dhopp1/local_rag_llm
Create and run a local LLM with RAG
This tool helps data analysts, researchers, or anyone working with large collections of documents to quickly find answers or generate content from their specific information. You feed it your own PDFs, websites, and CSV data, and it allows you to 'chat' with that content using a local AI model to get precise, evidence-based responses. The end-user persona is someone who needs to extract insights from private or specialized document sets without sending data to external AI services.
No commits in the last 6 months. Available on PyPI.
Use this if you need to build a secure, private AI chatbot that can answer questions or summarize information exclusively from your own documents, like policy manuals, research papers, or proprietary data.
Not ideal if you're looking for a simple, plug-and-play solution that doesn't require comfort with command-line operations or database setup.
Stars
7
Forks
—
Language
Python
License
MIT
Category
Last pushed
Jul 02, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/dhopp1/local_rag_llm"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
LearningCircuit/local-deep-research
Local Deep Research achieves ~95% on SimpleQA benchmark (tested with GPT-4.1-mini). Supports...
NVIDIA-AI-Blueprints/rag
This NVIDIA RAG blueprint serves as a reference solution for a foundational Retrieval Augmented...
Denis2054/RAG-Driven-Generative-AI
This repository provides programs to build Retrieval Augmented Generation (RAG) code for...
hienhayho/rag-colls
Collection of recent advanced RAG techniques.
jeremiahbohr/literature-mapper
Transform academic PDFs into a Knowledge Graph with typed claims, temporal analysis,...