README.md

metadata

title: Document RAG
emoji: ⚖️
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.56.0
python_version: 3.12
app_file: app.py
pinned: false

📄 DocuRAG — Advanced RAG Pipeline for Document Q&A

A production-grade Retrieval-Augmented Generation (RAG) system that lets you upload any PDF and have an intelligent conversation about its contents. Built with advanced retrieval techniques, hybrid search, and conversational memory.

🚀 Live Demo

Deployed on Hugging Face Spaces — [link coming soon]

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🧠 What Makes This GOOD

Although it might seem like an overkill for a personal project, I wanted to implement advanced and sophisticated approaches to learn the most!

Component	Technique
Chunking	Semantic chunking (sentence-transformers) + Recursive 512-Token
Embeddings	OpenAI text-embedding-3-small (dense, 1536d)
Sparse Vectors	BM25 via FastEmbed (Qdrant/bm25)
Retrieval	Hybrid search (dense + sparse) with Reciprocal Rank Fusion (RRF)
Reranking	Cohere Rerank v3.5
Generation	GPT-4o-mini with structured prompt engineering
Memory	Sliding window + LLM-based summarization of older turns
Vector Store	Qdrant Cloud (free tier, persistent)
UI	Streamlit with streaming responses

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🧪 Evaluation

The project includes a RAGAS evaluation pipeline (evaluation/evaluate.py) that measures:

• Faithfulness — are answers grounded in the retrieved context?
• Answer Relevancy — does the answer address the question?
• Context Precision — are the retrieved chunks actually relevant?
• Context Recall — are all relevant chunks being retrieved?

Based on a single Erasmus Italian PDF document that has 23 pages, the scores were:

• faithfulness: 0.8807
• answer_relevancy: 0.7479
• llm_context_precision_without_reference: 0.8843

Results saved to evaluation_results.csv

👤 Author

Built by Oussama Hassine as a portfolio project while transitioning into AI Engineering.

• LinkedIn: Oussama Hassine