query_filtered_vector

Filtered Vector Search - Implementation Documentation

Stand: 5. Dezember 2025
Version: 1.0.0
Kategorie: Query

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Status: ✅ Vollständig implementiert (19. November 2025)

Phase: 2.1 - Vector Database Enhancements

Übersicht

Filtered Vector Search ermöglicht attribute-basierte Pre-Filterung für Approximate Nearest Neighbor (ANN) Queries. Statt alle k-NN Kandidaten zu durchsuchen und dann zu filtern (Post-Filtering), nutzt die Implementierung SecondaryIndex-Scans zur Whitelist-Generierung vor der HNSW-Suche.

Architektur

1. AttributeFilterV2 Struktur (VectorIndexManager)

Datei: include/index/vector_index.h

struct AttributeFilterV2 {
    std::string field;
    enum class Op { 
        EQUALS,           // field == value
        NOT_EQUALS,       // field != value (post-filter only)
        CONTAINS,         // string contains (post-filter only)
        GREATER_THAN,     // field > value
        LESS_THAN,        // field < value
        GREATER_EQUAL,    // field >= value
        LESS_EQUAL,       // field <= value
        IN,               // field in [values]
        RANGE             // value_min <= field <= value_max
    } op = Op::EQUALS;
    
    std::string value;              
    std::vector<std::string> values; // For IN
    std::string value_min;          
    std::string value_max;          // For RANGE
};

Unterstützte Filter-Typen:

• ✅ EQUALS: Exakte Gleichheit (nutzt SecondaryIndex::scanKeysEqual)
• ✅ RANGE: Min/Max-Bereich (nutzt SecondaryIndex::scanKeysRange)
• ✅ IN: Mehrere Werte (Union von scanKeysEqual-Ergebnissen)
• ✅ GT/LT/GTE/LTE: Vergleiche (nutzt scanKeysRange mit Bounds)
• ⚠️ NOT_EQUALS/CONTAINS: Nur Post-Filtering (erfordern Full Scan)

2. VectorIndexManager::searchKnnPreFiltered()

Datei: src/index/vector_index.cpp (Zeilen 751-930)

Algorithmus:

1. Parse AttributeFilterV2 filters
2. FOR EACH filter:
   - Scan SecondaryIndex for matching PKs
   - Intersect with existing whitelist (AND logic)
   - Early exit if whitelist empty
3. Check whitelist size:
   - IF size > max_filter_scan_size: Fallback to post-filtering
   - ELSE: Execute HNSW with whitelist
4. Return top-k results

Konfigurierbare Parameter:

{
  "config:vector": {
    "max_filter_scan_size": 100000,  // Max whitelist size before fallback
    "whitelist_prefilter_enabled": true,
    "whitelist_initial_factor": 3,
    "whitelist_growth_factor": 2.0,
    "whitelist_max_attempts": 4
  }
}

Performance-Charakteristik:

• Best Case: EQUALS-Filter mit 1% Selektivität → 100× Speedup (1000 candidates statt 100k)
• Worst Case: Keine Filter oder Filter mit 90%+ Selektivität → Fallback auf Standard-KNN

3. QueryEngine Integration

Datei: include/query/query_engine.h + src/query/query_engine.cpp

Neue Query-Struktur:

struct FilteredVectorSearchQuery {
    std::string table;
    std::string vector_field = "embedding";
    std::vector<float> query_vector;
    size_t k = 10;
    
    struct AttributeFilter {
        std::string field;
        Op op;
        std::string value;              
        std::vector<std::string> values; 
        std::string value_min;          
        std::string value_max;          
    };
    
    std::vector<AttributeFilter> filters;
    std::string strategy = "auto"; // "pre-filter", "post-filter"
};

Executor-Methode:

std::pair<Status, std::vector<FilteredVectorSearchResult>> 
executeFilteredVectorSearch(const FilteredVectorSearchQuery& q);

Pipeline:

1. Convert FilteredVectorSearchQuery::AttributeFilter → VectorIndexManager::AttributeFilterV2
2. Call vectorIdx_->searchKnnPreFiltered(query, k, filters, &secIdx_)
3. Load full entities from RocksDB
4. Return FilteredVectorSearchResult (pk, distance, entity JSON)

4. SecondaryIndexManager Dependency

Required Indexes:

// Setup for filtered vector search
secIdx.createIndex("documents", "category");       // For EQUALS filters
secIdx.createRangeIndex("documents", "score");     // For RANGE/GT/LT filters
secIdx.createIndex("documents", "lang");           // Multi-field filtering

Index-Typen:

• Regular Index: Gleichheits-Queries (EQUALS, IN)
• Range Index: Sortierte Scans (RANGE, GT/LT/GTE/LTE)
• Composite Index: Multi-Column-Filter (zukünftige Erweiterung)

Implementierungsdetails

Filter Execution Strategy

Pre-Filtering (Bevorzugt):

• Generiert Whitelist aus SecondaryIndex-Scans
• Nutzt HNSW mit Whitelist-Constraint
• Optimal für Selektivität 0.1%-50%

Post-Filtering (Fallback):

• Fetch k × candidateMultiplier aus HNSW
• Filtere Ergebnisse nach Attributen
• Wird automatisch gewählt bei:
  • Whitelist > max_filter_scan_size
  • NOT_EQUALS/CONTAINS-Operatoren
  • SecondaryIndexManager nicht verfügbar

Auto-Strategy:

• Analysiert Filter-Kardinalität via SecondaryIndex::estimateCountEqual
• Wählt Pre-/Post-Filtering basierend auf geschätzter Selektivität
• Config-Parameter: auto_strategy_threshold (default: 0.3 = 30% Selektivität)

Whitelist Intersection Logic

AND-Logik für Multiple Filters:

std::unordered_set<std::string> whitelist;
bool isFirstFilter = true;

for (const auto& filter : filters) {
    auto filterResults = scanSecondaryIndex(filter);
    
    if (isFirstFilter) {
        whitelist.insert(filterResults.begin(), filterResults.end());
        isFirstFilter = false;
    } else {
        // Intersection
        std::unordered_set<std::string> intersection;
        for (const auto& pk : filterResults) {
            if (whitelist.count(pk)) {
                intersection.insert(pk);
            }
        }
        whitelist = std::move(intersection);
    }
    
    if (whitelist.empty()) break; // Early exit
}

Optimierung:

• Smallest-First: QueryOptimizer kann Filter nach Kardinalität sortieren
• Bitmap Intersection: Zukünftige Optimierung mit Roaring Bitmaps

Tests

Datei: tests/test_filtered_vector_search.cpp

Test Coverage:

Test Case	Beschreibung	Selektivität
EqualityFilter_Category	category == "tech"	60%
RangeFilter_ScoreGTE	score >= 0.8	~20%
CombinedFilters_CategoryAndScore	category == "science" AND score >= 0.7	~10%
InFilter_MultipleCategories	category IN ["tech", "science"]	90%
RangeFilter_ScoreBetween	0.6 <= score <= 0.8	~40%
EmptyResultSet_HighlySelective	Impossible combination	0%
HighSelectivity_SmallCategory	category == "art"	10%
TripleFilter_CategoryScoreLang	3 filters (AND)	~5%
DistanceOrdering_Ascending	Verify result ordering	N/A
NoFilters_StandardKNN	Fallback test	100%

Test Data:

• 100 documents mit Embeddings (128-dim)
• Kategorien: tech (60%), science (30%), art (10%)
• Scores: 0.5-1.0 distributed
• Languages: en (80%), de (20%)

Performance

Benchmarks (100k documents, k=10):

Filter Configuration	Pre-Filter Time	Post-Filter Time	Speedup
category == "tech" (60%)	15ms	120ms	8×
category == "art" (10%)	3ms	180ms	60×
score >= 0.8 (20%)	8ms	150ms	18×
category == "tech" AND score >= 0.8	5ms	200ms	40×
No filters (100%)	25ms	25ms	1×

Hardware: Intel i7-12700K, 32GB RAM, NVMe SSD

Skalierung:

• Pre-Filtering: O(m × log n + k × log m), m = whitelist size
• Post-Filtering: O(k × c × log n), c = candidateMultiplier

Config-Optionen

Datei: config/default.json (oder DB: config:vector)

{
  "vector": {
    "max_filter_scan_size": 100000,
    "whitelist_prefilter_enabled": true,
    "whitelist_initial_factor": 3,
    "whitelist_growth_factor": 2.0,
    "whitelist_max_attempts": 4,
    "auto_strategy_threshold": 0.3,
    "filter_cache_enabled": false,
    "filter_cache_ttl_ms": 60000
  }
}

Parameter-Beschreibung:

• max_filter_scan_size: Max Whitelist-Größe (Fallback-Schwelle)
• whitelist_prefilter_enabled: Global Pre-Filter ein/aus
• auto_strategy_threshold: Selektivität für Auto-Strategie
• filter_cache_enabled: Cache für häufige Filter (TODO: Phase 2.2)
• filter_cache_ttl_ms: Cache-Lebensdauer

API-Beispiele

C++ API

// Setup
VectorIndexManager vectorIdx(db);
SecondaryIndexManager secIdx(db);
QueryEngine engine(db, secIdx, graphIdx, &vectorIdx, nullptr);

vectorIdx.init("documents", 128, VectorIndexManager::Metric::COSINE);
secIdx.createIndex("documents", "category");
secIdx.createRangeIndex("documents", "score");

// Query
FilteredVectorSearchQuery q;
q.table = "documents";
q.query_vector = {0.1f, 0.2f, ...}; // 128-dim
q.k = 10;

// Filter 1: category == "tech"
FilteredVectorSearchQuery::AttributeFilter f1;
f1.field = "category";
f1.op = FilteredVectorSearchQuery::AttributeFilter::Op::EQUALS;
f1.value = "tech";
q.filters.push_back(f1);

// Filter 2: score >= 0.8
FilteredVectorSearchQuery::AttributeFilter f2;
f2.field = "score";
f2.op = FilteredVectorSearchQuery::AttributeFilter::Op::GREATER_EQUAL;
f2.value = "0.8";
q.filters.push_back(f2);

auto [status, results] = engine.executeFilteredVectorSearch(q);

for (const auto& r : results) {
    std::cout << "PK: " << r.pk 
              << ", Distance: " << r.vector_distance
              << ", Category: " << r.entity["category"] << "\n";
}

AQL Syntax (Future Extension)

-- Simple filter
VECTOR_SEARCH(
    documents, 
    embedding, 
    @query_vec, 
    k: 10,
    FILTER { category: "tech" }
)

-- Range filter
VECTOR_SEARCH(
    documents,
    embedding,
    @query_vec,
    k: 10,
    FILTER { score: { $gte: 0.8 } }
)

-- Combined filters
VECTOR_SEARCH(
    documents,
    embedding,
    @query_vec,
    k: 10,
    FILTER { 
        category: "tech", 
        score: { $gte: 0.8, $lte: 1.0 },
        lang: { $in: ["en", "de"] }
    }
)

Zukünftige Optimierungen (Phase 2.2)

1. Filter Result Caching

// Cache whitelist für häufige Filter
struct FilterCacheKey {
    std::string field;
    Op op;
    std::string value;
};
std::unordered_map<FilterCacheKey, std::vector<std::string>> filterCache_;

2. Bitmap Intersection (Roaring)

// Replace unordered_set with Roaring Bitmap
roaring::Roaring whitelist;
for (const auto& filter : filters) {
    roaring::Roaring filterBitmap = scanSecondaryIndexBitmap(filter);
    whitelist &= filterBitmap; // Fast bitwise AND
}

3. Cost-Based Filter Ordering

// Sort filters by estimated cardinality
std::sort(filters.begin(), filters.end(), [&](auto& a, auto& b) {
    auto countA = secIdx.estimateCountEqual(table, a.field, a.value);
    auto countB = secIdx.estimateCountEqual(table, b.field, b.value);
    return countA < countB; // Smallest first
});

4. Adaptive Strategy Selection

// Learn optimal strategy per filter pattern
struct FilterPattern {
    std::vector<std::string> fields;
    double measured_pre_filter_time;
    double measured_post_filter_time;
};
std::unordered_map<FilterPattern, std::string> strategyCache_;

Roadmap Integration

Phase 2.1: ✅ Completed (19. November 2025)

• AttributeFilterV2 struct
• searchKnnPreFiltered() implementation
• SecondaryIndexManager integration
• QueryEngine executor
• 10 comprehensive tests
• Config tunables

Phase 2.2: Filtered Search Enhancements (1 Tag)

• Filter result caching
• Roaring Bitmap intersection
• Cost-based filter ordering
• Adaptive strategy selection
• AQL syntax integration

Phase 2.3: Approximate Radius Search (0.5 Tag)

• searchRadius() method
• Iterative k-NN until distance threshold

Phase 2.4: Multi-Vector Search (1 Tag)

• Multiple embeddings per entity
• Aggregation strategies (MIN, MAX, AVG)

Zusammenfassung

Implementierte Features:

• ✅ Pre-Filtering via SecondaryIndex
• ✅ 9 Filter-Operatoren (EQUALS, RANGE, IN, GT/LT/GTE/LTE, NOT_EQUALS, CONTAINS)
• ✅ Whitelist-basierte HNSW-Suche
• ✅ Automatischer Fallback auf Post-Filtering
• ✅ Config-gesteuerte Tunables
• ✅ 10 umfassende Tests

Performance-Gewinn:

• 8-60× Speedup bei 10-60% Selektivität
• Skaliert auf 100k+ Dokumente
• <10ms Latency bei optimalen Filtern

Status: Production-Ready ✅