Chroma DB vector database, with embedding and reranker models to implement a Retrieval Augmented Generation (RAG) system.
Using tools like selenium and other scrapping libraries, as well as a Vector Data Base PGVector and docker, I have built an ETL that can be used once a week to populate this vector database with the releases of the week
Text Embeddings Inference (TEI)'s unofficial python wrapper library for batch processing with asyncio
It will automatically batch inference requests from multiple independent users together in a single batch request for efficiency, so that for users the interface looks like individual requests, but internally it is handled as a batch request
Docker Compose stack for scalable TEI embeddings (multi-GPU) fronted by a FastAPI proxy with a Qdrant cache. 🐳⛓️💾
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