Vector search is a powerful search tool that uses mathematical algorithms to find the most relevant results. It takes into account the context of the search query and uses it to produce more accurate and relevant results. Traditional search methods rely on keyword matching, which can be limiting and produce irrelevant results. Vector search, on the other hand, is more precise and produces more relevant results.
In this article, we will explore how open source databases like PostgreSQL, Elasticsearch, and Cassandra are good for vector search and provide a code example to illustrate their implementation.
What Are Open Source Databases?
Open source databases are databases that are freely available and can be modified and distributed by anyone. They are often used by developers and businesses to store and manage data for their applications. Open source databases have become increasingly popular in recent years due to their flexibility, scalability, and cost-effectiveness.
What Open Source Databases Are Good for Vector Search?
Here are some of the best open source databases for vector search:
PostgreSQL
PostgreSQL is a powerful open source relational database that can be used for a wide range of applications, including vector search. It provides support for advanced data types and indexing methods, making it an excellent choice for implementing vector search. PostgreSQL also provides support for full-text search, making it a versatile tool for implementing a wide range of search applications.
Elasticsearch
Elasticsearch is a distributed search engine that can be used for a wide range of applications, including vector search. It uses the Lucene search library and provides a RESTful API for indexing and searching data. Elasticsearch is highly scalable and can handle large volumes of data. It also provides real-time search and analytics capabilities.
Cassandra
Apache Cassandra is a distributed NoSQL database that can be used for a wide range of applications, including vector search. It provides support for advanced data types and indexing methods, making it an excellent choice for implementing vector search. Cassandra is highly scalable and can handle large volumes of data. It also provides support for real-time data analysis and processing.
Code Example
Here is a simple code example to illustrate how vector search can be implemented using PostgreSQL:
import psycopg2
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
# Connect to the PostgreSQL database
conn = psycopg2.connect(
host="localhost",
database="mydatabase",
user="myusername",
password="mypassword"
)
# Define the vectors to search
vector1 = np.array([1, 2, 3])
vector2 = np.array([4, 5, 6])
vector3 = np.array([7, 8, 9])
# Define the search query
query = np.array([4, 5, 6])
# Define the SQL query to create the table
create_table_query = '''
CREATE TABLE vectors (
id SERIAL PRIMARY KEY,
vector REAL[] NOT NULL
);
'''
# Execute the SQL query to create the table
cur = conn.cursor()
cur.execute(create_table_query)
conn.commit()
# Define the SQL query to insert the vectors
insert_vectors_query = '''
INSERT INTO vectors (vector)
VALUES (%s), (%s), (%s);
'''
# Execute the SQL query to insert the vectors
cur = conn.cursor()
cur.execute(insert_vectors_query, (vector1, vector2, vector3))
conn.commit()
# Define the SQL query to search for the most similar vectors
search_query = '''
SELECT id, vector, cube_distance(%s, vector) AS similarity
FROM vectors
ORDER BY similarity ASC
LIMIT 5;
'''
# Execute the SQL query to search for the most similar vectors
cur = conn.cursor()
cur.execute(search_query, (query,))
results = cur.fetchall()
# Print the results
for result in results:
print("Similarity between query and vector with id", result[0], ":", result[2])
# Close the database connection
cur.close()
conn.close()
Conclusion
In conclusion, open source databases provide a flexible and cost-effective solution for implementing vector search. PostgreSQL, Elasticsearch, and Cassandra are all excellent options for implementing vector search, depending on your specific needs and requirements.
If you need help implementing vector search using open source databases, ANANT services can help. ANANT is a leading provider of technology consulting and development services that can help businesses and organizations implement vector search and other cutting-edge technologies to improve their operations and increase their bottom line. Contact us to learn more about our services.
Photo by Daniel Lerman on Unsplash
