simranjeet97/Learn_RAG_from_Scratch_LLM
Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python
This project helps developers integrate large language models (LLMs) with custom data sources. It guides you through creating a system that can answer questions using information beyond its initial training, taking your documents or text as input and producing contextually accurate responses. Developers looking to build custom AI assistants or sophisticated search tools would find this useful.
No commits in the last 6 months.
Use this if you are a developer looking to build an AI application that can leverage specific documents or proprietary information to generate more accurate and relevant responses.
Not ideal if you are an end-user without programming knowledge, as this project is designed for developers to implement RAG systems.
Stars
10
Forks
7
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 20, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/simranjeet97/Learn_RAG_from_Scratch_LLM"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
bragai/bRAG-langchain
Everything you need to know to build your own RAG application
guyernest/advanced-rag
Jupyter Notebooks for Mastering LLM with Advanced RAG Course
liu673/rag-all-techniques
Implementation of all RAG techniques in a simpler way（以简单的方式实现所有 RAG 技术）
FareedKhan-dev/rag-ecosystem
Understand and code every important component of RAG architecture
FareedKhan-dev/14-rag-failures
Encountering 14 different Naive RAG fails and using KG to solve it