Our experiment proves it: HNSW indexes in PostgreSQL slash query times from 3.6s to 0.12s in RAG-based AI applications. Tested on 1 Million+ records!
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In the rapidly evolving landscape of Generative AI, Retrieval-Augmented Generation (RAG) models have emerged as powerful tools, combining the strengths of large language models with external knowledge bases. However, as the size of these knowledge bases grows, ensuring efficient and rapid retrieval becomes paramount. This is where Hierarchical Navigable Small World (HNSW) indexes come into play. In this blog post, we'll explore how HNSW indexes can significantly improve the performance of RAG-based applications using PostgreSQL and PgVector, backed by a practical experiment involving a dataset with one million rows.
Understanding RAG and Its Performance Challenges
Retrieval-Augmented Generation applications integrate large language models with external databases or knowledge bases. Instead of relying solely on the pre-trained knowledge embedded within the model, RAG models fetch relevant information from external sources in real time, enhancing the accuracy and relevance of generated responses.
However, as the size of the knowledge base grows, retrieval latency can become a bottleneck. Efficiently searching through millions of embeddings to find the most relevant pieces of information requires optimized indexing and search algorithms.
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Introducing HNSW Indexes
Hierarchical Navigable Small World (HNSW indexes) is an advanced graph-based indexing method designed for approximate nearest neighbor searches. Unlike traditional indexing methods, HNSW indexes in PostgreSQL create a multi-layered graph structure that allows for rapid traversal and efficient similarity searches, making it particularly suited for high-dimensional data like embeddings used in RAG models.
By leveraging HNSW indexes, databases like PostgreSQL—enhanced with extensions like PgVector—can perform similarity searches much faster, drastically reducing retrieval times in RAG applications.
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