examples/How_to_call_functions_for_knowledge_retrieval.ipynb

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   "source": [
    "# How to use functions with a knowledge base\n",
    "\n",
    "This notebook builds on the concepts in the [argument generation](How_to_call_functions_with_chat_models.ipynb) notebook, by creating an agent with access to a knowledge base and two functions that it can call based on the user requirement.\n",
    "\n",
    "We'll create an agent that uses data from arXiv to answer questions about academic subjects. It has two functions at its disposal:\n",
    "- **get_articles**: A function that gets arXiv articles on a subject and summarizes them for the user with links.\n",
    "- **read_article_and_summarize**: This function takes one of the previously searched articles, reads it in its entirety and summarizes the core argument, evidence and conclusions.\n",
    "\n",
    "This will get you comfortable with a multi-function workflow that can choose from multiple services, and where some of the data from the first function is persisted to be used by the second.\n",
    "\n",
    "## Walkthrough\n",
    "\n",
    "This cookbook takes you through the following workflow:\n",
    "\n",
    "- **Search utilities:** Creating the two functions that access arXiv for answers.\n",
    "- **Configure Agent:** Building up the Agent behaviour that will assess the need for a function and, if one is required, call that function and present results back to the agent.\n",
    "- **arXiv conversation:** Put all of this together in live conversation.\n"
   ]
  },
  {
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    }
   ],
   "source": [
    "!pip install scipy\n",
    "!pip install tenacity\n",
    "!pip install tiktoken==0.3.3\n",
    "!pip install termcolor \n",
    "!pip install openai\n",
    "!pip install requests\n",
    "!pip install arxiv\n",
    "!pip install pandas\n",
    "!pip install PyPDF2\n",
    "!pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dab872c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import arxiv\n",
    "import ast\n",
    "import concurrent\n",
    "from csv import writer\n",
    "from IPython.display import display, Markdown, Latex\n",
    "import json\n",
    "import openai\n",
    "import os\n",
    "import pandas as pd\n",
    "from PyPDF2 import PdfReader\n",
    "import requests\n",
    "from scipy import spatial\n",
    "from tenacity import retry, wait_random_exponential, stop_after_attempt\n",
    "import tiktoken\n",
    "from tqdm import tqdm\n",
    "from termcolor import colored\n",
    "\n",
    "GPT_MODEL = \"gpt-3.5-turbo-0613\"\n",
    "EMBEDDING_MODEL = \"text-embedding-ada-002\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2e47962",
   "metadata": {},
   "source": [
    "## Search utilities\n",
    "\n",
    "We'll first set up some utilities that will underpin our two functions.\n",
    "\n",
    "Downloaded papers will be stored in a directory (we use ```./data/papers``` here). We create a file ```arxiv_library.csv``` to store the embeddings and details for downloaded papers to retrieve against using ```summarize_text```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2de5d32d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set a directory to store downloaded papers\n",
    "data_dir = os.path.join(os.curdir, \"data\", \"papers\")\n",
    "paper_dir_filepath = \"./data/arxiv_library.csv\"\n",
    "\n",
    "# Generate a blank dataframe where we can store downloaded files\n",
    "df = pd.DataFrame(list())\n",
    "df.to_csv(paper_dir_filepath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57217b9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3))\n",
    "def embedding_request(text):\n",
    "    response = openai.Embedding.create(input=text, model=EMBEDDING_MODEL)\n",
    "    return response\n",
    "\n",
    "\n",
    "def get_articles(query, library=paper_dir_filepath, top_k=5):\n",
    "    \"\"\"This function gets the top_k articles based on a user's query, sorted by relevance.\n",
    "    It also downloads the files and stores them in arxiv_library.csv to be retrieved by the read_article_and_summarize.\n",
    "    \"\"\"\n",
    "    search = arxiv.Search(\n",
    "        query=query, max_results=top_k, sort_by=arxiv.SortCriterion.Relevance\n",
    "    )\n",
    "    result_list = []\n",
    "    for result in search.results():\n",
    "        result_dict = {}\n",
    "        result_dict.update({\"title\": result.title})\n",
    "        result_dict.update({\"summary\": result.summary})\n",
    "\n",
    "        # Taking the first url provided\n",
    "        result_dict.update({\"article_url\": [x.href for x in result.links][0]})\n",
    "        result_dict.update({\"pdf_url\": [x.href for x in result.links][1]})\n",
    "        result_list.append(result_dict)\n",
    "\n",
    "        # Store references in library file\n",
    "        response = embedding_request(text=result.title)\n",
    "        file_reference = [\n",
    "            result.title,\n",
    "            result.download_pdf(data_dir),\n",
    "            response[\"data\"][0][\"embedding\"],\n",
    "        ]\n",
    "\n",
    "        # Write to file\n",
    "        with open(library, \"a\") as f_object:\n",
    "            writer_object = writer(f_object)\n",
    "            writer_object.writerow(file_reference)\n",
    "            f_object.close()\n",
    "    return result_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dda02bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': 'Proximal Policy Optimization and its Dynamic Version for Sequence Generation',\n",
       " 'summary': 'In sequence generation task, many works use policy gradient for model\\noptimization to tackle the intractable backpropagation issue when maximizing\\nthe non-differentiable evaluation metrics or fooling the discriminator in\\nadversarial learning. In this paper, we replace policy gradient with proximal\\npolicy optimization (PPO), which is a proved more efficient reinforcement\\nlearning algorithm, and propose a dynamic approach for PPO (PPO-dynamic). We\\ndemonstrate the efficacy of PPO and PPO-dynamic on conditional sequence\\ngeneration tasks including synthetic experiment and chit-chat chatbot. The\\nresults show that PPO and PPO-dynamic can beat policy gradient by stability and\\nperformance.',\n",
       " 'article_url': 'http://arxiv.org/abs/1808.07982v1',\n",
       " 'pdf_url': 'http://arxiv.org/pdf/1808.07982v1'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test that the search is working\n",
    "result_output = get_articles(\"ppo reinforcement learning\")\n",
    "result_output[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11675627",
   "metadata": {},
   "outputs": [],
   "source": [
    "def strings_ranked_by_relatedness(\n",
    "    query: str,\n",
    "    df: pd.DataFrame,\n",
    "    relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y),\n",
    "    top_n: int = 100,\n",
    ") -> list[str]:\n",
    "    \"\"\"Returns a list of strings and relatednesses, sorted from most related to least.\"\"\"\n",
    "    query_embedding_response = embedding_request(query)\n",
    "    query_embedding = query_embedding_response[\"data\"][0][\"embedding\"]\n",
    "    strings_and_relatednesses = [\n",
    "        (row[\"filepath\"], relatedness_fn(query_embedding, row[\"embedding\"]))\n",
    "        for i, row in df.iterrows()\n",
    "    ]\n",
    "    strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True)\n",
    "    strings, relatednesses = zip(*strings_and_relatednesses)\n",
    "    return strings[:top_n]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7211df2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_pdf(filepath):\n",
    "    \"\"\"Takes a filepath to a PDF and returns a string of the PDF's contents\"\"\"\n",
    "    # creating a pdf reader object\n",
    "    reader = PdfReader(filepath)\n",
    "    pdf_text = \"\"\n",
    "    page_number = 0\n",
    "    for page in reader.pages:\n",
    "        page_number += 1\n",
    "        pdf_text += page.extract_text() + f\"\\nPage Number: {page_number}\"\n",
    "    return pdf_text\n",
    "\n",
    "\n",
    "# Split a text into smaller chunks of size n, preferably ending at the end of a sentence\n",
    "def create_chunks(text, n, tokenizer):\n",
    "    \"\"\"Returns successive n-sized chunks from provided text.\"\"\"\n",
    "    tokens = tokenizer.encode(text)\n",
    "    i = 0\n",
    "    while i < len(tokens):\n",
    "        # Find the nearest end of sentence within a range of 0.5 * n and 1.5 * n tokens\n",
    "        j = min(i + int(1.5 * n), len(tokens))\n",
    "        while j > i + int(0.5 * n):\n",
    "            # Decode the tokens and check for full stop or newline\n",
    "            chunk = tokenizer.decode(tokens[i:j])\n",
    "            if chunk.endswith(\".\") or chunk.endswith(\"\\n\"):\n",
    "                break\n",
    "            j -= 1\n",
    "        # If no end of sentence found, use n tokens as the chunk size\n",
    "        if j == i + int(0.5 * n):\n",
    "            j = min(i + n, len(tokens))\n",
    "        yield tokens[i:j]\n",
    "        i = j\n",
    "\n",
    "\n",
    "def extract_chunk(content, template_prompt):\n",
    "    \"\"\"This function applies a prompt to some input content. In this case it returns a summarize chunk of text\"\"\"\n",
    "    prompt = template_prompt + content\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=GPT_MODEL, messages=[{\"role\": \"user\", \"content\": prompt}], temperature=0\n",
    "    )\n",
    "    return response[\"choices\"][0][\"message\"][\"content\"]\n",
    "\n",
    "\n",
    "def summarize_text(query):\n",
    "    \"\"\"This function does the following:\n",
    "    - Reads in the arxiv_library.csv file in including the embeddings\n",
    "    - Finds the closest file to the user's query\n",
    "    - Scrapes the text out of the file and chunks it\n",
    "    - Summarizes each chunk in parallel\n",
    "    - Does one final summary and returns this to the user\"\"\"\n",
    "\n",
    "    # A prompt to dictate how the recursive summarizations should approach the input paper\n",
    "    summary_prompt = \"\"\"Summarize this text from an academic paper. Extract any key points with reasoning.\\n\\nContent:\"\"\"\n",
    "\n",
    "    # If the library is empty (no searches have been performed yet), we perform one and download the results\n",
    "    library_df = pd.read_csv(paper_dir_filepath).reset_index()\n",
    "    if len(library_df) == 0:\n",
    "        print(\"No papers searched yet, downloading first.\")\n",
    "        get_articles(query)\n",
    "        print(\"Papers downloaded, continuing\")\n",
    "        library_df = pd.read_csv(paper_dir_filepath).reset_index()\n",
    "    library_df.columns = [\"title\", \"filepath\", \"embedding\"]\n",
    "    library_df[\"embedding\"] = library_df[\"embedding\"].apply(ast.literal_eval)\n",
    "    strings = strings_ranked_by_relatedness(query, library_df, top_n=1)\n",
    "    print(\"Chunking text from paper\")\n",
    "    pdf_text = read_pdf(strings[0])\n",
    "\n",
    "    # Initialise tokenizer\n",
    "    tokenizer = tiktoken.get_encoding(\"cl100k_base\")\n",
    "    results = \"\"\n",
    "\n",
    "    # Chunk up the document into 1500 token chunks\n",
    "    chunks = create_chunks(pdf_text, 1500, tokenizer)\n",
    "    text_chunks = [tokenizer.decode(chunk) for chunk in chunks]\n",
    "    print(\"Summarizing each chunk of text\")\n",
    "\n",
    "    # Parallel process the summaries\n",
    "    with concurrent.futures.ThreadPoolExecutor(\n",
    "        max_workers=len(text_chunks)\n",
    "    ) as executor:\n",
    "        futures = [\n",
    "            executor.submit(extract_chunk, chunk, summary_prompt)\n",
    "            for chunk in text_chunks\n",
    "        ]\n",
    "        with tqdm(total=len(text_chunks)) as pbar:\n",
    "            for _ in concurrent.futures.as_completed(futures):\n",
    "                pbar.update(1)\n",
    "        for future in futures:\n",
    "            data = future.result()\n",
    "            results += data\n",
    "\n",
    "    # Final summary\n",
    "    print(\"Summarizing into overall summary\")\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=GPT_MODEL,\n",
    "        messages=[\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": f\"\"\"Write a summary collated from this collection of key points extracted from an academic paper.\n",
    "                        The summary should highlight the core argument, conclusions and evidence, and answer the user's query.\n",
    "                        User query: {query}\n",
    "                        The summary should be structured in bulleted lists following the headings Core Argument, Evidence, and Conclusions.\n",
    "                        Key points:\\n{results}\\nSummary:\\n\"\"\",\n",
    "            }\n",
    "        ],\n",
    "        temperature=0,\n",
    "    )\n",
    "    return response\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "898b94d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chunking text from paper\n",
      "Summarizing each chunk of text\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:04<00:00,  1.19s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summarizing into overall summary\n"
     ]
    }
   ],
   "source": [
    "# Test the summarize_text function works\n",
    "chat_test_response = summarize_text(\"PPO reinforcement learning sequence generation\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c715f60d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Core Argument:\n",
      "- The paper discusses the use of Proximal Policy Optimization (PPO) in sequence generation tasks, specifically in the context of chit-chat chatbots.\n",
      "- The authors argue that PPO is a more efficient reinforcement learning algorithm compared to policy gradient, commonly used in text generation tasks.\n",
      "- They propose a dynamic approach for PPO (PPO-dynamic) and demonstrate its efficacy in synthetic experiments and chit-chat chatbot tasks.\n",
      "\n",
      "Evidence:\n",
      "- PPO-dynamic achieves high precision scores comparable to other algorithms in a synthetic counting task.\n",
      "- PPO-dynamic shows faster progress and more stable learning curves compared to PPO in the synthetic counting task.\n",
      "- In the chit-chat chatbot task, PPO-dynamic achieves a slightly higher BLEU-2 score than other algorithms.\n",
      "- PPO and PPO-dynamic have more stable learning curves and converge faster than policy gradient.\n",
      "\n",
      "Conclusions:\n",
      "- PPO is a better optimization method for sequence learning compared to policy gradient.\n",
      "- PPO-dynamic further improves the optimization process by dynamically adjusting hyperparameters.\n",
      "- PPO can be used as a new optimization method for GAN-based sequence learning for better performance.\n"
     ]
    }
   ],
   "source": [
    "print(chat_test_response[\"choices\"][0][\"message\"][\"content\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab07e98",
   "metadata": {},
   "source": [
    "## Configure Agent\n",
    "\n",
    "We'll create our agent in this step, including a ```Conversation``` class to support multiple turns with the API, and some Python functions to enable interaction between the ```ChatCompletion``` API and our knowledge base functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77a6fb4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3))\n",
    "def chat_completion_request(messages, functions=None, model=GPT_MODEL):\n",
    "    headers = {\n",
    "        \"Content-Type\": \"application/json\",\n",
    "        \"Authorization\": \"Bearer \" + openai.api_key,\n",
    "    }\n",
    "    json_data = {\"model\": model, \"messages\": messages}\n",
    "    if functions is not None:\n",
    "        json_data.update({\"functions\": functions})\n",
    "    try:\n",
    "        response = requests.post(\n",
    "            \"https://api.openai.com/v1/chat/completions\",\n",
    "            headers=headers,\n",
    "            json=json_data,\n",
    "        )\n",
    "        return response\n",
    "    except Exception as e:\n",
    "        print(\"Unable to generate ChatCompletion response\")\n",
    "        print(f\"Exception: {e}\")\n",
    "        return e\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "73f7672d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Conversation:\n",
    "    def __init__(self):\n",
    "        self.conversation_history = []\n",
    "\n",
    "    def add_message(self, role, content):\n",
    "        message = {\"role\": role, \"content\": content}\n",
    "        self.conversation_history.append(message)\n",
    "\n",
    "    def display_conversation(self, detailed=False):\n",
    "        role_to_color = {\n",
    "            \"system\": \"red\",\n",
    "            \"user\": \"green\",\n",
    "            \"assistant\": \"blue\",\n",
    "            \"function\": \"magenta\",\n",
    "        }\n",
    "        for message in self.conversation_history:\n",
    "            print(\n",
    "                colored(\n",
    "                    f\"{message['role']}: {message['content']}\\n\\n\",\n",
    "                    role_to_color[message[\"role\"]],\n",
    "                )\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "978b7877",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate our get_articles and read_article_and_summarize functions\n",
    "arxiv_functions = [\n",
    "    {\n",
    "        \"name\": \"get_articles\",\n",
    "        \"description\": \"\"\"Use this function to get academic papers from arXiv to answer user questions.\"\"\",\n",
    "        \"parameters\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "                \"query\": {\n",
    "                    \"type\": \"string\",\n",
    "                    \"description\": f\"\"\"\n",
    "                            User query in JSON. Responses should be summarized and should include the article URL reference\n",
    "                            \"\"\",\n",
    "                }\n",
    "            },\n",
    "            \"required\": [\"query\"],\n",
    "        },\n",
    "        \"name\": \"read_article_and_summarize\",\n",
    "        \"description\": \"\"\"Use this function to read whole papers and provide a summary for users.\n",
    "        You should NEVER call this function before get_articles has been called in the conversation.\"\"\",\n",
    "        \"parameters\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "                \"query\": {\n",
    "                    \"type\": \"string\",\n",
    "                    \"description\": f\"\"\"\n",
    "                            Description of the article in plain text based on the user's query\n",
    "                            \"\"\",\n",
    "                }\n",
    "            },\n",
    "            \"required\": [\"query\"],\n",
    "        },\n",
    "    }\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c88ae15",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat_completion_with_function_execution(messages, functions=[None]):\n",
    "    \"\"\"This function makes a ChatCompletion API call with the option of adding functions\"\"\"\n",
    "    response = chat_completion_request(messages, functions)\n",
    "    full_message = response.json()[\"choices\"][0]\n",
    "    if full_message[\"finish_reason\"] == \"function_call\":\n",
    "        print(f\"Function generation requested, calling function\")\n",
    "        return call_arxiv_function(messages, full_message)\n",
    "    else:\n",
    "        print(f\"Function not required, responding to user\")\n",
    "        return response.json()\n",
    "\n",
    "\n",
    "def call_arxiv_function(messages, full_message):\n",
    "    \"\"\"Function calling function which executes function calls when the model believes it is necessary.\n",
    "    Currently extended by adding clauses to this if statement.\"\"\"\n",
    "\n",
    "    if full_message[\"message\"][\"function_call\"][\"name\"] == \"get_articles\":\n",
    "        try:\n",
    "            parsed_output = json.loads(\n",
    "                full_message[\"message\"][\"function_call\"][\"arguments\"]\n",
    "            )\n",
    "            print(\"Getting search results\")\n",
    "            results = get_articles(parsed_output[\"query\"])\n",
    "        except Exception as e:\n",
    "            print(parsed_output)\n",
    "            print(f\"Function execution failed\")\n",
    "            print(f\"Error message: {e}\")\n",
    "        messages.append(\n",
    "            {\n",
    "                \"role\": \"function\",\n",
    "                \"name\": full_message[\"message\"][\"function_call\"][\"name\"],\n",
    "                \"content\": str(results),\n",
    "            }\n",
    "        )\n",
    "        try:\n",
    "            print(\"Got search results, summarizing content\")\n",
    "            response = chat_completion_request(messages)\n",
    "            return response.json()\n",
    "        except Exception as e:\n",
    "            print(type(e))\n",
    "            raise Exception(\"Function chat request failed\")\n",
    "\n",
    "    elif (\n",
    "        full_message[\"message\"][\"function_call\"][\"name\"] == \"read_article_and_summarize\"\n",
    "    ):\n",
    "        parsed_output = json.loads(\n",
    "            full_message[\"message\"][\"function_call\"][\"arguments\"]\n",
    "        )\n",
    "        print(\"Finding and reading paper\")\n",
    "        summary = summarize_text(parsed_output[\"query\"])\n",
    "        return summary\n",
    "\n",
    "    else:\n",
    "        raise Exception(\"Function does not exist and cannot be called\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd3e7868",
   "metadata": {},
   "source": [
    "## arXiv conversation\n",
    "\n",
    "Let's put this all together by testing our functions out in conversation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c39a1d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start with a system message\n",
    "paper_system_message = \"\"\"You are arXivGPT, a helpful assistant pulls academic papers to answer user questions.\n",
    "You summarize the papers clearly so the customer can decide which to read to answer their question.\n",
    "You always provide the article_url and title so the user can understand the name of the paper and click through to access it.\n",
    "Begin!\"\"\"\n",
    "paper_conversation = Conversation()\n",
    "paper_conversation.add_message(\"system\", paper_system_message)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "253fd0f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Function generation requested, calling function\n",
      "Finding and reading paper\n",
      "Chunking text from paper\n",
      "Summarizing each chunk of text\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 17/17 [00:06<00:00,  2.65it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summarizing into overall summary\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Core Argument:\n",
       "- The paper focuses on the theoretical analysis of the PPO-Clip algorithm in the context of deep reinforcement learning.\n",
       "- The authors propose two core ideas: reinterpreting PPO-Clip from the perspective of hinge loss and introducing a two-step policy improvement scheme.\n",
       "- The paper establishes the global convergence of PPO-Clip and characterizes its convergence rate.\n",
       "\n",
       "Evidence:\n",
       "- The paper addresses the challenges posed by the clipping mechanism and neural function approximation.\n",
       "- The authors provide theoretical proofs, lemmas, and mathematical analysis to support their arguments.\n",
       "- The paper presents empirical experiments on various reinforcement learning benchmark tasks to validate the effectiveness of PPO-Clip.\n",
       "\n",
       "Conclusions:\n",
       "- The paper offers theoretical insights into the performance of PPO-Clip and provides a framework for analyzing its convergence properties.\n",
       "- PPO-Clip is shown to have a global convergence rate of O(1/sqrt(T)), where T is the number of iterations.\n",
       "- The hinge loss reinterpretation of PPO-Clip allows for variants with comparable empirical performance.\n",
       "- The paper contributes to a better understanding of PPO-Clip in the reinforcement learning community."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a user message\n",
    "paper_conversation.add_message(\"user\", \"Hi, how does PPO reinforcement learning work?\")\n",
    "chat_response = chat_completion_with_function_execution(\n",
    "    paper_conversation.conversation_history, functions=arxiv_functions\n",
    ")\n",
    "assistant_message = chat_response[\"choices\"][0][\"message\"][\"content\"]\n",
    "paper_conversation.add_message(\"assistant\", assistant_message)\n",
    "display(Markdown(assistant_message))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ca3e18a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Function generation requested, calling function\n",
      "Finding and reading paper\n",
      "Chunking text from paper\n",
      "Summarizing each chunk of text\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:04<00:00,  1.08s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summarizing into overall summary\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Core Argument:\n",
       "- The paper discusses the use of proximal policy optimization (PPO) in sequence generation tasks, specifically in the context of chit-chat chatbots.\n",
       "- The authors argue that PPO is a more efficient reinforcement learning algorithm compared to policy gradient, which is commonly used in text generation tasks.\n",
       "- They propose a dynamic approach for PPO (PPO-dynamic) and demonstrate its efficacy in synthetic experiments and chit-chat chatbot tasks.\n",
       "\n",
       "Evidence:\n",
       "- The authors derive the constraints for PPO-dynamic and provide the pseudo code for both PPO and PPO-dynamic.\n",
       "- They compare the performance of PPO-dynamic with other algorithms, including REINFORCE, MIXER, and SeqGAN, on a synthetic counting task and a chit-chat chatbot task using the OpenSubtitles dataset.\n",
       "- In the synthetic counting task, PPO-dynamic achieves a high precision score comparable to REINFORCE and MIXER, with a faster learning curve compared to PPO.\n",
       "- In the chit-chat chatbot task, PPO-dynamic achieves a slightly higher BLEU-2 score than REINFORCE and PPO, with a more stable and faster learning curve than policy gradient.\n",
       "\n",
       "Conclusions:\n",
       "- The results suggest that PPO is a better optimization method for sequence learning compared to policy gradient.\n",
       "- PPO-dynamic further improves the optimization process by dynamically adjusting the hyperparameters.\n",
       "- The authors conclude that PPO can be used as a new optimization method for GAN-based sequence learning for better performance."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add another user message to induce our system to use the second tool\n",
    "paper_conversation.add_message(\n",
    "    \"user\",\n",
    "    \"Can you read the PPO sequence generation paper for me and give me a summary\",\n",
    ")\n",
    "updated_response = chat_completion_with_function_execution(\n",
    "    paper_conversation.conversation_history, functions=arxiv_functions\n",
    ")\n",
    "display(Markdown(updated_response[\"choices\"][0][\"message\"][\"content\"]))\n"
   ]
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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