To build a production-ready Retrieval-Augmented Generation (RAG) pipeline in Python using BM25 retrieval via Meilisearch instead of a vector database, arguing that BM25 achieves 85–95% of the recall o…
"RAG Readiness," a tool designed to eliminate the vague "it depends" answers that plague Retrieval-Augmented Generation (RAG) architecture decisions. Instead of providing comparison tables, the tool u…
To implement retrieval-augmented generation (RAG) using the pgvector extension within an existing PostgreSQL database, combined with Amazon Bedrock's Titan embedding model and Claude for answer genera…
Production RAG systems often fail after moving beyond demo stage due to underbuilt indexing, retrieval, and observability layers. The indexing pipeline ingests documents into chunks and vector embeddi…
Public benchmarks like MTEB are unreliable for evaluating embedding models in real-world retrieval systems because their generic, artificially clean data does not reflect actual user behavior or domai…