timescale/pgai

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

5806 stars · 314 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

pgai is a Python library and set of PostgreSQL tools from Timescale that turns PostgreSQL into a retrieval engine for RAG and agentic applications, with automatic embedding generation and synchronization from database rows and S3 documents. It also provides a semantic catalog for natural-language-to-SQL and works with pgvector/pgvectorscale on any PostgreSQL instance.

Use cases

• build a rag application on postgres
• automatically generate and sync vector embeddings for database rows
• semantic search over documents stored in s3 and postgres
• natural language to sql for ai agents
• embed postgres data with openai or other embedding models
• add vector search to an existing postgres database

When to choose

• you already run postgres and want rag/semantic search without a separate vector database
• you need embeddings that stay in sync as underlying data changes
• you want text-to-sql grounded in database metadata for agentic apps

When to avoid

• you need actively maintained software - the project was discontinued in February 2026
• you want a managed vector database outside postgres
• you need non-postgres data stores

Facets

library · maturity abandoned

rag vector-database database search-engine llm-inference etl databases artificial-intelligence large-language-models python self-hosted cloud pgvector embeddings semantic-search text-to-sql postgres-extension vectorizer-worker discontinued retrieval-augmented-generation search data-engineering postgresql docker

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markdown · JSON · MCP: product_card(name="timescale/pgai")