//! 🤖 Candle Local LLM Provider Implementation
//!
//! "Bringing AI home with Rust-native local models!" - The Cheet 😺
use crate::proxy::{LlmProvider, LlmRequest, LlmResponse};
use anyhow::Result;
use async_trait::async_trait;
#[cfg(feature = "candle")]
use crate::proxy::LlmUsage;
#[cfg(feature = "candle")]
use candle_core::{Device, Tensor};
#[cfg(feature = "candle")]
use candle_transformers::models::llama;
pub struct CandleProvider {
// In a real implementation, we would hold model weights here
model_path: Option<String>,
}
impl CandleProvider {
pub fn new(model_path: Option<String>) -> Self {
Self { model_path }
}
}
impl Default for CandleProvider {
fn default() -> Self {
Self::new(None)
}
}
#[async_trait]
impl LlmProvider for CandleProvider {
async fn complete(&self, _request: LlmRequest) -> Result<LlmResponse> {
#[cfg(not(feature = "candle"))]
{
return Err(anyhow::anyhow!(
"Candle support is not enabled. Recompile with --features candle"
));
}
#[cfg(feature = "candle")]
{
// Suppress unused import warnings - these will be used when implementation is complete
let _suppress_warnings = (
std::marker::PhantomData::<LlmUsage>,
std::marker::PhantomData::<Device>,
std::marker::PhantomData::<Tensor>,
std::marker::PhantomData::<llama::Config>,
);
// This is a placeholder for the actual Candle implementation.
// In a real scenario, we would:
// 1. Load the model (if not already loaded)
// 2. Tokenize the input
// 3. Run inference
// 4. Decode the output
println!(
"🕯️ Running local inference with Candle (model: {})...",
_request.model
);
// For now, return a helpful message
Ok(LlmResponse {
content: format!("Local inference with Candle is configured but requires model weights. (Requested model: {})", _request.model),
model: _request.model,
usage: None,
})
}
}
fn name(&self) -> &'static str {
"Candle"
}
}
