docs/guides/RAG_SETUP_COMPLETE.md

# ✅ RAG Setup Complete!

## What Was Set Up

### 1. Extended RAG System
- **File**: `src/modal-rag-product-design.py`
- **Purpose**: Query the TokyoDrive Insurance product design document
- **Features**:
  - Supports both Markdown and Word documents
  - Uses separate ChromaDB collection (`product_design`)
  - Leverages existing Modal infrastructure
  - GPU-accelerated with Phi-3 model

### 2. Simple CLI Query Interface
- **File**: `query_product_design.py`
- **Features**:
  - Interactive mode for continuous queries
  - Single query mode for quick questions
  - Index command to set up the vector database
  - Clean, user-friendly output

### 3. Documentation
- `docs/QUICK_START_RAG.md` - Quick start guide
- `docs/setup_product_design_rag.md` - Detailed setup instructions
- `docs/next_steps_rag_recommendation.md` - Decision guide

## Files Created

```
src/
  └── modal-rag-product-design.py    # Extended RAG system

query_product_design.py                # CLI query interface

docs/
  ├── QUICK_START_RAG.md              # Quick start guide
  ├── setup_product_design_rag.md     # Setup instructions
  ├── next_steps_rag_recommendation.md # Decision guide
  └── RAG_SETUP_COMPLETE.md           # This file
```

## Next Steps

### 1. Index the Documents (Required First Step)

```bash
python query_product_design.py --index
```

This will:
- Load `tokyo_auto_insurance_product_design_filled.md`
- Load `tokyo_auto_insurance_product_design.docx`
- Create embeddings
- Store in ChromaDB

**Time**: 2-5 minutes

### 2. Test with a Query

```bash
# Single query
python query_product_design.py --query "What are the three product tiers?"

# Or interactive mode
python query_product_design.py --interactive
```

### 3. Use Cases

#### For Product Development
```bash
python query_product_design.py --query "What are the technical requirements for the digital platform?"
python query_product_design.py --query "What API integrations are needed?"
```

#### For Sales/Marketing
```bash
python query_product_design.py --query "What are the premium ranges for each tier?"
python query_product_design.py --query "What discounts are available?"
```

#### For Compliance
```bash
python query_product_design.py --query "What are the FSA licensing requirements?"
python query_product_design.py --query "What is the minimum capital requirement?"
```

#### For Financial Planning
```bash
python query_product_design.py --query "What are the Year 3 financial projections?"
python query_product_design.py --query "What is the break-even point?"
```

## Architecture

```
┌─────────────────────────────────────┐
│  Product Design Documents          │
│  - Markdown (.md)                  │
│  - Word (.docx)                    │
└──────────────┬──────────────────────┘
               │
               ▼
┌─────────────────────────────────────┐
│  Modal Volume                       │
│  mcp-hack-ins-products              │
└──────────────┬──────────────────────┘
               │
               ▼
┌─────────────────────────────────────┐
│  Indexing Function                   │
│  - Load documents                   │
│  - Split into chunks                │
│  - Generate embeddings              │
└──────────────┬──────────────────────┘
               │
               ▼
┌─────────────────────────────────────┐
│  ChromaDB (Remote)                  │
│  Collection: product_design          │
└──────────────┬──────────────────────┘
               │
               ▼
┌─────────────────────────────────────┐
│  Query Interface                    │
│  - CLI tool (query_product_design)  │
│  - Modal RAG class                  │
└──────────────┬──────────────────────┘
               │
               ▼
┌─────────────────────────────────────┐
│  LLM (Phi-3)                        │
│  - Retrieves relevant chunks        │
│  - Generates answers                │
└─────────────────────────────────────┘
```

## How It Works

1. **Indexing**: Documents are split into chunks, embedded, and stored in ChromaDB
2. **Query**: User asks a question
3. **Retrieval**: System finds relevant chunks using semantic search
4. **Generation**: LLM generates answer based on retrieved context
5. **Response**: Answer + sources returned to user

## Tips

### Best Practices
- **Be specific**: "What is the premium for Standard tier?" vs "What is the premium?"
- **Ask one thing**: Break complex questions into simpler ones
- **Use context**: Reference specific sections if you know them

### Performance
- First query: ~10-15 seconds (cold start)
- Subsequent queries: ~3-5 seconds (warm container)
- Indexing: 2-5 minutes (one-time)

### Troubleshooting
- **"No documents found"**: Check Modal volume has the files
- **"Collection not found"**: Run indexing first
- **Slow queries**: Normal on first query, should speed up

## Integration Ideas

1. **Development Workflow**: Extract requirements for Jira tickets
2. **Stakeholder Q&A**: Answer investor/partner questions quickly
3. **Documentation**: Auto-generate summaries for different audiences
4. **Compliance**: Generate compliance checklists automatically
5. **Sales**: Quick access to pricing and feature details

## Support

- See `docs/QUICK_START_RAG.md` for quick reference
- See `docs/setup_product_design_rag.md` for detailed setup
- Check Modal logs: `modal app logs insurance-rag-product-design`

---

**Status**: ✅ Ready to use!

**Next Action**: Run `python query_product_design.py --index` to get started.