ragsearch and Retrieval-Augmented-Generation-Intro-Project
These are ecosystem siblings: one is a practical educational introduction to RAG implementation in Jupyter Notebooks, while the other is a production-oriented RAG engine designed to deploy context-aware retrieval systems—representing different maturity stages of RAG technology in the same ecosystem.
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 Retrieval-Augmented-Generation-Intro-Project
HenryHengLUO/Retrieval-Augmented-Generation-Intro-Project
This project aims to introduce and demonstrate the practical applications of RAG using Python code in a Jupyter Notebook environment.
This project helps developers understand and implement Retrieval Augmented Generation (RAG) by walking them through practical applications. It takes custom documents and user queries as input, and outputs contextually relevant responses generated by a large language model. This is for developers interested in integrating RAG into their applications for enhanced information retrieval and text generation.
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