kyopark2014/agentic-rag
It shows how to realize agentic RAG.
This project provides practical examples for improving how AI models answer questions using your own documents, a process called Retrieval Augmented Generation (RAG). It shows you how to input various document types like PDFs, extract relevant information, and then use that information to generate more accurate and helpful responses from an AI. This is for AI application developers and engineers who want to build more reliable and intelligent conversational AI systems.
No commits in the last 6 months.
Use this if you are building generative AI applications and need to improve the accuracy and relevance of AI responses by integrating specific knowledge from your own document repositories.
Not ideal if you are a non-technical user looking for a ready-to-use AI chatbot without wanting to dive into system architecture or coding.
Stars
28
Forks
19
Language
Python
License
Apache-2.0
Category
Last pushed
Jun 20, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/kyopark2014/agentic-rag"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
deepsense-ai/ragbits
Building blocks for rapid development of GenAI applications
infiniflow/ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses...
GiovanniPasq/agentic-rag-for-dummies
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
truefoundry/cognita
RAG (Retrieval Augmented Generation) Framework for building modular, open source applications...
vectara/py-vectara-agentic
A python library for creating AI assistants with Vectara, using Agentic RAG