weizhepei/InstructRAG
[ICLR 2025] InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
This project helps AI developers build more reliable and trustworthy systems that answer questions using external information. It takes a query and a set of retrieved documents (which might be noisy) and produces a more accurate, verifiable answer by having the system explain its reasoning. AI engineers and researchers working on question-answering systems would use this to improve the quality of their AI's responses.
138 stars. No commits in the last 6 months.
Use this if you are building a question-answering AI and need it to provide more accurate, verifiable responses, especially when dealing with potentially noisy or irrelevant information.
Not ideal if you are looking for a pre-built, end-user application rather than a framework for developing AI models.
Stars
138
Forks
9
Language
Python
License
MIT
Category
Last pushed
Feb 06, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/weizhepei/InstructRAG"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Renumics/renumics-rag
Visualization for a Retrieval-Augmented Generation (RAG) Assistant 🤖❤️📚
VectorInstitute/retrieval-augmented-generation
Reference Implementations for the RAG bootcamp
naver/bergen
Benchmarking library for RAG
KalyanKS-NLP/rag-zero-to-hero-guide
Comprehensive guide to learn RAG from basics to advanced.
alan-turing-institute/t0-1
Application of Retrieval-Augmented Reasoning on a domain-specific body of knowledge