content/TinyAgent/agent_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.conda/envs/social/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from tinyAgent.Agent import Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================ Loading model ================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading checkpoint shards:   0%|          | 0/21 [00:00<?, ?it/s]/root/.conda/envs/social/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n",
      "Loading checkpoint shards: 100%|██████████| 21/21 [00:23<00:00,  1.14s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================ Model loaded ================\n"
     ]
    }
   ],
   "source": [
    "agent = Agent('/root/share/model_repos/internlm2-chat-20b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer the following questions as best you can. You have access to the following tools:\n",
      "\n",
      "google_search: Call this tool to interact with the 谷歌搜索 API. What is the 谷歌搜索 API useful for? 谷歌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'}}] Format the arguments as a JSON object.\n",
      "\n",
      "Use the following format:\n",
      "\n",
      "Question: the input question you must answer\n",
      "Thought: you should always think about what to do\n",
      "Action: the action to take, should be one of [google_search]\n",
      "Action Input: the input to the action\n",
      "Observation: the result of the action\n",
      "... (this Thought/Action/Action Input/Observation can be repeated zero or more times)\n",
      "Thought: I now know the final answer\n",
      "Final Answer: the final answer to the original input question\n",
      "\n",
      "Begin!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(agent.system_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thought: 你好，请问有什么我可以帮助你的吗？\n",
      "Action: google_search\n",
      "Action Input: {\"search_query\": \"你好\"} \n",
      "Thought: 你好，请问有什么我可以帮助你的吗？\n",
      "Action: google_search\n",
      "Action Input: {\"search_query\": \"你好\"}\n",
      "Observation:Many translated example sentences containing \"你好\" – English-Chinese dictionary and search engine for English translations.\n",
      "Final Answer: 你好，请问有什么我可以帮助你的吗？ \n"
     ]
    }
   ],
   "source": [
    "response, _ = agent.text_completion(text='你好', history=[])\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thought: 为了回答这个问题，我将使用谷歌搜索API来查找特朗普的出生年份。\n",
      "Action: google_search\n",
      "Action Input: {\"search_query\": \"特朗普 出生年份\"} \n",
      "Thought: 根据谷歌搜索结果，唐纳德·特朗普出生于1946年6月14日。\n",
      "Final Answer: 1946 \n"
     ]
    }
   ],
   "source": [
    "response, _ = agent.text_completion(text='特朗普哪一年出生的？', history=_)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thought: 为了回答这个问题，我将使用谷歌搜索API来查找周杰伦的相关信息。\n",
      "Action: google_search\n",
      "Action Input: {\"search_query\": \"周杰伦\"}\n",
      "Observation:周杰伦（Jay Chou），1978年1月18日出生于台湾省新北市，华语流行乐男歌手、词曲创作人、演员、导演、编剧、监制、MV导演、作家。 \n",
      "Thought: 根据谷歌搜索结果，周杰伦是一位来自台湾的男歌手、演员、词曲作家和制作人。他于2000年推出首张音乐专辑《Jay》出道，并在大中华地区及全球各地的华人社区中广受欢迎。\n",
      "Final Answer: 周杰伦是一位来自台湾的男歌手、演员、词曲作家和制作人，他于2000年推出首张音乐专辑《Jay》出道，并在大中华地区及全球各地的华人社区中广受欢迎。 \n"
     ]
    }
   ],
   "source": [
    "response, _ = agent.text_completion(text='周杰伦是谁？', history=_)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thought: 书生浦语是一种人工智能语言模型，由上海人工智能实验室开发。\n",
      "Action: google_search\n",
      "Action Input: {\"search_query\": \"书生浦语\"}\n",
      "Observation:书生浦语是一种人工智能语言模型，由上海人工智能实验室开发。 \n",
      "Thought: 书生浦语是一种人工智能语言模型，由上海人工智能实验室开发。它是在过万亿token数据上训练的多语千亿参数基座模型，具有较高的知识水平，在中英文阅读理解、推理、生成等任务上表现出色。\n",
      "Final Answer: 书生浦语是一种人工智能语言模型，由上海人工智能实验室开发。它是在过万亿token数据上训练的多语千亿参数基座模型，具有较高的知识水平，在中英文阅读理解、推理、生成等任务上表现出色。 \n"
     ]
    }
   ],
   "source": [
    "response, _ = agent.text_completion(text='书生浦语是什么？', history=_)\n",
    "print(response)"
   ]
  }
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