How to accelerate GenAI projects using Knowledge Bases On PostgreSQL
This Twitch show explores how to enhance Generative AI applications using Knowledge Bases on PostgreSQL, focusing on solving AI hallucination problems. Through practical examples and a live demo we will describe how Amazon Bedrock's Retrieval Augmented Generation (RAG) capabilities, combined with PostgreSQL's pgvector extension, can create more accurate and reliable AI applications.
The live stream show featured Tony Mullen with guests Domenico di Salvia and Steve Wood, all AWS database specialist solutions architects focusing on PostgreSQL. The episode explored how to accelerate GenAI projects using knowledge bases on PostgreSQL.
Main Discussion Points:
The team addressed the critical challenge of hallucinations in GenAI applications and chatbots, where AI systems generate fabricated information. They demonstrated how combining Amazon Bedrock knowledge bases with PostgreSQL vector storage can significantly reduce these hallucinations through Retrieval Augmented Generation (RAG). The solution involves storing vector embeddings—numerical representations of data that preserve semantic meaning—in PostgreSQL using the pgvector extension.
Steve demonstrated creating a knowledge base in Amazon Bedrock connected to Aurora PostgreSQL, showing how documents are chunked, converted to embeddings, and stored with specialized indexes (HNSW for vectors, full-text for hybrid search). The live demo compared responses from a standard LLM versus one enhanced with a knowledge base, clearly showing how the knowledge base provided accurate, source-cited answers while the standard LLM struggled with specific queries about obscure historical data.
The discussion highlighted PostgreSQL's advantages: 40 years of development, ACID compliance, extensive indexing support, and the pgvector extension that transforms it into an effective vector store. They covered optimization techniques including product quantization (using half-precision vectors), hybrid search combining semantic and lexical searches, and re-ranking strategies. AWS innovations like Aurora's optimized read instances and Graviton processor acceleration further enhance performance for vector workloads.
Key Highlights:
- Knowledge bases act as AI's "personal library" with organized, searchable information
- Vector embeddings enable computers to understand semantic relationships mathematically
- RAG provides both situational context (operational data) and semantic context (meaning and relationships)
- Amazon Bedrock orchestrates the entire RAG workflow automatically
- PostgreSQL with pgvector offers flexibility to co-locate relational and vector data
- Hybrid search and re-ranking significantly improve retrieval accuracy
- AWS ML extension (awsml) enables direct SQL interaction with Bedrock for automated embeddings
Hosts of the show
Tony Mullen - Senior Database Specialist @AWS
Guests
Steve Wood - Senior Database Specialist @AWS
Domenico di Salvia - Senior Database Specialist @AWS
Links from today's episode
- https://aws.amazon.com/blogs/database/accelerate-your-generative-ai-application-development-with-amazon-bedrock-knowledge-bases-quick-create-and-amazon-aurora-serverless/
- https://aws.amazon.com/blogs/database/automating-vector-embedding-generation-in-amazon-aurora-postgresql-with-amazon-bedrock/
- https://aws.amazon.com/blogs/database/load-vector-embeddings-up-to-67x-faster-with-pgvector-and-amazon-aurora/
- https://jkatz05.com/post/postgres/hybrid-search-postgres-pgvector/
- https://medium.com/@khandwanizaid/improving-rag-hybrid-search-with-re-ranking-for-better-retrieval-ada139e86340
- https://aws.amazon.com/blogs/database/new-amazon-aurora-optimized-reads-for-aurora-postgresql-with-up-to-8x-query-latency-improvement-for-i-o-intensive-applications/
- https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/postgresql-ml.html
- https://aws.amazon.com/blogs/database/part-1-adding-real-time-machine-learning-predictions-to-amazon-aurora/
- https://aws.amazon.com/blogs/database/accelerate-hnsw-indexing-and-searching-with-pgvector-on-amazon-aurora-postgresql-compatible-edition-and-amazon-rds-for-postgresql/
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