Hybrid Search with PostgreSQL and Pgvector

Hybrid search combines different search methods to enhance the relevancy of results in vector similarity searches. This approach is particularly useful in PostgreSQL with the pgvector extension, where it can improve key metrics like recall, index size, and query latency. Recall measures the relevancy of returned results, and boosting it often involves trade-offs with other metrics. Hybrid search employs multiple search methods, ranks the results from each, and merges them to produce a final ranking. A popular scoring method for this is reciprocal ranked fusion (RRF), which assigns weights to results based on their ranks. The blog post discusses implementing hybrid search using PostgreSQL's full-text search capabilities alongside vector similarity search. It outlines the necessary setup, including creating a PostgreSQL database, generating random data, and computing vector embeddings. The author demonstrates how to create indexes for both full-text and vector searches, and provides SQL queries to execute individual and hybrid searches. The results indicate that while vector searches can identify semantic relationships, full-text searches can pinpoint exact phrase matches, making their combination beneficial for improving search outcomes.

- Hybrid search enhances relevancy in vector similarity searches by combining multiple search methods.

- PostgreSQL with pgvector is used to implement hybrid search effectively.

- Reciprocal ranked fusion (RRF) is a key scoring method for merging results from different search methods.

- The approach can improve key metrics like recall, index size, and query latency.

- Combining vector and full-text searches can yield better results than using either method alone.