First, we need to install necessary Python packages. These packages provide the tools and functions that we’ll need throughout this project. To install the packages, you need to open your terminal and paste the below command:

pip install openai langchain pinecone-client unstructured

At this stage, we’re going to utilise the Unstructured PDF Loader and the Recursive Text Splitter. The former is used to load our book’s PDF, and the latter is employed to split the text into manageable chunks. Splitting the text will make it easier to handle and process.

from langchain.document_loaders import UnstructuredPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load the PDF
loader = UnstructuredPDFLoader('the_field_guide_to_data_science.pdf')
documents = loader.load()

# Split the text
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)

For this step, we’ll use OpenAI’s powerful models to convert the text chunks into numerical vector representations—which are more commonly referred to as embeddings.

import openai
from langchain.embeddings import OpenAIEmbeddings

# Set your OpenAI API key
openai.api_key = 'YOUR_OPENAI_API_KEY'

embeddings = OpenAIEmbeddings()

In the code snippet above, be sure to replace ‘YOUR_OPENAI_API_KEY’ with an actual API key from OpenAI.

The next step involves the use of Pinecone, which is an exceptional platform specializing in managing vector data. We will be storing our embeddings in Pinecone, and this would allow us to perform efficient similarity searches subsequently.

import pinecone

# Initialize Pinecone
pinecone.init(api_key='YOUR_PINECONE_API_KEY', environment='YOUR_PINECONE_ENVIRONMENT')

# Create an index
index_name = 'data-science-book'
if index_name not in pinecone.list_indexes():
    pinecone.create_index(index_name, dimension=1536)  # Ensure the dimension matches your embeddings

# Connect to the index
index = pinecone.Index(index_name)

While implementing the above lines of code, replace ‘YOUR_PINECONE_API_KEY’ and ‘YOUR_PINECONE_ENVIRONMENT’ with your actual Pinecone API key and environment.

Moving on to the next step, we will compute embeddings for different chunks of text and afterward, upload these computed embeddings to Pinecone. This is done so that later on, we can make efficient queries based on text similarity.

from tqdm.auto import tqdm

# Prepare data for indexing
for i, doc in enumerate(tqdm(texts)):
    # Compute embedding
    embedding = embeddings.embed_query(doc.page_content)
    # Create a unique ID
    id = f'doc-{i}'
    # Upsert into Pinecone
    index.upsert([(id, embedding, {'text': doc.page_content})])

At this stage, we’re about to construct a function that will take in user queries, utilize Pinecone to find the most similar text chunks (based on the embeddings), and then use OpenAI to generate comprehensive answers from these related text fragments.

def answer_query(query):
    # Embed the query
    query_embedding = embeddings.embed_query(query)
    # Search Pinecone for similar text chunks
    result = index.query(query_embedding, top_k=5, include_metadata=True)
    # Extract the relevant texts
    contexts = [match['metadata']['text'] for match in result['matches']]
    # Build the prompt for OpenAI
    prompt = ''.join(contexts) + f"Using the information above, answer the following question:{query}"
    # Let OpenAI generate the answer
    response = openai.Completion.create(
        engine='text-davinci-003',
        prompt=prompt,
        max_tokens=200,
        temperature=0.7,
    )
    answer = response.choices[0].text.strip()
    return answer

After setting up everything, it’s now time to test our system by asking it a question related to our book’s content. This would allow us to assess its performance and verify that it’s working as expected.

query = "What are the key qualities of a successful data scientist?"
answer = answer_query(query)
print("Question:", query)
print("Answer:", answer)

Once you’re comfortable with the process of querying large documents using AI, you might find these additional resources helpful for further exploring the possibilities:

• LangChain Documentation: A comprehensive guide to using LangChain for different purposes.
• Pinecone Homepage: Visit the homepage to understand more about the capabilities of Pinecone.
• OpenAI API Documentation: This offers a broad overview and detailed explanations of different functions of OpenAI, along with examples for better understanding.