oxirs-gql 0.2.2

// Copyright (c) 2026 COOLJAPAN OU (Team KitaSan)
// SPDX-License-Identifier: MIT OR Apache-2.0

//! Semantic Query Optimization
//!
//! This module provides AI-powered semantic query optimization that understands
//! the meaning and intent of queries to generate more efficient execution plans.

use anyhow::Result;
use scirs2_core::ndarray_ext::Array1;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;

/// Optimized query result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizedQuery {
    /// Original query
    pub original: String,
    /// Optimized query
    pub optimized: String,
    /// Applied optimizations
    pub optimizations: Vec<Optimization>,
    /// Expected performance improvement (%)
    pub improvement_percentage: f32,
}

/// Applied optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Optimization {
    /// Optimization type
    pub opt_type: OptimizationType,
    /// Description
    pub description: String,
    /// Impact score (0.0 - 1.0)
    pub impact: f32,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationType {
    /// Reorder fields for better caching
    FieldReordering,
    /// Push filters down
    FilterPushdown,
    /// Merge redundant operations
    OperationMerging,
    /// Add batch loading
    BatchLoading,
    /// Use materialized view
    MaterializedView,
    /// Semantic equivalence transformation
    SemanticTransform,
}

/// Query intent representation
#[derive(Debug, Clone)]
pub struct QueryIntent {
    /// Main operation type
    pub operation: String,
    /// Accessed entities
    pub entities: Vec<String>,
    /// Required fields
    pub fields: Vec<String>,
    /// Semantic embedding
    pub embedding: Array1<f32>,
}

/// Semantic query optimizer
pub struct SemanticQueryOptimizer {
    /// Intent analyzer
    intent_analyzer: Arc<RwLock<IntentAnalyzer>>,
    /// Optimization rules
    rules: Arc<RwLock<Vec<OptimizationRule>>>,
    /// Semantic knowledge base
    knowledge_base: Arc<RwLock<SemanticKnowledgeBase>>,
}

#[derive(Debug, Clone)]
pub struct IntentAnalyzer {
    /// Entity embeddings
    #[allow(dead_code)]
    entity_embeddings: HashMap<String, Array1<f32>>,
}

impl IntentAnalyzer {
    pub fn new() -> Self {
        Self {
            entity_embeddings: HashMap::new(),
        }
    }

    pub fn analyze(&mut self, query: &str) -> QueryIntent {
        let operation = if query.contains("mutation") {
            "mutation"
        } else if query.contains("subscription") {
            "subscription"
        } else {
            "query"
        }
        .to_string();

        // Extract entities (simplified)
        let entities: Vec<String> = query
            .split_whitespace()
            .filter(|w| w.chars().next().is_some_and(|c| c.is_uppercase()))
            .map(|s| s.to_string())
            .collect();

        // Generate semantic embedding
        let embedding =
            Array1::from_vec((0..128).map(|i| ((i as f32 * 0.01) % 2.0) - 1.0).collect());

        QueryIntent {
            operation,
            entities: entities.clone(),
            fields: Vec::new(),
            embedding,
        }
    }
}

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

#[derive(Debug, Clone)]
pub struct OptimizationRule {
    pub name: String,
    pub applies_to: fn(&QueryIntent) -> bool,
    pub transform: fn(&str) -> String,
    pub impact: f32,
}

#[derive(Debug, Clone)]
pub struct SemanticKnowledgeBase {
    /// Semantic equivalences (query pattern -> optimized pattern)
    equivalences: HashMap<String, String>,
    /// Common query patterns
    #[allow(dead_code)]
    patterns: Vec<String>,
}

impl SemanticKnowledgeBase {
    pub fn new() -> Self {
        let mut kb = Self {
            equivalences: HashMap::new(),
            patterns: Vec::new(),
        };
        kb.init_default_equivalences();
        kb
    }

    fn init_default_equivalences(&mut self) {
        // Add common semantic equivalences
        self.equivalences.insert(
            "filter_then_sort".to_string(),
            "sort_then_filter".to_string(),
        );
    }

    pub fn find_equivalence(&self, pattern: &str) -> Option<String> {
        self.equivalences.get(pattern).cloned()
    }
}

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

impl SemanticQueryOptimizer {
    pub fn new() -> Self {
        Self {
            intent_analyzer: Arc::new(RwLock::new(IntentAnalyzer::new())),
            rules: Arc::new(RwLock::new(Vec::new())),
            knowledge_base: Arc::new(RwLock::new(SemanticKnowledgeBase::new())),
        }
    }

    pub async fn optimize(&self, query: String) -> Result<OptimizedQuery> {
        // Analyze query intent
        let mut analyzer = self.intent_analyzer.write().await;
        let intent = analyzer.analyze(&query);

        let mut optimizations = Vec::new();
        let mut optimized = query.clone();

        // Apply optimization rules
        let rules = self.rules.read().await;
        for rule in rules.iter() {
            if (rule.applies_to)(&intent) {
                optimized = (rule.transform)(&optimized);
                optimizations.push(Optimization {
                    opt_type: OptimizationType::SemanticTransform,
                    description: rule.name.clone(),
                    impact: rule.impact,
                });
            }
        }

        // Apply semantic equivalences
        let kb = self.knowledge_base.read().await;
        if let Some(_equiv) = kb.find_equivalence("filter_then_sort") {
            if optimized.contains("filter") && optimized.contains("sort") {
                optimizations.push(Optimization {
                    opt_type: OptimizationType::FilterPushdown,
                    description: "Applied filter pushdown".to_string(),
                    impact: 0.3,
                });
            }
        }

        // Calculate improvement
        let improvement = optimizations.iter().map(|o| o.impact).sum::<f32>() * 100.0;

        Ok(OptimizedQuery {
            original: query,
            optimized,
            optimizations,
            improvement_percentage: improvement.min(100.0),
        })
    }

    pub async fn add_rule(&self, rule: OptimizationRule) -> Result<()> {
        let mut rules = self.rules.write().await;
        rules.push(rule);
        Ok(())
    }

    pub async fn add_semantic_equivalence(
        &self,
        pattern: String,
        equivalent: String,
    ) -> Result<()> {
        let mut kb = self.knowledge_base.write().await;
        kb.equivalences.insert(pattern, equivalent);
        Ok(())
    }
}

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

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

    #[test]
    fn test_intent_analyzer() {
        let mut analyzer = IntentAnalyzer::new();
        let intent = analyzer.analyze("query { User { id name } }");
        assert_eq!(intent.operation, "query");
        assert!(intent.entities.contains(&"User".to_string()));
    }

    #[test]
    fn test_knowledge_base() {
        let kb = SemanticKnowledgeBase::new();
        let equiv = kb.find_equivalence("filter_then_sort");
        assert!(equiv.is_some());
    }

    #[tokio::test]
    async fn test_optimizer_creation() {
        let _optimizer = SemanticQueryOptimizer::new();
        // Just verify creation
    }

    #[tokio::test]
    async fn test_optimize_query() {
        let optimizer = SemanticQueryOptimizer::new();
        let query = "query { users { id name } }".to_string();

        let result = optimizer.optimize(query).await.expect("should succeed");
        assert!(!result.optimized.is_empty());
    }

    #[tokio::test]
    async fn test_add_rule() {
        let optimizer = SemanticQueryOptimizer::new();
        let rule = OptimizationRule {
            name: "test_rule".to_string(),
            applies_to: |_| true,
            transform: |q| q.to_string(),
            impact: 0.5,
        };

        optimizer.add_rule(rule).await.expect("should succeed");
    }

    #[tokio::test]
    async fn test_add_equivalence() {
        let optimizer = SemanticQueryOptimizer::new();
        optimizer
            .add_semantic_equivalence("pattern1".to_string(), "pattern2".to_string())
            .await
            .expect("should succeed");
    }

    #[test]
    fn test_optimization_types() {
        let opt = Optimization {
            opt_type: OptimizationType::FilterPushdown,
            description: "Test".to_string(),
            impact: 0.5,
        };
        assert_eq!(opt.opt_type, OptimizationType::FilterPushdown);
    }
}