VectorInstitute/retrieval-augmented-generation
Reference Implementations for the RAG bootcamp
This collection provides examples for building applications that can answer questions using up-to-date or private information, going beyond what a large language model was originally trained on. You input a question and relevant external data (like documents, web pages, or database records), and it outputs an accurate, specific answer. It's designed for developers, data scientists, and AI engineers looking to create smart assistants or search tools.
Use this if you are an AI developer or data scientist who needs to build an application that can answer questions accurately by retrieving information from specific, external data sources.
Not ideal if you are an end-user simply looking to ask questions to an existing AI, as this project provides the building blocks for such systems, not a finished product.
Stars
33
Forks
24
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/VectorInstitute/retrieval-augmented-generation"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related tools
Renumics/renumics-rag
Visualization for a Retrieval-Augmented Generation (RAG) Assistant 🤖❤️📚
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
aihpi/workshop-rag
Retrieval Augmented Generation and Semantic-search Tools