vipune 0.2.3

//! Synchronous ONNX embedding engine for text-to-vector conversion.
//!
//! Uses bge-small-en-v1.5 model (384 dimensions) with mean pooling and L2 normalization.

use hf_hub::api::sync::Api;
use ort::inputs;
use ort::session::Session;
use ort::session::builder::GraphOptimizationLevel;
use ort::value::Tensor;
use tokenizers::Tokenizer;

use crate::errors::Error;
use tokenizers::TruncationParams;

/// Embedding dimensions for bge-small-en-v1.5 model.
///
/// All generated embeddings are 384-dimensional vectors.
pub const EMBEDDING_DIMS: usize = 384;

/// ONNX embedding engine for synchronous text-to-vector conversion.
///
/// Uses the bge-small-en-v1.5 model to generate 384-dimensional embeddings
/// with mean pooling and L2 normalization. All methods are synchronous,
/// matching vipune's no-async policy.
///
/// # Mutability Requirements
///
/// The `embed()` method requires `&mut self` because ONNX internally mutates
/// state for tensor allocations during inference.
pub struct EmbeddingEngine {
    session: Session,
    tokenizer: Tokenizer,
    requires_token_type_ids: bool,
}

impl EmbeddingEngine {
    /// Load model from cache or download on first use.
    ///
    /// # Sync API Choice
    ///
    /// Uses `hf_hub::api::sync::Api` with ureq feature for blocking I/O.
    /// This approach is fully synchronous, matching vipune's no-async policy.
    /// Files are cached locally in HF Hub cache, only downloaded once.
    pub fn new(model_id: &str) -> Result<Self, Error> {
        let api = Api::new()?;
        let repo = api.model(model_id.to_string());

        let model_path = repo
            .get("onnx/model.onnx")
            .or_else(|_| repo.get("model.onnx"))?;
        let tokenizer_path = repo.get("tokenizer.json")?;

        let mut tokenizer = Tokenizer::from_file(tokenizer_path)?;
        tokenizer
            .with_padding(None)
            .with_truncation(Some(TruncationParams {
                max_length: 512,
                ..Default::default()
            }))?;

        let session = Session::builder()?
            .with_optimization_level(GraphOptimizationLevel::Level1)?
            .commit_from_file(&model_path)?;

        // Check if model requires token_type_ids input
        let requires_token_type_ids = session
            .inputs
            .iter()
            .any(|input| input.name == "token_type_ids");

        Ok(EmbeddingEngine {
            session,
            tokenizer,
            requires_token_type_ids,
        })
    }

    /// Generate embedding for a single text.
    ///
    /// Returns exactly 384-dimensional f32 vector, L2-normalized.
    ///
    /// # Empty Input Handling
    ///
    /// Empty strings return a zero vector. This provides graceful handling
    /// without requiring error recovery from callers.
    ///
    /// # Token Truncation
    ///
    /// Texts exceeding 512 tokens are silently truncated via tokenizer truncation.
    pub fn embed(&mut self, text: &str) -> Result<Vec<f32>, Error> {
        if text.is_empty() {
            return Ok(vec![0.0f32; EMBEDDING_DIMS]);
        }

        let encoding = self.tokenizer.encode(text, true)?;
        let input_ids = encoding.get_ids();
        let attention_mask = encoding.get_attention_mask();

        if input_ids.is_empty() {
            return Ok(vec![0.0f32; EMBEDDING_DIMS]);
        }

        let seq_len = input_ids.len();

        let input_ids_vec: Vec<i64> = input_ids.iter().map(|&id| id as i64).collect();
        let attention_mask_vec: Vec<i64> = attention_mask.iter().map(|&m| m as i64).collect();

        let input_ids_tensor = Tensor::from_array(([1usize, seq_len], input_ids_vec))?;
        let attention_mask_tensor = Tensor::from_array(([1usize, seq_len], attention_mask_vec))?;

        // Only include token_type_ids if the model requires it
        let outputs = if self.requires_token_type_ids {
            let token_type_ids_vec: Vec<i64> = vec![0i64; seq_len]; // Single sentence, all zeros
            let token_type_ids_tensor =
                Tensor::from_array(([1usize, seq_len], token_type_ids_vec))?;
            let inputs = inputs![
                "input_ids" => input_ids_tensor,
                "attention_mask" => attention_mask_tensor,
                "token_type_ids" => token_type_ids_tensor
            ];
            self.session.run(inputs?)?
        } else {
            let inputs = inputs![
                "input_ids" => input_ids_tensor,
                "attention_mask" => attention_mask_tensor
            ];
            self.session.run(inputs?)?
        };

        let last_hidden_state = outputs
            .get("last_hidden_state")
            .or_else(|| outputs.get("token_embeddings"))
            .ok_or_else(|| {
                Error::Inference(
                    "Output tensor 'last_hidden_state' or 'token_embeddings' not found".to_string(),
                )
            })?
            .try_extract_tensor::<f32>()?;

        let shape = last_hidden_state.shape();
        let data = last_hidden_state.as_slice().unwrap();
        if shape.len() != 3 {
            return Err(Error::Inference(format!(
                "Expected 3D output (batch, seq_len, hidden), got {:?}",
                shape
            )));
        }

        let batch_size = shape[0];
        let hidden_dim = shape[2];

        if batch_size != 1 || hidden_dim != EMBEDDING_DIMS {
            return Err(Error::Inference(format!(
                "Unexpected output shape: {:?}, batch=1, hidden=384 expected",
                shape
            )));
        }

        let mut pooled = vec![0.0f32; EMBEDDING_DIMS];

        for (token_idx, chunk) in data.chunks(hidden_dim).take(seq_len).enumerate() {
            let mask_value = attention_mask.get(token_idx).copied().unwrap_or(0) as f32;

            for (dim, pooled_value) in pooled.iter_mut().enumerate() {
                *pooled_value += chunk[dim] * mask_value;
            }
        }

        let mask_sum: f32 = attention_mask
            .iter()
            .take(seq_len)
            .map(|&m| m as f32)
            .sum::<f32>()
            .max(1e-9);

        for value in pooled.iter_mut() {
            *value /= mask_sum;
        }

        let normalized = l2_normalize(&pooled);
        Ok(normalized)
    }
}

fn l2_normalize(vec: &[f32]) -> Vec<f32> {
    let norm: f32 = vec.iter().map(|&x| x * x).sum::<f32>().sqrt();
    let norm = norm.max(1e-9);

    vec.iter().map(|&x| x / norm).collect()
}

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

    #[test]
    fn test_embedding_dimensions() {
        assert_eq!(EMBEDDING_DIMS, 384);
    }

    #[test]
    fn test_l2_normalize_unit_vector() {
        let vec = vec![1.0, 0.0, 0.0];
        let normalized = l2_normalize(&vec);

        let norm: f32 = normalized.iter().map(|&x| x * x).sum::<f32>().sqrt();
        assert!((norm - 1.0).abs() < 0.01);
    }

    #[test]
    fn test_l2_normalize_zero_vector() {
        let vec = vec![0.0, 0.0, 0.0];
        let normalized = l2_normalize(&vec);

        assert_eq!(normalized, vec![0.0, 0.0, 0.0]);
    }

    #[test]
    fn test_l2_normalize_magnitude() {
        let vec = vec![3.0, 4.0];
        let normalized = l2_normalize(&vec);

        let norm: f32 = normalized.iter().map(|&x| x * x).sum::<f32>().sqrt();
        assert!((norm - 1.0).abs() < 0.01);
    }

    #[ignore]
    #[test]
    fn test_integration_whitespace_only() {
        let mut engine = EmbeddingEngine::new("BAAI/bge-small-en-v1.5").expect("load model");
        let embedding = engine.embed("   \t\n  ").expect("embed whitespace text");

        // Whitespace-only input should produce a valid embedding
        assert_eq!(embedding.len(), 384);
        assert!(embedding.iter().all(|&x| x.is_finite()));
    }

    #[ignore]
    #[test]
    fn test_integration_simple_text() {
        let mut engine = EmbeddingEngine::new("BAAI/bge-small-en-v1.5").expect("load model");
        let embedding = engine.embed("hello world").expect("embed text");

        assert_eq!(embedding.len(), 384);

        let norm: f32 = embedding.iter().map(|&x| x * x).sum::<f32>().sqrt();
        assert!(
            (norm - 1.0).abs() < 0.01,
            "Embedding should be L2-normalized"
        );

        assert!(embedding.iter().all(|&x| x.is_finite()));
    }

    #[ignore]
    #[test]
    fn test_integration_empty_string() {
        let mut engine = EmbeddingEngine::new("BAAI/bge-small-en-v1.5").expect("load model");
        let embedding = engine.embed("").expect("embed empty text");

        assert_eq!(embedding.len(), 384);
        assert_eq!(embedding, vec![0.0f32; 384]);
    }

    #[ignore]
    #[test]
    fn test_integration_long_text_truncation() {
        let mut engine = EmbeddingEngine::new("BAAI/bge-small-en-v1.5").expect("load model");

        let long_text = "This is a sentence. ".repeat(100);
        let embedding = engine.embed(&long_text).expect("embed long text");

        assert_eq!(embedding.len(), 384);

        let norm: f32 = embedding.iter().map(|&x| x * x).sum::<f32>().sqrt();
        assert!((norm - 1.0).abs() < 0.01);
    }
}