompt is not None:
-                        print(f"{each_tool_name} tool is added")
-                        tools.append(tool_prompt)
-        return tools
-
-    def add_finish_tools(self, tools):
-        tools.append(self.tooluniverse.get_one_tool_by_one_name(
-            'Finish', return_prompt=True))
-        print("Finish tool is added")
-        return tools
-
-    def set_system_prompt(self, conversation, sys_prompt):
-        if len(conversation) == 0:
-            conversation.append(
-                {"role": "system", "content": sys_prompt})
-        else:
-            conversation[0] = {"role": "system", "content": sys_prompt}
-        return conversation
-
-    def run_function_call(self, fcall_str,
-                          return_message=False,
-                          existing_tools_prompt=None,
-                          message_for_call_agent=None,
-                          call_agent=False,
-                          call_agent_level=None,
-                          temperature=None):
-
-        function_call_json, message = self.tooluniverse.extract_function_call_json(
-            fcall_str, return_message=return_message, verbose=False)
-        call_results = []
-        special_tool_call = ''
-        if function_call_json is not None:
-            if isinstance(function_call_json, list):
-                for i in range(len(function_call_json)):
-                    print("\033[94mTool Call:\033[0m", function_call_json[i])
-                    if function_call_json[i]["name"] == 'Finish':
-                        special_tool_call = 'Finish'
-                        break
-                    elif function_call_json[i]["name"] == 'Tool_RAG':
-                        new_tools_prompt, call_result = self.tool_RAG(
-                            message=message,
-                            existing_tools_prompt=existing_tools_prompt,
-                            rag_num=self.step_rag_num,
-                            return_call_result=True)
-                        existing_tools_prompt += new_tools_prompt
-                    elif function_call_json[i]["name"] == 'CallAgent':
-                        if call_agent_level < 2 and call_agent:
-                            solution_plan = function_call_json[i]['arguments']['solution']
-                            full_message = (
-                                message_for_call_agent +
-                                "\nYou must follow the following plan to answer the question: " +
-                                str(solution_plan)
-                            )
-                            call_result = self.run_multistep_agent(
-                                full_message, temperature=temperature,
-                                max_new_tokens=1024, max_token=99999,
-                                call_agent=False, call_agent_level=call_agent_level)
-                            call_result = call_result.split(
-                                '[FinalAnswer]')[-1].strip()
-                        else:
-                            call_result = "Error: The CallAgent has been disabled. Please proceed with your reasoning process to solve this question."
-                    else:
-                        call_result = self.tooluniverse.run_one_function(
-                            function_call_json[i])
-
-                    call_id = self.tooluniverse.call_id_gen()
-                    function_call_json[i]["call_id"] = call_id
-                    print("\033[94mTool Call Result:\033[0m", call_result)
-                    call_results.append({
-                        "role": "tool",
-                        "content": json.dumps({"content": call_result, "call_id": call_id})
-                    })
-        else:
-            call_results.append({
-                "role": "tool",
-                "content": json.dumps({"content": "Not a valid function call, please check the function call format."})
-            })
-
-        revised_messages = [{
-            "role": "assistant",
-            "content": message.strip(),
-            "tool_calls": json.dumps(function_call_json)
-        }] + call_results
-
-        # Yield the final result.
-        return revised_messages, existing_tools_prompt, special_tool_call
-
-    def run_function_call_stream(self, fcall_str,
-                                 return_message=False,
-                                 existing_tools_prompt=None,
-                                 message_for_call_agent=None,
-                                 call_agent=False,
-                                 call_agent_level=None,
-                                 temperature=None,
-                                 return_gradio_history=True):
-
-        function_call_json, message = self.tooluniverse.extract_function_call_json(
-            fcall_str, return_message=return_message, verbose=False)
-        call_results = []
-        special_tool_call = ''
-        if return_gradio_history:
-            gradio_history = []
-        if function_call_json is not None:
-            if isinstance(function_call_json, list):
-                for i in range(len(function_call_json)):
-                    if function_call_json[i]["name"] == 'Finish':
-                        special_tool_call = 'Finish'
-                        break
-                    elif function_call_json[i]["name"] == 'Tool_RAG':
-                        new_tools_prompt, call_result = self.tool_RAG(
-                            message=message,
-                            existing_tools_prompt=existing_tools_prompt,
-                            rag_num=self.step_rag_num,
-                            return_call_result=True)
-                        existing_tools_prompt += new_tools_prompt
-                    elif function_call_json[i]["name"] == 'DirectResponse':
-                        call_result = function_call_json[i]['arguments']['respose']
-                        special_tool_call = 'DirectResponse'
-                    elif function_call_json[i]["name"] == 'RequireClarification':
-                        call_result = function_call_json[i]['arguments']['unclear_question']
-                        special_tool_call = 'RequireClarification'
-                    elif function_call_json[i]["name"] == 'CallAgent':
-                        if call_agent_level < 2 and call_agent:
-                            solution_plan = function_call_json[i]['arguments']['solution']
-                            full_message = (
-                                message_for_call_agent +
-                                "\nYou must follow the following plan to answer the question: " +
-                                str(solution_plan)
-                            )
-                            sub_agent_task = "Sub TxAgent plan: " + \
-                                str(solution_plan)
-                            # When streaming, yield responses as they arrive.
-                            call_result = yield from self.run_gradio_chat(
-                                full_message, history=[], temperature=temperature,
-                                max_new_tokens=1024, max_token=99999,
-                                call_agent=False, call_agent_level=call_agent_level,
-                                conversation=None,
-                                sub_agent_task=sub_agent_task)
-
-                            call_result = call_result.split(
-                                '[FinalAnswer]')[-1]
-                        else:
-                            call_result = "Error: The CallAgent has been disabled. Please proceed with your reasoning process to solve this question."
-                    else:
-                        call_result = self.tooluniverse.run_one_function(
-                            function_call_json[i])
-
-                    call_id = self.tooluniverse.call_id_gen()
-                    function_call_json[i]["call_id"] = call_id
-                    call_results.append({
-                        "role": "tool",
-                        "content": json.dumps({"content": call_result, "call_id": call_id})
-                    })
-                    if return_gradio_history and function_call_json[i]["name"] != 'Finish':
-                        if function_call_json[i]["name"] == 'Tool_RAG':
-                            gradio_history.append(ChatMessage(role="assistant", content=str(call_result), metadata={
-                                                  "title": "🧰 "+function_call_json[i]['name'], "log": str(function_call_json[i]['arguments'])}))
-
-                        else:
-                            gradio_history.append(ChatMessage(role="assistant", content=str(call_result), metadata={
-                                                  "title": "⚒️ "+function_call_json[i]['name'], "log": str(function_call_json[i]['arguments'])}))
-        else:
-            call_results.append({
-                "role": "tool",
-                "content": json.dumps({"content": "Not a valid function call, please check the function call format."})
-            })
-
-        revised_messages = [{
-            "role": "assistant",
-            "content": message.strip(),
-            "tool_calls": json.dumps(function_call_json)
-        }] + call_results
-
-        # Yield the final result.
-        if return_gradio_history:
-            return revised_messages, existing_tools_prompt, special_tool_call, gradio_history
-        else:
-            return revised_messages, existing_tools_prompt, special_tool_call
-
-    def get_answer_based_on_unfinished_reasoning(self, conversation, temperature, max_new_tokens, max_token, outputs=None):
-        if conversation[-1]['role'] == 'assisant':
-            conversation.append(
-                {'role': 'tool', 'content': 'Errors happen during the function call, please come up with the final answer with the current information.'})
-        finish_tools_prompt = self.add_finish_tools([])
-
-        last_outputs_str = self.llm_infer(messages=conversation,
-                                          temperature=temperature,
-                                          tools=finish_tools_prompt,
-                                          output_begin_string='Since I cannot continue reasoning, I will provide the final answer based on the current information and general knowledge.\n\n[FinalAnswer]',
-                                          skip_special_tokens=True,
-                                          max_new_tokens=max_new_tokens, max_token=max_token)
-        print(last_outputs_str)
-        return last_outputs_str
-
-    def run_multistep_agent(self, message: str,
-                            temperature: float,
-                            max_new_tokens: int,
-                            max_token: int,
-                            max_round: int = 20,
-                            call_agent=False,
-                            call_agent_level=0) -> str:
-        """
-        Generate a streaming response using the llama3-8b model.
-        Args:
-            message (str): The input message.
-            temperature (float): The temperature for generating the response.
-            max_new_tokens (int): The maximum number of new tokens to generate.
-        Returns:
-            str: The generated response.
-        """
-        print("\033[1;32;40mstart\033[0m")
-        picked_tools_prompt, call_agent_level = self.initialize_tools_prompt(
-            call_agent, call_agent_level, message)
-        conversation = self.initialize_conversation(message)
-
-        outputs = []
-        last_outputs = []
-        next_round = True
-        function_call_messages = []
-        current_round = 0
-        token_overflow = False
-        enable_summary = False
-        last_status = {}
-
-        if self.enable_checker:
-            checker = ReasoningTraceChecker(message, conversation)
-        try:
-            while next_round and current_round < max_round:
-                current_round += 1
-                if len(outputs) > 0:
-                    function_call_messages, picked_tools_prompt, special_tool_call = self.run_function_call(
-                        last_outputs, return_message=True,
-                        existing_tools_prompt=picked_tools_prompt,
-                        message_for_call_agent=message,
-                        call_agent=call_agent,
-                        call_agent_level=call_agent_level,
-                        temperature=temperature)
-
-                    if special_tool_call == 'Finish':
-                        next_round = False
-                        conversation.extend(function_call_messages)
-                        if isinstance(function_call_messages[0]['content'], types.GeneratorType):
-                            function_call_messages[0]['content'] = next(
-                                function_call_messages[0]['content'])
-                        return function_call_messages[0]['content'].split('[FinalAnswer]')[-1]
-
-                    if (self.enable_summary or token_overflow) and not call_agent:
-                        if token_overflow:
-                            print("token_overflow, using summary")
-                        enable_summary = True
-                    last_status = self.function_result_summary(
-                        conversation, status=last_status, enable_summary=enable_summary)
-
-                    if function_call_messages is not None:
-                        conversation.extend(function_call_messages)
-                        outputs.append(tool_result_format(
-                            function_call_messages))
-                    else:
-                        next_round = False
-                        conversation.extend(
-                            [{"role": "assistant", "content": ''.join(last_outputs)}])
-                        return ''.join(last_outputs).replace("</s>", "")
-                if self.enable_checker:
-                    good_status, wrong_info = checker.check_conversation()
-                    if not good_status:
-                        next_round = False
-                        print(
-                            "Internal error in reasoning: " + wrong_info)
-                        break
-                last_outputs = []
-                outputs.append("### TxAgent:\n")
-                last_outputs_str, token_overflow = self.llm_infer(messages=conversation,
-                                                                  temperature=temperature,
-                                                                  tools=picked_tools_prompt,
-                                                                  skip_special_tokens=False,
-                                                                  max_new_tokens=max_new_tokens, max_token=max_token,
-                                                                  check_token_status=True)
-                if last_outputs_str is None:
-                    next_round = False
-                    print(
-                        "The number of tokens exceeds the maximum limit.")
-                else:
-                    last_outputs.append(last_outputs_str)
-            if max_round == current_round:
-                print("The number of rounds exceeds the maximum limit!")
-            if self.force_finish:
-                return self.get_answer_based_on_unfinished_reasoning(conversation, temperature, max_new_tokens, max_token)
-            else:
-                return None
-
-        except Exception as e:
-            print(f"Error: {e}")
-            if self.force_finish:
-                return self.get_answer_based_on_unfinished_reasoning(conversation, temperature, max_new_tokens, max_token)
-            else:
-                return None
-
-    def build_logits_processor(self, messages, llm):
-        # Use the tokenizer from the LLM instance.
-        tokenizer = llm.get_tokenizer()
-        if self.avoid_repeat and len(messages) > 2:
-            assistant_messages = []
-            for i in range(1, len(messages) + 1):
-                if messages[-i]['role'] == 'assistant':
-                    assistant_messages.append(messages[-i]['content'])
-                    if len(assistant_messages) == 2:
-                        break
-            forbidden_ids = [tokenizer.encode(
-                msg, add_special_tokens=False) for msg in assistant_messages]
-            return [NoRepeatSentenceProcessor(forbidden_ids, 5)]
-        else:
-            return None
-
-    def llm_infer(self, messages, temperature=0.1, tools=None,
-                  output_begin_string=None, max_new_tokens=2048,
-                  max_token=None, skip_special_tokens=True,
-                  model=None, tokenizer=None, terminators=None, seed=None, check_token_status=False):
-
-        if model is None:
-            model = self.model
-
-        logits_processor = self.build_logits_processor(messages, model)
-        sampling_params = SamplingParams(
-            temperature=temperature,
-            max_tokens=max_new_tokens,
-            logits_processors=logits_processor,
-            seed=seed if seed is not None else self.seed,
-        )
-
-        prompt = self.chat_template.render(
-            messages=messages, tools=tools, add_generation_prompt=True)
-        if output_begin_string is not None:
-            prompt += output_begin_string
-
-        if check_token_status and max_token is not None:
-            token_overflow = False
-            num_input_tokens = len(self.tokenizer.encode(
-                prompt, return_tensors="pt")[0])
-            if max_token is not None:
-                if num_input_tokens > max_token:
-                    torch.cuda.empty_cache()
-                    gc.collect()
-                    print("Number of input tokens before inference:",
-                          num_input_tokens)
-                    logger.info(
-                        "The number of tokens exceeds the maximum limit!!!!")
-                    token_overflow = True
-                    return None, token_overflow
-        output = model.generate(
-            prompt,
-            sampling_params=sampling_params,
-        )
-        output = output[0].outputs[0].text
-        print("\033[92m" + output + "\033[0m")
-        if check_token_status and max_token is not None:
-            return output, token_overflow
-
-        return output
-
-    def run_self_agent(self, message: str,
-                       temperature: float,
-                       max_new_tokens: int,
-                       max_token: int) -> str:
-
-        print("\033[1;32;40mstart self agent\033[0m")
-        conversation = []
-        conversation = self.set_system_prompt(conversation, self.self_prompt)
-        conversation.append({"role": "user", "content": message})
-        return self.llm_infer(messages=conversation,
-                              temperature=temperature,
-                              tools=None,
-                              max_new_tokens=max_new_tokens, max_token=max_token)
-
-    def run_chat_agent(self, message: str,
-                       temperature: float,
-                       max_new_tokens: int,
-                       max_token: int) -> str:
-
-        print("\033[1;32;40mstart chat agent\033[0m")
-        conversation = []
-        conversation = self.set_system_prompt(conversation, self.chat_prompt)
-        conversation.append({"role": "user", "content": message})
-        return self.llm_infer(messages=conversation,
-                              temperature=temperature,
-                              tools=None,
-                              max_new_tokens=max_new_tokens, max_token=max_token)
-
-    def run_format_agent(self, message: str,
-                         answer: str,
-                         temperature: float,
-                         max_new_tokens: int,
-                         max_token: int) -> str:
-
-        print("\033[1;32;40mstart format agent\033[0m")
-        if '[FinalAnswer]' in answer:
-            possible_final_answer = answer.split("[FinalAnswer]")[-1]
-        elif "\n\n" in answer:
-            possible_final_answer = answer.split("\n\n")[-1]
-        else:
-            possible_final_answer = answer.strip()
-        if len(possible_final_answer) == 1:
-            choice = possible_final_answer[0]
-            if choice in ['A', 'B', 'C', 'D', 'E']:
-                return choice
-        elif len(possible_final_answer) > 1:
-            if possible_final_answer[1] == ':':
-                choice = possible_final_answer[0]
-                if choice in ['A', 'B', 'C', 'D', 'E']:
-                    print("choice", choice)
-                    return choice
-
-        conversation = []
-        format_prompt = f"You are helpful assistant to transform the answer of agent to the final answer of 'A', 'B', 'C', 'D'."
-        conversation = self.set_system_prompt(conversation, format_prompt)
-        conversation.append({"role": "user", "content": message +
-                            "\nThe final answer of agent:" + answer + "\n The answer is (must be a letter):"})
-        return self.llm_infer(messages=conversation,
-                              temperature=temperature,
-                              tools=None,
-                              max_new_tokens=max_new_tokens, max_token=max_token)
-
-    def run_summary_agent(self, thought_calls: str,
-                          function_response: str,
-                          temperature: float,
-                          max_new_tokens: int,
-                          max_token: int) -> str:
-        print("\033[1;32;40mSummarized Tool Result:\033[0m")
-        generate_tool_result_summary_training_prompt = """Thought and function calls: 
-{thought_calls}
-
-Function calls' responses:
-\"\"\"
-{function_response}
-\"\"\"
-
-Based on the Thought and function calls, and the function calls' responses, you need to generate a summary of the function calls' responses that fulfills the requirements of the thought. The summary MUST BE ONE sentence and include all necessary information.
-
-Directly respond with the summarized sentence of the function calls' responses only. 
-
-Generate **one summarized sentence** about "function calls' responses" with necessary information, and respond with a string:
-            """.format(thought_calls=thought_calls, function_response=function_response)
-        conversation = []
-        conversation.append(
-            {"role": "user", "content": generate_tool_result_summary_training_prompt})
-        output = self.llm_infer(messages=conversation,
-                                temperature=temperature,
-                                tools=None,
-                                max_new_tokens=max_new_tokens, max_token=max_token)
-
-        if '[' in output:
-            output = output.split('[')[0]
-        return output
-
-    def function_result_summary(self, input_list, status, enable_summary):
-        """
-        Processes the input list, extracting information from sequences of 'user', 'tool', 'assistant' roles.
-        Supports 'length' and 'step' modes, and skips the last 'k' groups.
-
-        Parameters:
-            input_list (list): A list of dictionaries containing role and other information.
-            summary_skip_last_k (int): Number of groups to skip from the end. Defaults to 0.
-            summary_context_length (int): The context length threshold for the 'length' mode.
-            last_processed_index (tuple or int): The last processed index.
-
-        Returns:
-            list: A list of extracted information from valid sequences.
-        """
-        if 'tool_call_step' not in status:
-            status['tool_call_step'] = 0
-
-        for idx in range(len(input_list)):
-            pos_id = len(input_list)-idx-1
-            if input_list[pos_id]['role'] == 'assistant':
-                if 'tool_calls' in input_list[pos_id]:
-                    if 'Tool_RAG' in str(input_list[pos_id]['tool_calls']):
-                        status['tool_call_step'] += 1
-                break
-
-        if 'step' in status:
-            status['step'] += 1
-        else:
-            status['step'] = 0
-
-        if not enable_summary:
-            return status
-
-        if 'summarized_index' not in status:
-            status['summarized_index'] = 0
-
-        if 'summarized_step' not in status:
-            status['summarized_step'] = 0
-
-        if 'previous_length' not in status:
-            status['previous_length'] = 0
-
-        if 'history' not in status:
-            status['history'] = []
-
-        function_response = ''
-        idx = 0
-        current_summarized_index = status['summarized_index']
-
-        status['history'].append(self.summary_mode == 'step' and status['summarized_step']
-                                 < status['step']-status['tool_call_step']-self.summary_skip_last_k)
-
-        idx = current_summarized_index
-        while idx < len(input_list):
-            if (self.summary_mode == 'step' and status['summarized_step'] < status['step']-status['tool_call_step']-self.summary_skip_last_k) or (self.summary_mode == 'length' and status['previous_length'] > self.summary_context_length):
-
-                if input_list[idx]['role'] == 'assistant':
-                    if 'Tool_RAG' in str(input_list[idx]['tool_calls']):
-                        this_thought_calls = None
-                    else:
-                        if len(function_response) != 0:
-                            print("internal summary")
-                            status['summarized_step'] += 1
-                            result_summary = self.run_summary_agent(
-                                thought_calls=this_thought_calls,
-                                function_response=function_response,
-                                temperature=0.1,
-                                max_new_tokens=1024,
-                                max_token=99999
-                            )
-
-                            input_list.insert(
-                                last_call_idx+1, {'role': 'tool', 'content': result_summary})
-                            status['summarized_index'] = last_call_idx + 2
-                            idx += 1
-
-                        last_call_idx = idx
-                        this_thought_calls = input_list[idx]['content'] + \
-                            input_list[idx]['tool_calls']
-                        function_response = ''
-
-                elif input_list[idx]['role'] == 'tool' and this_thought_calls is not None:
-                    function_response += input_list[idx]['content']
-                    del input_list[idx]
-                    idx -= 1
-
-            else:
-                break
-            idx += 1
-
-        if len(function_response) != 0:
-            status['summarized_step'] += 1
-            result_summary = self.run_summary_agent(
-                thought_calls=this_thought_calls,
-                function_response=function_response,
-                temperature=0.1,
-                max_new_tokens=1024,
-                max_token=99999
-            )
-
-            tool_calls = json.loads(input_list[last_call_idx]['tool_calls'])
-            for tool_call in tool_calls:
-                del tool_call['call_id']
-            input_list[last_call_idx]['tool_calls'] = json.dumps(tool_calls)
-            input_list.insert(
-                last_call_idx+1, {'role': 'tool', 'content': result_summary})
-            status['summarized_index'] = last_call_idx + 2
-
-        return status
-
-    # Following are Gradio related functions
-
-    # General update method that accepts any new arguments through kwargs
-    def update_parameters(self, **kwargs):
-        for key, value in kwargs.items():
-            if hasattr(self, key):
-                setattr(self, key, value)
-
-        # Return the updated attributes
-        updated_attributes = {key: value for key,
-                              value in kwargs.items() if hasattr(self, key)}
-        return updated_attributes
-
-    def run_gradio_chat(self, message: str,
-                        history: list,
-                        temperature: float,
-                        max_new_tokens: int,
-                        max_token: int,
-                        call_agent: bool,
-                        conversation: gr.State,
-                        max_round: int = 20,
-                        seed: int = None,
-                        call_agent_level: int = 0,
-                        sub_agent_task: str = None) -> str:
-        """
-        Generate a streaming response using the llama3-8b model.
-        Args:
-            message (str): The input message.
-            history (list): The conversation history used by ChatInterface.
-            temperature (float): The temperature for generating the response.
-            max_new_tokens (int): The maximum number of new tokens to generate.
-        Returns:
-            str: The generated response.
-        """
-        print("\033[1;32;40mstart\033[0m")
-        print("len(message)", len(message))
-        if len(message) <= 10:
-            yield "Hi, I am TxAgent, an assistant for answering biomedical questions. Please provide a valid message with a string longer than 10 characters."
-            return "Please provide a valid message."
-        outputs = []
-        outputs_str = ''
-        last_outputs = []
-
-        picked_tools_prompt, call_agent_level = self.initialize_tools_prompt(
-            call_agent,
-            call_agent_level,
-            message)
-
-        conversation = self.initialize_conversation(
-            message,
-            conversation=conversation,
-            history=history)
-        history = []
-
-        next_round = True
-        function_call_messages = []
-        current_round = 0
-        enable_summary = False
-        last_status = {}  # for summary
-        token_overflow = False
-        if self.enable_checker:
-            checker = ReasoningTraceChecker(
-                message, conversation, init_index=len(conversation))
-
-        try:
-            while next_round and current_round < max_round:
-                current_round += 1
-                if len(last_outputs) > 0:
-                    function_call_messages, picked_tools_prompt, special_tool_call, current_gradio_history = yield from self.run_function_call_stream(
-                        last_outputs, return_message=True,
-                        existing_tools_prompt=picked_tools_prompt,
-                        message_for_call_agent=message,
-                        call_agent=call_agent,
-                        call_agent_level=call_agent_level,
-                        temperature=temperature)
-                    history.extend(current_gradio_history)
-                    if special_tool_call == 'Finish':
-                        yield history
-                        next_round = False
-                        conversation.extend(function_call_messages)
-                        return function_call_messages[0]['content']
-                    elif special_tool_call == 'RequireClarification' or special_tool_call == 'DirectResponse':
-                        history.append(
-                            ChatMessage(role="assistant", content=history[-1].content))
-                        yield history
-                        next_round = False
-                        return history[-1].content
-                    if (self.enable_summary or token_overflow) and not call_agent:
-                        if token_overflow:
-                            print("token_overflow, using summary")
-                        enable_summary = True
-                    last_status = self.function_result_summary(
-                        conversation, status=last_status,
-                        enable_summary=enable_summary)
-                    if function_call_messages is not None:
-                        conversation.extend(function_call_messages)
-                        formated_md_function_call_messages = tool_result_format(
-                            function_call_messages)
-                        yield history
-                    else:
-                        next_round = False
-                        conversation.extend(
-                            [{"role": "assistant", "content": ''.join(last_outputs)}])
-                        return ''.join(last_outputs).replace("</s>", "")
-                if self.enable_checker:
-                    good_status, wrong_info = checker.check_conversation()
-                    if not good_status:
-                        next_round = False
-                        print("Internal error in reasoning: " + wrong_info)
-                        break
-                last_outputs = []
-                last_outputs_str, token_overflow = self.llm_infer(
-                    messages=conversation,
-                    temperature=temperature,
-                    tools=picked_tools_prompt,
-                    skip_special_tokens=False,
-                    max_new_tokens=max_new_tokens,
-                    max_token=max_token,
-                    seed=seed,
-                    check_token_status=True)
-                last_thought = last_outputs_str.split("[TOOL_CALLS]")[0]
-                for each in history:
-                    if each.metadata is not None:
-                        each.metadata['status'] = 'done'
-                if '[FinalAnswer]' in last_thought:
-                    final_thought, final_answer = last_thought.split(
-                        '[FinalAnswer]')
-                    history.append(
-                        ChatMessage(role="assistant",
-                                    content=final_thought.strip())
-                    )
-                    yield history
-                    history.append(
-                        ChatMessage(
-                            role="assistant", content="**Answer**:\n"+final_answer.strip())
-                    )
-                    yield history
-                else:
-                    history.append(ChatMessage(
-                        role="assistant", content=last_thought))
-                    yield history
-
-                last_outputs.append(last_outputs_str)
-
-            if next_round:
-                if self.force_finish:
-                    last_outputs_str = self.get_answer_based_on_unfinished_reasoning(
-                        conversation, temperature, max_new_tokens, max_token)
-                    for each in history:
-                        if each.metadata is not None:
-                            each.metadata['status'] = 'done'
-                    if '[FinalAnswer]' in last_thought:
-                        final_thought, final_answer = last_thought.split(
-                            '[FinalAnswer]')
-                        history.append(
-                            ChatMessage(role="assistant",
-                                        content=final_thought.strip())
-                        )
-                        yield history
-                        history.append(
-                            ChatMessage(
-                                role="assistant", content="**Answer**:\n"+final_answer.strip())
-                        )
-                        yield history
-                else:
-                    yield "The number of rounds exceeds the maximum limit!"
-
-        except Exception as e:
-            print(f"Error: {e}")
-            if self.force_finish:
-                last_outputs_str = self.get_answer_based_on_unfinished_reasoning(
-                    conversation,
-                    temperature,
-                    max_new_tokens,
-                    max_token)
-                for each in history:
-                    if each.metadata is not None:
-                        each.metadata['status'] = 'done'
-                if '[FinalAnswer]' in last_thought or '"name": "Finish",' in last_outputs_str:
-                    final_thought, final_answer = last_thought.split(
-                        '[FinalAnswer]')
-                    history.append(
-                        ChatMessage(role="assistant",
-                                    content=final_thought.strip())
-                    )
-                    yield history
-                    history.append(
-                        ChatMessage(
-                            role="assistant", content="**Answer**:\n"+final_answer.strip())
-                    )
-                    yield history
-            else:
-                return None
+import gradio as gr
+import os
+import sys
+import json
+import gc
+import numpy as np
+from vllm import LLM, SamplingParams
+from jinja2 import Template
+from typing import List
+import types
+from tooluniverse import ToolUniverse
+from gradio import ChatMessage
+from .toolrag import ToolRAGModel
+
+from .utils import NoRepeatSentenceProcessor, ReasoningTraceChecker, tool_result_format
+
+
+class TxAgent:
+    def __init__(self, model_name,
+                 rag_model_name,
+                 tool_files_dict=None,  # None leads to the default tool files in ToolUniverse
+                 enable_finish=True,
+                 enable_rag=True,
+                 enable_summary=False,
+                 init_rag_num=0,
+                 step_rag_num=10,
+                 summary_mode='step',
+                 summary_skip_last_k=0,
+                 summary_context_length=None,
+                 force_finish=True,
+                 avoid_repeat=True,
+                 seed=None,
+                 enable_checker=False,
+                 enable_chat=False,
+                 additional_default_tools=None,
+                 ):
+        self.model_name = model_name
+        self.tokenizer = None
+        self.terminators = None
+        self.rag_model_name = rag_model_name
+        self.tool_files_dict = tool_files_dict
+        self.model = None
+        self.rag_model = ToolRAGModel(rag_model_name)
+        self.tooluniverse = None
+        # self.tool_desc = None
+        self.prompt_multi_step = "You are a helpful assistant that will solve problems through detailed, step-by-step reasoning and actions based on your reasoning. Typically, your actions will use the provided functions. You have access to the following functions."
+        self.self_prompt = "Strictly follow the instruction."
+        self.chat_prompt = "You are helpful assistant to chat with the user."
+        self.enable_finish = enable_finish
+        self.enable_rag = enable_rag
+        self.enable_summary = enable_summary
+        self.summary_mode = summary_mode
+        self.summary_skip_last_k = summary_skip_last_k
+        self.summary_context_length = summary_context_length
+        self.init_rag_num = init_rag_num
+        self.step_rag_num = step_rag_num
+        self.force_finish = force_finish
+        self.avoid_repeat = avoid_repeat
+        self.seed = seed
+        self.enable_checker = enable_checker
+        self.additional_default_tools = additional_default_tools
+        self.print_self_values()
+
+    def init_model(self):
+        self.load_models()
+        self.load_tooluniverse()
+        self.load_tool_desc_embedding()
+
+    def print_self_values(self):
+        for attr, value in self.__dict__.items():
+            print(f"{attr}: {value}")
+
+    def load_models(self, model_name=None):
+        if model_name is not None:
+            if model_name == self.model_name:
+                return f"The model {model_name} is already loaded."
+            self.model_name = model_name
+
+        self.model = LLM(model=self.model_name)
+        self.chat_template = Template(self.model.get_tokenizer().chat_template)
+        self.tokenizer = self.model.get_tokenizer()
+
+        return f"Model {model_name} loaded successfully."
+
+    def load_tooluniverse(self):
+        self.tooluniverse = ToolUniverse(tool_files=self.tool_files_dict)
+        self.tooluniverse.load_tools()
+        special_tools = self.tooluniverse.prepare_tool_prompts(
+            self.tooluniverse.tool_category_dicts["special_tools"])
+        self.special_tools_name = [tool['name'] for tool in special_tools]
+
+    def load_tool_desc_embedding(self):
+        self.rag_model.load_tool_desc_embedding(self.tooluniverse)
+
+    def rag_infer(self, query, top_k=5):
+        return self.rag_model.rag_infer(query, top_k)
+
+    def initialize_tools_prompt(self, call_agent, call_agent_level, message):
+        picked_tools_prompt = []
+        picked_tools_prompt = self.add_special_tools(
+            picked_tools_prompt, call_agent=call_agent)
+        if call_agent:
+            call_agent_level += 1
+            if call_agent_level >= 2:
+                call_agent = False
+
+        if not call_agent:
+            picked_tools_prompt += self.tool_RAG(
+                message=message, rag_num=self.init_rag_num)
+        return picked_tools_prompt, call_agent_level
+
+    def initialize_conversation(self, message, conversation=None, history=None):
+        if conversation is None:
+            conversation = []
+
+        conversation = self.set_system_prompt(
+            conversation, self.prompt_multi_step)
+        if history is not None:
+            if len(history) == 0:
+                conversation = []
+                print("clear conversation successfully")
+            else:
+                for i in range(len(history)):
+                    if history[i]['role'] == 'user':
+                        if i-1 >= 0 and history[i-1]['role'] == 'assistant':
+                            conversation.append(
+                                {"role": "assistant", "content": history[i-1]['content']})
+                        conversation.append(
+                            {"role": "user", "content": history[i]['content']})
+                    if i == len(history)-1 and history[i]['role'] == 'assistant':
+                        conversation.append(
+                            {"role": "assistant", "content": history[i]['content']})
+
+        conversation.append({"role": "user", "content": message})
+
+        return conversation
+
+    def tool_RAG(self, message=None,
+                 picked_tool_names=None,
+                 existing_tools_prompt=[],
+                 rag_num=5,
+                 return_call_result=False):
+        extra_factor = 30  # Factor to retrieve more than rag_num
+        if picked_tool_names is None:
+            assert picked_tool_names is not None or message is not None
+            picked_tool_names = self.rag_infer(
+                message, top_k=rag_num*extra_factor)
+
+        picked_tool_names_no_special = []
+        for tool in picked_tool_names:
+            if tool not in self.special_tools_name:
+                picked_tool_names_no_special.append(tool)
+        picked_tool_names_no_special = picked_tool_names_no_special[:rag_num]
+        picked_tool_names = picked_tool_names_no_special[:rag_num]
+
+        picked_tools = self.tooluniverse.get_tool_by_name(picked_tool_names)
+        picked_tools_prompt = self.tooluniverse.prepare_tool_prompts(
+            picked_tools)
+        if return_call_result:
+            return picked_tools_prompt, picked_tool_names
+        return picked_tools_prompt
+
+    def add_special_tools(self, tools, call_agent=False):
+        if self.enable_finish:
+            tools.append(self.tooluniverse.get_one_tool_by_one_name(
+                'Finish', return_prompt=True))
+            print("Finish tool is added")
+        if call_agent:
+            tools.append(self.tooluniverse.get_one_tool_by_one_name(
+                'CallAgent', return_prompt=True))
+            print("CallAgent tool is added")
+        else:
+            if self.enable_rag:
+                tools.append(self.tooluniverse.get_one_tool_by_one_name(
+                    'Tool_RAG', return_prompt=True))
+                print("Tool_RAG tool is added")
+
+            if self.additional_default_tools is not None:
+                for each_tool_name in self.additional_default_tools:
+                    tool_prompt = self.tooluniverse.get_one_tool_by_one_name(
+                        each_tool_name, return_prompt=True)
+                    if tool_prompt is not None:
+                        print(f"{each_tool_name} tool is added")
+                        tools.append(tool_prompt)
+        return tools
+
+    def add_finish_tools(self, tools):
+        tools.append(self.tooluniverse.get_one_tool_by_one_name(
+            'Finish', return_prompt=True))
+        print("Finish tool is added")
+        return tools
+
+    def set_system_prompt(self, conversation, sys_prompt):
+        if len(conversation) == 0:
+            conversation.append(
+                {"role": "system", "content": sys_prompt})
+        else:
+            conversation[0] = {"role": "system", "content": sys_prompt}
+        return conversation
+
+    def run_function_call(self, fcall_str,
+                          return_message=False,
+                          existing_tools_prompt=None,
+                          message_for_call_agent=None,
+                          call_agent=False,
+                          call_agent_level=None,
+                          temperature=None):
+
+        function_call_json, message = self.tooluniverse.extract_function_call_json(
+            fcall_str, return_message=return_message, verbose=False)
+        call_results = []
+        special_tool_call = ''
+        if function_call_json is not None:
+            if isinstance(function_call_json, list):
+                for i in range(len(function_call_json)):
+                    print("\033[94mTool Call:\033[0m", function_call_json[i])
+                    if function_call_json[i]["name"] == 'Finish':
+                        special_tool_call = 'Finish'
+                        break
+                    elif function_call_json[i]["name"] == 'Tool_RAG':
+                        new_tools_prompt, call_result = self.tool_RAG(
+                            message=message,
+                            existing_tools_prompt=existing_tools_prompt,
+                            rag_num=self.step_rag_num,
+                            return_call_result=True)
+                        existing_tools_prompt += new_tools_prompt
+                    elif function_call_json[i]["name"] == 'CallAgent':
+                        if call_agent_level < 2 and call_agent:
+                            solution_plan = function_call_json[i]['arguments']['solution']
+                            full_message = (
+                                message_for_call_agent +
+                                "\nYou must follow the following plan to answer the question: " +
+                                str(solution_plan)
+                            )
+                            call_result = self.run_multistep_agent(
+                                full_message, temperature=temperature,
+                                max_new_tokens=1024, max_token=99999,
+                                call_agent=False, call_agent_level=call_agent_level)
+                            call_result = call_result.split(
+                                '[FinalAnswer]')[-1].strip()
+                        else:
+                            call_result = "Error: The CallAgent has been disabled. Please proceed with your reasoning process to solve this question."
+                    else:
+                        call_result = self.tooluniverse.run_one_function(
+                            function_call_json[i])
+
+                    call_id = self.tooluniverse.call_id_gen()
+                    function_call_json[i]["call_id"] = call_id
+                    print("\033[94mTool Call Result:\033[0m", call_result)
+                    call_results.append({
+                        "role": "tool",
+                        "content": json.dumps({"content": call_result, "call_id": call_id})
+                    })
+        else:
+            call_results.append({
+                "role": "tool",
+                "content": json.dumps({"content": "Not a valid function call, please check the function call format."})
+            })
+
+        revised_messages = [{
+            "role": "assistant",
+            "content": message.strip(),
+            "tool_calls": json.dumps(function_call_json)
+        }] + call_results
+
+        # Yield the final result.
+        return revised_messages, existing_tools_prompt, special_tool_call
+
+    def run_function_call_stream(self, fcall_str,
+                                 return_message=False,
+                                 existing_tools_prompt=None,
+                                 message_for_call_agent=None,
+                                 call_agent=False,
+                                 call_agent_level=None,
+                                 temperature=None,
+                                 return_gradio_history=True):
+
+        function_call_json, message = self.tooluniverse.extract_function_call_json(
+            fcall_str, return_message=return_message, verbose=False)
+        call_results = []
+        special_tool_call = ''
+        if return_gradio_history:
+            gradio_history = []
+        if function_call_json is not None:
+            if isinstance(function_call_json, list):
+                for i in range(len(function_call_json)):
+                    if function_call_json[i]["name"] == 'Finish':
+                        special_tool_call = 'Finish'
+                        break
+                    elif function_call_json[i]["name"] == 'Tool_RAG':
+                        new_tools_prompt, call_result = self.tool_RAG(
+                            message=message,
+                            existing_tools_prompt=existing_tools_prompt,
+                            rag_num=self.step_rag_num,
+                            return_call_result=True)
+                        existing_tools_prompt += new_tools_prompt
+                    elif function_call_json[i]["name"] == 'DirectResponse':
+                        call_result = function_call_json[i]['arguments']['respose']
+                        special_tool_call = 'DirectResponse'
+                    elif function_call_json[i]["name"] == 'RequireClarification':
+                        call_result = function_call_json[i]['arguments']['unclear_question']
+                        special_tool_call = 'RequireClarification'
+                    elif function_call_json[i]["name"] == 'CallAgent':
+                        if call_agent_level < 2 and call_agent:
+                            solution_plan = function_call_json[i]['arguments']['solution']
+                            full_message = (
+                                message_for_call_agent +
+                                "\nYou must follow the following plan to answer the question: " +
+                                str(solution_plan)
+                            )
+                            sub_agent_task = "Sub TxAgent plan: " + \
+                                str(solution_plan)
+                            # When streaming, yield responses as they arrive.
+                            call_result = yield from self.run_gradio_chat(
+                                full_message, history=[], temperature=temperature,
+                                max_new_tokens=1024, max_token=99999,
+                                call_agent=False, call_agent_level=call_agent_level,
+                                conversation=None,
+                                sub_agent_task=sub_agent_task)
+
+                            call_result = call_result.split(
+                                '[FinalAnswer]')[-1]
+                        else:
+                            call_result = "Error: The CallAgent has been disabled. Please proceed with your reasoning process to solve this question."
+                    else:
+                        call_result = self.tooluniverse.run_one_function(
+                            function_call_json[i])
+
+                    call_id = self.tooluniverse.call_id_gen()
+                    function_call_json[i]["call_id"] = call_id
+                    call_results.append({
+                        "role": "tool",
+                        "content": json.dumps({"content": call_result, "call_id": call_id})
+                    })
+                    if return_gradio_history and function_call_json[i]["name"] != 'Finish':
+                        if function_call_json[i]["name"] == 'Tool_RAG':
+                            gradio_history.append(ChatMessage(role="assistant", content=str(call_result), metadata={
+                                                  "title": "🧰 "+function_call_json[i]['name'], "log": str(function_call_json[i]['arguments'])}))
+
+                        else:
+                            gradio_history.append(ChatMessage(role="assistant", content=str(call_result), metadata={
+                                                  "title": "⚒️ "+function_call_json[i]['name'], "log": str(function_call_json[i]['arguments'])}))
+        else:
+            call_results.append({
+                "role": "tool",
+                "content": json.dumps({"content": "Not a valid function call, please check the function call format."})
+            })
+
+        revised_messages = [{
+            "role": "assistant",
+            "content": message.strip(),
+            "tool_calls": json.dumps(function_call_json)
+        }] + call_results
+
+        # Yield the final result.
+        if return_gradio_history:
+            return revised_messages, existing_tools_prompt, special_tool_call, gradio_history
+        else:
+            return revised_messages, existing_tools_prompt, special_tool_call
+
+    def get_answer_based_on_unfinished_reasoning(self, conversation, temperature, max_new_tokens, max_token, outputs=None):
+        if conversation[-1]['role'] == 'assisant':
+            conversation.append(
+                {'role': 'tool', 'content': 'Errors happen during the function call, please come up with the final answer with the current information.'})
+        finish_tools_prompt = self.add_finish_tools([])
+
+        last_outputs_str = self.llm_infer(messages=conversation,
+                                          temperature=temperature,
+                                          tools=finish_tools_prompt,
+                                          output_begin_string='Since I cannot continue reasoning, I will provide the final answer based on the current information and general knowledge.\n\n[FinalAnswer]',
+                                          skip_special_tokens=True,
+                                          max_new_tokens=max_new_tokens, max_token=max_token)
+        print(last_outputs_str)
+        return last_outputs_str
+
+    def run_multistep_agent(self, message: str,
+                            temperature: float,
+                            max_new_tokens: int,
+                            max_token: int,
+                            max_round: int = 20,
+                            call_agent=False,
+                            call_agent_level=0) -> str:
+        """
+        Generate a streaming response using the llama3-8b model.
+        Args:
+            message (str): The input message.
+            temperature (float): The temperature for generating the response.
+            max_new_tokens (int): The maximum number of new tokens to generate.
+        Returns:
+            str: The generated response.
+        """
+        print("\033[1;32;40mstart\033[0m")
+        picked_tools_prompt, call_agent_level = self.initialize_tools_prompt(
+            call_agent, call_agent_level, message)
+        conversation = self.initialize_conversation(message)
+
+        outputs = []
+        last_outputs = []
+        next_round = True
+        function_call_messages = []
+        current_round = 0
+        token_overflow = False
+        enable_summary = False
+        last_status = {}
+
+        if self.enable_checker:
+            checker = ReasoningTraceChecker(message, conversation)
+        try:
+            while next_round and current_round < max_round:
+                current_round += 1
+                if len(outputs) > 0:
+                    function_call_messages, picked_tools_prompt, special_tool_call = self.run_function_call(
+                        last_outputs, return_message=True,
+                        existing_tools_prompt=picked_tools_prompt,
+                        message_for_call_agent=message,
+                        call_agent=call_agent,
+                        call_agent_level=call_agent_level,
+                        temperature=temperature)
+
+                    if special_tool_call == 'Finish':
+                        next_round = False
+                        conversation.extend(function_call_messages)
+                        if isinstance(function_call_messages[0]['content'], types.GeneratorType):
+                            function_call_messages[0]['content'] = next(
+                                function_call_messages[0]['content'])
+                        return function_call_messages[0]['content'].split('[FinalAnswer]')[-1]
+
+                    if (self.enable_summary or token_overflow) and not call_agent:
+                        if token_overflow:
+                            print("token_overflow, using summary")
+                        enable_summary = True
+                    last_status = self.function_result_summary(
+                        conversation, status=last_status, enable_summary=enable_summary)
+
+                    if function_call_messages is not None:
+                        conversation.extend(function_call_messages)
+                        outputs.append(tool_result_format(
+                            function_call_messages))
+                    else:
+                        next_round = False
+                        conversation.extend(
+                            [{"role": "assistant", "content": ''.join(last_outputs)}])
+                        return ''.join(last_outputs).replace("</s>", "")
+                if self.enable_checker:
+                    good_status, wrong_info = checker.check_conversation()
+                    if not good_status:
+                        next_round = False
+                        print(
+                            "Internal error in reasoning: " + wrong_info)
+                        break
+                last_outputs = []
+                outputs.append("### TxAgent:\n")
+                last_outputs_str, token_overflow = self.llm_infer(messages=conversation,
+                                                                  temperature=temperature,
+                                                                  tools=picked_tools_prompt,
+                                                                  skip_special_tokens=False,
+                                                                  max_new_tokens=max_new_tokens, max_token=max_token,
+                                                                  check_token_status=True)
+                if last_outputs_str is None:
+                    next_round = False
+                    print(
+                        "The number of tokens exceeds the maximum limit.")
+                else:
+                    last_outputs.append(last_outputs_str)
+            if max_round == current_round:
+                print("The number of rounds exceeds the maximum limit!")
+            if self.force_finish:
+                return self.get_answer_based_on_unfinished_reasoning(conversation, temperature, max_new_tokens, max_token)
+            else:
+                return None
+
+        except Exception as e:
+            print(f"Error: {e}")
+            if self.force_finish:
+                return self.get_answer_based_on_unfinished_reasoning(conversation, temperature, max_new_tokens, max_token)
+            else:
+                return None
+
+    def build_logits_processor(self, messages, llm):
+        # Use the tokenizer from the LLM instance.
+        tokenizer = llm.get_tokenizer()
+        if self.avoid_repeat and len(messages) > 2:
+            assistant_messages = []
+            for i in range(1, len(messages) + 1):
+                if messages[-i]['role'] == 'assistant':
+                    assistant_messages.append(messages[-i]['content'])
+                    if len(assistant_messages) == 2:
+                        break
+            forbidden_ids = [tokenizer.encode(
+                msg, add_special_tokens=False) for msg in assistant_messages]
+            return [NoRepeatSentenceProcessor(forbidden_ids, 5)]
+        else:
+            return None
+
+def llm_infer(self, messages, temperature=0.7, tools=None,
+              output_begin_string=None, max_new_tokens=2048,
+              max_token=None, skip_special_tokens=True,
+              model=None, tokenizer=None, terminators=None, seed=None, check_token_status=False):
+
+    if model is None:
+        model = self.model
+
+    # Removed logits_processors for vLLM compatibility
+    sampling_params = {
+        "temperature": temperature,
+        "top_k": 40,
+        "top_p": 0.95,
+        "max_tokens": max_new_tokens,
+        "stop": ["</s>", "<|eot_id|>", "<|endoftext|>"],
+        "seed": seed if seed is not None else self.seed
+    }
+
+    prompt = self.chat_template.render(
+        messages=messages, tools=tools, add_generation_prompt=True
+    )
+
+    if output_begin_string is not None:
+        prompt += output_begin_string
+
+    token_overflow = False
+    if check_token_status and max_token is not None:
+        num_input_tokens = len(self.tokenizer.encode(
+            prompt, return_tensors="pt")[0])
+        if num_input_tokens > max_token:
+            import gc
+            import torch
+            torch.cuda.empty_cache()
+            gc.collect()
+            print("Number of input tokens before inference:", num_input_tokens)
+            token_overflow = True
+            return None, token_overflow
+
+    outputs = model.generate(prompt, sampling_params=sampling_params)
+
+    if isinstance(outputs, list) and len(outputs) > 0:
+        output = outputs[0].outputs[0].text
+    else:
+        output = "[Error: No output]"
+
+    print("\033[92m" + output + "\033[0m")
+
+    if check_token_status and max_token is not None:
+        return output, token_overflow
+
+    return output
+
+    def run_self_agent(self, message: str,
+                       temperature: float,
+                       max_new_tokens: int,
+                       max_token: int) -> str:
+
+        print("\033[1;32;40mstart self agent\033[0m")
+        conversation = []
+        conversation = self.set_system_prompt(conversation, self.self_prompt)
+        conversation.append({"role": "user", "content": message})
+        return self.llm_infer(messages=conversation,
+                              temperature=temperature,
+                              tools=None,
+                              max_new_tokens=max_new_tokens, max_token=max_token)
+
+    def run_chat_agent(self, message: str,
+                       temperature: float,
+                       max_new_tokens: int,
+                       max_token: int) -> str:
+
+        print("\033[1;32;40mstart chat agent\033[0m")
+        conversation = []
+        conversation = self.set_system_prompt(conversation, self.chat_prompt)
+        conversation.append({"role": "user", "content": message})
+        return self.llm_infer(messages=conversation,
+                              temperature=temperature,
+                              tools=None,
+                              max_new_tokens=max_new_tokens, max_token=max_token)
+
+    def run_format_agent(self, message: str,
+                         answer: str,
+                         temperature: float,
+                         max_new_tokens: int,
+                         max_token: int) -> str:
+
+        print("\033[1;32;40mstart format agent\033[0m")
+        if '[FinalAnswer]' in answer:
+            possible_final_answer = answer.split("[FinalAnswer]")[-1]
+        elif "\n\n" in answer:
+            possible_final_answer = answer.split("\n\n")[-1]
+        else:
+            possible_final_answer = answer.strip()
+        if len(possible_final_answer) == 1:
+            choice = possible_final_answer[0]
+            if choice in ['A', 'B', 'C', 'D', 'E']:
+                return choice
+        elif len(possible_final_answer) > 1:
+            if possible_final_answer[1] == ':':
+                choice = possible_final_answer[0]
+                if choice in ['A', 'B', 'C', 'D', 'E']:
+                    print("choice", choice)
+                    return choice
+
+        conversation = []
+        format_prompt = f"You are helpful assistant to transform the answer of agent to the final answer of 'A', 'B', 'C', 'D'."
+        conversation = self.set_system_prompt(conversation, format_prompt)
+        conversation.append({"role": "user", "content": message +
+                            "\nThe final answer of agent:" + answer + "\n The answer is (must be a letter):"})
+        return self.llm_infer(messages=conversation,
+                              temperature=temperature,
+                              tools=None,
+                              max_new_tokens=max_new_tokens, max_token=max_token)
+
+    def run_summary_agent(self, thought_calls: str,
+                          function_response: str,
+                          temperature: float,
+                          max_new_tokens: int,
+                          max_token: int) -> str:
+        print("\033[1;32;40mSummarized Tool Result:\033[0m")
+        generate_tool_result_summary_training_prompt = """Thought and function calls: 
+{thought_calls}
+
+Function calls' responses:
+\"\"\"
+{function_response}
+\"\"\"
+
+Based on the Thought and function calls, and the function calls' responses, you need to generate a summary of the function calls' responses that fulfills the requirements of the thought. The summary MUST BE ONE sentence and include all necessary information.
+
+Directly respond with the summarized sentence of the function calls' responses only. 
+
+Generate **one summarized sentence** about "function calls' responses" with necessary information, and respond with a string:
+            """.format(thought_calls=thought_calls, function_response=function_response)
+        conversation = []
+        conversation.append(
+            {"role": "user", "content": generate_tool_result_summary_training_prompt})
+        output = self.llm_infer(messages=conversation,
+                                temperature=temperature,
+                                tools=None,
+                                max_new_tokens=max_new_tokens, max_token=max_token)
+
+        if '[' in output:
+            output = output.split('[')[0]
+        return output
+
+    def function_result_summary(self, input_list, status, enable_summary):
+        """
+        Processes the input list, extracting information from sequences of 'user', 'tool', 'assistant' roles.
+        Supports 'length' and 'step' modes, and skips the last 'k' groups.
+
+        Parameters:
+            input_list (list): A list of dictionaries containing role and other information.
+            summary_skip_last_k (int): Number of groups to skip from the end. Defaults to 0.
+            summary_context_length (int): The context length threshold for the 'length' mode.
+            last_processed_index (tuple or int): The last processed index.
+
+        Returns:
+            list: A list of extracted information from valid sequences.
+        """
+        if 'tool_call_step' not in status:
+            status['tool_call_step'] = 0
+
+        for idx in range(len(input_list)):
+            pos_id = len(input_list)-idx-1
+            if input_list[pos_id]['role'] == 'assistant':
+                if 'tool_calls' in input_list[pos_id]:
+                    if 'Tool_RAG' in str(input_list[pos_id]['tool_calls']):
+                        status['tool_call_step'] += 1
+                break
+
+        if 'step' in status:
+            status['step'] += 1
+        else:
+            status['step'] = 0
+
+        if not enable_summary:
+            return status
+
+        if 'summarized_index' not in status:
+            status['summarized_index'] = 0
+
+        if 'summarized_step' not in status:
+            status['summarized_step'] = 0
+
+        if 'previous_length' not in status:
+            status['previous_length'] = 0
+
+        if 'history' not in status:
+            status['history'] = []
+
+        function_response = ''
+        idx = 0
+        current_summarized_index = status['summarized_index']
+
+        status['history'].append(self.summary_mode == 'step' and status['summarized_step']
+                                 < status['step']-status['tool_call_step']-self.summary_skip_last_k)
+
+        idx = current_summarized_index
+        while idx < len(input_list):
+            if (self.summary_mode == 'step' and status['summarized_step'] < status['step']-status['tool_call_step']-self.summary_skip_last_k) or (self.summary_mode == 'length' and status['previous_length'] > self.summary_context_length):
+
+                if input_list[idx]['role'] == 'assistant':
+                    if 'Tool_RAG' in str(input_list[idx]['tool_calls']):
+                        this_thought_calls = None
+                    else:
+                        if len(function_response) != 0:
+                            print("internal summary")
+                            status['summarized_step'] += 1
+                            result_summary = self.run_summary_agent(
+                                thought_calls=this_thought_calls,
+                                function_response=function_response,
+                                temperature=0.1,
+                                max_new_tokens=1024,
+                                max_token=99999
+                            )
+
+                            input_list.insert(
+                                last_call_idx+1, {'role': 'tool', 'content': result_summary})
+                            status['summarized_index'] = last_call_idx + 2
+                            idx += 1
+
+                        last_call_idx = idx
+                        this_thought_calls = input_list[idx]['content'] + \
+                            input_list[idx]['tool_calls']
+                        function_response = ''
+
+                elif input_list[idx]['role'] == 'tool' and this_thought_calls is not None:
+                    function_response += input_list[idx]['content']
+                    del input_list[idx]
+                    idx -= 1
+
+            else:
+                break
+            idx += 1
+
+        if len(function_response) != 0:
+            status['summarized_step'] += 1
+            result_summary = self.run_summary_agent(
+                thought_calls=this_thought_calls,
+                function_response=function_response,
+                temperature=0.1,
+                max_new_tokens=1024,
+                max_token=99999
+            )
+
+            tool_calls = json.loads(input_list[last_call_idx]['tool_calls'])
+            for tool_call in tool_calls:
+                del tool_call['call_id']
+            input_list[last_call_idx]['tool_calls'] = json.dumps(tool_calls)
+            input_list.insert(
+                last_call_idx+1, {'role': 'tool', 'content': result_summary})
+            status['summarized_index'] = last_call_idx + 2
+
+        return status
+
+    # Following are Gradio related functions
+
+    # General update method that accepts any new arguments through kwargs
+    def update_parameters(self, **kwargs):
+        for key, value in kwargs.items():
+            if hasattr(self, key):
+                setattr(self, key, value)
+
+        # Return the updated attributes
+        updated_attributes = {key: value for key,
+                              value in kwargs.items() if hasattr(self, key)}
+        return updated_attributes
+
+    def run_gradio_chat(self, message: str,
+                        history: list,
+                        temperature: float,
+                        max_new_tokens: int,
+                        max_token: int,
+                        call_agent: bool,
+                        conversation: gr.State,
+                        max_round: int = 20,
+                        seed: int = None,
+                        call_agent_level: int = 0,
+                        sub_agent_task: str = None) -> str:
+        """
+        Generate a streaming response using the llama3-8b model.
+        Args:
+            message (str): The input message.
+            history (list): The conversation history used by ChatInterface.
+            temperature (float): The temperature for generating the response.
+            max_new_tokens (int): The maximum number of new tokens to generate.
+        Returns:
+            str: The generated response.
+        """
+        print("\033[1;32;40mstart\033[0m")
+        print("len(message)", len(message))
+        if len(message) <= 10:
+            yield "Hi, I am TxAgent, an assistant for answering biomedical questions. Please provide a valid message with a string longer than 10 characters."
+            return "Please provide a valid message."
+        outputs = []
+        outputs_str = ''
+        last_outputs = []
+
+        picked_tools_prompt, call_agent_level = self.initialize_tools_prompt(
+            call_agent,
+            call_agent_level,
+            message)
+
+        conversation = self.initialize_conversation(
+            message,
+            conversation=conversation,
+            history=history)
+        history = []
+
+        next_round = True
+        function_call_messages = []
+        current_round = 0
+        enable_summary = False
+        last_status = {}  # for summary
+        token_overflow = False
+        if self.enable_checker:
+            checker = ReasoningTraceChecker(
+                message, conversation, init_index=len(conversation))
+
+        try:
+            while next_round and current_round < max_round:
+                current_round += 1
+                if len(last_outputs) > 0:
+                    function_call_messages, picked_tools_prompt, special_tool_call, current_gradio_history = yield from self.run_function_call_stream(
+                        last_outputs, return_message=True,
+                        existing_tools_prompt=picked_tools_prompt,
+                        message_for_call_agent=message,
+                        call_agent=call_agent,
+                        call_agent_level=call_agent_level,
+                        temperature=temperature)
+                    history.extend(current_gradio_history)
+                    if special_tool_call == 'Finish':
+                        yield history
+                        next_round = False
+                        conversation.extend(function_call_messages)
+                        return function_call_messages[0]['content']
+                    elif special_tool_call == 'RequireClarification' or special_tool_call == 'DirectResponse':
+                        history.append(
+                            ChatMessage(role="assistant", content=history[-1].content))
+                        yield history
+                        next_round = False
+                        return history[-1].content
+                    if (self.enable_summary or token_overflow) and not call_agent:
+                        if token_overflow:
+                            print("token_overflow, using summary")
+                        enable_summary = True
+                    last_status = self.function_result_summary(
+                        conversation, status=last_status,
+                        enable_summary=enable_summary)
+                    if function_call_messages is not None:
+                        conversation.extend(function_call_messages)
+                        formated_md_function_call_messages = tool_result_format(
+                            function_call_messages)
+                        yield history
+                    else:
+                        next_round = False
+                        conversation.extend(
+                            [{"role": "assistant", "content": ''.join(last_outputs)}])
+                        return ''.join(last_outputs).replace("</s>", "")
+                if self.enable_checker:
+                    good_status, wrong_info = checker.check_conversation()
+                    if not good_status:
+                        next_round = False
+                        print("Internal error in reasoning: " + wrong_info)
+                        break
+                last_outputs = []
+                last_outputs_str, token_overflow = self.llm_infer(
+                    messages=conversation,
+                    temperature=temperature,
+                    tools=picked_tools_prompt,
+                    skip_special_tokens=False,
+                    max_new_tokens=max_new_tokens,
+                    max_token=max_token,
+                    seed=seed,
+                    check_token_status=True)
+                last_thought = last_outputs_str.split("[TOOL_CALLS]")[0]
+                for each in history:
+                    if each.metadata is not None:
+                        each.metadata['status'] = 'done'
+                if '[FinalAnswer]' in last_thought:
+                    final_thought, final_answer = last_thought.split(
+                        '[FinalAnswer]')
+                    history.append(
+                        ChatMessage(role="assistant",
+                                    content=final_thought.strip())
+                    )
+                    yield history
+                    history.append(
+                        ChatMessage(
+                            role="assistant", content="**Answer**:\n"+final_answer.strip())
+                    )
+                    yield history
+                else:
+                    history.append(ChatMessage(
+                        role="assistant", content=last_thought))
+                    yield history
+
+                last_outputs.append(last_outputs_str)
+
+            if next_round:
+                if self.force_finish:
+                    last_outputs_str = self.get_answer_based_on_unfinished_reasoning(
+                        conversation, temperature, max_new_tokens, max_token)
+                    for each in history:
+                        if each.metadata is not None:
+                            each.metadata['status'] = 'done'
+                    if '[FinalAnswer]' in last_thought:
+                        final_thought, final_answer = last_thought.split(
+                            '[FinalAnswer]')
+                        history.append(
+                            ChatMessage(role="assistant",
+                                        content=final_thought.strip())
+                        )
+                        yield history
+                        history.append(
+                            ChatMessage(
+                                role="assistant", content="**Answer**:\n"+final_answer.strip())
+                        )
+                        yield history
+                else:
+                    yield "The number of rounds exceeds the maximum limit!"
+
+        except Exception as e:
+            print(f"Error: {e}")
+            if self.force_finish:
+                last_outputs_str = self.get_answer_based_on_unfinished_reasoning(
+                    conversation,
+                    temperature,
+                    max_new_tokens,
+                    max_token)
+                for each in history:
+                    if each.metadata is not None:
+                        each.metadata['status'] = 'done'
+                if '[FinalAnswer]' in last_thought or '"name": "Finish",' in last_outputs_str:
+                    final_thought, final_answer = last_thought.split(
+                        '[FinalAnswer]')
+                    history.append(
+                        ChatMessage(role="assistant",
+                                    content=final_thought.strip())
+                    )
+                    yield history
+                    history.append(
+                        ChatMessage(
+                            role="assistant", content="**Answer**:\n"+final_answer.strip())
+                    )
+                    yield history
+            else:
+                return None