oxirs-chat 0.2.4

//! Local Model Provider Implementation
//!
//! Implements the LLM provider trait for local model inference using llama.cpp or similar engines.

use anyhow::{anyhow, Result};
use async_trait::async_trait;
use std::{
    collections::HashMap,
    path::PathBuf,
    time::{Duration, Instant},
};

use super::{
    config::ProviderConfig,
    providers::LLMProvider,
    types::{LLMRequest, LLMResponse, LLMResponseChunk, LLMResponseStream, Usage},
};

/// Local model provider implementation (using llama.cpp or similar)
pub struct LocalModelProvider {
    config: ProviderConfig,
    model_path: PathBuf,
}

impl LocalModelProvider {
    pub fn new(config: ProviderConfig) -> Result<Self> {
        let model_path = PathBuf::from(
            config
                .base_url
                .as_ref()
                .ok_or_else(|| anyhow!("Model path not specified for local provider"))?,
        );

        if !model_path.exists() {
            return Err(anyhow!("Model file does not exist: {:?}", model_path));
        }

        Ok(Self { config, model_path })
    }
}

#[async_trait]
impl LLMProvider for LocalModelProvider {
    async fn generate(&self, model: &str, request: &LLMRequest) -> Result<LLMResponse> {
        // This is a placeholder implementation
        // In production, this would interface with llama.cpp, candle, or another local inference engine

        let prompt = format!(
            "{}\n\n{}",
            request.system_prompt.as_deref().unwrap_or(""),
            request
                .messages
                .iter()
                .map(|m| format!("{:?}: {}", m.role, m.content))
                .collect::<Vec<_>>()
                .join("\n")
        );

        // Simulate local model response
        let content = format!(
            "Local model response to: {}... (Model: {})",
            &prompt.chars().take(50).collect::<String>(),
            model
        );

        let prompt_tokens = prompt.split_whitespace().count();
        let completion_tokens = content.split_whitespace().count();

        Ok(LLMResponse {
            content,
            model_used: model.to_string(),
            provider_used: "local".to_string(),
            usage: Usage {
                prompt_tokens,
                completion_tokens,
                total_tokens: prompt_tokens + completion_tokens,
                cost: 0.0, // Local models are free
            },
            latency: Duration::from_millis(100),
            quality_score: Some(0.7),
            metadata: HashMap::new(),
        })
    }

    fn get_available_models(&self) -> Vec<String> {
        vec![
            "llama-2-7b".to_string(),
            "llama-2-13b".to_string(),
            "mistral-7b".to_string(),
            "codellama-7b".to_string(),
        ]
    }

    fn supports_streaming(&self) -> bool {
        true
    }

    fn get_provider_name(&self) -> &str {
        "local"
    }

    fn estimate_cost(&self, _model: &str, _input_tokens: usize, _output_tokens: usize) -> f64 {
        0.0 // Local models are free
    }

    async fn generate_stream(
        &self,
        model: &str,
        request: &LLMRequest,
    ) -> Result<LLMResponseStream> {
        // Simulate streaming for local models
        let response = self.generate(model, request).await?;
        let words: Vec<String> = response
            .content
            .split_whitespace()
            .map(String::from)
            .collect();
        let chunk_size = 3; // Words per chunk

        let model_name = model.to_string();
        let provider_name = "local".to_string();
        let started_at = Instant::now();

        // Create chunks with owned data
        let chunks: Vec<Result<LLMResponseChunk>> = words
            .chunks(chunk_size)
            .enumerate()
            .map(|(index, chunk)| {
                let total_words = words.len();
                let is_final = (index + 1) * chunk_size >= total_words;
                Ok(LLMResponseChunk {
                    content: chunk.join(" ") + if !is_final { " " } else { "" },
                    is_final,
                    chunk_index: index,
                    model_used: model_name.clone(),
                    provider_used: provider_name.clone(),
                    latency: started_at.elapsed(),
                    metadata: HashMap::new(),
                })
            })
            .collect();

        // Create stream from owned chunks
        let stream = futures_util::stream::iter(chunks);

        Ok(LLMResponseStream {
            stream: Box::pin(stream),
            model_used: model.to_string(),
            provider_used: "local".to_string(),
            started_at,
        })
    }
}