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766 lines
31 KiB
Plaintext
766 lines
31 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "3e67f200",
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"metadata": {},
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"source": [
|
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"# How to use functions with a knowledge base\n",
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"\n",
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"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",
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"\n",
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"We'll create an agent that uses data from arXiv to answer questions about academic subjects. It has two functions at its disposal:\n",
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"- **get_articles**: A function that gets arXiv articles on a subject and summarizes them for the user with links.\n",
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"- **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",
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"\n",
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"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",
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"\n",
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"## Walkthrough\n",
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"\n",
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"This cookbook takes you through the following workflow:\n",
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"\n",
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"- **Search utilities:** Creating the two functions that access arXiv for answers.\n",
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"- **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",
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"- **arXiv conversation:** Put all of this together in live conversation.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "80e71f33",
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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"!pip install scipy --quiet\n",
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"!pip install tenacity --quiet\n",
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"!pip install tiktoken==0.3.3 --quiet\n",
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"!pip install termcolor --quiet\n",
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"!pip install openai --quiet\n",
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"!pip install arxiv --quiet\n",
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"!pip install pandas --quiet\n",
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"!pip install PyPDF2 --quiet\n",
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"!pip install tqdm --quiet"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "dab872c5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import arxiv\n",
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"import ast\n",
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"import concurrent\n",
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"import json\n",
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"import os\n",
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"import pandas as pd\n",
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"import tiktoken\n",
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"from csv import writer\n",
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"from IPython.display import display, Markdown, Latex\n",
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"from openai import OpenAI\n",
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"from PyPDF2 import PdfReader\n",
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"from scipy import spatial\n",
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"from tenacity import retry, wait_random_exponential, stop_after_attempt\n",
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"from tqdm import tqdm\n",
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"from termcolor import colored\n",
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"\n",
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"GPT_MODEL = \"gpt-3.5-turbo-0613\"\n",
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"EMBEDDING_MODEL = \"text-embedding-ada-002\"\n",
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"client = OpenAI(api_key=\"\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "f2e47962",
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"metadata": {},
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"source": [
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"## Search utilities\n",
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"\n",
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"We'll first set up some utilities that will underpin our two functions.\n",
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"\n",
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"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```."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "2de5d32d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Directory './data/papers' already exists.\n"
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]
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}
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],
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"source": [
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"directory = './data/papers'\n",
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"\n",
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"# Check if the directory already exists\n",
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"if not os.path.exists(directory):\n",
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" # If the directory doesn't exist, create it and any necessary intermediate directories\n",
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" os.makedirs(directory)\n",
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" print(f\"Directory '{directory}' created successfully.\")\n",
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"else:\n",
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" # If the directory already exists, print a message indicating it\n",
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" print(f\"Directory '{directory}' already exists.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "ae5cb7a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set a directory to store downloaded papers\n",
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"data_dir = os.path.join(os.curdir, \"data\", \"papers\")\n",
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"paper_dir_filepath = \"./data/arxiv_library.csv\"\n",
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"\n",
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"# Generate a blank dataframe where we can store downloaded files\n",
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"df = pd.DataFrame(list())\n",
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"df.to_csv(paper_dir_filepath)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "57217b9d",
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"metadata": {},
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"outputs": [],
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"source": [
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"@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3))\n",
|
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"def embedding_request(text):\n",
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" response = client.embeddings.create(input=text, model=EMBEDDING_MODEL)\n",
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" return response\n",
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"\n",
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"\n",
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"@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3))\n",
|
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"def get_articles(query, library=paper_dir_filepath, top_k=10):\n",
|
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" \"\"\"This function gets the top_k articles based on a user's query, sorted by relevance.\n",
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" It also downloads the files and stores them in arxiv_library.csv to be retrieved by the read_article_and_summarize.\n",
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" \"\"\"\n",
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" client = arxiv.Client()\n",
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" search = arxiv.Search(\n",
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" query = query,\n",
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" max_results = top_k,\n",
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" sort_by = arxiv.SortCriterion.SubmittedDate\n",
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" )\n",
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" result_list = []\n",
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" for result in client.results(search):\n",
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" result_dict = {}\n",
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" result_dict.update({\"title\": result.title})\n",
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" result_dict.update({\"summary\": result.summary})\n",
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"\n",
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" # Taking the first url provided\n",
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" result_dict.update({\"article_url\": [x.href for x in result.links][0]})\n",
|
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" result_dict.update({\"pdf_url\": [x.href for x in result.links][1]})\n",
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" result_list.append(result_dict)\n",
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"\n",
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" # Store references in library file\n",
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" response = embedding_request(text=result.title)\n",
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" file_reference = [\n",
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" result.title,\n",
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" result.download_pdf(data_dir),\n",
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" response.data[0].embedding,\n",
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" ]\n",
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"\n",
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" # Write to file\n",
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" with open(library, \"a\") as f_object:\n",
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" writer_object = writer(f_object)\n",
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" writer_object.writerow(file_reference)\n",
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" f_object.close()\n",
|
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" return result_list\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "dda02bdb",
|
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'title': 'Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models',\n",
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" 'summary': \"This paper introduces a novel and significant challenge for Vision Language\\nModels (VLMs), termed Unsolvable Problem Detection (UPD). UPD examines the\\nVLM's ability to withhold answers when faced with unsolvable problems in the\\ncontext of Visual Question Answering (VQA) tasks. UPD encompasses three\\ndistinct settings: Absent Answer Detection (AAD), Incompatible Answer Set\\nDetection (IASD), and Incompatible Visual Question Detection (IVQD). To deeply\\ninvestigate the UPD problem, extensive experiments indicate that most VLMs,\\nincluding GPT-4V and LLaVA-Next-34B, struggle with our benchmarks to varying\\nextents, highlighting significant room for the improvements. To address UPD, we\\nexplore both training-free and training-based solutions, offering new insights\\ninto their effectiveness and limitations. We hope our insights, together with\\nfuture efforts within the proposed UPD settings, will enhance the broader\\nunderstanding and development of more practical and reliable VLMs.\",\n",
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" 'article_url': 'http://arxiv.org/abs/2403.20331v1',\n",
|
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" 'pdf_url': 'http://arxiv.org/pdf/2403.20331v1'}"
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]
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},
|
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"execution_count": 11,
|
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"metadata": {},
|
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"output_type": "execute_result"
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|
}
|
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],
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"source": [
|
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"# Test that the search is working\n",
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"result_output = get_articles(\"ppo reinforcement learning\")\n",
|
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"result_output[0]"
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": 13,
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"id": "11675627",
|
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def strings_ranked_by_relatedness(\n",
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" query: str,\n",
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" df: pd.DataFrame,\n",
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" relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y),\n",
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" top_n: int = 100,\n",
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") -> list[str]:\n",
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" \"\"\"Returns a list of strings and relatednesses, sorted from most related to least.\"\"\"\n",
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" query_embedding_response = embedding_request(query)\n",
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" query_embedding = query_embedding_response.data[0].embedding\n",
|
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" strings_and_relatednesses = [\n",
|
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" (row[\"filepath\"], relatedness_fn(query_embedding, row[\"embedding\"]))\n",
|
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" for i, row in df.iterrows()\n",
|
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" ]\n",
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" strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True)\n",
|
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" strings, relatednesses = zip(*strings_and_relatednesses)\n",
|
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" return strings[:top_n]\n"
|
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": 17,
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|
"id": "7211df2c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def read_pdf(filepath):\n",
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" \"\"\"Takes a filepath to a PDF and returns a string of the PDF's contents\"\"\"\n",
|
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" # creating a pdf reader object\n",
|
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" reader = PdfReader(filepath)\n",
|
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" pdf_text = \"\"\n",
|
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" page_number = 0\n",
|
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" for page in reader.pages:\n",
|
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" page_number += 1\n",
|
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" pdf_text += page.extract_text() + f\"\\nPage Number: {page_number}\"\n",
|
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" return pdf_text\n",
|
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"\n",
|
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"\n",
|
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"# Split a text into smaller chunks of size n, preferably ending at the end of a sentence\n",
|
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"def create_chunks(text, n, tokenizer):\n",
|
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" \"\"\"Returns successive n-sized chunks from provided text.\"\"\"\n",
|
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" tokens = tokenizer.encode(text)\n",
|
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" i = 0\n",
|
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" while i < len(tokens):\n",
|
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" # Find the nearest end of sentence within a range of 0.5 * n and 1.5 * n tokens\n",
|
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" j = min(i + int(1.5 * n), len(tokens))\n",
|
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" while j > i + int(0.5 * n):\n",
|
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" # Decode the tokens and check for full stop or newline\n",
|
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" chunk = tokenizer.decode(tokens[i:j])\n",
|
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" if chunk.endswith(\".\") or chunk.endswith(\"\\n\"):\n",
|
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" break\n",
|
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" j -= 1\n",
|
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" # If no end of sentence found, use n tokens as the chunk size\n",
|
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" if j == i + int(0.5 * n):\n",
|
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" j = min(i + n, len(tokens))\n",
|
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" yield tokens[i:j]\n",
|
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" i = j\n",
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"\n",
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"\n",
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"def extract_chunk(content, template_prompt):\n",
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" \"\"\"This function applies a prompt to some input content. In this case it returns a summarized chunk of text\"\"\"\n",
|
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" prompt = template_prompt + content\n",
|
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" response = client.chat.completions.create(\n",
|
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" model=GPT_MODEL, messages=[{\"role\": \"user\", \"content\": prompt}], temperature=0\n",
|
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" )\n",
|
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" return response.choices[0].message.content\n",
|
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"\n",
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"\n",
|
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"def summarize_text(query):\n",
|
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" \"\"\"This function does the following:\n",
|
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" - Reads in the arxiv_library.csv file in including the embeddings\n",
|
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" - Finds the closest file to the user's query\n",
|
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" - Scrapes the text out of the file and chunks it\n",
|
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" - Summarizes each chunk in parallel\n",
|
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" - Does one final summary and returns this to the user\"\"\"\n",
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"\n",
|
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" # A prompt to dictate how the recursive summarizations should approach the input paper\n",
|
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" summary_prompt = \"\"\"Summarize this text from an academic paper. Extract any key points with reasoning.\\n\\nContent:\"\"\"\n",
|
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"\n",
|
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" # If the library is empty (no searches have been performed yet), we perform one and download the results\n",
|
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" library_df = pd.read_csv(paper_dir_filepath).reset_index()\n",
|
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" if len(library_df) == 0:\n",
|
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" print(\"No papers searched yet, downloading first.\")\n",
|
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" get_articles(query)\n",
|
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" print(\"Papers downloaded, continuing\")\n",
|
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" library_df = pd.read_csv(paper_dir_filepath).reset_index()\n",
|
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" library_df.columns = [\"title\", \"filepath\", \"embedding\"]\n",
|
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" library_df[\"embedding\"] = library_df[\"embedding\"].apply(ast.literal_eval)\n",
|
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" strings = strings_ranked_by_relatedness(query, library_df, top_n=1)\n",
|
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" print(\"Chunking text from paper\")\n",
|
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" pdf_text = read_pdf(strings[0])\n",
|
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"\n",
|
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" # Initialise tokenizer\n",
|
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" tokenizer = tiktoken.get_encoding(\"cl100k_base\")\n",
|
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" results = \"\"\n",
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"\n",
|
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" # Chunk up the document into 1500 token chunks\n",
|
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" chunks = create_chunks(pdf_text, 1500, tokenizer)\n",
|
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" text_chunks = [tokenizer.decode(chunk) for chunk in chunks]\n",
|
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" print(\"Summarizing each chunk of text\")\n",
|
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"\n",
|
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" # Parallel process the summaries\n",
|
|
" with concurrent.futures.ThreadPoolExecutor(\n",
|
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" max_workers=len(text_chunks)\n",
|
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" ) as executor:\n",
|
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" futures = [\n",
|
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" executor.submit(extract_chunk, chunk, summary_prompt)\n",
|
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" for chunk in text_chunks\n",
|
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" ]\n",
|
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" with tqdm(total=len(text_chunks)) as pbar:\n",
|
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" for _ in concurrent.futures.as_completed(futures):\n",
|
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" pbar.update(1)\n",
|
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" for future in futures:\n",
|
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" data = future.result()\n",
|
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" results += data\n",
|
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"\n",
|
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" # Final summary\n",
|
|
" print(\"Summarizing into overall summary\")\n",
|
|
" response = client.chat.completions.create(\n",
|
|
" model=GPT_MODEL,\n",
|
|
" messages=[\n",
|
|
" {\n",
|
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" \"role\": \"user\",\n",
|
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" \"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": 18,
|
|
"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%|██████████| 8/8 [00:05<00:00, 1.43it/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": 19,
|
|
"id": "c715f60d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Core Argument:\n",
|
|
"- The academic paper introduces ConvPrompt, a novel approach for continual learning (CL) that combines convolutional prompting with language models.\n",
|
|
"- ConvPrompt addresses the limitations of existing CL approaches by allowing for both layer-specific learning and better concept transfer across tasks.\n",
|
|
"- ConvPrompt uses convolution to create task-specific prompts based on task-shared embeddings, enabling efficient adaptation to new tasks with low parameter overhead.\n",
|
|
"- Language models are leveraged to determine task similarity and dynamically decide the number of prompts to be learned.\n",
|
|
"\n",
|
|
"Evidence:\n",
|
|
"- Experimental results show that ConvPrompt outperforms state-of-the-art prompt-based CL approaches with a lower number of parameters.\n",
|
|
"- The paper provides a comprehensive analysis of different components and their importance.\n",
|
|
"- ConvPrompt achieves high accuracy and low forgetting rates while reducing the number of parameters.\n",
|
|
"- ConvPrompt utilizes shared inter-task concepts better than other prompt-based approaches, leading to higher maximum accuracy by the tasks.\n",
|
|
"- ConvPrompt combined with Slow Learner with Classifier Alignment (SLCA) outperforms SLCA in two out of three datasets, achieving a new state-of-the-art in continual learning.\n",
|
|
"\n",
|
|
"Conclusions:\n",
|
|
"- ConvPrompt is a promising approach for continual learning in computer vision tasks, outperforming existing methods while using fewer parameters.\n",
|
|
"- ConvPrompt allows for efficient adaptation to new tasks and better concept transfer across tasks.\n",
|
|
"- The use of convolution and language models in ConvPrompt improves knowledge transfer and task similarity determination.\n",
|
|
"- ConvPrompt achieves high accuracy and low forgetting rates, making it a valuable approach for continual learning.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(chat_test_response.choices[0].message.content)"
|
|
]
|
|
},
|
|
{
|
|
"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": 20,
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"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": 22,
|
|
"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",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"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": 29,
|
|
"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": 30,
|
|
"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 that is designed to optimize policies for sequential decision-making tasks. Here is a paper that provides an overview of PPO and its workings:\n",
|
|
"\n",
|
|
"Title: \"Proximal Policy Optimization Algorithms\"\n",
|
|
"Article URL: [arxiv.org/abs/1707.06347v2](http://arxiv.org/abs/1707.06347v2)\n",
|
|
"Summary: This paper introduces Proximal Policy Optimization (PPO), an algorithm for reinforcement learning. PPO is designed to strike a balance between stability and sample efficiency in policy optimization. It uses a surrogate objective function that is updated iteratively through multiple epochs of optimization. The authors demonstrate the effectiveness of PPO by comparing it with other popular algorithms on a range of benchmark tasks.\n",
|
|
"\n",
|
|
"Reading this paper will provide a detailed understanding of how PPO works and its key components."
|
|
],
|
|
"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))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"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%|██████████| 7/7 [00:06<00:00, 1.07it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Summarizing into overall summary\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/markdown": [
|
|
"Core Argument:\n",
|
|
"- The paper proposes using heuristic search methods on the output probability distribution of machine learning policies to improve the performance of multi-agent path finding (MAPF) algorithms.\n",
|
|
"- The main contributions of the paper are the creation of a \"smart\" collision shield using heuristic search and a neural network agnostic framework for using a learnt 1-step policy with heuristic search for full horizon planning.\n",
|
|
"\n",
|
|
"Evidence:\n",
|
|
"- The paper discusses the limitations of current ML approaches for MAPF, which produce \"local\" policies that only plan for a single timestep and have poor success rates and scalability.\n",
|
|
"- The paper demonstrates several model-agnostic ways to use heuristic search with learnt policies, which significantly improve the policies' success rates and scalability.\n",
|
|
"- The paper compares ML-based approaches with classical heuristic search approaches and discusses the strengths and weaknesses of each approach.\n",
|
|
"- Experimental results show the effectiveness of the proposed method in improving success rates and scalability.\n",
|
|
"\n",
|
|
"Conclusions:\n",
|
|
"- The proposed method of using heuristic search with learnt policies improves the success rates and scalability of MAPF algorithms.\n",
|
|
"- The combination of a learnt policy with a heuristic in the LaCAM framework shows promising results.\n",
|
|
"- CS-PIBT is an effective collision shield that improves performance in MAPF problems.\n",
|
|
"- The best way of combining the learnt policy with the heuristic depends on the specific scenario and can be determined through experimentation."
|
|
],
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e5d88f8e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|