muqadasejaz/PDF-QA-RAG-System
A PDF Question-Answering App built with RAG (Retrieval-Augmented Generation), allowing users to upload PDFs and ask context-based questions. Powered by Streamlit, LangChain, Ollama, and Chroma for efficient and accurate answers.
Implements semantic search via LangChain's text chunking and Chroma/FAISS vector embeddings (using `nomic-embed-text`), ensuring retrieved context directly grounds LLM responses from Ollama's `llama3.1` model to reduce hallucination. The pipeline runs entirely on local infrastructure—no cloud dependencies—with PyPDFLoader handling PDF ingestion and temporary file cleanup for secure processing.
No commits in the last 6 months.
Stars
6
Forks
—
Language
Python
License
MIT
Category
Last pushed
Sep 16, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/muqadasejaz/PDF-QA-RAG-System"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
VectifyAI/PageIndex
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
thearpankumar/GPUaccelerated-multilingual-RAG
GPU - vector DB - AI-powered document processing platform for financial services. Features...
praj2408/RAG-Enhanced-NCERT-Tutor
RAG-Enhanced-NCERT-Tutor is an AI-powered tutor for NCERT curriculum, using Retrieval-Augmented...
justine-george/ai-markdown-llm-retrieval
AI-powered document query system using LangChain, ChromaDB, and OpenAI for efficient RAG-based...
Vikas-ai56/Contextual_RAG
An Advanced RAG system using Python and Langgraph for intelligent, stateful question-answering...