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

In the build a philosophy quote generator with vector search and astra db (part 2) first part of this project, we explored the foundational elements for building a philosophy quote generator. We integrated Astra DB for managing our data and used Python for coding the core logic. Now, we’ll dive deeper into vector search and enhance the functionality of our quote generator. This part will show you how to improve search accuracy, handle user queries efficiently, and scale the system with ease.

Let’s get started with the core components: vector search and optimization of Astra DB queries.

1. Understanding Vector Search

Vector search offers a powerful way to search and retrieve data, especially when working with high-dimensional data such as text, images, or multimedia. In our philosophy quote generator, vector search allows users to input a text query and receive quotes that match not only exact keywords but also the context and meaning behind those words.

At the heart of vector search lies a word embedding model, which transforms text data into vectors. These vectors represent words in a continuous vector space, where similar words or phrases are mapped close to each other. Instead of relying solely on exact matches, vector search looks for semantic similarities. For example, searching for “existentialism” will return quotes related to existential philosophy even if the word itself doesn’t appear in the quote.

2. Setting Up Vector Search with Astra DB

Astra DB serves as the backbone of our database for this project. It’s a NoSQL database based on Apache Cassandra and is designed for handling large-scale, build a philosophy quote generator with vector search and astra db (part 2) distributed data. It also integrates seamlessly with vector search capabilities.

To implement vector search, we’ll use sentence-transformers in Python, which provides state-of-the-art embeddings for text data. You’ll need to install the required libraries first:

bash

pip install sentence-transformers cassandra-driver

Next, load the pre-trained model and encode your philosophy quotes:

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
# Sample philosophy quotes

quotes = [

    "The unexamined life is not worth living. - Socrates",

    "To be is to do. - Immanuel Kant",

    "Man is condemned to be free. - Jean-Paul Sartre",

]

# Convert quotes to vectors quote_vectors = model.encode(quotes)

Once we convert the quotes into vectors, store both the quotes and vectors in Astra DB.

3. Storing Vectors in Astra DB

Astra DB can handle large datasets efficiently. For storing vectors, we’ll create a dedicated table for quotes and their embeddings. Each entry will contain a quote and its corresponding vector:

sql

CREATE TABLE philosophy_quotes (

    id UUID PRIMARY KEY,

    quote TEXT,

    vector VECTOR<FLOAT, 384>

);

The VECTOR data type stores high-dimensional vectors. We’ve defined it with 384 dimensions because the sentence-transformer model produces vectors of that size.

You can now insert quotes and their vectors:

python

from cassandra.cluster import Cluster

from cassandra.auth import PlainTextAuthProvider

import uuid
cloud_config = {

    'secure_connect_bundle': '/path/to/secure-connect-database.zip'

}
auth_provider = PlainTextAuthProvider('client_id', 'client_secret')

cluster = Cluster(cloud=cloud_config, auth_provider=auth_provider)

session = cluster.connect('your_keyspace')

# Insert quotes with vectors into Astra DB for quote, vector in zip(quotes, quote_vectors): session.execute( """ INSERT INTO philosophy_quotes (id, quote, vector) VALUES (%s, %s, %s) """, (uuid.uuid4(), quote, vector.tolist()) )

4. Implementing Vector Search

With the quotes and vectors stored in Astra DB, you can now implement the vector search logic. This will enable the system to return philosophy quotes that semantically match a user’s query.

To perform a vector search, we’ll encode the user query into a vector and compare it against the vectors stored in the database. The closer the vectors are, the more build a philosophy quote generator with vector search and astra db (part 2) semantically similar the quotes will be to the query.

First, encode the user query:

python

user_query = "freedom and responsibility"

query_vector = model.encode([user_query])[0]

Now, retrieve quotes with the closest vectors using nearest neighbor search. Astra DB supports efficient querying of vectors based on cosine similarity:

sql

SELECT id, quote FROM philosophy_quotes

ORDER BY vector ANN OF %s LIMIT 5;

You will need to pass the encoded query vector and fetch the closest quotes. Here’s how you can do that programmatically:

python

rows = session.execute(

    """

    SELECT id, quote FROM philosophy_quotes

    ORDER BY vector ANN OF %s LIMIT 5;

    """,

    (query_vector.tolist(),)

)

for row in rows: print(row.quote)

5. Improving the Search Algorithm

While vector search delivers quotes based on semantic meaning, we can further enhance the relevance of search results by considering other factors such as popularity, author, or date of the quote. You can achieve this by adding more fields to the philosophy_quotes table, such as:

author TEXT
popularity_score INT
created_at TIMESTAMP

To integrate these into your search, modify the SQL query to combine vector similarity with additional filters:

sql

SELECT id, quote, author FROM philosophy_quotes

WHERE author = 'Jean-Paul Sartre'

ORDER BY vector ANN OF %s, popularity_score DESC

LIMIT 5;

This will return quotes by Jean-Paul Sartre that are the closest matches to the query and rank them by popularity.

6. Building the User Interface

Now that the backend logic is set up, the next step involves building a user-friendly interface for your quote generator. Use a framework like Flask or FastAPI to create the frontend. Here’s a simple API example using Flask:

python

from flask import Flask, request, jsonify

app = Flask(__name__)
@app.route('/get_quote', methods=['POST'])

def get_quote():

    user_query = request.json['query']

    query_vector = model.encode([user_query])[0]
    rows = session.execute(

        """

        SELECT quote FROM philosophy_quotes

        ORDER BY vector ANN OF %s LIMIT 5;

        """,

        (query_vector.tolist(),)

    )
    return jsonify([row.quote for row in rows])

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

Users can send a POST request with their query, and the system will return the most relevant quotes based on vector search.

7. Scaling the System

Once the philosophy build a philosophy quote generator with vector search and astra db (part 2) quote generator gains traction, you’ll need to think about scaling. Astra DB provides automatic scaling for data storage and querying, so you won’t need to worry about the database’s capacity. However, you can optimize performance by:

Indexing popular queries and caching results.
Using pagination to limit the number of quotes returned.
Adding a load balancer to distribute traffic evenly across multiple servers.

With the flexibility of Astra DB and vector search, the quote generator will handle increasing traffic and large datasets efficiently.

8. Conclusion

In this build a philosophy quote generator with vector search and astra db (part 2) second part, we explored the use of vector search to improve the accuracy of our philosophy quote generator. By leveraging Astra DB’s high-performance data storage and vector capabilities, we created a system that not only retrieves quotes based on exact matches but also understands the meaning behind user queries. As you continue building, remember to optimize search algorithms and explore different ways to enhance the user experience.

This quote generator provides a powerful and scalable way to engage with philosophical texts. Now you can continue expanding its features or apply the same vector search principles to other projects!