# gemini-rs [![Rust](https://github.com/andreban/gemini-rs/actions/workflows/rust.yml/badge.svg)](https://github.com/andreban/gemini-rs/actions/workflows/rust.yml)

A Rust wrapper for the Google Cloud Gemini REST API (Vertex AI).

## Features

- **Text Generation**: Generate text responses from Gemini models
- **Streaming**: Stream responses in real-time using SSE
- **Text Embeddings**: Generate text embeddings for ML pipelines
- **Token Counting**: Count tokens before making API calls
- **Image Generation**: Generate and predict images
- **Conversations**: Maintain multi-turn dialogue context
- **Safety Settings**: Configure content safety thresholds
- **Function Calling**: Declare and use tools/functions
- **Google Search Retrieval**: Enable dynamic search-backed responses
- **JSON Schema Responses**: Get structured JSON output

## Installation

Add this to your `Cargo.toml`:

```toml
[dependencies]
gemini-rs = { path = "path/to/gemini-rs" }
tokio = { version = "1", features = ["full"] }
```

## Quick Start

```rust
use gemini_rs::prelude::*;
use gcp_auth::AuthenticationManager;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Authenticate with GCP
    let provider = AuthenticationManager::new();
    
    // Create client
    let client = GeminiClient::new(
        provider,
        "us-central1-aiplatform.googleapis.com".to_string(),
        "your-project-id".to_string(),
        "us-central1".to_string(),
    );
    
    // Generate content
    let request = GenerateContentRequest::builder()
        .contents(vec![Content {
            role: Some(Role::User),
            parts: Some(vec![Part::Text("Hello, Gemini!".to_string())]),
        }])
        .build();
    
    let response = client.generate_content(&request, "gemini-2.0-flash").await?;
    
    for candidate in &response.candidates {
        if let Some(text) = candidate.get_text() {
            println!("{}", text);
        }
    }
    
    Ok(())
}
```

## Examples

Run the examples in the `examples/` directory:

```bash
# Generate text
cargo run --example text-from-text

# Streaming text generation
cargo run --example text-from-text-streaming

# Multi-turn conversation
cargo run --example conversation

# Count tokens
cargo run --example count-tokens

# Generate images
cargo run --example generate_image

# Text embeddings
cargo run --example text-embedding

# JSON schema responses
cargo run --example json-schema

# Safety settings
cargo run --example safety-setting

# System instructions
cargo run --example system_instruction

# Google Search Retrieval
cargo run --example google-search-retrieval

# Google Search tool
cargo run --example google-search
```

## API Reference

### Client Methods

| Method | Description |
|--------|-------------|
| `generate_content()` | Generate content from a model |
| `generate_content_stream()` | Stream content generation responses |
| `stream_generate_content()` | Stream content using a queue-based approach |
| `prompt_conversation()` | Send a conversation prompt and get response |
| `text_embeddings()` | Generate text embeddings |
| `count_tokens()` | Count tokens in a request |
| `predict_image()` | Generate/predict images |

### Supported Models

The library works with all Vertex AI Gemini models, including:
- `gemini-2.0-flash`
- `gemini-2.0-flash-lite`
- `gemini-1.5-pro`
- `gemini-1.5-flash`
- `text-embedding-004`
- `imagen-3.0-generate-002`

## License

See [LICENSE](LICENSE) for details.