NeumAI

Why we included this project

Neum AI handles the glue work of a retrieval-augmented generation setup: pulling content from sources like Postgres or S3, embedding it, and getting the vectors into a store such as Weaviate or Qdrant without hand-writing each connector. You describe the whole pipeline in plain Python, and a short run() call takes the data from raw documents to searchable vectors. Real-time synchronization keeps the index current as underlying sources change, and metadata survives ingestion, so hybrid retrieval still works later without extra plumbing. For small teams that would otherwise patch together separate ETL, embedding, and database integrations, having this data layer in one place saves real setup time.