In Part 1 of this series, we explored the foundational steps in building a philosophy quote generator using Astra DB and vector search. We covered the initial setup of Astra DB, the implementation of a basic database schema, and how to store and retrieve quotes efficiently. Now, in build a philosophy quote generator with vector search and astra db (part 2), we’ll dive deeper into integrating vector search capabilities, refining the generator, and enhancing the user experience.
Recap of Part 1
Before moving ahead, let’s quickly recap what we’ve covered so far:
- Astra DB Setup: Astra DB, a powerful NoSQL database, allows efficient storage and retrieval of data. We set up an Astra DB instance and created a basic schema for storing philosophy quotes, including fields like the quote text, author, and category.
- Initial Quote Generator: We built a simple quote generator that fetches quotes from Astra DB and displays them randomly.
- Introduction to Vector Search: We discussed how vector search allows the system to perform more meaningful searches by comparing the semantic meaning of phrases, enabling more accurate results for user queries.
Now, we’ll focus on implementing vector search, improving data ingestion, and making the system more interactive.
Step 1: Implementing Vector Search in Astra DB
Vector build a philosophy quote generator with vector search and astra db (part 2) search revolutionizes traditional keyword-based search methods by capturing the deeper meaning of text. For instance, when a user searches for “wisdom,” the system can return quotes about knowledge, learning, or understanding, which may not explicitly contain the word “wisdom” but convey related concepts.
Why Use Vector Search?
Vector search transforms quotes into mathematical representations (vectors) that encode their meaning. Astra DB supports this through integrations with machine learning models and vector embeddings, allowing for more advanced search capabilities.
How to Integrate Vector Search
To integrate vector search, you will need to:
- Obtain Word Embeddings: Use pre-trained models like BERT, OpenAI’s GPT, or others to convert your philosophy quotes into vector embeddings. These models convert text into high-dimensional vectors that represent the semantic meaning of the text.
- Store Vectors in Astra DB: Astra DB can store these vector representations. Modify the schema created in Part 1 to include a field for storing vector embeddings.
For example:
cqlCREATE TABLE philosophy_quotes (
id UUID PRIMARY KEY,
quote_text TEXT,
author TEXT,
category TEXT,
embedding VECTOR<FLOAT, 1536>
);
The
embeddingfield holds the vector representation of the quote. - Query with Vectors: Instead of matching quotes based on keywords, you can now compare vectors. When a user inputs a search term, convert the term into a vector using the same model that created the quote embeddings. Astra DB’s vector search functionality will find the most similar quotes based on cosine similarity between the vectors.
Example of Querying with Vectors
Here’s how a vector search query might look:
query_embedding = get_embedding_from_text("wisdom")
result = session.execute("""
SELECT quote_text, author FROM philosophy_quotes
WHERE embedding = ?
ORDER BY embedding <-> ?
LIMIT 1;
""", (query_embedding,))
This query finds the closest match to the user’s search query by calculating the distance between vector embeddings.
Step 2: Ingesting and Preprocessing Data
To fully build a philosophy quote generator with vector search and astra db (part 2) leverage the power of vector search, you need to ingest a large set of philosophy quotes. The more quotes you include, the richer the generator becomes.
Sources of Quotes
You can find philosophy quotes from various open-source platforms, books, and websites. For this example, let’s assume we’re using a dataset of quotes by famous philosophers like Socrates, Nietzsche, and Confucius.
Preprocessing the Data
Before inserting quotes into Astra DB, ensure they are clean and well-structured. Preprocessing may involve:
- Removing Duplicates: Duplicate quotes can reduce the diversity of responses.
- Standardizing Formats: Ensure quotes are consistently formatted, with proper punctuation and quotation marks.
- Converting Text to Embeddings: As mentioned, use a machine learning model to convert each quote into a vector embedding. This step is crucial for enabling vector search.
For example:
from transformers import BertModel, BertTokenizer
# Load pre-trained BERT model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
# Function to get embeddings
def get_embedding_from_text(text):
inputs = tokenizer(text, return_tensors='pt', max_length=512, truncation=True)
outputs = model(**inputs)
return outputs.last_hidden_state.mean(dim=1).detach().numpy()
# Example
quote_embedding = get_embedding_from_text("The unexamined life is not worth living.")
Once you’ve preprocessed and vectorized your dataset, insert the data into Astra DB.
Step 3: Enhancing the User Interface
A philosophy quote generator should feel seamless and intuitive to the user. By improving the front end, you’ll enhance user experience and engagement.
Building the Interface
You can use a basic HTML/JavaScript front-end to collect user inputs and display quotes. Integrating with a back-end API that interfaces with Astra DB enables dynamic responses to user searches.
Example User Workflow
- User Input: The user types a word or phrase, like “truth” or “life,” into a search bar.
- Vector Search: The system converts the input into a vector, queries the Astra DB for the most semantically similar quote, and returns the result.
- Displaying Results: The quote, along with the author’s name, appears in the user interface, offering an interactive experience. You can add additional features, like “Next Quote” or “Save Quote” buttons.
Here’s a simple HTML structure for the search bar:
<div>
<input type="text" id="searchTerm" placeholder="Enter a philosophical concept..." />
<button onclick="getQuote()">Get Quote</button>
</div>
<div id="quoteDisplay"></div>
<script>
async function getQuote() {
let searchTerm = document.getElementById('searchTerm').value;
// Send search term to the back-end API for vector search
let response = await fetch(`/search_quote?term=${searchTerm}`);
let data = await response.json();
document.getElementById('quoteDisplay').innerHTML = `${data.quote_text} - ${data.author}`;
}
</script>
The back end, powered by Flask or Node.js, handles the connection to Astra DB and runs the vector search queries.
Step 4: Testing and Optimizing the Generator
Once you’ve implemented the core functionality, it’s essential to thoroughly test your quote generator. Focus on both functionality and performance.
Testing the Vector Search
- Accuracy: Does the build a philosophy quote generator with vector search and astra db (part 2) generator return meaningful quotes for various search terms? Test a range of philosophical concepts and phrases to ensure that vector search delivers high-quality results.
- Performance: Ensure the generator handles large datasets and returns results quickly. You might need to optimize queries or tune your Astra DB instance for speed.
Continuous Improvement
Regularly update the dataset by adding more quotes and expanding your database. As new machine learning models improve, consider retraining your embeddings for better accuracy.
Conclusion
By build a philosophy quote generator with vector search and astra db (part 2) integrating vector search into Astra DB, your philosophy quote generator becomes far more intelligent and user-friendly. Users can search for deep, meaningful quotes using abstract concepts, and the generator will return insightful results. In this part of the guide, we explored how to implement vector search, ingest and preprocess data, and enhance the user interface. With these tools in hand, you can now create an engaging, thoughtful philosophy quote generator that brings wisdom to life.
