Emelia In the build a philosophy quote generator with vector search and astra db (part 3) previous articles, we explored how to set up the foundation for our philosophy quote generator. In part one, we set up our environment, and part two covered the initial integration of Astra DB. Now, it’s time to take a deeper dive into the implementation of vector search. We will incorporate vector search functionality into our project to make it more robust, allowing for more intelligent and relevant quote suggestions.
This part will build a philosophy quote generator with vector search and astra db (part 3) guide you through configuring vector search for better search results in the quote generator and integrating Astra DB effectively. We will also explore the importance of vector search, how it works, and how to utilize it with Astra DB to make your philosophy quote generator more insightful and relevant.
Understanding Vector Search
Traditional search engines rely heavily on keyword matching to return results, often missing context or relationships between words. Vector search, on the other hand, works by understanding the meaning behind the text. It converts the text into vectors or mathematical representations, allowing for a more nuanced search based on meaning rather than just keywords.
Philosophical quotes often convey complex ideas. By using vector search, we ensure that the quotes returned reflect the intended meaning of the user’s input rather than just words that happen to match.
Astra DB: The Power of NoSQL with Vector Search
Astra DB is a managed database-as-a-service built on Apache Cassandra. It provides a high level of scalability, fault tolerance, and performance, making it perfect for large datasets. When combined with vector search, it offers advanced functionality for storing and retrieving data, enabling us to handle vast amounts of philosophical quotes efficiently.
Getting Started with Vector Search in Astra DB
1. Install the Required Libraries
Before starting, build a philosophy quote generator with vector search and astra db (part 3) ensure you have the necessary libraries installed. Astra DB provides a Python client, and for vector search, we will need libraries such as sentence-transformers for transforming text into vectors.
Here’s a basic setup:
pip install cassandra-driver sentence-transformers
The sentence-transformers library helps in converting text into vectors that are easier to search in a semantic way. You’ll also need the cassandra-driver for interacting with Astra DB.
2. Set Up Astra DB
First, ensure that Astra DB is set up. If you haven’t already, sign up for Astra DB and create a database instance. Download the credentials file, which will allow your application to interact with the database securely.
To connect to Astra DB:
from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
def create_session():
cloud_config = {'secure_connect_bundle': 'path_to_your_secure_connect_bundle.zip'}
auth_provider = PlainTextAuthProvider('client_id', 'client_secret')
cluster = Cluster(cloud=cloud_config, auth_provider=auth_provider)
session = cluster.connect('philosophy_quotes')
return session
In the code above, replace the placeholders with your credentials from Astra DB.
3. Incorporate Vector Search into Your Application
Now that you’ve set up Astra DB, the next step is to transform the philosophy quotes into vectors using sentence-transformers.
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
def generate_vector(text):
return model.encode(text)
This function will convert any input text, including philosophy quotes, into a vector. These vectors are build a philosophy quote generator with vector search and astra db (part 3) essential for the vector search process.
4. Store Vectors in Astra DB
Before storing the vectors, ensure you have a table ready for the quotes and their respective vectors.
CREATE TABLE philosophy_quotes (
id UUID PRIMARY KEY,
quote TEXT,
vector VECTOR<FLOAT, 768>
);
The VECTOR<FLOAT, 768> type stores the vector representation of each quote. The number 768 represents the dimensions of the vectors generated by the model.
To store a quote along with its vector, use the following:
import uuid
def store_quote(session, quote, vector):
session.execute("""
INSERT INTO philosophy_quotes (id, quote, vector)
VALUES (%s, %s, %s)
""", (uuid.uuid4(), quote, vector.tolist()))
This function inserts the quote and its vector into the database. The vector.tolist() method converts the vector into a list of floats, which can be stored in the database.
5. Perform Vector Search
To perform vector search, we compare the vector of the user’s input with the vectors stored in the database. The closest match (based on cosine similarity) will be returned as the most relevant quote.
import numpy as np
def vector_search(session, input_text):
input_vector = generate_vector(input_text)
rows = session.execute("SELECT quote, vector FROM philosophy_quotes")
best_quote = None
highest_similarity = -1
for row in rows:
stored_vector = np.array(row.vector)
similarity = np.dot(stored_vector, input_vector) / (np.linalg.norm(stored_vector) * np.linalg.norm(input_vector))
if similarity > highest_similarity:
highest_similarity = similarity
best_quote = row.quote
return best_quote
This function calculates the cosine similarity between the input vector and each stored vector. The quote with the highest similarity score is returned as the most relevant.
6. User Interface and Input
Now that we build a philosophy quote generator with vector search and astra db (part 3) have the backend set up, let’s integrate a simple user interface where users can enter their query to receive philosophy quotes.
Here’s an example using Flask:
pip install flask
from flask import Flask, request, render_template
app = Flask(__name__)
def index():
if request.method == "POST":
query = request.form["query"]
quote = vector_search(session, query)
return render_template("index.html", quote=quote)
return render_template("index.html")
if __name__ == "__main__":
app.run(debug=True)
In this Flask app, the user inputs a phrase or a concept. The app then returns the most relevant philosophy quote based on the vector search results.
7. Testing the Quote Generator
With everything set up, it’s time to test the quote generator. Try inputting different philosophical ideas or terms, and the generator will return relevant quotes.
For example:
- Input: “truth”
- Output: “Truth is the offspring of silence and unbroken meditation. — Isaac Newton”
- Input: “life”
- Output: “Life must be understood backward. But it must be lived forward. — Søren Kierkegaard”
8. Optimizing the Quote Generator
To improve the performance of the quote generator, consider:
- Indexing the vectors: Indexing can improve search speed, especially with larger datasets. You can create a vector index in Astra DB for faster vector search.
- Caching the model: If the quote generator is used frequently, you may want to cache the sentence transformer model to avoid reloading it every time.
- Using embeddings: Instead of using raw vectors, embeddings allow for more meaningful and compact representations of the text, which may improve search accuracy.
Conclusion
Building a build a philosophy quote generator with vector search and astra db (part 3) philosophy quote generator using vector search and Astra DB unlocks more intelligent and context-aware search capabilities. The use of vector search ensures that philosophical concepts are matched based on meaning rather than keywords alone, providing a deeper understanding of the user’s input.
Astra DB’s scalability and performance make it an ideal choice for storing and retrieving large amounts of data, especially when working with complex datasets like philosophy quotes. By combining these powerful tools, you can create an application that not only retrieves quotes but does so with insight and meaning.
This is part three of our journey in building a robust philosophy quote generator. In future parts, we may explore further optimizations, additional features like user preferences, and ways to scale the application to handle more users and a wider range of quotes. See more
