oxirs-chat 0.2.4

//! Advanced Retrieval Strategies for RAG
//!
//! Implements state-of-the-art retrieval techniques including:
//! - Dense Passage Retrieval (DPR)
//! - ColBERT-style late interaction
//! - BM25+ with learned parameters
//! - Learning-to-Rank (LTR)
//! - Query expansion and reformulation
//! - Multi-hop reasoning retrieval

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use tracing::{debug, info};

use super::types::{QueryContext, RagDocument, SearchResult};

/// Advanced retrieval strategies configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedRetrievalConfig {
    /// Enable dense passage retrieval
    pub enable_dpr: bool,
    /// Enable ColBERT late interaction
    pub enable_colbert: bool,
    /// Enable BM25+ with learned params
    pub enable_bm25_plus: bool,
    /// Enable learning-to-rank
    pub enable_ltr: bool,
    /// Enable query expansion
    pub enable_query_expansion: bool,
    /// Enable multi-hop retrieval
    pub enable_multi_hop: bool,
    /// Maximum expansion terms
    pub max_expansion_terms: usize,
    /// Multi-hop depth
    pub multi_hop_depth: usize,
    /// LTR model path (optional)
    pub ltr_model_path: Option<String>,
}

impl Default for AdvancedRetrievalConfig {
    fn default() -> Self {
        Self {
            enable_dpr: true,
            enable_colbert: false, // Expensive, off by default
            enable_bm25_plus: true,
            enable_ltr: true,
            enable_query_expansion: true,
            enable_multi_hop: false,
            max_expansion_terms: 10,
            multi_hop_depth: 2,
            ltr_model_path: None,
        }
    }
}

/// Advanced retrieval engine
pub struct AdvancedRetriever {
    config: AdvancedRetrievalConfig,
    dpr_retriever: Option<DensePassageRetriever>,
    colbert_retriever: Option<ColBERTRetriever>,
    bm25_plus: BM25Plus,
    ltr_ranker: Option<LearningToRank>,
    query_expander: QueryExpander,
}

impl AdvancedRetriever {
    /// Create a new advanced retriever
    pub fn new(config: AdvancedRetrievalConfig) -> Self {
        info!("Initializing advanced retrieval strategies");

        let dpr_retriever = if config.enable_dpr {
            Some(DensePassageRetriever::new())
        } else {
            None
        };

        let colbert_retriever = if config.enable_colbert {
            Some(ColBERTRetriever::new())
        } else {
            None
        };

        let ltr_ranker = if config.enable_ltr {
            Some(LearningToRank::new(config.ltr_model_path.clone()))
        } else {
            None
        };

        Self {
            config,
            dpr_retriever,
            colbert_retriever,
            bm25_plus: BM25Plus::new(),
            ltr_ranker,
            query_expander: QueryExpander::new(),
        }
    }

    /// Retrieve documents using advanced strategies
    pub async fn retrieve(
        &self,
        query: &str,
        context: &QueryContext,
        candidate_docs: &[RagDocument],
    ) -> Result<Vec<SearchResult>> {
        let mut all_results = Vec::new();

        // Query expansion
        let expanded_query = if self.config.enable_query_expansion {
            self.query_expander.expand(query, context).await?
        } else {
            query.to_string()
        };

        debug!("Expanded query: {} -> {}", query, expanded_query);

        // Dense Passage Retrieval
        if let Some(ref dpr) = self.dpr_retriever {
            let dpr_results = dpr.retrieve(&expanded_query, candidate_docs).await?;
            all_results.extend(dpr_results);
        }

        // ColBERT late interaction
        if let Some(ref colbert) = self.colbert_retriever {
            let colbert_results = colbert.retrieve(&expanded_query, candidate_docs).await?;
            all_results.extend(colbert_results);
        }

        // BM25+ with learned parameters
        if self.config.enable_bm25_plus {
            let bm25_results = self
                .bm25_plus
                .retrieve(&expanded_query, candidate_docs)
                .await?;
            all_results.extend(bm25_results);
        }

        // Multi-hop retrieval
        if self.config.enable_multi_hop {
            let multi_hop_results = self
                .multi_hop_retrieve(&expanded_query, candidate_docs)
                .await?;
            all_results.extend(multi_hop_results);
        }

        // Learning-to-Rank reranking
        let final_results = if let Some(ref ltr) = self.ltr_ranker {
            ltr.rerank(all_results, &expanded_query, context).await?
        } else {
            // Default ranking by score
            let mut sorted = all_results;
            sorted.sort_by(|a, b| {
                b.score
                    .partial_cmp(&a.score)
                    .unwrap_or(std::cmp::Ordering::Equal)
            });
            sorted
        };

        Ok(final_results)
    }

    /// Multi-hop retrieval for complex queries
    async fn multi_hop_retrieve(
        &self,
        query: &str,
        candidate_docs: &[RagDocument],
    ) -> Result<Vec<SearchResult>> {
        let mut hop_results = Vec::new();
        let mut current_query = query.to_string();

        for hop in 0..self.config.multi_hop_depth {
            debug!("Multi-hop retrieval: hop {}", hop);

            // Retrieve documents for current query
            let hop_docs = self
                .bm25_plus
                .retrieve(&current_query, candidate_docs)
                .await?;

            if hop_docs.is_empty() {
                break;
            }

            // Extract key information from top documents for next hop
            let top_doc = &hop_docs[0].document;
            current_query = format!(
                "{} {}",
                current_query,
                top_doc
                    .content
                    .split_whitespace()
                    .take(20)
                    .collect::<Vec<_>>()
                    .join(" ")
            );

            hop_results.extend(hop_docs);
        }

        Ok(hop_results)
    }
}

/// Dense Passage Retrieval (DPR)
pub struct DensePassageRetriever {
    // TODO: Integrate with actual embedding model
    // For now, use similarity-based approach
}

impl DensePassageRetriever {
    pub fn new() -> Self {
        Self {}
    }

    pub async fn retrieve(
        &self,
        query: &str,
        documents: &[RagDocument],
    ) -> Result<Vec<SearchResult>> {
        // Simplified DPR - in production, use actual dual encoder model
        let query_embedding = self.encode_query(query)?;

        let mut results = Vec::new();
        for doc in documents {
            let doc_embedding = self.encode_passage(&doc.content)?;
            let score = self.compute_similarity(&query_embedding, &doc_embedding);

            let mut result = SearchResult::new(doc.clone(), score);
            result.relevance_factors.push("DPR".to_string());
            results.push(result);
        }

        results.sort_by(|a, b| {
            b.score
                .partial_cmp(&a.score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(results)
    }

    fn encode_query(&self, query: &str) -> Result<Vec<f32>> {
        // Simplified encoding - use actual embedding model in production
        Ok(query
            .split_whitespace()
            .enumerate()
            .map(|(i, _)| (i as f32 + 1.0) / query.split_whitespace().count() as f32)
            .collect())
    }

    fn encode_passage(&self, passage: &str) -> Result<Vec<f32>> {
        // Simplified encoding - use actual embedding model in production
        Ok(passage
            .split_whitespace()
            .enumerate()
            .map(|(i, _)| (i as f32 + 1.0) / passage.split_whitespace().count() as f32)
            .collect())
    }

    fn compute_similarity(&self, query_emb: &[f32], doc_emb: &[f32]) -> f64 {
        // Cosine similarity
        let dot_product: f32 = query_emb
            .iter()
            .zip(doc_emb.iter())
            .map(|(a, b)| a * b)
            .sum();

        let query_norm: f32 = query_emb.iter().map(|x| x * x).sum::<f32>().sqrt();
        let doc_norm: f32 = doc_emb.iter().map(|x| x * x).sum::<f32>().sqrt();

        if query_norm > 0.0 && doc_norm > 0.0 {
            (dot_product / (query_norm * doc_norm)) as f64
        } else {
            0.0
        }
    }
}

impl Default for DensePassageRetriever {
    fn default() -> Self {
        Self::new()
    }
}

/// ColBERT-style late interaction retriever
pub struct ColBERTRetriever {
    // Token-level representations for late interaction
}

impl ColBERTRetriever {
    pub fn new() -> Self {
        Self {}
    }

    pub async fn retrieve(
        &self,
        query: &str,
        documents: &[RagDocument],
    ) -> Result<Vec<SearchResult>> {
        // Simplified ColBERT - use actual ColBERT model in production
        let query_tokens = self.tokenize_and_encode(query)?;

        let mut results = Vec::new();
        for doc in documents {
            let doc_tokens = self.tokenize_and_encode(&doc.content)?;
            let score = self.max_sim_interaction(&query_tokens, &doc_tokens);

            let mut result = SearchResult::new(doc.clone(), score);
            result.relevance_factors.push("ColBERT".to_string());
            results.push(result);
        }

        results.sort_by(|a, b| {
            b.score
                .partial_cmp(&a.score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(results)
    }

    fn tokenize_and_encode(&self, text: &str) -> Result<Vec<Vec<f32>>> {
        // Simplified tokenization and encoding
        Ok(text
            .split_whitespace()
            .map(|token| {
                token
                    .chars()
                    .enumerate()
                    .map(|(i, _)| (i as f32 + 1.0) / token.len() as f32)
                    .collect()
            })
            .collect())
    }

    fn max_sim_interaction(&self, query_tokens: &[Vec<f32>], doc_tokens: &[Vec<f32>]) -> f64 {
        // MaxSim late interaction
        let mut total_score = 0.0;

        for q_token in query_tokens {
            let mut max_sim = 0.0;
            for d_token in doc_tokens {
                let sim = self.cosine_similarity(q_token, d_token);
                if sim > max_sim {
                    max_sim = sim;
                }
            }
            total_score += max_sim;
        }

        total_score / query_tokens.len() as f64
    }

    fn cosine_similarity(&self, a: &[f32], b: &[f32]) -> f64 {
        if a.is_empty() || b.is_empty() {
            return 0.0;
        }

        let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
        let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();

        if norm_a > 0.0 && norm_b > 0.0 {
            (dot / (norm_a * norm_b)) as f64
        } else {
            0.0
        }
    }
}

impl Default for ColBERTRetriever {
    fn default() -> Self {
        Self::new()
    }
}

/// BM25+ with learned parameters
pub struct BM25Plus {
    k1: f64,    // Term frequency saturation parameter
    b: f64,     // Length normalization parameter
    delta: f64, // BM25+ delta parameter
}

impl BM25Plus {
    pub fn new() -> Self {
        Self {
            k1: 1.5,    // Learned parameter
            b: 0.75,    // Learned parameter
            delta: 0.5, // BM25+ improvement
        }
    }

    pub async fn retrieve(
        &self,
        query: &str,
        documents: &[RagDocument],
    ) -> Result<Vec<SearchResult>> {
        let query_terms: Vec<&str> = query.split_whitespace().collect();
        let avg_doc_length = self.compute_avg_doc_length(documents);

        let mut results = Vec::new();
        for doc in documents {
            let score = self.compute_bm25_plus_score(
                &query_terms,
                &doc.content,
                avg_doc_length,
                documents.len(),
            );

            let mut result = SearchResult::new(doc.clone(), score);
            result.relevance_factors.push("BM25+".to_string());
            results.push(result);
        }

        results.sort_by(|a, b| {
            b.score
                .partial_cmp(&a.score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(results)
    }

    fn compute_avg_doc_length(&self, documents: &[RagDocument]) -> f64 {
        if documents.is_empty() {
            return 0.0;
        }

        let total_length: usize = documents
            .iter()
            .map(|d| d.content.split_whitespace().count())
            .sum();

        total_length as f64 / documents.len() as f64
    }

    fn compute_bm25_plus_score(
        &self,
        query_terms: &[&str],
        document: &str,
        avg_doc_length: f64,
        corpus_size: usize,
    ) -> f64 {
        let doc_terms: Vec<&str> = document.split_whitespace().collect();
        let doc_length = doc_terms.len() as f64;

        let mut score = 0.0;

        for term in query_terms {
            let term_freq = doc_terms.iter().filter(|&&t| t == *term).count() as f64;

            // IDF component (simplified - should count document frequency)
            let idf = ((corpus_size as f64 + 1.0) / 2.0).ln();

            // BM25+ score
            let tf_component = (term_freq * (self.k1 + 1.0))
                / (term_freq + self.k1 * (1.0 - self.b + self.b * doc_length / avg_doc_length));

            // BM25+ delta improvement
            score += idf * (tf_component + self.delta);
        }

        score
    }
}

impl Default for BM25Plus {
    fn default() -> Self {
        Self::new()
    }
}

/// Learning-to-Rank (LTR) reranker
pub struct LearningToRank {
    model_path: Option<String>,
    feature_weights: HashMap<String, f64>,
}

impl LearningToRank {
    pub fn new(model_path: Option<String>) -> Self {
        // Initialize with default feature weights
        let mut feature_weights = HashMap::new();
        feature_weights.insert("semantic_score".to_string(), 0.4);
        feature_weights.insert("bm25_score".to_string(), 0.3);
        feature_weights.insert("query_term_coverage".to_string(), 0.2);
        feature_weights.insert("document_freshness".to_string(), 0.1);

        Self {
            model_path,
            feature_weights,
        }
    }

    pub async fn rerank(
        &self,
        results: Vec<SearchResult>,
        query: &str,
        _context: &QueryContext,
    ) -> Result<Vec<SearchResult>> {
        let mut reranked = results;

        // Extract features and compute LTR scores
        for result in &mut reranked {
            let features = self.extract_features(&result.document, query);
            let ltr_score = self.compute_ltr_score(&features);
            result.score = ltr_score;
        }

        // Re-sort by LTR scores
        reranked.sort_by(|a, b| {
            b.score
                .partial_cmp(&a.score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(reranked)
    }

    fn extract_features(&self, document: &RagDocument, query: &str) -> HashMap<String, f64> {
        let mut features = HashMap::new();

        // Query term coverage
        let query_terms: Vec<&str> = query.split_whitespace().collect();
        let doc_terms: Vec<&str> = document.content.split_whitespace().collect();
        let coverage = query_terms
            .iter()
            .filter(|&term| doc_terms.contains(term))
            .count() as f64
            / query_terms.len() as f64;
        features.insert("query_term_coverage".to_string(), coverage);

        // Document freshness (days since creation)
        let age_days = (chrono::Utc::now() - document.timestamp).num_days() as f64;
        let freshness = 1.0 / (1.0 + age_days / 365.0); // Decay over year
        features.insert("document_freshness".to_string(), freshness);

        // Semantic score (placeholder - would use actual embeddings)
        features.insert("semantic_score".to_string(), 0.5);

        // BM25 score (placeholder)
        features.insert("bm25_score".to_string(), 0.5);

        features
    }

    fn compute_ltr_score(&self, features: &HashMap<String, f64>) -> f64 {
        let mut score = 0.0;

        for (feature, value) in features {
            if let Some(&weight) = self.feature_weights.get(feature) {
                score += weight * value;
            }
        }

        score
    }
}

/// Query expansion engine
pub struct QueryExpander {
    expansion_terms: HashMap<String, Vec<String>>,
}

impl QueryExpander {
    pub fn new() -> Self {
        // Initialize with common expansions
        let mut expansion_terms = HashMap::new();

        // Example expansions
        expansion_terms.insert(
            "search".to_string(),
            vec!["find".to_string(), "look".to_string(), "query".to_string()],
        );
        expansion_terms.insert(
            "person".to_string(),
            vec![
                "people".to_string(),
                "individual".to_string(),
                "human".to_string(),
            ],
        );

        Self { expansion_terms }
    }

    pub async fn expand(&self, query: &str, _context: &QueryContext) -> Result<String> {
        let mut expanded_terms = Vec::new();

        for term in query.split_whitespace() {
            expanded_terms.push(term.to_string());

            // Add expansion terms if available
            if let Some(expansions) = self.expansion_terms.get(&term.to_lowercase()) {
                expanded_terms.extend(expansions.iter().take(2).cloned());
            }
        }

        Ok(expanded_terms.join(" "))
    }
}

impl Default for QueryExpander {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[test]
    fn test_bm25_plus_scoring() {
        let bm25 = BM25Plus::new();
        let docs = [
            RagDocument {
                id: "1".to_string(),
                content: "machine learning artificial intelligence".to_string(),
                embedding: None,
                metadata: HashMap::new(),
                timestamp: Utc::now(),
                source: "test".to_string(),
            },
            RagDocument {
                id: "2".to_string(),
                content: "deep learning neural networks".to_string(),
                embedding: None,
                metadata: HashMap::new(),
                timestamp: Utc::now(),
                source: "test".to_string(),
            },
        ];

        let score =
            bm25.compute_bm25_plus_score(&["machine", "learning"], &docs[0].content, 10.0, 2);

        assert!(score > 0.0);
    }

    #[tokio::test]
    async fn test_query_expansion() {
        let expander = QueryExpander::new();
        let expanded = expander
            .expand("search person", &QueryContext::new("test".to_string()))
            .await
            .expect("should succeed");

        assert!(expanded.contains("search"));
        assert!(expanded.contains("person"));
        assert!(expanded.split_whitespace().count() > 2);
    }
}