README.md

metadata

title: RAG Document Q&A Assistant
emoji: 📄
colorFrom: green
colorTo: purple
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: false
license: mit

📄 RAG Document Q&A Assistant

A Retrieval-Augmented Generation (RAG) system that answers questions about uploaded documents with source citations and chunking strategy comparison.

🎯 What This Does

1. Upload a PDF or TXT document
2. Choose a chunking strategy (Fixed-size or Paragraph-based)
3. Process the document (chunks it and creates embeddings)
4. Ask questions about the document
5. Get accurate answers with relevance scores and source citations

🏗️ Architecture

Document → Chunking → Embedding → Vector Store (ChromaDB)
                                        ↓
User Question → Embedding → Semantic Search → Retrieved Chunks → GPT-4o-mini → Answer

Component	Technology
Embeddings	sentence-transformers/all-MiniLM-L6-v2 (384 dimensions)
Vector Store	ChromaDB (in-memory)
Chunking	Fixed-size (500 chars, 100 overlap) or Paragraph-based
LLM	OpenAI GPT-4o-mini
Framework	Gradio

🔬 Chunking Strategies Compared

This app lets you compare two chunking approaches:

Strategy	How It Works	Best For
Fixed-size	Splits text into 500-char chunks with 100-char overlap	Uniform documents, consistent retrieval
Paragraph-based	Splits on double newlines, preserves natural boundaries	Structured documents, better context

Key Insight: Fixed-size chunking may cut mid-sentence but creates more chunks for better retrieval granularity. Paragraph-based preserves context but may create uneven chunk sizes.

🛠️ Technical Implementation

Vector Search Flow

# 1. Document Processing
chunks = chunk_by_strategy(document_text)
collection.add(documents=chunks, ids=chunk_ids)

# 2. Query Processing  
results = collection.query(query_texts=[question], n_results=3)

# 3. Answer Generation
context = format_retrieved_chunks(results)
answer = gpt4o_mini.generate(context + question)

Similarity Scoring

Distances from ChromaDB are converted to intuitive relevance percentages:

similarity = 1 / (1 + distance)  # Higher = more relevant

🧪 Development Challenges

Challenge 1: Collection Already Exists Error

Problem: chromadb.errors.InternalError: Collection [documents] already exists Cause: Re-uploading documents without clearing the previous collection. Solution: Delete existing collection before creating new one:

try:
    chroma_client.delete_collection(name="documents")
except:
    pass
collection = chroma_client.create_collection(name="documents", ...)

Challenge 2: PDF Text Extraction

Problem: Some PDFs have unusual formatting resulting in few chunks. Solution: PyMuPDF (fitz) handles most PDF formats reliably. For problematic PDFs, fixed-size chunking provides more consistent results than paragraph-based.

📊 Features

• ✅ PDF and TXT file support
• ✅ Two chunking strategies for comparison
• ✅ Source citations with relevance scores
• ✅ Real-time document statistics
• ✅ Clean, intuitive UI

📝 Limitations

• Requires OpenAI API key (uses GPT-4o-mini)
• In-memory vector store (resets on each session)
• English language optimized
• Maximum file size limited by HF Spaces

📚 Research References

• RAG Original Paper (Lewis et al., 2020) - Introduced Retrieval-Augmented Generation
• RAG Survey (Gao et al., 2023) - Comprehensive survey of RAG techniques
• Chunking Strategies for RAG (Merola & Singh, 2025) - Analysis of chunking approaches

👤 Author

Nav772 - Built as part of AI/ML Engineering portfolio

📚 Related Projects

• RAG Document Q&A (LangChain version)
• Movie Sentiment Analyzer
• Amazon Review Rating Predictor
• Food Image Classifier