openai-cookbook/examples/How_to_call_functions_for_knowledge_retrieval.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3e67f200",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80e71f33",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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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 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 os\n",
    "import arxiv\n",
    "import ast\n",
    "import concurrent\n",
    "import json\n",
    "import os\n",
    "import pandas as pd\n",
    "import tiktoken\n",
    "from csv import writer\n",
    "from IPython.display import display, Markdown, Latex\n",
    "from openai import OpenAI\n",
    "from PyPDF2 import PdfReader\n",
    "from scipy import spatial\n",
    "from tenacity import retry, wait_random_exponential, stop_after_attempt\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",
    "client = OpenAI()"
   ]
  },
  {
   "attachments": {},
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory './data/papers' already exists.\n"
     ]
    }
   ],
   "source": [
    "directory = './data/papers'\n",
    "\n",
    "# Check if the directory already exists\n",
    "if not os.path.exists(directory):\n",
    "    # If the directory doesn't exist, create it and any necessary intermediate directories\n",
    "    os.makedirs(directory)\n",
    "    print(f\"Directory '{directory}' created successfully.\")\n",
    "else:\n",
    "    # If the directory already exists, print a message indicating it\n",
    "    print(f\"Directory '{directory}' already exists.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae5cb7a1",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 = client.embeddings.create(input=text, model=EMBEDDING_MODEL)\n",
    "    return response\n",
    "\n",
    "\n",
    "@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3))\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",
    "    client = arxiv.Client()\n",
    "    search = arxiv.Search(\n",
    "        query = \"quantum\",\n",
    "        max_results = 10,\n",
    "        sort_by = arxiv.SortCriterion.SubmittedDate\n",
    "    )\n",
    "    result_list = []\n",
    "    for result in client.results(search):\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": 6,
   "id": "dda02bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': 'Entanglement entropy and deconfined criticality: emergent SO(5) symmetry and proper lattice bipartition',\n",
       " 'summary': \"We study the R\\\\'enyi entanglement entropy (EE) of the two-dimensional $J$-$Q$\\nmodel, the emblematic quantum spin model of deconfined criticality at the phase\\ntransition between antiferromagnetic and valence-bond-solid ground states.\\nQuantum Monte Carlo simulations with an improved EE scheme reveal critical\\ncorner contributions that scale logarithmically with the system size, with a\\ncoefficient in remarkable agreement with the form expected from a large-$N$\\nconformal field theory with SO($N=5$) symmetry. However, details of the\\nbipartition of the lattice are crucial in order to observe this behavior. If\\nthe subsystem for the reduced density matrix does not properly accommodate\\nvalence-bond fluctuations, logarithmic contributions appear even for\\ncorner-less bipartitions. We here use a $45^\\\\circ$ tilted cut on the square\\nlattice. Beyond supporting an SO($5$) deconfined quantum critical point, our\\nresults for both the regular and tilted cuts demonstrate important microscopic\\naspects of the EE that are not captured by conformal field theory.\",\n",
       " 'article_url': 'http://arxiv.org/abs/2401.14396v1',\n",
       " 'pdf_url': 'http://arxiv.org/pdf/2401.14396v1'}"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "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": 8,
   "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 summarized chunk of text\"\"\"\n",
    "    prompt = template_prompt + content\n",
    "    response = client.chat.completions.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 = client.chat.completions.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": 9,
   "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%|██████████| 15/15 [00:08<00:00,  1.76it/s]\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": 10,
   "id": "c715f60d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The academic paper discusses the unique decomposition of generators of completely positive dynamical semigroups in infinite dimensions. The main result of the paper is that for any separable complex Hilbert space, any trace-class operator B that does not have a purely imaginary trace, and any generator L of a norm-continuous one-parameter semigroup of completely positive maps, there exists a unique bounded operator K and a unique completely positive map Φ such that L=K(·) + (·)K∗+ Φ. The paper also introduces a modified version of the Choi formalism, which relates completely positive maps to positive semi-definite operators, and characterizes when this correspondence is injective and surjective. The paper concludes by discussing the challenges and questions that arise when generalizing the results to non-separable Hilbert spaces.\n"
     ]
    }
   ],
   "source": [
    "print(chat_test_response.choices[0].message.content)\n"
   ]
  },
  {
   "attachments": {},
   "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": 11,
   "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",
    "    try:\n",
    "        response = client.chat.completions.create(\n",
    "            model=model,\n",
    "            messages=messages,\n",
    "            functions=functions,\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": 12,
   "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": 13,
   "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",
    "    },\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": 14,
   "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.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\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\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"
   ]
  },
  {
   "attachments": {},
   "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": 15,
   "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": 16,
   "id": "253fd0f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Function generation requested, calling function\n",
      "Getting search results\n",
      "Got search results, summarizing content\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "PPO (Proximal Policy Optimization) is a reinforcement learning algorithm used in training agents to make sequential decisions in dynamic environments. It belongs to the family of policy optimization algorithms and addresses the challenge of optimizing policies in a stable and sample-efficient manner. \n",
       "\n",
       "PPO works by iteratively collecting a batch of data from interacting with the environment, computing advantages to estimate the quality of actions, and then performing multiple policy updates using a clipped surrogate objective. This objective function helps prevent excessive policy updates that could lead to policy divergence and instability. \n",
       "\n",
       "By iteratively updating the policy using the collected data, PPO seeks to maximize the expected cumulative rewards obtained by the agent. It has been used successfully in a variety of reinforcement learning tasks, including robotic control, game playing, and simulated environments. \n",
       "\n",
       "To learn more about PPO reinforcement learning, you can read the following papers:\n",
       "\n",
       "1. Title: \"Proximal Policy Optimization Algorithms\"\n",
       "   Article URL: [arxiv.org/abs/1707.06347v2](http://arxiv.org/abs/1707.06347v2)\n",
       "   Summary: This paper introduces PPO and presents two versions of the algorithm: PPO-Penalty and PPO-Clip. It provides a detailed description of PPO's update rule and compares its performance against other popular reinforcement learning algorithms.\n",
       "\n",
       "2. Title: \"Emergent Properties of PPO Reinforcement Learning in Resource-Limited Environments\"\n",
       "   Article URL: [arxiv.org/abs/2001.14342v1](http://arxiv.org/abs/2001.14342v1)\n",
       "   Summary: This paper explores the emergent properties of PPO reinforcement learning algorithms in resource-limited environments. It discusses the impact of varying the resource constraints and agent population sizes on the learning process and performance.\n",
       "\n",
       "Reading these papers will give you a deeper understanding of PPO reinforcement learning and its applications in different domains."
      ],
      "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": 17,
   "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%|██████████| 15/15 [00:09<00:00,  1.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summarizing into overall summary\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "The paper discusses the unique decomposition of generators of completely positive dynamical semigroups in infinite dimensions. The main result is that for any separable complex Hilbert space, any trace-class operator B that does not have a purely imaginary trace, and any generator L of a norm-continuous one-parameter semigroup of completely positive maps, there exists a unique bounded operator K and a unique completely positive map Φ such that L=K(·) + (·)K∗+ Φ. The paper also introduces a modified version of the Choi formalism and characterizes when this correspondence is injective and surjective. The paper concludes by discussing the challenges and questions that arise when generalizing the results to non-separable Hilbert spaces."
      ],
      "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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