# Quickstart: Gemini API Migration

**Feature Branch**: `006-gemini-api-migration`
**Created**: 2025-12-14

## Prerequisites

1. Python 3.9+ installed
2. Google AI API key (get one at https://aistudio.google.com/apikey)
3. Existing backend codebase

## Setup

### 1. Install Dependencies

```bash
# Remove old OpenAI dependency and add Gemini
pip uninstall openai -y
pip install google-genai
```

Or update requirements.txt:
```txt
# Remove this line:
# openai==1.35.13

# Add this line:
google-genai>=0.3.0
```

Then run:
```bash
pip install -r requirements.txt
```

### 2. Configure Environment

Update your `.env` file:

```bash
# Remove or comment out:
# OPENAI_API_KEY=sk-proj-xxxxx

# Add:
GEMINI_API_KEY=your-gemini-api-key-here
```

### 3. Verify Configuration

```bash
# Test that the API key is set
python -c "import os; print('GEMINI_API_KEY set:', bool(os.getenv('GEMINI_API_KEY')))"
```

## Testing the Migration

### Start the Server

```bash
uvicorn app.main:app --reload
```

### Test Chat Endpoint

```bash
curl -X POST http://localhost:8000/api/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello, how are you?"}'
```

Expected: JSON response with AI-generated message.

### Test Translation Endpoint

```bash
curl -X POST http://localhost:8000/api/translate \
  -H "Content-Type: application/json" \
  -d '{"content": "Hello, world!"}'
```

Expected: JSON response with Urdu translation.

### Test Personalization Endpoint

```bash
curl -X POST http://localhost:8000/api/personalize \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Machine learning uses algorithms to find patterns in data.",
    "user_id": 1
  }'
```

Expected: JSON response with personalized content and adjustments made.

## Code Examples

### Using GeminiService Directly

```python
from app.services.gemini_service import GeminiService

# Initialize service
service = GeminiService()

# Chat response
response = await service.get_chat_response("What is Python?")
print(response)

# Translation
urdu_text = await service.translate_to_urdu("Hello, how are you?")
print(urdu_text)

# Personalization
result = await service.personalize_content(
    content="Neural networks are computing systems...",
    software_level="beginner",
    hardware_level="none",
    learning_goals="learn AI basics"
)
print(result)
```

### Using EmbeddingsService

```python
from app.services.embeddings_service import EmbeddingsService

# Initialize service
embeddings = EmbeddingsService()

# Generate embedding
vector = await embeddings.create_embedding("Hello world")
print(f"Embedding dimension: {len(vector)}")  # Should be 768
```

## Troubleshooting

### Error: "GEMINI_API_KEY not set"

Make sure your `.env` file has the key set and the app is loading environment variables:

```python
from dotenv import load_dotenv
load_dotenv()
```

### Error: "Model not found"

Verify you have access to the specified models:
- `gemini-2.0-flash-exp` for chat/translation/personalization
- `text-embedding-004` for embeddings

### Error: "Rate limit exceeded"

The Gemini API has rate limits. For development:
- Free tier: 15 RPM (requests per minute)
- Consider implementing retry logic with exponential backoff

### Embedding Dimension Mismatch

If you see Qdrant errors about vector dimensions:
- OpenAI embeddings: 1536 dimensions
- Gemini embeddings: 768 dimensions

You may need to recreate Qdrant collections with the new dimension size (out of scope for this migration).

## Verification Checklist

- [ ] Server starts without errors
- [ ] Chat endpoint returns responses
- [ ] Translation endpoint works
- [ ] Personalization endpoint works
- [ ] No references to OpenAI in logs
- [ ] No `OPENAI_API_KEY` required

## Related Documentation

- [Spec](./spec.md)
- [Research](./research.md)
- [Data Model](./data-model.md)
- [Implementation Plan](./plan.md)