timerring/rag101
LangChain and RAG best practices
This project helps developers integrate custom data with large language models to build applications that can answer questions based on specific documents or web pages. It takes various data sources like PDFs or websites, processes them into a format LLMs can understand, and then allows the LLM to provide relevant, context-aware answers to user queries. This is for developers building AI-powered applications that need to chat with or retrieve information from private or specialized datasets.
No commits in the last 6 months.
Use this if you are a developer looking for a practical guide to implement Retrieval-Augmented Generation (RAG) using LangChain to enhance your LLM applications with external, domain-specific data.
Not ideal if you are looking for a pre-built, production-ready application; this is a foundational guide for building your own.
Stars
8
Forks
—
Language
Python
License
MIT
Category
Last pushed
Mar 04, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/timerring/rag101"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
run-llama/llama_index
LlamaIndex is the leading document agent and OCR platform
emarco177/documentation-helper
Reference implementation of a RAG-based documentation helper using LangChain, Pinecone, and Tavily..
janus-llm/janus-llm
Leveraging LLMs for modernization through intelligent chunking, iterative prompting and...
JetXu-LLM/llama-github
Llama-github is an open-source Python library that empowers LLM Chatbots, AI Agents, and...
Vasallo94/ObsidianRAG
RAG system to query your Obsidian notes using LangGraph and local LLMs (Ollama)