Sunday, November 17, 2024

AI tool: pgai: develop RAG, semantic search in Postgres DB

timescale/pgai: A suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL GitHub


pgai allows you to develop RAG, semantic search, and other AI applications directly in PostgreSQL

Timescale Products | Timescale


pgai simplifies the process of building search, Retrieval Augmented Generation (RAG), and other AI applications with PostgreSQL. It complements popular extensions for vector search in PostgreSQL like pgvector and pgvectorscale, building on top of their capabilities.

Auto Create and Sync Vector Embeddings in 1 Line of SQL (pgai Vectorizer) - YouTube



timescale/pgvectorscale: A complement to pgvector for high performance, cost efficient vector search on large workloads. @GitHub


Timescale Documentation | Install TimescaleDB on Docker

docker pull timescale/timescaledb-ha:pg16

docker run -d --name timescaledb -p 5432:5432 -e POSTGRES_PASSWORD=password timescale/timescaledb-ha:pg16

psql -d "postgres://<username>:<password>@<host>:<port>/<database-name>"

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Cloud, for 4-10x less than AWS, Azure

 AWS and Azure are At Least 4x–10x More Expensive Than Hetzner

  • AWS On-Demand vs. Hetzner: $226.59 / $32.70 ≈ 6.93 times more expensive
  • AWS 1-Year Reserved vs. Hetzner: $180.60 / $32.70 ≈ 5.52 times more expensive
  • Azure vs. Hetzner: $331.42 / $32.70 ≈ 10.14 times more expensive