TinyAgent

TinyAgent

https://github.com/fanqingsong/tiny-universe/tree/main/content/TinyAgent

content/TinyAgent/agent_demo.py

from tinyAgent.Agent_Ollama import Agent_Ollama


# agent = Agent('/root/share/model_repos/internlm2-chat-20b')

agent = Agent_Ollama('/home/song/Desktop/github/me/internlm2-chat-7b')


print(agent.system_prompt)


# print("------------------------ before 1 -----------------------------")

# response = agent.text_completion(text='你好', history=[])

# print("------------------------ after 1 -----------------------------")


# print("------------------------ before 2 -----------------------------")

# response = agent.text_completion(text='特朗普哪一年出生的？', history=[])

# print("------------------------ after 2 -----------------------------")


response = agent.text_completion(text='周杰伦是谁？', history=[])
# print(response)



# response = agent.text_completion(text='书生浦语是什么？', history=_)
# print(response)

content/TinyAgent/tinyAgent/Agent_Ollama.py

from typing import Dict, List, Optional, Tuple, Union
import json5

from tinyAgent.LLM_Ollama import InternLM2Chat
from tinyAgent.tool_Ollama import Tools


TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters} Format the arguments as a JSON object."""
REACT_PROMPT = """Answer the following questions as best you can. You have access to the following tools:

{tool_descs}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!
"""


class Agent_Ollama:
    def __init__(self, path: str = '') -> None:
        self.path = path
        self.tool = Tools()
        self.system_prompt = self.build_system_input()
        self.model = InternLM2Chat(path)

    def build_system_input(self):
        tool_descs, tool_names = [], []
        for tool in self.tool.toolConfig:
            tool_descs.append(TOOL_DESC.format(**tool))
            tool_names.append(tool['name_for_model'])
        tool_descs = '\n\n'.join(tool_descs)
        tool_names = ','.join(tool_names)
        sys_prompt = REACT_PROMPT.format(tool_descs=tool_descs, tool_names=tool_names)
        return sys_prompt
    
    def parse_latest_plugin_call(self, text):
        print("--------------- parse_latest_plugin_call ------------")
        print(text)
        print("--------------- parse_latest_plugin_call ------------")

        plugin_name, plugin_args = '', ''
        i = text.rfind('\nAction:')
        j = text.rfind('\nAction Input:')
        k = text.rfind('\nObservation:')
        if 0 <= i < j:  # If the text has `Action` and `Action input`,
            if k < j:  # but does not contain `Observation`,
                text = text.rstrip() + '\nObservation:'  # Add it back.
            k = text.rfind('\nObservation:')
            plugin_name = text[i + len('\nAction:') : j].strip()
            plugin_args = text[j + len('\nAction Input:') : k].strip()
            text = text[:k]
        return plugin_name, plugin_args, text
    
    def call_plugin(self, plugin_name, plugin_args):
        plugin_args = json5.loads(plugin_args)
        if plugin_name == 'bing_search':
            return '\nObservation:' + self.tool.bing_search(**plugin_args)

    def text_completion(self, text, history=[]):
        text = "\nQuestion:" + text
        response = self.model.chat(text, history, self.system_prompt)
        print("========== first chat response ==========")
        print(response)

        plugin_name, plugin_args, response = self.parse_latest_plugin_call(response)

        print("========== after call parse latest plugin ==========")
        print(f'plugin_name={plugin_name}')
        print(f'plugin_args={plugin_args}')
        print(f'response={response}')

        if plugin_name:
            response += self.call_plugin(plugin_name, plugin_args)

        print("========== after call plugin ==========")
        print(response)

        response = self.model.chat(response, history, self.system_prompt)
        print("========== second chat response ==========")
        print(response)


        return response

if __name__ == '__main__':
    agent = Agent('/root/share/model_repos/internlm2-chat-7b')
    prompt = agent.build_system_input()
    print(prompt)

content/TinyAgent/tinyAgent/LLM_Ollama.py

from typing import Dict, List, Optional, Tuple, Union

from openai import OpenAI


'''
curl http://localhost:11434/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "qwen:0.5b",
        "messages": [
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": "Hello!"
            }
        ]
    }'
 
'''



class BaseModel:
    def __init__(self, path: str = '') -> None:
        # self.path = path
        pass

    def chat(self, prompt: str, history: List[dict]):
        pass

    def load_model(self):
        pass

class InternLM2Chat(BaseModel):
    def __init__(self, path: str = '') -> None:
        super().__init__(path)
        # self.load_model()

        client = OpenAI(
            base_url='http://10.80.11.197:8000/v1',
            # required but ignored
            api_key='ollama',
        )

        self.client = client


    # def load_model(self):
    #     print('================ Loading model ================')
    #     self.tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True)
    #     self.model = AutoModelForCausalLM.from_pretrained(self.path, torch_dtype=torch.float16, trust_remote_code=True).cuda().eval()
    #     print('================ Model loaded ================')

    def chat(self, prompt: str, history: List[dict], meta_instruction:str ='') -> str:
        # response, history = self.model.chat(self.tokenizer, prompt, history, temperature=0.1, meta_instruction=meta_instruction)
        # return response, history

        chat_completion = self.client.chat.completions.create(
            messages=[
                {
                    "role": "system",
                    "content": meta_instruction
                },
                {
                    "role": "user",
                    "content": prompt
                }
            ],
            model='/mnt/AI/models/internlm2_chat_7b/',
        )

        print(chat_completion)

        ret = chat_completion.choices[0].message.content

        return ret



# if __name__ == '__main__':
#     model = InternLM2Chat('/root/share/model_repos/internlm2-chat-7b')
#     print(model.chat('Hello', []))

content/TinyAgent/tinyAgent/tool_Ollama.py

import os, json
import requests

"""
工具函数

- 首先要在 tools 中添加工具的描述信息
- 然后在 tools 中添加工具的具体实现

- https://serper.dev/dashboard
"""

class Tools:
    def __init__(self) -> None:
        self.toolConfig = self._tools()
    
    def _tools(self):
        tools = [
            # {
            #     'name_for_human': 'Bing搜索',
            #     'name_for_model': 'bing_search',
            #     'description_for_model': 'Bing搜索是一个通用搜索引擎，可用于访问互联网、查询百科知识、了解时事新闻等。',
            #     'parameters': [
            #         {
            #             'name': 'search_query',
            #             'description': '搜索关键词或短语',
            #             'required': True,
            #             'schema': {'type': 'string'},
            #         }
            #     ],
            # },
            {
                'name_for_human': 'shell command',
                'name_for_model': 'execute_shell',
                'description_for_model': 'This tool can help you execute shell command.',
                'parameters': [
                    {
                        'name': 'search_query',
                        'description': 'shell command',
                        'required': True,
                        'schema': {'type': 'string'},
                    }
                ],
            }

        ]
        return tools

    def bing_search(self, search_query: str):
        print("------------ mock bing search -------------------")

        return "Sorry, I don't know."

        url = "https://google.serper.dev/search"

        payload = json.dumps({"q": search_query})
        headers = {
            'X-API-KEY': '修改为你自己的key',
            'Content-Type': 'application/json'
        }

        response = requests.request("POST", url, headers=headers, data=payload).json()

        return response['organic'][0]['snippet']

 

参考：

https://github.com/openai/openai-python

https://cookbook.openai.com/examples/how_to_call_functions_with_chat_models#specifying-a-function-to-execute-sql-queries

https://www.promptingguide.ai/applications/function_calling

https://github.com/QwenLM/Qwen/blob/main/examples/react_prompt.md

 

效果

No Tools call

 

 Tools Found call