ragsearch and Awesome-RAG-Production
One project is a RAG engine for context-aware recommendations, while the other is a curated collection of resources for building and scaling RAG systems, making them complements.
About ragsearch
mrutunjay-kinagi/ragsearch
This project aims to build a Retrieval-Augmented Generation (RAG) engine to provide context-aware recommendations based on user queries.
Supports multiple data input formats (CSV, JSON, Parquet) and integrates with Cohere for embeddings alongside dual vector storage backends—FAISS for in-memory performance or ChromaDB for persistent SQLite-backed search. Built as a Python library with Flask-based web UI, targeting natural language queries over structured datasets with configurable embedding and retrieval pipelines.
About Awesome-RAG-Production
mowael07/Awesome-RAG-Production
🚀 Build and scale reliable Retrieval-Augmented Generation (RAG) systems with this curated collection of tools, frameworks, and best practices.
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