Efficient Vector Similarity Search with Redis Tutorial

Enhancing Search Results with Vector Embeddings and Redis

In today's digital landscape, the ability to search for information effectively is crucial. Users expect quick and precise search functionality in nearly every application and website. To enhance search outcomes, architects and developers must continuously evaluate new methods and architectures. One of the most promising approaches is utilizing vector embeddings generated by deep learning models, which significantly improve the accuracy and relevance of search results.

Understanding Vector Embeddings

Many organizations are leveraging indexing techniques to convert their data into a vector space. By representing data as vectors, similarity searches can be conducted to identify the most relevant results. This article explores how deep learning models can create vector embeddings and how Redis can be used for efficient and accurate searching.

Creating Vector Embeddings

To illustrate the process, we'll work with an Amazon product dataset. This tutorial outlines the steps:

• Creating vector embeddings for the Amazon product dataset
• Indexing them with Redis
• Searching for similar vectors

Setting Up Your Environment

Begin by creating a new directory and a Jupyter notebook. Download the dataset CSV file from the designated source and store it in the ./data/ directory. Ensure you are using Python 3.8 and install the necessary dependencies:

pip install redis pandas sentence-transformers

Loading the Data

Once the dependencies are installed, import the necessary libraries:

import pandas as pd
import redis
from redis.commands.search.field import VectorField, TextField, TagField
from redis.commands.search.query import Query
from redis.commands.search.result import Result

Load the Amazon product data into a Pandas DataFrame, truncating long text fields to a maximum of 512 characters, which is optimal for the pre-trained embedding generator:

df = pd.read_csv('./data/amazon_products.csv')
df['keywords'] = df['keywords'].str.slice(0, 512)
# Add a primary key and filter data

Connecting to Redis

With the product data loaded, connect to Redis. You can use a free Redis instance from RedisLabs. Sign up and create a Redis instance, noting down the connection details, including the password.

Generating Embeddings

We will generate embeddings using the Sentence Transformer model distilroberta-v1. This model can be loaded to create the embeddings:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilroberta-v1')
embeddings = model.encode(df['keywords'].tolist())

Utility Functions

Define utility functions to load product data and create indices:

def load_product_data():
    # Function to load and process product data
    pass

def create_index(vector_field):
    # Function to create Redis index
    pass

Indexing Methods

Two key indexing methods are explored:

• Flat Indexing: A simple method where all data points are indexed. It performs a brute-force search, which can be computationally intensive for large datasets.
• HNSW (Hierarchical Navigable Small World): A more complex algorithm that organizes data into a graph structure, providing efficient approximate nearest neighbor search.

Indexing and Querying Data

Start with flat indexing:

create_index(VectorField('embeddings'))

Query the index to find the top 5 nearest neighbors for a given query vector:

query_vector = embeddings[0]
query_result = redis_connection.search(Query(f'*=>[KNN 5 @embeddings $vec]').sort_by('distance'))

Now, repeat the query with HNSW indexing:

create_index(VectorField('embeddings', 'HNSW'))

With this configuration, query to find similar items efficiently.

Final Thoughts

This tutorial demonstrates how vector embeddings and Redis can take your search capabilities to the next level. For a deeper dive, check out the full code in our GitHub repository, where you can also find additional context and examples, including how to perform similarity searches with images.

Learn More About AI-enhanced Searches

Implementing AI technology in search can vastly enhance user experience. Explore various models and approaches to discover what best suits your needs.