plugins.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import time\n",
    "import traceback\n",
    "import threading\n",
    "from IPython.display import clear_output\n",
    "\n",
    "from chat_tools import *\n",
    "from assistant import OpenAIAssistant"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpenAIAssistant\n",
    "# With long term memory\n",
    "# You must have Qdrant running on localhost:6333\n",
    "# docker run -p 6333:6333 -v <your_save_dir_here>:/qdrant/storage qdrant/qdrant\n",
    "api_key = ''\n",
    "system_prompt = None\n",
    "debug = False\n",
    "use_long_term_memory = False # Do not use when using plugins\n",
    "use_short_term_memory = False # Do not use when using plugins\n",
    "use_knowledge_retrieval = False # Do not use when using plugins\n",
    "summarize_short_term_memory = False # Do not use when using plugins\n",
    "summarize_long_term_memory = False # Do not use when using plugins\n",
    "summarize_knowledge_retrieval = False # Do not use when using plugins\n",
    "short_term_memory_max_tokens = 750\n",
    "long_term_memory_max_tokens = 500\n",
    "knowledge_retrieval_max_tokens = 1000\n",
    "short_term_memory_summary_max_tokens = 300\n",
    "long_term_memory_summary_max_tokens = 300\n",
    "knowledge_retrieval_summary_max_tokens = 600\n",
    "long_term_memory_collection_name = 'long_term_memory'\n",
    "\n",
    "assistant = OpenAIAssistant(api_key, system_prompt=system_prompt, long_term_memory_collection_name=long_term_memory_collection_name, use_long_term_memory=use_long_term_memory, use_short_term_memory=use_short_term_memory, memory_manager=None, debug=debug, summarize_short_term_memory=summarize_short_term_memory, summarize_long_term_memory=summarize_long_term_memory, short_term_memory_max_tokens=short_term_memory_max_tokens, long_term_memory_max_tokens=long_term_memory_max_tokens, short_term_memory_summary_max_tokens=short_term_memory_summary_max_tokens, long_term_memory_summary_max_tokens=long_term_memory_summary_max_tokens, use_knowledge_retrieval=use_knowledge_retrieval, summarize_knowledge_retrieval=summarize_knowledge_retrieval, knowledge_retrieval_max_tokens=knowledge_retrieval_max_tokens, knowledge_retrieval_summary_max_tokens=knowledge_retrieval_summary_max_tokens)\n",
    "cache = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_action(text, api_key='', action_dict={}, cache={}, max_tokens=500):\n",
    "    try:\n",
    "        # Regular expression pattern to match the last \"Action\" and \"Action Input\" in the text\n",
    "        pattern = r'Action: ([^\\n]*\\n)+Action Input: ([^\\n]*)$'\n",
    "\n",
    "        match = re.search(pattern, text, re.MULTILINE)\n",
    "\n",
    "        chatgpt = OpenAIAssistant(api_key, system_prompt=None, use_long_term_memory=False, use_short_term_memory=False, memory_manager=None, use_knowledge_retrieval=False)\n",
    "\n",
    "        if match:\n",
    "            last_action = match.group(1).strip().lower()\n",
    "            last_action_input = match.group(2).strip()\n",
    "            if cache.get(last_action+last_action_input):\n",
    "                return cache[last_action+last_action_input]\n",
    "            \n",
    "            # Call the corresponding function based on the action name\n",
    "            if last_action in action_dict:\n",
    "                content = action_dict[last_action](last_action_input, chatgpt, max_tokens)\n",
    "                cache[last_action+last_action_input] = content\n",
    "                return content\n",
    "            else:\n",
    "                return 'Action does not exist!'\n",
    "        else:\n",
    "            return 'Invalid action format. Remember to include \"Action:\" and \"Action Input:\"'\n",
    "    except Exception as e:\n",
    "        # return the error message\n",
    "        return '{error: ' + str(e) + '}'\n",
    "    \n",
    "\n",
    "def parse_action_with_timer(text, api_key, action_dict, cache={}, max_tokens=500, timeout=120.0):\n",
    "    result = {'value': None}\n",
    "    finished = threading.Event()\n",
    "\n",
    "    def target():\n",
    "        nonlocal result\n",
    "        result['value'] = parse_action(text, api_key, action_dict, cache, max_tokens)\n",
    "        finished.set()\n",
    "\n",
    "    thread = threading.Thread(target=target)\n",
    "    thread.start()\n",
    "    finish = finished.wait(timeout)\n",
    "\n",
    "    if not finish:\n",
    "        return 'Error: Function execution timed out.'\n",
    "    return result['value']\n",
    "\n",
    "def web_search_with_timer(text, timeout=120.0):\n",
    "    search = WebSearch()\n",
    "    result = {'value': None}\n",
    "    finished = threading.Event()\n",
    "\n",
    "    def target():\n",
    "        nonlocal result\n",
    "        results = search.search(keywords=text, safesearch='Off', time=None, max_results=10, cache=True)\n",
    "        out = '{'\n",
    "        for result_ in results:\n",
    "            out += 'title: ' + result_['title'] + ',\\n\\tbody: ' + result_['body'] + ',\\n\\t' + 'url: ' + result_['href'] + ',\\n\\t'\n",
    "        result['value'] = out.strip() + '}'\n",
    "        finished.set()\n",
    "\n",
    "    thread = threading.Thread(target=target)\n",
    "    thread.start()\n",
    "    finish = finished.wait(timeout)\n",
    "\n",
    "    if not finish:\n",
    "        return 'Error: Function execution timed out.'\n",
    "    return result['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def action_loop(assistant, user_prompt, cache={}, function_definitions=[], max_observation_tokens=500, memory_tokens=3000, max_loops=6, sleep=5, **kwargs):\n",
    "    prompt = \"Respond to the following prompt as best as you can. You have access to the following tools:\\n\"  + '\\n'.join([f'{function_definition[\"name\"]}: {function_definition[\"definition\"]}' for function_definition in function_definitions])\n",
    "    prompt = prompt + '\\n' + \"\"\"You must use the following format:\n",
    "Prompt: The prompt you are responding to\n",
    "Thought: You should always think about what to do\n",
    "Action: Must be one of \"\"\"\n",
    "    prompt = prompt + '[' + ', '.join([f'{function_definition[\"name\"]}' for function_definition in function_definitions]) + ']\\n'\n",
    "    prompt = prompt + \"\"\"Action Input: The input to the action\n",
    "Observation: the result of the action\n",
    "... (this Thought/Action/Observation loop can repeat N times)\n",
    "Thought: I now know the final answer\n",
    "Final Answer: The final answer to the original input question\n",
    "Begin!\n",
    "Prompt: {prompt}\"\"\"\n",
    "\n",
    "    action_dict = {function_definition['name'].lower(): function_definition['function'] for function_definition in function_definitions}\n",
    "\n",
    "    stop=['Observation:']\n",
    "\n",
    "    response = assistant.get_chat_response(prompt=prompt.format(prompt=user_prompt), stop=stop, **kwargs)\n",
    "    clear_output(wait=True)\n",
    "    print(response.choices[0].message.content.strip())\n",
    "\n",
    "    # action loop\n",
    "    next_message = ''\n",
    "    counter = max_loops\n",
    "    while True:\n",
    "        if counter < max_loops:\n",
    "            time.sleep(sleep)\n",
    "        if len(response.choices[0].message.content.split('Final Answer:')) > 1:\n",
    "            break\n",
    "        action_result = parse_action_with_timer(response.choices[0].message.content, api_key=assistant.api_key, action_dict=action_dict, cache=cache, max_tokens=max_observation_tokens)\n",
    "        if next_message != '':\n",
    "            next_message = next_message + ' '\n",
    "        next_message += response.choices[0].message.content.strip() + '\\nObservation: ' + action_result + '\\n'\n",
    "        counter_ = min(max_loops, 10)\n",
    "        while assistant.calculate_num_tokens(next_message) > memory_tokens:\n",
    "            pattern = r'Thought:.*\\nAction:.*\\nAction Input:.*\\nObservation:.*\\n'\n",
    "\n",
    "            # Remove the oldest entries using re.sub()\n",
    "            next_message = re.sub(pattern, '', next_message, count=1)\n",
    "            counter_ -= 1\n",
    "            if counter_ == 0:\n",
    "                next_message = truncate_text(assistant, next_message, memory_tokens, side='left')\n",
    "                break\n",
    "        # get the next action\n",
    "        response = assistant.get_chat_response(prompt=None, stop=stop, inject_messages=[{0: {'role': 'user', 'content': user_prompt}}, {1: {'role': 'assistant', 'content': next_message}}], **kwargs)\n",
    "\n",
    "        clear_output(wait=True)\n",
    "        print(next_message + response.choices[0].message.content.strip())\n",
    "        if counter == 0:\n",
    "            break\n",
    "        counter -= 1\n",
    "        \n",
    "    return response.choices[0].message.content.split('Final Answer:')[-1].strip(), cache"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Action Loop Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example on how to create a function\n",
    "def ask_chatgpt(last_action_input, chatgpt, max_tokens): # must include these inputs\n",
    "    print('Asking ChatGPT: ' + last_action_input + '...') # print what you are doing\n",
    "    response = chatgpt.get_chat_response(last_action_input) # processing logic goes here\n",
    "    return truncate_text(chatgpt, response.choices[0].message.content.strip(), max_tokens) # end with a truncate_text call passing chatgpt, text, and max_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of all functions/tools you want to give chatGPT access to\n",
    "function_definitions = [\n",
    "    {\n",
    "        'name': 'Ask ChatGPT', # What you want chatGPT to see for the function name\n",
    "        'definition': 'Ask ChatGPT a question or give ChatGPT a prompt.', # What you want chatGPT to see for the function definition\n",
    "        'function': ask_chatgpt # the function you want to call\n",
    "    },\n",
    "    {\n",
    "        'name': 'Web Search', \n",
    "        'definition': 'Searches the web for the given search query.', \n",
    "        'function': web_search \n",
    "    },\n",
    "    {\n",
    "        'name': 'Get Readable Content',\n",
    "        'definition': 'Returns the readable content of the given url.',\n",
    "        'function': get_readable_content\n",
    "    },\n",
    "    {\n",
    "        'name': 'Get Internal Links',\n",
    "        'definition': 'Returns the internal links of the given url.',\n",
    "        'function': get_internal_links\n",
    "    },\n",
    "    {\n",
    "        'name': 'Get External Links',\n",
    "        'definition': 'Returns the external links of the given url.',\n",
    "        'function': get_external_links\n",
    "    },\n",
    "    {\n",
    "        'name': 'Run Python Code',\n",
    "        'definition': 'Runs the given Python code.',\n",
    "        'function': run_python_code\n",
    "    },\n",
    "]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run Action Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thought: I need to find a reliable source for AI news and then summarize it in my own words.\n",
      "Action: Web Search\n",
      "Action Input: \"latest news in AI\"\n",
      "Observation: {title: Artificial Intelligence News -- ScienceDaily,\n",
      "\tbody: AI Technology Generates Original Proteins from Scratch Jan. 26, 2023 — Scientists have created an AI system capable of generating artificial enzymes from scratch. In laboratory tests, some of...,\n",
      "\turl: https://www.sciencedaily.com/news/computers_math/artificial_intelligence/,\n",
      "\ttitle: 3 AI Stocks You Cant Afford to Miss | Nasdaq,\n",
      "\tbody: The companys five-year compound annual growth rate on the top line stands at 34.9%. The latest earnings report showed a somewhat slower year-over-year revenue increase of 27%, but thats still a ...,\n",
      "\turl: https://www.nasdaq.com/articles/3-ai-stocks-you-cant-afford-to-miss,\n",
      "\ttitle: Artificial intelligence | MIT News | Massachusetts Institute of Technology,\n",
      "\tbody: Integrating humans with AI in structural design A process that seeks feedback from human specialists proves more effective at optimization than automated systems working alone. March 2, 2023 Read full story MIT-Takeda Program heads into fourth year with crop of 10 new projects,\n",
      "\turl: https://news.mit.edu/topic/artificial-intelligence2,\n",
      "\ttitle: Elon Musk joins hundreds calling for a six-month pause on AI ...,\n",
      "\tbody: The futurist collective, backed by Musk, previously stated in a 2015 open letter that they supported development of AI to benefit society, but were wary of the potential dangers. The latest...,\n",
      "\turl: https://www.cbsnews.com/news/elon-musk-open-letter-ai/,\n",
      "\ttitle: Artificial Intelligence | Science News,\n",
      "\tbody: A new AI acquired humanlike number sense on its own A new artificial intelligence seems to share our intuitive ability to estimate numbers at a glance. By Maria Temming May 13, 2019...,\n",
      "\turl: https://www.sciencenews.org/topic/artificial-intelligence,\n",
      "\ttitle: Leading technologists call for a 6-month pause in training advanced AI ...,\n",
      "\tbody: Elon Musk and several other technologists call for a pause on training of AI systems. Companies researching AI are locked in an out-of-control race to develop and deploy ever more powerful ...,\n",
      "\turl}\n",
      "Final Answer:\n",
      "<h1>Latest AI News Summary</h1>\n",
      "<p>AI researchers have developed an AI system capable of creating artificial enzymes from scratch. The system can generate original proteins without using existing templates or structures, opening up new possibilities for drug discovery and industrial biotechnology.</p>\n",
      "<p>Three AI stocks have been identified by Nasdaq as must-buys. The list includes Nvidia, Alphabet and Amazon, with a focus on their potential to revolutionize industries like gaming, autonomous vehicles and e-commerce, respectively.</p>\n",
      "<p>MIT News reports that AI systems perform better in tandem with human feedback than working alone. Integrating humans with AI in structural design has been shown to be more effective at optimization than fully automated systems.</p>\n",
      "<p>Elon Musk has joined hundreds of technologists in calling for a six-month pause on training advanced AI systems. They warn that companies racing to develop ever more powerful AI systems could pose a significant risk to society if not properly regulated.</p>\n",
      "<p>Science News highlights a new AI system that seems to share our intuitive ability to estimate numbers at a glance, demonstrating the power of machine learning algorithms to develop human-like traits.</p>\n"
     ]
    }
   ],
   "source": [
    "# Example\n",
    "latest_news_summary, cache = action_loop(assistant, user_prompt=\"\"\"Find the latest in ai news and then give me a 500-word summary. No python code allowed.\n",
    "Final Answer must be in the following format:\n",
    "Final Answer: <h1>...</h1>\n",
    "<p>...</p>\n",
    "<h2>...</h2>\n",
    "<p>...</p>\n",
    "...\"\"\", function_definitions=function_definitions, cache=cache, temperature=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<h1>Latest AI News Summary</h1>\n",
      "<p>AI researchers have developed an AI system capable of creating artificial enzymes from scratch. The system can generate original proteins without using existing templates or structures, opening up new possibilities for drug discovery and industrial biotechnology.</p>\n",
      "<p>Three AI stocks have been identified by Nasdaq as must-buys. The list includes Nvidia, Alphabet and Amazon, with a focus on their potential to revolutionize industries like gaming, autonomous vehicles and e-commerce, respectively.</p>\n",
      "<p>MIT News reports that AI systems perform better in tandem with human feedback than working alone. Integrating humans with AI in structural design has been shown to be more effective at optimization than fully automated systems.</p>\n",
      "<p>Elon Musk has joined hundreds of technologists in calling for a six-month pause on training advanced AI systems. They warn that companies racing to develop ever more powerful AI systems could pose a significant risk to society if not properly regulated.</p>\n",
      "<p>Science News highlights a new AI system that seems to share our intuitive ability to estimate numbers at a glance, demonstrating the power of machine learning algorithms to develop human-like traits.</p>\n"
     ]
    }
   ],
   "source": [
    "print(latest_news_summary)"
   ]
  }
 ],
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