Products & Services
Timescale Cloud
A reliable PostgreSQL cloud for your workloads
Support Services
Support options for your use case, infrastructure, and budget
Workloads
Time Series
Lightning-fast ingest and querying on PostgreSQL
Real-Time Analytics
The fastest real-time analytics on PostgreSQL
AI and Vector
Build RAG, search, and Al agents, all on PostgreSQL
Open-Source Extensions and Tools
Time Series and Real-Time Analytics
A reliable PostgreSQL cloud for your workloads
AI and Vector
Security Scanner
Security, reliability, and support for demanding businesses.
Industries That Rely On Us
Featured Articles
Scale PostgreSQL via Partitioning: A Dev’s Intro to Hypertables
Read more
Boosting Postgres INSERT Performance by 2x With UNNEST
Read more
Documentation
Timescale Docs
Learn how to make PostgreSQL faster with our documentation
AI and Vector
Learn how to use PostgreSQL for Al with our documentation
Learn PostgreSQL
Learn PostgreSQL
Learn the PostgreSQL basics and scale your database performance
Timescale Benchmarks
Timescale benchmarks
See how Timescale performs against the competition
More

Beyond RAG Applications: PostgreSQL for Agentic AI

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


– 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

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

Build RAG and Agentic AI apps with Postgres

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

Start with any language or use infrastructure as code.

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

Case Studies

Technical Videos & Webinars

Case Studies

Ready to scale further and spend less?