Crate ai_client

ai_client

A Rust crate for interacting with AI language model APIs, supporting multiple providers (Grok, Anthropic, OpenAI) through a unified ChatCompletionClient trait.

Features

• Unified interface for chat completions across different LLM providers
• Caching of responses using an LRU cache
• Exponential backoff for retrying failed requests
• Metrics tracking for requests, successes, errors, and cache hits
• Environment-based configuration
• Robust error handling

Usage

use ai_client::clients::{ChatCompletionClient, GrokClient};
use ai_client::entities::Message;
use tokio;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = GrokClient::new()?;
    let messages = vec![
        Message {
            role: "system".to_string(),
            content: "You are a helpful assistant.".to_string(),
        },
        Message {
            role: "user".to_string(),
            content: "What is 101*3?".to_string(),
        },
    ];
    let response = client.send_chat_completion(messages, "low").await?;
    println!("Response: {:?}", response.choices[0].message.content);
    Ok(())
}

Environment Variables

• GROK_API_KEY: API key for Grok
• GROK_API_ENDPOINT: API endpoint (default: https://api.x.ai/v1/chat/completions)
• GROK_MODEL: Model name (default: grok-3-mini-fast-latest)
• GROK_CACHE_SIZE: Cache size for responses (default: 100)

Re-exports

pub use clients::ChatCompletionClient;

pub use clients::GrokClient;

pub use clients::AnthropicClient;

pub use clients::OpenAIClient;

pub use entities::Message;

pub use entities::ChatCompletionResponse;

pub use entities::Usage;

pub use entities::Choice;

pub use entities::ResponseMessage;

pub use metrics::Metrics;

pub use error::LlmClientError;

Modules

cache

clients

entities

error

metrics