smart-tree 8.0.1

//! 🤖 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"
    }
}