Added Project Structure

README.md

@@ -59,6 +59,7 @@ This system implements a complete RAG pipeline with the following components:
 - Comprehensive error handling and validation
 - Modular architecture for easy extension
 
+---
 ## Local Development
 
 ### Prerequisites
@@ -100,43 +101,6 @@ python app/main.py
 
 The application will start on `http://localhost:7860`
 
-## Deployment to Hugging Face Spaces
-
-### Method 1: Direct Upload
-
-1. Create a new Space on [Hugging Face](https://huggingface.co/new-space)
-2. Select "Gradio" as SDK
-3. Upload repository files
-4. Add repository secret:
-   - Navigate to Settings → Repository secrets
-   - Create `OPENROUTER_API_KEY` with your API key
-5. Space will auto-deploy
-
-### Method 2: Git Push
-
-```bash
-# Add Hugging Face remote
-git remote add hf https://huggingface.co/spaces/YOUR_USERNAME/SPACE_NAME
-
-# Push to Hugging Face
-git push hf main
-```
-
-**Important**: Ensure the YAML frontmatter (lines 1-9) remains at the top of README.md for proper Space configuration.
-
-## Usage
-
-1. **Upload Document**: Select PDF, DOCX, or TXT file (max recommended: 50MB)
-2. **Process**: Click "Process Document" to chunk and index
-3. **Query**: Ask natural language questions about the content
-4. **Review**: Receive markdown-formatted answers with context
-
-### Example Queries
-
-- "What are the main conclusions of this research paper?"
-- "Summarize the key points from section 3"
-- "What methodology was used in this study?"
-- "Extract all mentioned dates and events"
 
 ## Project Structure
 
@@ -158,56 +122,6 @@ ai-rag-document/
 └── README.md
 ```
 
-## Technical Implementation Details
-
-### Text Chunking Strategy
-
-Uses `RecursiveCharacterTextSplitter` with:
-- **Chunk size**: 1000 characters (balances context vs. precision)
-- **Overlap**: 200 characters (prevents context loss at boundaries)
-- **Metadata preservation**: Tracks source file and document type
-
-### Embedding Model Selection
-
-BAAI/bge-small-en-v1.5 chosen for:
-- Superior performance on MTEB benchmark vs. all-MiniLM-L6-v2
-- 384-dimension vectors (compact yet effective)
-- Instruction-tuned for retrieval tasks
-- L2 normalization for cosine similarity
-
-### LLM Configuration
-
-Google Gemma 3-4B-IT via OpenRouter:
-- **Free tier**: No cost, suitable for demos and light production
-- **Temperature 0.1**: Reduces hallucination, increases factuality
-- **Max tokens 512**: Concise answers, faster responses
-- **OpenRouter benefits**: Unified API, no vendor lock-in
-
-### Prompt Engineering
-
-The system uses a carefully designed prompt:
-- Explicit instruction against hallucination
-- Context grounding requirement
-- Markdown formatting for readability
-- Fallback response for insufficient context
-
-## Testing
-
-```bash
-# Run tests
-python -m pytest tests/
-
-# Run specific test
-python -m pytest tests/test_rag_pipeline.py -v
-```
-
-## Limitations and Considerations
-
-- **Rate limit**: 10 queries/hour (configurable in `rag_pipeline.py`)
-- **Document size**: Large files (>100MB) may cause memory issues
-- **Context window**: Limited to 4 retrieved chunks per query
-- **Free tier**: OpenRouter free tier has usage limits
-
 ## Future Enhancements
 
 - Multi-document cross-referencing
@@ -225,4 +139,4 @@ This project is open source and available for portfolio and educational purposes
 
 **Prateek Kumar Goel**
 - GitHub: [@pkgprateek](https://github.com/pkgprateek)
-- Project deployed on [Hugging Face Spaces](https://huggingface.co/spaces)
+- Project deployed on [Hugging Face Spaces](https://huggingface.co/spaces/pkgprateek)