ChatGPT but for your data
The possibilities are endless: Build customer service bots, chat with your developer documentation, and get easy answers to questions over 100+ page documents.Retrieval Augmented Generation
Use Retrieval Augmented Generation (RAG) to supplement foundation models with your own data. Augment base large language models like GPT4 from OpenAI, LLaMa, Falcon, Claude from Anthropic, and more.Long term memory
Create AI agents using PostgreSQL as vector storage for AutoGPT. Leverage popular development frameworks like Langchain and LlamaIndex.No need to learn a new database or query language
pgvector extends PostgreSQL to handle vector similarity search and storage of embeddings needed for LLM AI applications with reliability and ease.All the tools you need for vector similarity search
Exact and approximate nearest neighbor search, L2 distance, inner product, and cosine distance, and out-of-the-box support for up to 2000-dimensional vectors.Yes, it works with that
Leverage the wide ecosystem of PostgreSQL libraries, ORMs, connectors, and tools. From Python and Javascript to the rest of your favorite languages.Timescale is cloud PostgreSQL++
Run pgvector on Timescale’s fully-managed PostgreSQL cloud platform for an easy, cost-effective experience.Developer conveniences
One-click database forking for testing new models and different embeddings. Read replicas to scale query load. Free consultative support to guide you as you grow.Secure and private
SOC2 Type II and GDPR compliance. Data encryption at rest and in motion. VPC peering for your Amazon VPC.Transparent pricing
No “pay per query” or “pay per index”. Decoupled compute and storage for flexible scaling as your needs change.Timescale Vector Cookbook
Create, store and query OpenAI embeddings with PostgreSQL and pgvector
What Are ivfflat Indexes in pgvector and How Do They Work
LangChain and pgvector: Up and Running
No credit card required • Free for 30 days