Announcing `google-ai-rs`: Type-Safe Google Generative AI Client for Rust

Hi Rustaceans! I'm excited to share my new crate for working with Google's AI APIs: google-ai-rs Why this exists: Provides strict type safety for Google's Gemini API responses Handles JSON schema validation at compile time Supports both gRPC and REST with async/await Implements full API surface (chat, embeddings, model management) Key Features: // Schema validation example #[derive(AsSchema)] #[schema(description = "Validated customer purchase order")] #[schema(rename_all = "camelCase")] pub struct Order { #[schema( description = "Order items with quantity and pricing" )] pub items: Vec, #[schema( description = "Customer contact information" )] pub customer: Customer, #[schema(description = "Total amount in USD")] pub total: f64, pub status: OrderStatus } #[derive(AsSchema)] #[schema(rename_all = "camelCase")] enum OrderStatus { Pending, Processing, Shipped, Canceled, } let result = model.as_schema::() .generate_content("Return top 5 orders").await?; Flexible Input Handling: // Mix text and binary inputs model.generate_content(( "Explain this diagram", Part::blob("image/png", diagram_bytes), "Include technical specs" )).await?; Production Ready: Connection pooling via tonic/hyper Configurable retry policies Proper error handling variants Benchmark: 2-3x faster than pure REST implementations Getting Started: [dependencies] google-ai-rs = "0.1.0" use google_ai_rs::{Client, generative::GenerativeModel}; #[tokio::main] async fn main() -> Result { let client = Client::new(env!("API_KEY").into())?; let response = client.generative_model("gemini-pro") .generate_content("Explain borrow checker using cooking analogies") .await?; println!("{}", response.text()); Ok(()) } Links: Crates.io Docs.rs GitHub Repo Would love feedback from anyone working with AI APIs in Rust! Particularly interested in: Schema derivation patterns Error handling ergonomics gRPC vs REST tradeoffs

May 9, 2025 - 16:09

Announcing `google-ai-rs`: Type-Safe Google Generative AI Client for Rust

Hi Rustaceans! I'm excited to share my new crate for working with Google's AI APIs: google-ai-rs

Why this exists:

Provides strict type safety for Google's Gemini API responses
Handles JSON schema validation at compile time
Supports both gRPC and REST with async/await
Implements full API surface (chat, embeddings, model management)

Key Features:

// Schema validation example

#[derive(AsSchema)]
#[schema(description = "Validated customer purchase order")]
#[schema(rename_all = "camelCase")]
pub struct Order {
    #[schema(
        description = "Order items with quantity and pricing"
    )]
    pub items: Vec<OrderItem>,

    #[schema(
        description = "Customer contact information"
    )]
    pub customer: Customer,

    #[schema(description = "Total amount in USD")]
    pub total: f64,

    pub status: OrderStatus
}

#[derive(AsSchema)]
#[schema(rename_all = "camelCase")]
enum OrderStatus {
    Pending,
    Processing,
    Shipped,
    Canceled,
}

let result = model.as_schema::<Vec<Order>>()
        .generate_content("Return top 5 orders").await?;

Flexible Input Handling:

// Mix text and binary inputs
model.generate_content((
    "Explain this diagram",
    Part::blob("image/png", diagram_bytes),
    "Include technical specs"
)).await?;

Production Ready:

Connection pooling via tonic/hyper
Configurable retry policies
Proper error handling variants
Benchmark: 2-3x faster than pure REST implementations

Getting Started:

[dependencies]
google-ai-rs = "0.1.0"

use google_ai_rs::{Client, generative::GenerativeModel};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::new(env!("API_KEY").into())?;
    let response = client.generative_model("gemini-pro")
        .generate_content("Explain borrow checker using cooking analogies")
        .await?;

    println!("{}", response.text());
    Ok(())
}

Links:

Would love feedback from anyone working with AI APIs in Rust! Particularly interested in:

Schema derivation patterns
Error handling ergonomics
gRPC vs REST tradeoffs