oxirs-chat 0.2.4

//! Multi-stage retrieval engine for the RAG system
//!
//! Implements semantic search, graph traversal, hybrid retrieval,
//! and intelligent document ranking and filtering.

use super::quantum::QuantumRanker;
use super::types::*;
use anyhow::Result;
use oxirs_core::Store;
use oxirs_vec::{Vector, VectorIndex};
use std::collections::{HashMap, HashSet};
use std::sync::Arc;
use tokio::sync::RwLock;
use tracing::{debug, info};

/// Multi-stage retrieval engine
pub struct MultiStageRetrieval {
    config: RetrievalConfig,
    semantic_retriever: SemanticRetriever,
    graph_retriever: GraphRetriever,
    hybrid_retriever: HybridRetriever,
    result_cache: Arc<RwLock<HashMap<String, Vec<SearchResult>>>>,
}

impl MultiStageRetrieval {
    /// Create a new multi-stage retrieval engine
    pub fn new(rag_config: &super::RAGConfig) -> Self {
        let config = RetrievalConfig {
            max_documents: rag_config.retrieval.max_results,
            similarity_threshold: rag_config.retrieval.similarity_threshold as f64,
            enable_reranking: true,
            reranking_model: None,
            enable_temporal_filtering: true,
            temporal_window: Some(std::time::Duration::from_secs(365 * 24 * 3600)),
        };

        Self {
            semantic_retriever: SemanticRetriever::new(),
            graph_retriever: GraphRetriever::new(),
            hybrid_retriever: HybridRetriever::new(),
            result_cache: Arc::new(RwLock::new(HashMap::new())),
            config,
        }
    }

    /// Perform multi-stage retrieval
    pub async fn retrieve(
        &self,
        query: &str,
        context: &QueryContext,
        vector_index: &Arc<dyn VectorIndex>,
        store: &Arc<dyn Store>,
    ) -> Result<Vec<SearchResult>> {
        let start_time = std::time::Instant::now();

        // Check cache first
        let cache_key = format!("{}:{}", query, context.session_id);
        let cache = self.result_cache.read().await;
        if let Some(cached_results) = cache.get(&cache_key) {
            debug!("Cache hit for query: {}", query);
            return Ok(cached_results.clone());
        }

        let mut all_results = Vec::new();

        // Stage 1: Semantic retrieval
        info!("Starting semantic retrieval for query: {}", query);
        let semantic_results = self
            .semantic_retriever
            .retrieve(query, context, vector_index)
            .await?;
        all_results.extend(semantic_results);

        // Stage 2: Graph traversal (if enabled)
        if context.query_intent != QueryIntent::Information {
            info!("Starting graph traversal");
            let graph_results = self.graph_retriever.retrieve(query, context, store).await?;
            all_results.extend(graph_results);
        }

        // Stage 3: Hybrid search (if enabled)
        info!("Starting hybrid retrieval");
        let hybrid_results = self
            .hybrid_retriever
            .retrieve(query, context, vector_index, store)
            .await?;
        all_results.extend(hybrid_results);

        // Stage 4: Deduplication and ranking
        let final_results = self.deduplicate_and_rank(all_results, query).await?;

        // Stage 5: Apply filters
        let filtered_results = self.apply_filters(final_results, context).await?;

        // Cache results
        let mut cache = self.result_cache.write().await;
        cache.insert(cache_key, filtered_results.clone());
        // Keep cache size reasonable
        if cache.len() > 1000 {
            cache.clear();
        }

        let retrieval_time = start_time.elapsed();
        info!(
            "Multi-stage retrieval completed in {:?}, found {} documents",
            retrieval_time,
            filtered_results.len()
        );

        Ok(filtered_results)
    }

    /// Update configuration
    pub fn update_config(&mut self, rag_config: &super::RAGConfig) {
        self.config.max_documents = rag_config.retrieval.max_results;
        self.config.similarity_threshold = rag_config.retrieval.similarity_threshold as f64;
    }

    /// Deduplicate and rank results
    async fn deduplicate_and_rank(
        &self,
        mut results: Vec<SearchResult>,
        query: &str,
    ) -> Result<Vec<SearchResult>> {
        // Deduplicate by document ID
        let mut seen_ids = HashSet::new();
        results.retain(|result| seen_ids.insert(result.document.id.clone()));

        // Apply quantum-inspired ranking if available
        let query_complexity = query.split_whitespace().count() as f64 / 10.0;
        if query_complexity > 0.5 {
            let documents: Vec<RagDocument> = results.iter().map(|r| r.document.clone()).collect();

            let mut quantum_ranker = QuantumRanker::new(query_complexity);
            let quantum_results = quantum_ranker.rank_documents(&documents);

            // Convert quantum results back to search results
            results = quantum_results
                .into_iter()
                .map(|qr| qr.to_classical())
                .collect();
        } else {
            // Standard ranking by score
            results.sort_by(|a, b| {
                b.score
                    .partial_cmp(&a.score)
                    .unwrap_or(std::cmp::Ordering::Equal)
            });
        }

        // Limit to max documents
        results.truncate(self.config.max_documents);

        Ok(results)
    }

    /// Apply various filters to results
    async fn apply_filters(
        &self,
        mut results: Vec<SearchResult>,
        context: &QueryContext,
    ) -> Result<Vec<SearchResult>> {
        // Filter by similarity threshold
        results.retain(|result| result.score >= self.config.similarity_threshold);

        // Apply temporal filtering if enabled
        if self.config.enable_temporal_filtering {
            if let Some(window) = self.config.temporal_window {
                let cutoff = chrono::Utc::now() - chrono::Duration::from_std(window)?;
                results.retain(|result| result.document.timestamp >= cutoff);
            }
        }

        // Apply domain constraints
        if !context.domain_constraints.is_empty() {
            results.retain(|result| {
                context.domain_constraints.iter().any(|constraint| {
                    result.document.content.contains(constraint)
                        || result
                            .document
                            .metadata
                            .values()
                            .any(|v| v.contains(constraint))
                })
            });
        }

        Ok(results)
    }
}

/// Semantic retrieval using vector similarity
pub struct SemanticRetriever {
    embedding_cache: Arc<RwLock<HashMap<String, Vec<f32>>>>,
}

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

impl SemanticRetriever {
    pub fn new() -> Self {
        Self {
            embedding_cache: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    pub async fn retrieve(
        &self,
        query: &str,
        _context: &QueryContext,
        vector_index: &Arc<dyn VectorIndex>,
    ) -> Result<Vec<SearchResult>> {
        // Get query embedding (simplified - would use actual embedding model)
        let query_embedding_vec = self.get_query_embedding(query).await?;
        let query_embedding = Vector::new(query_embedding_vec);

        // Perform vector search
        let vector_results = vector_index.search_knn(&query_embedding, 20)?;

        // Convert to search results
        let search_results = vector_results
            .into_iter()
            .map(|(id, score)| {
                SearchResult::new(
                    RagDocument::new(
                        id.clone(),
                        format!("Content for document {id}"),
                        "vector_index".to_string(),
                    ),
                    score.into(),
                )
                .add_relevance_factor("semantic_similarity".to_string())
            })
            .collect();

        Ok(search_results)
    }

    async fn get_query_embedding(&self, query: &str) -> Result<Vec<f32>> {
        // Check cache first
        let cache = self.embedding_cache.read().await;
        {
            if let Some(embedding) = cache.get(query) {
                return Ok(embedding.clone());
            }
        }

        // Generate embedding (simplified implementation)
        let embedding = query
            .chars()
            .map(|c| (c as u8 as f32) / 255.0)
            .take(128)
            .collect::<Vec<f32>>();

        // Pad to fixed size
        let mut padded = embedding;
        padded.resize(128, 0.0);

        // Cache the result
        let mut cache = self.embedding_cache.write().await;
        {
            cache.insert(query.to_string(), padded.clone());
        }

        Ok(padded)
    }
}

/// Graph-based retrieval using SPARQL queries
pub struct GraphRetriever {
    query_cache: Arc<RwLock<HashMap<String, Vec<SearchResult>>>>,
}

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

impl GraphRetriever {
    pub fn new() -> Self {
        Self {
            query_cache: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    pub async fn retrieve(
        &self,
        query: &str,
        context: &QueryContext,
        _store: &Arc<dyn Store>,
    ) -> Result<Vec<SearchResult>> {
        // Check cache
        let cache_key = format!("graph:{}:{}", query, context.session_id);
        let cache = self.query_cache.read().await;
        {
            if let Some(cached) = cache.get(&cache_key) {
                return Ok(cached.clone());
            }
        }

        // Generate SPARQL query based on natural language query
        let sparql_query = self.generate_sparql_query(query, context)?;

        // Execute query (simplified implementation)
        let results = self.execute_sparql_query(&sparql_query).await?;

        // Cache results
        {
            let mut cache = self.query_cache.write().await;
            cache.insert(cache_key, results.clone());
        }

        Ok(results)
    }

    fn generate_sparql_query(&self, query: &str, _context: &QueryContext) -> Result<String> {
        // Simplified SPARQL generation
        let query_terms: Vec<&str> = query.split_whitespace().collect();

        let sparql = format!(
            r#"
            SELECT ?subject ?predicate ?object WHERE {{
                ?subject ?predicate ?object .
                FILTER(contains(lcase(str(?object)), "{}"))
            }}
            LIMIT 10
            "#,
            query_terms.join(" ").to_lowercase()
        );

        Ok(sparql)
    }

    async fn execute_sparql_query(&self, _query: &str) -> Result<Vec<SearchResult>> {
        // Placeholder implementation - would execute actual SPARQL query
        Ok(vec![SearchResult::new(
            RagDocument::new(
                "graph_result_1".to_string(),
                "Graph-based result content".to_string(),
                "knowledge_graph".to_string(),
            ),
            0.8,
        )
        .add_relevance_factor("graph_connectivity".to_string())])
    }
}

/// Hybrid retrieval combining multiple approaches
pub struct HybridRetriever {
    fusion_weights: HashMap<String, f64>,
}

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

impl HybridRetriever {
    pub fn new() -> Self {
        let mut weights = HashMap::new();
        weights.insert("semantic".to_string(), 0.6);
        weights.insert("keyword".to_string(), 0.3);
        weights.insert("graph".to_string(), 0.1);

        Self {
            fusion_weights: weights,
        }
    }

    pub async fn retrieve(
        &self,
        query: &str,
        context: &QueryContext,
        vector_index: &Arc<dyn VectorIndex>,
        _store: &Arc<dyn Store>,
    ) -> Result<Vec<SearchResult>> {
        let mut combined_results = Vec::new();

        // Keyword-based search
        let keyword_results = self.keyword_search(query, context).await?;
        combined_results.extend(keyword_results);

        // Additional semantic search with different parameters
        let additional_semantic = self.enhanced_semantic_search(query, vector_index).await?;
        combined_results.extend(additional_semantic);

        // Apply fusion scoring
        self.apply_fusion_scoring(&mut combined_results);

        Ok(combined_results)
    }

    async fn keyword_search(
        &self,
        query: &str,
        _context: &QueryContext,
    ) -> Result<Vec<SearchResult>> {
        // Simplified keyword search implementation
        let keywords: Vec<&str> = query.split_whitespace().collect();

        let results = keywords
            .iter()
            .enumerate()
            .map(|(i, &keyword)| {
                SearchResult::new(
                    RagDocument::new(
                        format!("keyword_result_{i}"),
                        format!("Document containing keyword: {keyword}"),
                        "keyword_search".to_string(),
                    ),
                    0.7 - (i as f64 * 0.1),
                )
                .add_relevance_factor(format!("keyword_match: {keyword}"))
            })
            .collect();

        Ok(results)
    }

    async fn enhanced_semantic_search(
        &self,
        query: &str,
        vector_index: &Arc<dyn VectorIndex>,
    ) -> Result<Vec<SearchResult>> {
        // Enhanced semantic search with query expansion
        let expanded_query = format!("{query} related context information");

        // Use semantic retriever logic (simplified)
        let semantic_retriever = SemanticRetriever::new();
        let dummy_context = QueryContext::new("hybrid_search".to_string());

        semantic_retriever
            .retrieve(&expanded_query, &dummy_context, vector_index)
            .await
    }

    fn apply_fusion_scoring(&self, results: &mut [SearchResult]) {
        for result in results.iter_mut() {
            let mut fused_score = 0.0;
            let mut total_weight = 0.0;

            // Apply weights based on retrieval method
            for factor in &result.relevance_factors {
                if factor.contains("semantic") {
                    fused_score +=
                        result.score * self.fusion_weights.get("semantic").unwrap_or(&0.0);
                    total_weight += self.fusion_weights.get("semantic").unwrap_or(&0.0);
                } else if factor.contains("keyword") {
                    fused_score +=
                        result.score * self.fusion_weights.get("keyword").unwrap_or(&0.0);
                    total_weight += self.fusion_weights.get("keyword").unwrap_or(&0.0);
                } else if factor.contains("graph") {
                    fused_score += result.score * self.fusion_weights.get("graph").unwrap_or(&0.0);
                    total_weight += self.fusion_weights.get("graph").unwrap_or(&0.0);
                }
            }

            if total_weight > 0.0 {
                result.score = fused_score / total_weight;
            }
        }

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

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

    #[test]
    fn test_semantic_retriever_creation() {
        let retriever = SemanticRetriever::new();
        assert!(retriever.embedding_cache.try_read().is_ok());
    }

    #[test]
    fn test_graph_retriever_sparql_generation() {
        let retriever = GraphRetriever::new();
        let context = QueryContext::new("test_session".to_string());
        let sparql = retriever.generate_sparql_query("test query", &context);
        assert!(sparql.is_ok());
        assert!(sparql.expect("should succeed").contains("SELECT"));
    }

    #[test]
    fn test_hybrid_retriever_weights() {
        let retriever = HybridRetriever::new();
        assert_eq!(retriever.fusion_weights.len(), 3);
        assert!(retriever.fusion_weights.contains_key("semantic"));
    }
}