Isn't RAG "just" dynamically injecting relevant text in a prompt? What more would one implement to achieve RAG, beyond using Postgres' built in full text or knn search?

what i'm looking for is a neat python library (or equivalent) that integrates end to end say with postgres/pgvector using sqlalchemy, enables parallel processing of large number of documents, create interfaces for embeddings using openai/ollama etc. It looks like FastRAG [0] from intel looks close to what i'm envisioning but it doesnt appear to have integration to postgres ecosystem yet i guess.

[0] https://github.com/IntelLabs/fastRAG

Through the platform (Neum AI) we support the ability to do this with Postgres, it is just a cloud platform so not a python library.

Curious on what type of customization are you looking to add that you would want something like a library?

We need something we can orchestrate and control locally and be able make changes if need be. The GUI based interface is good for more mature workflows but our workflows are constantly evolving and requires tweaking that its hard to do with GUI and web interface

Or generally what are good vector dbs have tried LlaMaindex, pinecone and milvus but all kinda sucked different way.

What about then sucked?

Timescale recently released Timescale Vector [0] a scalable search index (DiskANN) and efficient time-based vector search, in addition to all capabilities of pgvector and vanilla PostgreSQL. We plan to add the document processing and embedding creation capabilities you discuss into our Python client library [1] next, but Timescale Vector integrates with LangChain and LlamaIndex today [2], which both have document chunking and embedding creation capabilities. (I work on Timescale Vector)

[0]: https://www.timescale.com/blog/how-we-made-postgresql-the-be... [1]: https://github.com/timescale/python-vector [2]: https://www.timescale.com/ai/#resources