Building a Philosophy Quote Generator with Vector Search and Astra DB (Part 2)

In the first part of our series, we laid the groundwork for creating a philosophy quote generator using Astra DB and vector search technology. In this second part, we will delve deeper into implementing the generator, including how to set up the vector search, populate your database with quotes, and build a user-friendly interface. Whether you’re a developer looking to expand your skill set or an enthusiast interested in philosophical insights, this guide will provide you with the tools needed to create a functional and engaging quote generator.

Step-by-Step Implementation

1. Setting Up Astra DB

To begin, ensure you have an Astra DB account and have created a database. Follow these steps:

• Create an Astra DB Account: Go to the Astra DB website and sign up for an account if you haven’t done so.
• Create a Database: After logging in, create a new database instance, making sure to select the correct cloud provider and region.
• Secure Your Database: Generate an application token for authentication, which will be necessary for connecting your application to Astra DB.

2. Structuring Your Database

Once your database is set up, it’s time to define the schema for storing quotes. You might consider a simple structure like the following:

CREATE TABLE quotes (
    id UUID PRIMARY KEY,
    quote TEXT,
    author TEXT,
    category TEXT,
    vector BLOB
);

Here, the vector column will store the embeddings for each quote, enabling efficient vector search capabilities.

3. Populating the Database with Quotes

Next, you need to gather a collection of philosophical quotes. You can either compile these manually or use an existing dataset. To convert quotes into vector embeddings, use a language model such as OpenAI’s GPT or Hugging Face Transformers. Here’s how you can do this:

• Select a Pre-trained Model: Choose a suitable model for embedding text, such as BERT or GPT-3.
• Generate Embeddings: Write a script to iterate through your quotes and generate embeddings. Store these embeddings in the vector column in your Astra DB.

Example Python code to generate embeddings might look like this:

from cassandra.cluster import Cluster
from transformers import pipeline

# Connect to Astra DB
cluster = Cluster(['<Astra DB Contact Point>'], port=9042)
session = cluster.connect('<keyspace_name>')

# Load embedding model
embedder = pipeline('feature-extraction')

# List of quotes
quotes = [
    {"quote": "The unexamined life is not worth living.", "author": "Socrates"},
    {"quote": "I think, therefore I am.", "author": "René Descartes"},
    # Add more quotes
]

# Populate database
for item in quotes:
    vector = embedder(item["quote"])[0][0]
    session.execute(
        """
        INSERT INTO quotes (id, quote, author, vector) 
        VALUES (uuid(), %s, %s, %s)
        """,
        (item["quote"], item["author"], vector)
    )

4. Implementing Vector Search

Now that your database is populated with quotes and their corresponding vectors, it’s time to implement vector search. Astra DB supports vector search using the K-Nearest Neighbors (KNN) algorithm.

Example Query for Vector Search

To search for quotes similar to a user-provided input, you will first generate an embedding for the input text and then perform a vector search:

def find_similar_quotes(input_quote, session):
    input_vector = embedder(input_quote)[0][0]
    # Perform vector search
    results = session.execute(
        """
        SELECT quote, author FROM quotes 
        WHERE vector KNN %s 
        LIMIT 5
        """,
        (input_vector,)
    )
    return results

5. Building the User Interface

For the user interface, you can use a simple web framework like Flask or React. The interface should allow users to input their thoughts or a topic they’re interested in and then display similar quotes.

Example Flask Endpoint

Here’s a simple Flask endpoint for your quote generator:

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/get_quote', methods=['POST'])
def get_quote():
    input_quote = request.json['quote']
    results = find_similar_quotes(input_quote, session)
    return jsonify([{"quote": row.quote, "author": row.author} for row in results])

if __name__ == '__main__':
    app.run(debug=True)

Conclusion

In this part of our guide, we covered the essential steps to build a philosophy quote generator utilizing Astra DB and vector search technology. By following these steps, you can create a dynamic application that offers users a rich experience in exploring philosophical quotes based on their interests.

Frequently Asked Questions (FAQs)

1. What is vector search?

Vector search is a method of searching through high-dimensional data by comparing vector representations, allowing for efficient and relevant retrieval of similar items based on embeddings.

2. Can I use other databases instead of Astra DB?

Yes, you can use other databases that support vector storage and querying, such as PostgreSQL with the pgvector extension or dedicated vector databases like Pinecone or Milvus.

3. How do I generate text embeddings?

You can use pre-trained models from libraries like Hugging Face Transformers or OpenAI’s API to convert text into numerical embeddings.

4. What programming languages can I use to implement this?

You can implement the quote generator using any programming language that supports web development and database interactions, but Python is commonly used due to its rich ecosystem.

5. Are there any limitations to using vector search?

While vector search is powerful, it can be computationally intensive, especially with large datasets. Proper indexing and optimization are necessary to ensure performance.

By understanding these components, you can create a robust philosophy quote generator that not only showcases your coding skills but also provides users with profound philosophical insights. Happy coding!