lattice-tune 0.3.0

//! Distillation pipeline orchestration.

use super::DistillationConfig;
use super::types::{DistillationStats, LabelingResult, RawExample};
use crate::data::{ExampleMetadata, IntentLabels, TrainingExample};
use crate::distill::teacher::TeacherConfig;
use crate::error::{Result, TuneError};
use chrono::Utc;

/// Distillation pipeline that orchestrates labeling from teacher models
///
/// This is a placeholder implementation. The actual API calls would need
/// to be implemented with a proper HTTP client like `reqwest`.
pub struct DistillationPipeline {
    /// Teacher model configuration
    teacher: TeacherConfig,

    /// Pipeline configuration
    config: DistillationConfig,

    /// Statistics
    stats: DistillationStats,
}

impl DistillationPipeline {
    /// Create a new distillation pipeline
    pub fn new(teacher: TeacherConfig, config: DistillationConfig) -> Result<Self> {
        teacher.validate().map_err(TuneError::InvalidConfig)?;
        config.validate()?;

        Ok(Self {
            teacher,
            config,
            stats: DistillationStats::default(),
        })
    }

    /// Create with default configuration
    pub fn with_teacher(teacher: TeacherConfig) -> Result<Self> {
        Self::new(teacher, DistillationConfig::default())
    }

    /// Get the teacher configuration
    pub fn teacher(&self) -> &TeacherConfig {
        &self.teacher
    }

    /// Get the pipeline configuration
    pub fn config(&self) -> &DistillationConfig {
        &self.config
    }

    /// Get current statistics
    pub fn stats(&self) -> &DistillationStats {
        &self.stats
    }

    /// Reset statistics
    pub fn reset_stats(&mut self) {
        self.stats = DistillationStats::default();
    }

    /// Label a single raw example
    ///
    /// This is a placeholder - actual implementation would call the teacher API.
    pub fn label_single(&mut self, raw: &RawExample) -> Result<LabelingResult> {
        let start = std::time::Instant::now();

        // Placeholder: simulate labeling
        // In real implementation, this would:
        // 1. Format the prompt from raw example
        // 2. Call the teacher API
        // 3. Parse the response into IntentLabels

        let _prompt = raw.to_prompt();

        // Simulate labels (in production, these come from teacher)
        let mut labels = IntentLabels {
            continuation: 0.4,
            topic_shift: 0.1,
            explicit_query: 0.3,
            person_lookup: 0.1,
            health_check: 0.05,
            task_status: 0.05,
        };

        if self.config.normalize_labels {
            labels
                .softmax_normalize()
                .map_err(TuneError::InvalidConfig)?;
        }

        let confidence = 0.85; // Would come from teacher response
        let latency_ms = start.elapsed().as_millis() as u64;

        // Check confidence threshold
        if let Some(min_conf) = self.config.min_confidence {
            if confidence < min_conf {
                self.stats.skipped += 1;
                return Err(TuneError::Validation(format!(
                    "Confidence {confidence} below threshold {min_conf}"
                )));
            }
        }

        let result = LabelingResult::success(raw.id, labels, confidence, latency_ms);
        self.stats.update(&result);

        Ok(result)
    }

    /// Label a batch of raw examples
    ///
    /// Returns results for all examples, including failures.
    pub fn label_batch(&mut self, raws: &[RawExample]) -> Vec<LabelingResult> {
        let mut results = Vec::with_capacity(raws.len());

        for raw in raws {
            let result = match self.label_single(raw) {
                Ok(r) => r,
                Err(e) => {
                    let r = LabelingResult::failure(raw.id, e.to_string(), 0);
                    self.stats.update(&r);
                    r
                }
            };
            results.push(result);
        }

        results
    }

    /// Convert labeled results to training examples
    ///
    /// Requires embeddings to be provided separately.
    pub fn to_training_examples(
        &self,
        results: &[LabelingResult],
        context_embeddings: &[Vec<Vec<f32>>],
        message_embeddings: &[Vec<f32>],
    ) -> Result<Vec<TrainingExample>> {
        if results.len() != context_embeddings.len() || results.len() != message_embeddings.len() {
            return Err(TuneError::DimensionMismatch {
                expected: results.len(),
                actual: context_embeddings.len(),
            });
        }

        let mut examples = Vec::with_capacity(results.len());

        for (i, result) in results.iter().enumerate() {
            if !result.is_success() {
                continue;
            }

            let mut example = TrainingExample::with_id(
                result.example_id,
                context_embeddings[i].clone(),
                message_embeddings[i].clone(),
                result.labels.clone(),
            );

            // Add metadata about the labeling
            let metadata = ExampleMetadata::with_source(result.example_id.to_string())
                .teacher(self.teacher.display_name())
                .labeled_at(Utc::now())
                .confidence(result.confidence);

            example = example.with_metadata(metadata);
            examples.push(example);
        }

        Ok(examples)
    }
}