marklysze/LangChain-RAG-Linux
Examples of RAG using LangChain with local LLMs - Mixtral 8x7B, Llama 2, Mistral 7B, Orca 2, Phi-2, Neural 7B
This project helps you answer specific questions using information from your own Word documents, generating summaries or detailed answers. You provide your documents, and it uses powerful AI models running on your own computer to give you direct responses. This is ideal for researchers, analysts, or anyone who needs to quickly extract precise information from their private document collections.
No commits in the last 6 months.
Use this if you need to query your own Word documents and get AI-generated answers or summaries, ensuring your data stays private and is processed on your local machine.
Not ideal if you don't have a powerful Nvidia graphics card and a Linux system, or if you need to process data types other than Word documents.
Stars
40
Forks
2
Language
Jupyter Notebook
License
—
Category
Last pushed
Jan 20, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/marklysze/LangChain-RAG-Linux"
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