Shreyas2409/book-rag
Built a secure multi-modal retrieval system for document ingestion using Neo4j vector database and LangChain orchestration. Engineered ReAct reasoning prompts and LLM-as-Judge evaluation prompts to enhance answer faithfulness and grounding, driving a 70% increase in user engagement.
Stars
—
Forks
—
Language
Python
License
—
Category
Last pushed
Feb 17, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/Shreyas2409/book-rag"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yichuan-w/LEANN
[MLsys2026]: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast,...
aws-samples/layout-aware-document-processing-and-retrieval-augmented-generation
Advanced document extraction and chunking techniques for retrieval augmented generation that is...
byerlikaya/SmartRAG
Multi-Modal RAG for .NET — query databases, documents, images and audio in natural language....
mrutunjay-kinagi/ragsearch
This project aims to build a Retrieval-Augmented Generation (RAG) engine to provide...
Omkar-Wagholikar/adora
Python package that makes it easy to spin up a custom Retrieval-Augmented Generation (RAG) pipeline.