How to use functions with a knowledge base

Jun 14, 2023
Open in Github

This notebook builds on the concepts in the argument generation notebook, by creating an agent with access to a knowledge base and two functions that it can call based on the user requirement.

We'll create an agent that uses data from arXiv to answer questions about academic subjects. It has two functions at its disposal:

  • get_articles: A function that gets arXiv articles on a subject and summarizes them for the user with links.
  • 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.

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.


This cookbook takes you through the following workflow:

  • Search utilities: Creating the two functions that access arXiv for answers.
  • 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.
  • arXiv conversation: Put all of this together in live conversation.
!pip install scipy --quiet
!pip install tenacity --quiet
!pip install tiktoken==0.3.3 --quiet
!pip install termcolor --quiet
!pip install openai --quiet
!pip install arxiv --quiet
!pip install pandas --quiet
!pip install PyPDF2 --quiet
!pip install tqdm --quiet
import os
import arxiv
import ast
import concurrent
import json
import os
import pandas as pd
import tiktoken
from csv import writer
from IPython.display import display, Markdown, Latex
from openai import OpenAI
from PyPDF2 import PdfReader
from scipy import spatial
from tenacity import retry, wait_random_exponential, stop_after_attempt
from tqdm import tqdm
from termcolor import colored

GPT_MODEL = "gpt-3.5-turbo-0613"
EMBEDDING_MODEL = "text-embedding-ada-002"
client = OpenAI()

Search utilities

We'll first set up some utilities that will underpin our two functions.

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.

directory = './data/papers'

# Check if the directory already exists
if not os.path.exists(directory):
    # If the directory doesn't exist, create it and any necessary intermediate directories
    print(f"Directory '{directory}' created successfully.")
    # If the directory already exists, print a message indicating it
    print(f"Directory '{directory}' already exists.")
Directory './data/papers' already exists.
# Set a directory to store downloaded papers
data_dir = os.path.join(os.curdir, "data", "papers")
paper_dir_filepath = "./data/arxiv_library.csv"

# Generate a blank dataframe where we can store downloaded files
df = pd.DataFrame(list())
@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3))
def embedding_request(text):
    response = client.embeddings.create(input=text, model=EMBEDDING_MODEL)
    return response

@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3))
def get_articles(query, library=paper_dir_filepath, top_k=5):
    """This function gets the top_k articles based on a user's query, sorted by relevance.
    It also downloads the files and stores them in arxiv_library.csv to be retrieved by the read_article_and_summarize.
    client = arxiv.Client()
    search = arxiv.Search(
        query = "quantum",
        max_results = 10,
        sort_by = arxiv.SortCriterion.SubmittedDate
    result_list = []
    for result in client.results(search):
        result_dict = {}
        result_dict.update({"title": result.title})
        result_dict.update({"summary": result.summary})

        # Taking the first url provided
        result_dict.update({"article_url": [x.href for x in result.links][0]})
        result_dict.update({"pdf_url": [x.href for x in result.links][1]})

        # Store references in library file
        response = embedding_request(text=result.title)
        file_reference = [

        # Write to file
        with open(library, "a") as f_object:
            writer_object = writer(f_object)
    return result_list
# Test that the search is working
result_output = get_articles("ppo reinforcement learning")
{'title': 'Quantum types: going beyond qubits and quantum gates',
 'summary': 'Quantum computing is a growing field with significant potential applications.\nLearning how to code quantum programs means understanding how qubits work and\nlearning to use quantum gates. This is analogous to creating classical\nalgorithms using logic gates and bits. Even after learning all concepts, it is\ndifficult to create new algorithms, which hinders the acceptance of quantum\nprogramming by most developers. This article outlines the need for higher-level\nabstractions and proposes some of them in a developer-friendly programming\nlanguage called Rhyme. The new quantum types are extensions of classical types,\nincluding bits, integers, floats, characters, arrays, and strings. We show how\nto use such types with code snippets.',
 'article_url': '',
 'pdf_url': ''}
def strings_ranked_by_relatedness(
    query: str,
    df: pd.DataFrame,
    relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y),
    top_n: int = 100,
) -> list[str]:
    """Returns a list of strings and relatednesses, sorted from most related to least."""
    query_embedding_response = embedding_request(query)
    query_embedding =[0].embedding
    strings_and_relatednesses = [
        (row["filepath"], relatedness_fn(query_embedding, row["embedding"]))
        for i, row in df.iterrows()
    strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True)
    strings, relatednesses = zip(*strings_and_relatednesses)
    return strings[:top_n]
def read_pdf(filepath):
    """Takes a filepath to a PDF and returns a string of the PDF's contents"""
    # creating a pdf reader object
    reader = PdfReader(filepath)
    pdf_text = ""
    page_number = 0
    for page in reader.pages:
        page_number += 1
        pdf_text += page.extract_text() + f"\nPage Number: {page_number}"
    return pdf_text

# Split a text into smaller chunks of size n, preferably ending at the end of a sentence
def create_chunks(text, n, tokenizer):
    """Returns successive n-sized chunks from provided text."""
    tokens = tokenizer.encode(text)
    i = 0
    while i < len(tokens):
        # Find the nearest end of sentence within a range of 0.5 * n and 1.5 * n tokens
        j = min(i + int(1.5 * n), len(tokens))
        while j > i + int(0.5 * n):
            # Decode the tokens and check for full stop or newline
            chunk = tokenizer.decode(tokens[i:j])
            if chunk.endswith(".") or chunk.endswith("\n"):
            j -= 1
        # If no end of sentence found, use n tokens as the chunk size
        if j == i + int(0.5 * n):
            j = min(i + n, len(tokens))
        yield tokens[i:j]
        i = j

def extract_chunk(content, template_prompt):
    """This function applies a prompt to some input content. In this case it returns a summarized chunk of text"""
    prompt = template_prompt + content
    response =
        model=GPT_MODEL, messages=[{"role": "user", "content": prompt}], temperature=0
    return response.choices[0].message.content

def summarize_text(query):
    """This function does the following:
    - Reads in the arxiv_library.csv file in including the embeddings
    - Finds the closest file to the user's query
    - Scrapes the text out of the file and chunks it
    - Summarizes each chunk in parallel
    - Does one final summary and returns this to the user"""

    # A prompt to dictate how the recursive summarizations should approach the input paper
    summary_prompt = """Summarize this text from an academic paper. Extract any key points with reasoning.\n\nContent:"""

    # If the library is empty (no searches have been performed yet), we perform one and download the results
    library_df = pd.read_csv(paper_dir_filepath).reset_index()
    if len(library_df) == 0:
        print("No papers searched yet, downloading first.")
        print("Papers downloaded, continuing")
        library_df = pd.read_csv(paper_dir_filepath).reset_index()
    library_df.columns = ["title", "filepath", "embedding"]
    library_df["embedding"] = library_df["embedding"].apply(ast.literal_eval)
    strings = strings_ranked_by_relatedness(query, library_df, top_n=1)
    print("Chunking text from paper")
    pdf_text = read_pdf(strings[0])

    # Initialise tokenizer
    tokenizer = tiktoken.get_encoding("cl100k_base")
    results = ""

    # Chunk up the document into 1500 token chunks
    chunks = create_chunks(pdf_text, 1500, tokenizer)
    text_chunks = [tokenizer.decode(chunk) for chunk in chunks]
    print("Summarizing each chunk of text")

    # Parallel process the summaries
    with concurrent.futures.ThreadPoolExecutor(
    ) as executor:
        futures = [
            executor.submit(extract_chunk, chunk, summary_prompt)
            for chunk in text_chunks
        with tqdm(total=len(text_chunks)) as pbar:
            for _ in concurrent.futures.as_completed(futures):
        for future in futures:
            data = future.result()
            results += data

    # Final summary
    print("Summarizing into overall summary")
    response =
                "role": "user",
                "content": f"""Write a summary collated from this collection of key points extracted from an academic paper.
                        The summary should highlight the core argument, conclusions and evidence, and answer the user's query.
                        User query: {query}
                        The summary should be structured in bulleted lists following the headings Core Argument, Evidence, and Conclusions.
                        Key points:\n{results}\nSummary:\n""",
    return response
# Test the summarize_text function works
chat_test_response = summarize_text("PPO reinforcement learning sequence generation")
Chunking text from paper
Summarizing each chunk of text
100%|██████████| 6/6 [00:06<00:00,  1.08s/it]
Summarizing into overall summary
Core Argument:
- The academic paper explores the connection between the transverse field Ising (TFI) model and the ϕ4 model, highlighting the analogy between topological solitary waves in the ϕ4 model and the effect of the transverse field on spin flips in the TFI model.
- The study reveals regimes of memory/loss of memory and coherence/decoherence in the classical ϕ4 model subjected to periodic perturbations, which are essential in annealing phenomena.
- The exploration of the analogy between lower-dimensional linear quantum systems and higher-dimensional classical nonlinear systems can lead to a deeper understanding of information processing in these systems.

- The authors analyze the dynamics and relaxation of weakly coupled ϕ4 chains through numerical simulations, observing kink and breather excitations and investigating the structural phase transition associated with the double well potential.
- The critical temperature (Tc) approaches zero as the inter-chain coupling strength (C⊥) approaches zero, but there is a finite Tc for C⊥>0.
- The spectral function shows peaks corresponding to particle motion across the double-well potential at higher temperatures and oscillations in a single well at lower temperatures.
- The soft-mode frequency (ωs) decreases as temperature approaches Ts, the dynamical crossover temperature.
- The relaxation process of the average displacement (QD) is controlled by spatially extended vibrations and large kink densities.
- The mean domain size (⟨DS⟩) exhibits an algebraic decay for finite C⊥>0.
- The probability of larger domain sizes is higher before a kick compared to after a kick for C⊥>0.

- The authors suggest further exploration of the crossover between decoherence and finite coherence in periodic-kick strength space.
- They propose extending the study to different kick profiles, introducing kink defects, and studying weakly-coupled chains in higher dimensions.
- Recognizing similarities between classical nonlinear equations and quantum linear ones in information processing is important.
- Future research directions include investigating the dynamics of quantum annealing, measurement and memory in the periodically driven complex Ginzburg-Landau equation, and the behavior of solitons and domain walls in various systems.

Configure Agent

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.

@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3))
def chat_completion_request(messages, functions=None, model=GPT_MODEL):
        response =
        return response
    except Exception as e:
        print("Unable to generate ChatCompletion response")
        print(f"Exception: {e}")
        return e
class Conversation:
    def __init__(self):
        self.conversation_history = []

    def add_message(self, role, content):
        message = {"role": role, "content": content}

    def display_conversation(self, detailed=False):
        role_to_color = {
            "system": "red",
            "user": "green",
            "assistant": "blue",
            "function": "magenta",
        for message in self.conversation_history:
                    f"{message['role']}: {message['content']}\n\n",
# Initiate our get_articles and read_article_and_summarize functions
arxiv_functions = [
        "name": "get_articles",
        "description": """Use this function to get academic papers from arXiv to answer user questions.""",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": f"""
                            User query in JSON. Responses should be summarized and should include the article URL reference
            "required": ["query"],
        "name": "read_article_and_summarize",
        "description": """Use this function to read whole papers and provide a summary for users.
        You should NEVER call this function before get_articles has been called in the conversation.""",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": f"""
                            Description of the article in plain text based on the user's query
            "required": ["query"],
def chat_completion_with_function_execution(messages, functions=[None]):
    """This function makes a ChatCompletion API call with the option of adding functions"""
    response = chat_completion_request(messages, functions)
    full_message = response.choices[0]
    if full_message.finish_reason == "function_call":
        print(f"Function generation requested, calling function")
        return call_arxiv_function(messages, full_message)
        print(f"Function not required, responding to user")
        return response

def call_arxiv_function(messages, full_message):
    """Function calling function which executes function calls when the model believes it is necessary.
    Currently extended by adding clauses to this if statement."""

    if == "get_articles":
            parsed_output = json.loads(
            print("Getting search results")
            results = get_articles(parsed_output["query"])
        except Exception as e:
            print(f"Function execution failed")
            print(f"Error message: {e}")
                "role": "function",
                "content": str(results),
            print("Got search results, summarizing content")
            response = chat_completion_request(messages)
            return response
        except Exception as e:
            raise Exception("Function chat request failed")

    elif ( == "read_article_and_summarize"
        parsed_output = json.loads(
        print("Finding and reading paper")
        summary = summarize_text(parsed_output["query"])
        return summary

        raise Exception("Function does not exist and cannot be called")

arXiv conversation

Let's put this all together by testing our functions out in conversation.

# Start with a system message
paper_system_message = """You are arXivGPT, a helpful assistant pulls academic papers to answer user questions.
You summarize the papers clearly so the customer can decide which to read to answer their question.
You always provide the article_url and title so the user can understand the name of the paper and click through to access it.
paper_conversation = Conversation()
paper_conversation.add_message("system", paper_system_message)
# Add a user message
paper_conversation.add_message("user", "Hi, how does PPO reinforcement learning work?")
chat_response = chat_completion_with_function_execution(
    paper_conversation.conversation_history, functions=arxiv_functions
assistant_message = chat_response.choices[0].message.content
paper_conversation.add_message("assistant", assistant_message)
Function generation requested, calling function
Getting search results
Got search results, summarizing content
<IPython.core.display.Markdown object>
# Add another user message to induce our system to use the second tool
    "Can you read the PPO sequence generation paper for me and give me a summary",
updated_response = chat_completion_with_function_execution(
    paper_conversation.conversation_history, functions=arxiv_functions
Function generation requested, calling function
Finding and reading paper
Chunking text from paper
Summarizing each chunk of text
100%|██████████| 6/6 [00:07<00:00,  1.19s/it]
Summarizing into overall summary
<IPython.core.display.Markdown object>