Hero detail

Beyond RAG Applications: PostgreSQL for Agentic AI

Build intelligent, contextual AI apps without leaving the PostgreSQL you know and love.

Hero detail
Start Building on CloudTry Open Source
– create index – Create vector index on embeddings CREATE INDEX documents_embedding_idx ON document_embedding USING diskann (embedding); – query -- Use case: A RAG system that retrieves the most relevant documents based on -- semantic similarity and additional metadata filtering. SELECT document_title, document_url, (document_embedding <=> query_embedding) AS semantic_similarity FROM knowledge_base WHERE category IN ('Technical_Documentation', 'Research_Papers') AND last_updated_date >= '2023-01-01' AND access_level = 'Public' AND document_length BETWEEN 500 AND 10000 AND confidence_score >= 0.7 ORDER BY semantic_similarity LIMIT 5;

Trusted by great companies building AI products

28x

Performance gain

Query latency graph

PostgreSQL with pgvectorscale outperformed Pinecone—28x lower latency, 16x higher throughput, and 75% lower costs on 50M embeddings

See the Benchmark
Hero detail

Build RAG and Agentic AI apps with Postgres

Hero detail

Step-by-Step: Build a Retrieval Pipeline with Postgres

Start with any language or use infrastructure as code.

Hero detail

Stay Updated on AI Insights

Get notified when new articles in this series are published, along with the latest AI news and insights—straight to your inbox

Guides & Best Practices

Hero detail

Technical Videos & Webinars

Hero detail

Case Studies

Page detail

Ready to scale further and spend less?

Start building your RAG appCheck out DocsDiscord Community