# Gemini

## Implementation

### Provider Setup

```typescript
import { GoogleGenerativeAI, GenerativeModel } from '@google/generative-ai';

class GeminiProvider implements LLMProvider {
  private model: GenerativeModel;

  constructor() {
    const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY!);
    this.model = genAI.getGenerativeModel({ model: 'gemini-pro' });
  }
}
```

### Message Generation

```typescript
async generateResponse(
  messages: Array<{ role: string; content: string }>, 
  cleanContent: string
): Promise<LLMResponse> {
  const chat = this.model.startChat({
    history: messages.slice(0, -1).map(msg => ({
      role: msg.role === 'assistant' ? 'model' : 'user',
      parts: [{ text: msg.content }],
    })),
  });

  const result = await chat.sendMessage(
    messages[messages.length - 1].content
  );
  const response = await result.response;

  return {
    content: response.text(),
    totalTokens: undefined,
    toolCalls: undefined
  };
}
```

## Features

### Supported Capabilities

* Chat history management
* Role-based messaging
* Text generation
* Error handling

### Limitations

* No function calling support
* No token counting
* No streaming support

## Error Handling

### Implementation

```typescript
try {
  // Message generation logic
} catch (error) {
  console.error('Gemini Error:', error);
  throw new Error('Failed to generate Gemini response');
}
```

### Common Errors

* API key issues
* Rate limiting
* Invalid requests
* Model availability

## Best Practices

### Configuration

* Secure API key management
* Model version selection
* Error logging
* Response validation

### Message Handling

* Proper role mapping
* History management
* Content validation
* Error recovery

## Related Documentation

* [LLM Factory](https://docs.lillo.ai/llm/factory)
* [Model Configuration](https://github.com/lillo-ai/website/blob/master/docs/core/framework/configuration.md)
* [API Reference](https://github.com/lillo-ai/website/blob/master/docs/reference/api.md)