oxirs_arq/
vector_query_optimizer.rs

1//! Vector-Aware Query Optimizer
2//!
3//! This module provides integration between SPARQL query optimization and vector search
4//! capabilities from oxirs-vec. It enables intelligent query planning that can leverage
5//! vector indexes for semantic similarity queries and hybrid text/vector search.
6
7use std::collections::{HashMap, HashSet};
8use std::sync::{Arc, Mutex};
9use std::time::{Duration, Instant};
10
11use anyhow::{anyhow, Result};
12use tracing::{info, span, Level};
13
14use crate::algebra::{Algebra, Expression, Term, TriplePattern, Variable};
15use crate::integrated_query_planner::{
16    IntegratedExecutionPlan, IntegratedPlannerConfig, IntegratedQueryPlanner,
17};
18
19/// Vector search integration configuration
20#[derive(Debug, Clone)]
21pub struct VectorOptimizerConfig {
22    /// Enable vector similarity search optimization
23    pub enable_vector_optimization: bool,
24    /// Threshold for semantic similarity search (0.0 to 1.0)
25    pub similarity_threshold: f32,
26    /// Maximum number of vector candidates to consider
27    pub max_vector_candidates: usize,
28    /// Vector index cache size
29    pub vector_cache_size: usize,
30    /// Enable hybrid text-vector search
31    pub enable_hybrid_search: bool,
32    /// Vector embedding dimension
33    pub embedding_dimension: usize,
34    /// Distance metric for vector search
35    pub distance_metric: VectorDistanceMetric,
36    /// Vector index types to consider
37    pub preferred_index_types: Vec<VectorIndexType>,
38    /// Minimum query complexity to enable vector optimization
39    pub complexity_threshold: f64,
40}
41
42impl Default for VectorOptimizerConfig {
43    fn default() -> Self {
44        Self {
45            enable_vector_optimization: true,
46            similarity_threshold: 0.8,
47            max_vector_candidates: 1000,
48            vector_cache_size: 10_000,
49            enable_hybrid_search: true,
50            embedding_dimension: 768, // Common for transformer models
51            distance_metric: VectorDistanceMetric::Cosine,
52            preferred_index_types: vec![
53                VectorIndexType::Hnsw,
54                VectorIndexType::IvfFlat,
55                VectorIndexType::IvfPq,
56            ],
57            complexity_threshold: 10.0,
58        }
59    }
60}
61
62/// Supported vector distance metrics
63#[derive(Debug, Clone, Copy, PartialEq, Eq)]
64pub enum VectorDistanceMetric {
65    Cosine,
66    Euclidean,
67    DotProduct,
68    Manhattan,
69}
70
71/// Types of vector indexes available
72#[derive(Debug, Clone, Copy, PartialEq, Eq)]
73pub enum VectorIndexType {
74    Hnsw,
75    IvfFlat,
76    IvfPq,
77    FlatIndex,
78    Lsh,
79}
80
81/// Vector search strategy for SPARQL queries
82#[derive(Debug, Clone)]
83pub enum VectorSearchStrategy {
84    /// Pure vector similarity search
85    PureVector {
86        query_vector: Vec<f32>,
87        similarity_threshold: f32,
88        k: usize,
89    },
90    /// Hybrid search combining text and vector
91    Hybrid {
92        text_query: String,
93        query_vector: Option<Vec<f32>>,
94        text_weight: f32,
95        vector_weight: f32,
96    },
97    /// Vector-constrained SPARQL query
98    VectorConstrained {
99        sparql_patterns: Vec<TriplePattern>,
100        vector_filter: VectorFilter,
101    },
102    /// Semantic expansion using vectors
103    SemanticExpansion {
104        original_terms: Vec<Term>,
105        expansion_candidates: Vec<(Term, f32)>,
106        max_expansions: usize,
107    },
108}
109
110/// Vector-based filters for SPARQL queries
111#[derive(Debug, Clone)]
112pub struct VectorFilter {
113    pub subject_vector: Option<Vec<f32>>,
114    pub object_vector: Option<Vec<f32>>,
115    pub predicate_vector: Option<Vec<f32>>,
116    pub similarity_threshold: f32,
117    pub max_matches: usize,
118}
119
120/// Vector-aware query optimizer
121pub struct VectorQueryOptimizer {
122    config: VectorOptimizerConfig,
123    integrated_planner: IntegratedQueryPlanner,
124    vector_indexes: Arc<Mutex<HashMap<String, VectorIndexInfo>>>,
125    #[allow(dead_code)]
126    embedding_cache: Arc<Mutex<HashMap<String, Vec<f32>>>>,
127    #[allow(dead_code)]
128    #[allow(clippy::type_complexity)]
129    semantic_cache: Arc<Mutex<HashMap<String, Vec<(String, f32)>>>>,
130    #[allow(dead_code)]
131    query_patterns: Arc<Mutex<HashMap<u64, VectorSearchStrategy>>>,
132    performance_metrics: Arc<Mutex<VectorPerformanceMetrics>>,
133}
134
135/// Information about available vector indexes
136#[derive(Debug, Clone)]
137pub struct VectorIndexInfo {
138    pub index_type: VectorIndexType,
139    pub dimension: usize,
140    pub size: usize,
141    pub distance_metric: VectorDistanceMetric,
142    pub build_time: Duration,
143    pub last_updated: Instant,
144    pub accuracy_stats: IndexAccuracyStats,
145    pub performance_stats: IndexPerformanceStats,
146}
147
148/// Accuracy statistics for vector indexes
149#[derive(Debug, Clone, Default)]
150pub struct IndexAccuracyStats {
151    pub recall_at_k: HashMap<usize, f32>,
152    pub precision_at_k: HashMap<usize, f32>,
153    pub average_distance_error: f32,
154    pub query_count: usize,
155}
156
157/// Performance statistics for vector indexes
158#[derive(Debug, Clone, Default)]
159pub struct IndexPerformanceStats {
160    pub average_query_time: Duration,
161    pub queries_per_second: f32,
162    pub memory_usage: usize,
163    pub cache_hit_rate: f32,
164    pub index_efficiency: f32,
165}
166
167/// Vector optimization performance metrics
168#[derive(Debug, Clone, Default)]
169pub struct VectorPerformanceMetrics {
170    pub vector_queries_optimized: usize,
171    pub hybrid_queries_optimized: usize,
172    pub semantic_expansions_performed: usize,
173    pub average_optimization_speedup: f32,
174    pub vector_cache_hit_rate: f32,
175    pub embedding_generation_time: Duration,
176    pub total_optimization_time: Duration,
177}
178
179/// Vector-enhanced execution plan
180#[derive(Debug, Clone)]
181pub struct VectorEnhancedPlan {
182    /// Base integrated execution plan
183    pub base_plan: IntegratedExecutionPlan,
184    /// Vector search strategy to apply
185    pub vector_strategy: Option<VectorSearchStrategy>,
186    /// Recommended vector index to use
187    pub recommended_vector_index: Option<String>,
188    /// Expected vector search performance
189    pub vector_performance_estimate: VectorPerformanceEstimate,
190    /// Hybrid search configuration
191    pub hybrid_config: Option<HybridSearchConfig>,
192}
193
194/// Performance estimate for vector operations
195#[derive(Debug, Clone, Default)]
196pub struct VectorPerformanceEstimate {
197    pub estimated_query_time: Duration,
198    pub estimated_recall: f32,
199    pub estimated_memory_usage: usize,
200    pub confidence: f32,
201}
202
203/// Configuration for hybrid search
204#[derive(Debug, Clone)]
205pub struct HybridSearchConfig {
206    pub text_weight: f32,
207    pub vector_weight: f32,
208    pub reranking_k: usize,
209    pub fusion_method: ResultFusionMethod,
210}
211
212/// Methods for fusing text and vector search results
213#[derive(Debug, Clone, Copy)]
214pub enum ResultFusionMethod {
215    LinearCombination,
216    RankFusion,
217    BayesianFusion,
218    LearningToRank,
219}
220
221impl VectorQueryOptimizer {
222    /// Create a new vector-aware query optimizer
223    pub fn new(
224        vector_config: VectorOptimizerConfig,
225        planner_config: IntegratedPlannerConfig,
226    ) -> Result<Self> {
227        let integrated_planner = IntegratedQueryPlanner::new(planner_config)?;
228
229        Ok(Self {
230            config: vector_config,
231            integrated_planner,
232            vector_indexes: Arc::new(Mutex::new(HashMap::new())),
233            embedding_cache: Arc::new(Mutex::new(HashMap::new())),
234            semantic_cache: Arc::new(Mutex::new(HashMap::new())),
235            query_patterns: Arc::new(Mutex::new(HashMap::new())),
236            performance_metrics: Arc::new(Mutex::new(VectorPerformanceMetrics::default())),
237        })
238    }
239
240    /// Register a vector index for use in query optimization
241    pub fn register_vector_index(&self, name: String, index_info: VectorIndexInfo) -> Result<()> {
242        let mut indexes = self.vector_indexes.lock().expect("lock poisoned");
243        let size = index_info.size;
244        indexes.insert(name.clone(), index_info);
245
246        info!("Registered vector index: {} with {} vectors", name, size);
247        Ok(())
248    }
249
250    /// Create an optimized execution plan with vector awareness
251    pub fn create_vector_enhanced_plan(&mut self, algebra: &Algebra) -> Result<VectorEnhancedPlan> {
252        let span = span!(Level::DEBUG, "vector_enhanced_planning");
253        let _enter = span.enter();
254
255        // First get the base plan from integrated planner
256        let base_plan = self.integrated_planner.create_plan(algebra)?;
257
258        // Analyze the query for vector optimization opportunities
259        let vector_opportunities = self.analyze_vector_opportunities(algebra)?;
260
261        if vector_opportunities.is_empty() {
262            // No vector optimization opportunities
263            return Ok(VectorEnhancedPlan {
264                base_plan,
265                vector_strategy: None,
266                recommended_vector_index: None,
267                vector_performance_estimate: VectorPerformanceEstimate::default(),
268                hybrid_config: None,
269            });
270        }
271
272        // Select the best vector optimization strategy
273        let vector_strategy = self.select_vector_strategy(&vector_opportunities, algebra)?;
274
275        // Choose the optimal vector index
276        let recommended_vector_index = self.select_vector_index(&vector_strategy)?;
277
278        // Estimate vector performance
279        let vector_performance_estimate =
280            self.estimate_vector_performance(&vector_strategy, &recommended_vector_index)?;
281
282        // Configure hybrid search if applicable
283        let hybrid_config = self.configure_hybrid_search(&vector_strategy)?;
284
285        // Update performance metrics
286        self.update_optimization_metrics(&vector_strategy);
287
288        Ok(VectorEnhancedPlan {
289            base_plan,
290            vector_strategy: Some(vector_strategy),
291            recommended_vector_index,
292            vector_performance_estimate,
293            hybrid_config,
294        })
295    }
296
297    /// Analyze SPARQL algebra for vector optimization opportunities
298    fn analyze_vector_opportunities(&self, algebra: &Algebra) -> Result<Vec<VectorOpportunity>> {
299        let mut opportunities = Vec::new();
300
301        match algebra {
302            Algebra::Bgp(patterns) => {
303                opportunities.extend(self.analyze_bgp_patterns(patterns)?);
304            }
305            Algebra::Filter { pattern, condition } => {
306                // Check if filter condition involves semantic similarity
307                if self.is_semantic_filter(condition) {
308                    opportunities.push(VectorOpportunity::SemanticFilter {
309                        condition: condition.clone(),
310                        estimated_selectivity: 0.1, // Conservative estimate
311                    });
312                }
313                opportunities.extend(self.analyze_vector_opportunities(pattern)?);
314            }
315            Algebra::Join { left, right } => {
316                opportunities.extend(self.analyze_vector_opportunities(left)?);
317                opportunities.extend(self.analyze_vector_opportunities(right)?);
318
319                // Check for join optimization opportunities
320                if let Some(join_opportunity) = self.analyze_join_opportunity(left, right)? {
321                    opportunities.push(join_opportunity);
322                }
323            }
324            Algebra::Union { left, right } => {
325                opportunities.extend(self.analyze_vector_opportunities(left)?);
326                opportunities.extend(self.analyze_vector_opportunities(right)?);
327            }
328            Algebra::LeftJoin {
329                left,
330                right,
331                filter: _,
332            } => {
333                opportunities.extend(self.analyze_vector_opportunities(left)?);
334                opportunities.extend(self.analyze_vector_opportunities(right)?);
335            }
336            _ => {
337                // Recursively analyze sub-patterns
338                if let Some(subpattern) = self.extract_subpattern(algebra) {
339                    opportunities.extend(self.analyze_vector_opportunities(&subpattern)?);
340                }
341            }
342        }
343
344        Ok(opportunities)
345    }
346
347    /// Analyze BGP patterns for vector opportunities
348    fn analyze_bgp_patterns(&self, patterns: &[TriplePattern]) -> Result<Vec<VectorOpportunity>> {
349        let mut opportunities = Vec::new();
350
351        for pattern in patterns {
352            // Check for text matching patterns that could benefit from semantic search
353            if self.is_text_matching_pattern(pattern) {
354                opportunities.push(VectorOpportunity::TextSimilarity {
355                    pattern: pattern.clone(),
356                    estimated_matches: 100, // Conservative estimate
357                });
358            }
359
360            // Check for entity similarity patterns
361            if self.is_entity_similarity_pattern(pattern) {
362                opportunities.push(VectorOpportunity::EntitySimilarity {
363                    pattern: pattern.clone(),
364                    similarity_type: EntitySimilarityType::Conceptual,
365                });
366            }
367
368            // Check for property path patterns that could benefit from vector expansion
369            if self.is_expandable_property_pattern(pattern) {
370                opportunities.push(VectorOpportunity::PropertyExpansion {
371                    pattern: pattern.clone(),
372                    expansion_depth: 2,
373                });
374            }
375        }
376
377        Ok(opportunities)
378    }
379
380    /// Check if a pattern involves text matching
381    fn is_text_matching_pattern(&self, pattern: &TriplePattern) -> bool {
382        // Look for patterns with literal objects that might be text
383        match &pattern.object {
384            Term::Literal(literal) => {
385                // Check if literal contains text that could benefit from semantic search
386                literal.value.len() > 5 && literal.value.chars().any(|c| c.is_alphabetic())
387            }
388            _ => false,
389        }
390    }
391
392    /// Check if a pattern involves entity similarity
393    fn is_entity_similarity_pattern(&self, pattern: &TriplePattern) -> bool {
394        // Look for patterns that query for related entities
395        match &pattern.predicate {
396            Term::Iri(iri) => {
397                // Common predicates that indicate entity relationships
398                iri.as_str().contains("similar")
399                    || iri.as_str().contains("related")
400                    || iri.as_str().contains("type")
401                    || iri.as_str().contains("category")
402            }
403            _ => false,
404        }
405    }
406
407    /// Check if a pattern could benefit from property expansion
408    fn is_expandable_property_pattern(&self, pattern: &TriplePattern) -> bool {
409        // Look for patterns with specific predicates that could be semantically expanded
410        match &pattern.predicate {
411            Term::Variable(_) => true, // Variable predicates can often be expanded
412            Term::Iri(iri) => {
413                // Common expandable predicates
414                let expandable_predicates = [
415                    "type",
416                    "category",
417                    "topic",
418                    "subject",
419                    "theme",
420                    "describes",
421                    "about",
422                    "concerns",
423                    "deals_with",
424                ];
425
426                expandable_predicates
427                    .iter()
428                    .any(|pred| iri.as_str().contains(pred))
429            }
430            _ => false,
431        }
432    }
433
434    /// Check if an expression is a semantic filter
435    fn is_semantic_filter(&self, expression: &Expression) -> bool {
436        // Look for filter expressions that involve text similarity functions
437        match expression {
438            Expression::Function { name, .. } => {
439                name.as_str().contains("similarity")
440                    || name.as_str().contains("match")
441                    || name.as_str().contains("distance")
442                    || name.as_str().contains("semantic")
443            }
444            _ => false,
445        }
446    }
447
448    /// Analyze join opportunities for vector optimization
449    fn analyze_join_opportunity(
450        &self,
451        left: &Algebra,
452        right: &Algebra,
453    ) -> Result<Option<VectorOpportunity>> {
454        // Check if the join involves patterns that could benefit from vector-based join optimization
455        let left_vars = self.extract_variables(left);
456        let right_vars = self.extract_variables(right);
457        let shared_vars: Vec<_> = left_vars.intersection(&right_vars).collect();
458
459        if !shared_vars.is_empty() {
460            // Check if any shared variables represent entities that could benefit from vector similarity
461            for var in shared_vars {
462                if self.is_vector_suitable_variable(var, left)
463                    || self.is_vector_suitable_variable(var, right)
464                {
465                    return Ok(Some(VectorOpportunity::VectorJoin {
466                        left_pattern: Box::new(left.clone()),
467                        right_pattern: Box::new(right.clone()),
468                        join_variable: var.clone(),
469                        estimated_selectivity: 0.2,
470                    }));
471                }
472            }
473        }
474
475        Ok(None)
476    }
477
478    /// Extract variables from algebra expression
479    fn extract_variables(&self, algebra: &Algebra) -> HashSet<Variable> {
480        let mut vars = HashSet::new();
481
482        match algebra {
483            Algebra::Bgp(patterns) => {
484                for pattern in patterns {
485                    if let Term::Variable(var) = &pattern.subject {
486                        vars.insert(var.clone());
487                    }
488                    if let Term::Variable(var) = &pattern.predicate {
489                        vars.insert(var.clone());
490                    }
491                    if let Term::Variable(var) = &pattern.object {
492                        vars.insert(var.clone());
493                    }
494                }
495            }
496            _ => {
497                // Recursively extract from subpatterns
498                // Implementation would continue for other algebra types
499            }
500        }
501
502        vars
503    }
504
505    /// Check if a variable is suitable for vector operations
506    fn is_vector_suitable_variable(&self, _var: &Variable, _context: &Algebra) -> bool {
507        // Heuristics to determine if a variable represents entities suitable for vector similarity
508        // This could be based on type information, predicates used, etc.
509        true // Simplified for now
510    }
511
512    /// Extract subpattern from algebra for recursive analysis
513    fn extract_subpattern(&self, algebra: &Algebra) -> Option<Algebra> {
514        match algebra {
515            Algebra::Project { pattern, .. } => Some((**pattern).clone()),
516            Algebra::Distinct { pattern } => Some((**pattern).clone()),
517            Algebra::Reduced { pattern } => Some((**pattern).clone()),
518            Algebra::OrderBy { pattern, .. } => Some((**pattern).clone()),
519            Algebra::Slice { pattern, .. } => Some((**pattern).clone()),
520            Algebra::Group { pattern, .. } => Some((**pattern).clone()),
521            Algebra::Having { pattern, .. } => Some((**pattern).clone()),
522            _ => None,
523        }
524    }
525
526    /// Select the best vector search strategy
527    fn select_vector_strategy(
528        &self,
529        opportunities: &[VectorOpportunity],
530        _algebra: &Algebra,
531    ) -> Result<VectorSearchStrategy> {
532        if opportunities.is_empty() {
533            return Err(anyhow!("No vector opportunities available"));
534        }
535
536        // Simple strategy selection - could be enhanced with ML
537        let primary_opportunity = &opportunities[0];
538
539        match primary_opportunity {
540            VectorOpportunity::TextSimilarity { pattern, .. } => {
541                Ok(VectorSearchStrategy::Hybrid {
542                    text_query: self.extract_text_from_pattern(pattern)?,
543                    query_vector: None, // Will be generated during execution
544                    text_weight: 0.6,
545                    vector_weight: 0.4,
546                })
547            }
548            VectorOpportunity::EntitySimilarity { pattern, .. } => {
549                Ok(VectorSearchStrategy::SemanticExpansion {
550                    original_terms: vec![pattern.subject.clone()],
551                    expansion_candidates: Vec::new(), // Will be populated during execution
552                    max_expansions: 10,
553                })
554            }
555            VectorOpportunity::VectorJoin { .. } => {
556                Ok(VectorSearchStrategy::VectorConstrained {
557                    sparql_patterns: vec![], // Will be populated based on join patterns
558                    vector_filter: VectorFilter {
559                        subject_vector: None,
560                        object_vector: None,
561                        predicate_vector: None,
562                        similarity_threshold: self.config.similarity_threshold,
563                        max_matches: self.config.max_vector_candidates,
564                    },
565                })
566            }
567            _ => {
568                Ok(VectorSearchStrategy::PureVector {
569                    query_vector: Vec::new(), // Will be generated during execution
570                    similarity_threshold: self.config.similarity_threshold,
571                    k: 100,
572                })
573            }
574        }
575    }
576
577    /// Extract text content from a triple pattern
578    fn extract_text_from_pattern(&self, pattern: &TriplePattern) -> Result<String> {
579        match &pattern.object {
580            Term::Literal(literal) => Ok(literal.value.clone()),
581            Term::Iri(iri) => {
582                // Extract local name from IRI
583                let iri_str = iri.as_str();
584                if let Some(fragment) = iri_str.split('#').next_back() {
585                    Ok(fragment.to_string())
586                } else if let Some(local) = iri_str.split('/').next_back() {
587                    Ok(local.to_string())
588                } else {
589                    Ok(iri_str.to_string())
590                }
591            }
592            _ => Err(anyhow!("Cannot extract text from pattern")),
593        }
594    }
595
596    /// Select the optimal vector index for the strategy
597    fn select_vector_index(&self, strategy: &VectorSearchStrategy) -> Result<Option<String>> {
598        let indexes = self.vector_indexes.lock().expect("lock poisoned");
599
600        if indexes.is_empty() {
601            return Ok(None);
602        }
603
604        // Select index based on strategy requirements and performance characteristics
605        let mut best_index = None;
606        let mut best_score = 0.0f32;
607
608        for (name, info) in indexes.iter() {
609            let score = self.calculate_index_score(info, strategy);
610            if score > best_score {
611                best_score = score;
612                best_index = Some(name.clone());
613            }
614        }
615
616        Ok(best_index)
617    }
618
619    /// Calculate suitability score for an index given a strategy
620    fn calculate_index_score(
621        &self,
622        info: &VectorIndexInfo,
623        strategy: &VectorSearchStrategy,
624    ) -> f32 {
625        let mut score = 0.0f32;
626
627        // Base score from index type preferences
628        let type_bonus = match info.index_type {
629            VectorIndexType::Hnsw => 1.0,
630            VectorIndexType::IvfPq => 0.8,
631            VectorIndexType::IvfFlat => 0.7,
632            VectorIndexType::FlatIndex => 0.5,
633            VectorIndexType::Lsh => 0.6,
634        };
635        score += type_bonus;
636
637        // Performance-based scoring
638        score += info.performance_stats.queries_per_second / 1000.0; // Normalize QPS
639        score += info.performance_stats.cache_hit_rate;
640        score += info.performance_stats.index_efficiency;
641
642        // Accuracy-based scoring
643        if let Some(recall_10) = info.accuracy_stats.recall_at_k.get(&10) {
644            score += recall_10;
645        }
646
647        // Strategy-specific adjustments
648        match strategy {
649            VectorSearchStrategy::PureVector { k, .. }
650                // Prefer indexes optimized for k-NN search
651                if *k <= 100 && matches!(info.index_type, VectorIndexType::Hnsw) => {
652                    score += 0.2;
653                }
654            VectorSearchStrategy::Hybrid { .. } => {
655                // Prefer indexes with good recall for hybrid search
656                score += 0.1;
657            }
658            _ => {}
659        }
660
661        score
662    }
663
664    /// Estimate vector search performance
665    fn estimate_vector_performance(
666        &self,
667        strategy: &VectorSearchStrategy,
668        index_name: &Option<String>,
669    ) -> Result<VectorPerformanceEstimate> {
670        let mut estimate = VectorPerformanceEstimate::default();
671
672        if let Some(name) = index_name {
673            let indexes = self.vector_indexes.lock().expect("lock poisoned");
674            if let Some(info) = indexes.get(name) {
675                estimate.estimated_query_time = info.performance_stats.average_query_time;
676                estimate.estimated_memory_usage = info.performance_stats.memory_usage;
677
678                // Estimate recall based on strategy
679                estimate.estimated_recall = match strategy {
680                    VectorSearchStrategy::PureVector { .. } => {
681                        *info.accuracy_stats.recall_at_k.get(&10).unwrap_or(&0.9)
682                    }
683                    VectorSearchStrategy::Hybrid { .. } => {
684                        // Hybrid search typically has higher effective recall
685                        info.accuracy_stats.recall_at_k.get(&10).unwrap_or(&0.9) * 1.1
686                    }
687                    _ => 0.8, // Conservative estimate
688                };
689
690                estimate.confidence = 0.8; // Base confidence
691            }
692        } else {
693            // No vector index available - provide conservative estimates
694            estimate.estimated_query_time = Duration::from_millis(100);
695            estimate.estimated_recall = 0.7;
696            estimate.estimated_memory_usage = 1024 * 1024; // 1MB
697            estimate.confidence = 0.5;
698        }
699
700        Ok(estimate)
701    }
702
703    /// Configure hybrid search parameters
704    fn configure_hybrid_search(
705        &self,
706        strategy: &VectorSearchStrategy,
707    ) -> Result<Option<HybridSearchConfig>> {
708        match strategy {
709            VectorSearchStrategy::Hybrid {
710                text_weight,
711                vector_weight,
712                ..
713            } => Ok(Some(HybridSearchConfig {
714                text_weight: *text_weight,
715                vector_weight: *vector_weight,
716                reranking_k: 100,
717                fusion_method: ResultFusionMethod::LinearCombination,
718            })),
719            _ => Ok(None),
720        }
721    }
722
723    /// Update optimization performance metrics
724    fn update_optimization_metrics(&self, strategy: &VectorSearchStrategy) {
725        let mut metrics = self.performance_metrics.lock().expect("lock poisoned");
726
727        match strategy {
728            VectorSearchStrategy::PureVector { .. } => {
729                metrics.vector_queries_optimized += 1;
730            }
731            VectorSearchStrategy::Hybrid { .. } => {
732                metrics.hybrid_queries_optimized += 1;
733            }
734            VectorSearchStrategy::SemanticExpansion { .. } => {
735                metrics.semantic_expansions_performed += 1;
736            }
737            _ => {}
738        }
739    }
740
741    /// Get current performance metrics
742    pub fn get_performance_metrics(&self) -> VectorPerformanceMetrics {
743        self.performance_metrics
744            .lock()
745            .expect("lock poisoned")
746            .clone()
747    }
748
749    /// Update execution feedback for vector operations
750    pub fn update_vector_execution_feedback(
751        &mut self,
752        _strategy_hash: u64,
753        actual_duration: Duration,
754        _actual_recall: f32,
755        _actual_memory: usize,
756        success: bool,
757    ) -> Result<()> {
758        // Update performance metrics and adaptive thresholds based on execution feedback
759        let mut metrics = self.performance_metrics.lock().expect("lock poisoned");
760
761        if success {
762            // Update average speedup calculation
763            let base_time = Duration::from_millis(500); // Estimated base query time
764            let speedup = base_time.as_millis() as f32 / actual_duration.as_millis() as f32;
765
766            let total_optimizations =
767                metrics.vector_queries_optimized + metrics.hybrid_queries_optimized;
768
769            if total_optimizations > 0 {
770                metrics.average_optimization_speedup = (metrics.average_optimization_speedup
771                    * (total_optimizations - 1) as f32
772                    + speedup)
773                    / total_optimizations as f32;
774            }
775        }
776
777        Ok(())
778    }
779}
780
781/// Vector optimization opportunities discovered in SPARQL queries
782#[derive(Debug, Clone)]
783pub enum VectorOpportunity {
784    /// Text similarity search opportunity
785    TextSimilarity {
786        pattern: TriplePattern,
787        estimated_matches: usize,
788    },
789    /// Entity similarity search opportunity
790    EntitySimilarity {
791        pattern: TriplePattern,
792        similarity_type: EntitySimilarityType,
793    },
794    /// Property expansion opportunity
795    PropertyExpansion {
796        pattern: TriplePattern,
797        expansion_depth: usize,
798    },
799    /// Semantic filter opportunity
800    SemanticFilter {
801        condition: Expression,
802        estimated_selectivity: f32,
803    },
804    /// Vector-based join opportunity
805    VectorJoin {
806        left_pattern: Box<Algebra>,
807        right_pattern: Box<Algebra>,
808        join_variable: Variable,
809        estimated_selectivity: f32,
810    },
811}
812
813/// Types of entity similarity
814#[derive(Debug, Clone, Copy)]
815pub enum EntitySimilarityType {
816    Conceptual,
817    Taxonomic,
818    Relational,
819    Contextual,
820}
821
822/// Index recommendation for vector search
823#[derive(Debug, Clone)]
824pub struct VectorIndexRecommendation {
825    pub recommended_type: VectorIndexType,
826    pub estimated_benefit: f32,
827    pub creation_cost_estimate: Duration,
828    pub memory_requirement: usize,
829    pub maintenance_overhead: f32,
830}
oxirs_arq/vector_query_optimizer.rs

oxirs_arq/
vector_query_optimizer.rs
