sevensense-api 0.1.0

//! Embedding model service.
//!
//! This module provides the `EmbeddingModel` service for generating
//! vector embeddings from audio segments.

use thiserror::Error;

use super::{Segment, SegmentEmbedding};

/// Embedding model error.
#[derive(Debug, Error)]
pub enum EmbeddingError {
    /// Model loading error
    #[error("Failed to load model: {0}")]
    ModelLoadError(String),

    /// Inference error
    #[error("Inference failed: {0}")]
    InferenceError(String),

    /// Invalid input
    #[error("Invalid input: {0}")]
    InvalidInput(String),

    /// Model not initialized
    #[error("Model not initialized")]
    NotInitialized,
}

/// Embedding model configuration.
#[derive(Debug, Clone)]
pub struct EmbeddingModelConfig {
    /// Model path or identifier
    pub model_id: String,
    /// Embedding dimension
    pub embedding_dim: usize,
    /// Batch size for inference
    pub batch_size: usize,
    /// Use GPU if available
    pub use_gpu: bool,
}

impl Default for EmbeddingModelConfig {
    fn default() -> Self {
        Self {
            model_id: "birdnet-v2.4".to_string(),
            embedding_dim: 1024,
            batch_size: 32,
            use_gpu: false,
        }
    }
}

/// Embedding model for generating audio embeddings.
///
/// Wraps ONNX model inference for generating fixed-size vector
/// representations of audio segments.
pub struct EmbeddingModel {
    config: EmbeddingModelConfig,
}

impl EmbeddingModel {
    /// Create a new embedding model with the given configuration.
    pub async fn new(config: EmbeddingModelConfig) -> Result<Self, EmbeddingError> {
        // In a real implementation, this would:
        // 1. Load ONNX model from path
        // 2. Initialize ONNX runtime session
        // 3. Configure GPU/CPU execution providers

        Ok(Self { config })
    }

    /// Generate embeddings for a batch of segments.
    pub async fn embed_batch(
        &self,
        segments: &[Segment],
    ) -> Result<Vec<SegmentEmbedding>, EmbeddingError> {
        // In a real implementation, this would:
        // 1. Preprocess segments (mel spectrogram)
        // 2. Batch and run inference
        // 3. L2 normalize embeddings

        let embeddings = segments
            .iter()
            .map(|seg| SegmentEmbedding {
                id: seg.id,
                recording_id: seg.recording_id,
                embedding: vec![0.0; self.config.embedding_dim],
                start_time: seg.start_time,
                end_time: seg.end_time,
                species: seg.species.clone(),
            })
            .collect();

        Ok(embeddings)
    }

    /// Generate embedding for text (for text-to-audio search).
    pub async fn embed_text(&self, text: &str) -> Result<Vec<f32>, EmbeddingError> {
        if text.is_empty() {
            return Err(EmbeddingError::InvalidInput("Empty text".to_string()));
        }

        // In a real implementation, this would use a text encoder
        // For now, return a zero vector
        Ok(vec![0.0; self.config.embedding_dim])
    }

    /// Get the embedding dimension.
    #[must_use]
    pub fn embedding_dim(&self) -> usize {
        self.config.embedding_dim
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    async fn test_embedding_model_creation() {
        let model = EmbeddingModel::new(Default::default()).await;
        assert!(model.is_ok());
    }

    #[tokio::test]
    async fn test_embed_text_empty() {
        let model = EmbeddingModel::new(Default::default()).await.unwrap();
        let result = model.embed_text("").await;
        assert!(matches!(result, Err(EmbeddingError::InvalidInput(_))));
    }
}