SINGHxTUSHAR/Document-QA-Llama-GROQ
Document-Q&A using the GROQ and Llama3 is a sophisticated question-answering system designed to interactively retrieve and process information from PDF documents. The project leverages a Retrieval Augmented Generation (RAG) approach by integrating vector embeddings, similarity search, and language model inference.
No commits in the last 6 months.
Stars
1
Forks
—
Language
Python
License
MIT
Category
Last pushed
Mar 05, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/SINGHxTUSHAR/Document-QA-Llama-GROQ"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Future-House/paper-qa
High accuracy RAG for answering questions from scientific documents with citations
weiwill88/Local_Pdf_Chat_RAG
🧠 纯原生 Python 实现的 RAG 框架 | FAISS + BM25 混合检索 | 支持 Ollama / SiliconFlow | 适合新手入门学习
shubham0204/OnDevice-RAG-Android
A custom RAG pipeline for multi-document QA from PDF/DOCX documents, in Android
dev-it-with-me/RagUltimateAdvisor
A complete Retrieval-Augmented Generation (RAG) application that demonstrates modern AI...
EarthlyAlien/Document-Assistant
RAG based Document Assistant for Search