Build a Philosophy Quote Generator With Vector Search And Astra db (part 2)

In Part 1 of this series, we explored how to set up a basic framework for our philosophy quote generator. We utilized Apache Cassandra via Astra DB to manage our data and vector search to retrieve quotes that best match user queries. In this part, we’ll dive deeper into implementing vector search, optimizing our database, and adding features like similarity scoring and contextual understanding. By the end of this tutorial, you’ll have a fully functional quote generator that can provide meaningful, contextually relevant philosophical insights. build a philosophy quote generator with vector search and astra db (part 2)

Recap of Part 1

To briefly recap, here’s what we achieved in Part 1:

1. Set up Astra DB – We established a database in Astra DB, which is built on Apache Cassandra and offers a managed, scalable NoSQL database solution.
2. Quote Storage – We loaded a collection of famous philosophy quotes into our Astra DB instance.
3. Basic Querying – We created a simple query mechanism to retrieve quotes based on keywords.

In this part, we will:

• Implement vector search for deeper semantic searching.
• Use machine learning models to transform text into vectors.
• Fine-tune the quote retrieval using vector-based similarity scoring.

1. Setting Up Vector Search

What Is Vector Search?

Vector search allows us to find similar items (in this case, philosophy quotes) based on their meaning, rather than just matching keywords. This approach relies on embeddings – dense numerical representations of text, produced by machine learning models. By searching based on these embeddings, we can retrieve quotes that match the intent and context of the user’s input.

For our project, we’ll use Astra DB to store our quotes and an embedding model to convert those quotes into vector representations. Let’s break it down step by step. build a philosophy quote generator with vector search and astra db (part 2)

Step 1: Choose an Embedding Model

To transform text (philosophical quotes) into vectors, we need a model capable of generating embeddings. One popular choice is Sentence-BERT (SBERT), which is fine-tuned to produce sentence-level embeddings for semantic similarity tasks.

To implement this:

• Install the sentence-transformers library:
• Load the pre-trained SBERT model in Python:

This model will convert both the user’s input and our stored quotes into vectors that we can compare using cosine similarity.

Step 2: Generate Embeddings for Philosophy Quotes

Next, we need to generate embeddings for all the quotes stored in Astra DB. For this, we retrieve the quotes, pass them through the SBERT model, and store the embeddings back into the database.

Here’s how:

1. Retrieve the quotes from Astra DB.
2. Generate embeddings for each quote.
3. Store the embeddings in Astra DB as an additional column.

Now, each quote in our database will have an embedding that represents its semantic meaning.

2. Implementing Vector Search

With the quotes stored as vectors, we can now implement the vector search. The goal is to take a user’s input, convert it into an embedding, and then search for the most similar quote embeddings in Astra DB. build a philosophy quote generator with vector search and astra db (part 2)

Step 1: Convert User Input into a Vector

When a user submits a query, we need to generate an embedding for their input. This is done using the same SBERT model.

Step 2: Vector-Based Search in Astra DB

Vector search compares the user’s embedding with the embeddings of all quotes in our database. To efficiently perform this, we use cosine similarity, which measures the similarity between two vectors.

Astra DB doesn’t natively support vector search yet, but we can implement it by pulling all embeddings from the database and calculating cosine similarity manually.

Here’s how you would implement the cosine similarity search:

Optimizing the Search

The approach of pulling all embeddings from the database works for small datasets, but it’s inefficient as the dataset grows. For larger datasets, we can use approximate nearest neighbor (ANN) techniques, such as FAISS (Facebook AI Similarity Search) to speed up the search. build a philosophy quote generator with vector search and astra db (part 2)

To integrate FAISS into our pipeline:

1. Index the embeddings: Before performing the search, we need to index all embeddings using FAISS. This step significantly reduces the search time for larger datasets.
2. Query the index: Instead of comparing the user’s query with every embedding, FAISS quickly finds the nearest neighbors.

3. Fine-Tuning and Additional Features

Adding Contextual Understanding

To make the quote generator more intelligent, we can add additional layers of contextual understanding. One approach is to integrate Natural Language Processing (NLP) techniques, such as topic modeling or named entity recognition (NER), to better understand the themes and topics present in the user’s query and the quotes.

For example, if the user’s query is about “freedom,” we can prioritize quotes that specifically deal with existentialism or political philosophy by tagging quotes with relevant topics during the embedding phase. build a philosophy quote generator with vector search and astra db (part 2)

Providing Explanations

We can also enhance the user experience by providing explanations or interpretations of the retrieved quotes. This could involve offering additional context on the philosopher who said the quote or breaking down the meaning of the quote.

By adding these interpretive layers, we can create a more engaging and educational experience for users.

4. Conclusion

In Part 2, we have taken significant steps to build a robust and intelligent philosophy quote generator using vector search and Astra DB. We learned how to:

• Generate embeddings for our quotes using Sentence-BERT.
• Implement vector search to retrieve quotes based on semantic similarity.
• Optimize search using tools like FAISS for efficient nearest-neighbor search.
• Add features to improve the user experience, such as contextual understanding and explanations.

With this foundation, the quote generator can serve as a powerful tool for discovering philosophical insights. Whether you’re searching for wisdom on life’s deepest questions or just looking for an inspiring quote, this generator will provide relevant, meaningful responses based on your input. build a philosophy quote generator with vector search and astra db (part 2)