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.
No commits in the last 6 months. Available on PyPI.
Stars
3
Forks
2
Language
Python
License
MIT
Category
Last pushed
Sep 07, 2025
Monthly downloads
37
Commits (30d)
0
Dependencies
10
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