Search Engine Applications

This page documents how uubed’s position-safe encoding can be integrated into search engines for improved performance and accuracy.

Overview

Search engines benefit from uubed’s position-safe encoding in several key areas:

Index Optimization: Efficient encoding of document embeddings
Query Processing: Fast similarity matching with encoded vectors
Storage Efficiency: Reduced index size without losing accuracy

Implementation Examples

Basic Integration

from uubed import encode, decode

# Encode document embeddings for search index
document_embedding = [0.1, 0.5, 0.8, 0.2]  # Your document vector
encoded = encode(document_embedding, method="shq64")

# Store encoded representation in search index
search_index.add_document(doc_id, encoded)

Query Processing

# Encode query vector for similarity search
query_embedding = [0.15, 0.52, 0.75, 0.25]
encoded_query = encode(query_embedding, method="shq64")

# Perform similarity search with encoded vectors
results = search_index.similarity_search(encoded_query, top_k=10)

Performance Benefits

Reduced Memory Usage: Up to 50% reduction in index size
Faster Retrieval: Position-safe encoding enables efficient similarity matching
Scalability: Better performance with large-scale document collections

Best Practices

Choose appropriate encoding method based on your accuracy requirements
Use consistent encoding across all documents and queries
Consider batch encoding for large document collections
Monitor performance metrics and adjust encoding parameters as needed