future_work

Search & Relevance – Future Work

Status: v1 Complete (BM25 HTTP + Hybrid Fusion) – v2 Planning

<<<<<<< Updated upstream

Verification – 16. November 2025

• Kurze Überprüfung gegen den Quellcode:
  • Gefunden/implementiert: BM25 + FULLTEXT AQL Integration, Hybrid Text+Vector Fusion, Stemming/Analyzer, VectorIndex (HNSW optional), SemanticCache, HKDFCache, TSStore + Gorilla Codec, ContentManager ZSTD Wrapper.
  • Fehlend / nur dokumentiert: CDC/Changefeed HTTP Endpoints (GET /changefeed, SSE), FieldEncryption batch API (encryptEntityBatch) und PKI/eIDAS Signaturen (Design vorhanden, produktive Implementierung fehlt).
  • Empfehlung: Nächster Implementierungsschritt: CDC/Changefeed (MVP) — siehe docs/development/todo.md für Details.

Stashed changes

Implemented Features (v1)

✅ BM25 Fulltext Search (Commit 94af141)

• API: POST /search/fulltext
• Scoring: Okapi BM25 (k1=1.2, b=0.75)
• Index: TF/DocLength automatic maintenance
• Response: {pk, score} sorted by relevance
• Tests: 10/10 passed

✅ Hybrid Text+Vector Fusion (Commit e55508a)

• API: POST /search/fusion
• Modes: RRF (rank-based) and Weighted (score-based)
• Flexibility: Text-only, Vector-only, or combined
• Normalization: Min-Max for weighted, reciprocal rank for RRF
• Tests: No regressions in fulltext suite

✅ Stemming & Analyzer Extensions (v1.2)

• Implementation: Porter-Subset (EN), simplified suffix removal (DE)
• Configuration: Per-index via POST /index/create with:

  {
    "type": "fulltext",
    "config": {
      "stemming_enabled": true,
      "language": "en"  // en | de | none
    }
  }

• Index Maintenance: Consistent tokenization in Put/Delete/Rebuild
• Query-Time: Automatically uses index config for query tokens
• Storage: Config persisted in ftidxmeta:table:column as JSON
• Backward Compatible: Default {stemming_enabled: false, language: "none"}
• Tests: 16/16 stemming tests passed + 10/10 fulltext regression tests
• HTTP API: /index/create with type: "fulltext" and optional config
• OpenAPI: Documented in openapi.yaml with examples
• Stopwords: Pro-Index konfigurierbar (Default-Listen EN/DE, Custom-Liste)

✅ AQL Integration: FULLTEXT Operator (v1.3)

Goal: Implement FULLTEXT(field, query) operator in AQL

Status: ✅ Implementiert (aql_translator.cpp lines 101-174)

Features:

• Syntax: FULLTEXT(doc.field, "query" [, limit])
• Standalone FULLTEXT queries
• FULLTEXT + AND Kombinationen (hybride Suche)
• FULLTEXT + OR via DisjunctiveQuery
• Integration mit BM25() Scoring

Beispiel-Queries:

-- Simple FULLTEXT
FOR doc IN articles
  FILTER FULLTEXT(doc.content, "machine learning")
  RETURN doc

-- FULLTEXT + BM25 scoring
FOR doc IN articles
  FILTER FULLTEXT(doc.content, "machine learning")
  SORT BM25(doc) DESC
  LIMIT 10
  RETURN {title: doc.title, score: BM25(doc)}

-- FULLTEXT + AND (hybrid)
FOR doc IN articles
  FILTER FULLTEXT(doc.content, "neural networks") AND doc.year == "2024"
  RETURN doc

-- FULLTEXT + OR (disjunctive)
FOR doc IN articles
  FILTER FULLTEXT(doc.content, "AI") OR doc.category == "research"
  RETURN doc

Tests: 23/23 green (test_aql_fulltext.cpp, test_aql_fulltext_hybrid.cpp)

✅ AQL Integration: BM25(doc) Function (v1.3)

Goal: Enable BM25 scoring in AQL queries with SORT support

Status: ✅ Implementiert

Implementation Details:

1. Query Engine Extension (query_engine.cpp)

   • Neue Methode: executeAndKeysWithScores() liefert KeysWithScores
   • Score-Map aus scanFulltextWithScores()
   • Scores bleiben über AND-Intersections mit Strukturprädikaten erhalten

2. Function Evaluation (query_engine.cpp lines 963-982)

   • BM25(doc) liest Score aus ctx.getBm25ScoreForPk(pk)
   • 0.0 Fallback, wenn kein Score vorhanden
   • Extrahiert _key oder _pk aus dem Dokumentobjekt

3. SORT Integration

   • SORT BM25(doc) DESC nutzt Score aus EvaluationContext
   • Automatische Befüllung via ctx.setBm25Scores() bei FULLTEXT

Beispiel-Query:

FOR doc IN articles
  FILTER FULLTEXT(doc.content, "machine learning")
  SORT BM25(doc) DESC
  LIMIT 10
  RETURN {title: doc.title, score: BM25(doc)}

Tests: 4/4 grün (test_aql_bm25.cpp)

• BasicBM25FunctionParsing
• ExecuteAndKeysWithScores
• BM25ScoresDecreaseWithRelevance
• NoScoresForNonFulltextQuery

Future Work (v2+)

✅ Advanced Analyzer Extensions

Goal: Extend stemming with additional linguistic features

Potential Enhancements:

1. Stopword Filtering

• Implemented in v1.2 (Default EN/DE + Custom per Index)

2. Umlaut Normalization (German)

   • ✅ Implemented in v1.2 (normalize_umlauts config option)
   • Normalize "ä→a", "ö→o", "ü→u", "ß→ss"
   • Improves matching for search queries without special chars
   • Example: "läuft" → "lauft" (stems to "lauf")
   • Implementation: utils::Normalizer::normalizeUmlauts()
   • Tests: test_normalization.cpp (2/2 passing)

3. Compound Word Splitting (German)

   • Split "Fußballweltmeisterschaft" → "fußball welt meisterschaft"
   • Critical for German precision/recall
   • Requires dictionary or ML-based approach

4. Lemmatization (vs. Stemming)

   • More accurate morphological analysis
   • "running" → "run", "better" → "good"
   • Requires POS tagging and lexicon

Effort Estimate: 2-5 days (depending on scope)

• Stopwords: 4-6 hours
• Umlaut normalization: 2-3 hours
• Compound splitting: 1-2 days (complex)
• Lemmatization: 2-3 days (requires NLP library)

Complexity: Medium-High

• Stopwords: Low
• Normalization: Low
• Compound splitting: High (ambiguity resolution)
• Lemmatization: High (dependency on NLP toolkit)

Priority: Medium

• Stopwords: High value/effort ratio
• Umlaut normalization: High for German content
• Compound splitting: Nice-to-have (complex)
• Lemmatization: Overkill for most use cases (stemming sufficient)

Alternative Analyzers (Future):

• N-Grams (for partial matching, typo tolerance)
• Phonetic matching (Soundex, Metaphone for fuzzy search)
• Synonym expansion
• Stop-word removal

---

🔲 Position-based Phrase Search

Goal: Replace substring-based phrases with true position-aware phrase matching

Example:

{
  "query": "\"machine learning\"",
  "match": "exact phrase only, not 'machine' and 'learning' separately"
}

Requirements:

• Extend index to store token positions (position arrays alongside TF)
• Phrase query parser: detect quoted strings
• Proximity verification: ensure tokens appear consecutively (or within k-window)

Effort: 2-3 days (incremental over current substring approach)

---

🔲 Query Highlighting

Goal: Return matched terms/snippets in response

Example Response:

{
  "pk": "doc123",
  "score": 8.5,
  "highlights": {
    "content": "...with <em>machine learning</em> algorithms..."
  }
}

Requirements:

• Extract matched tokens from query
• Locate occurrences in document text
• Generate snippets with highlighting markup

Effort: 1-2 days

---

🔲 Learned Fusion (ML-based Ranking)

Goal: Replace hand-tuned fusion with learned weights

Approach:

• Collect query logs with relevance judgments
• Train LambdaMART/LightGBM ranker
• Features: BM25 score, Vector similarity, metadata signals
• Online serving: predict fusion weights per query

Effort: 1-2 weeks (requires ML infrastructure)

---

🔲 Multi-Stage Retrieval Pipeline

Goal: Efficient retrieval → reranking architecture

Stages:

1. Retrieval (fast, high recall): Fusion search with k=1000
2. Reranking (slow, high precision): Cross-encoder on top-100
3. Diversification (optional): MMR for result diversity

Effort: 2-3 days (without Cross-Encoder integration)

---

Implementation Priority

High Priority (v2):

1. ✅ BM25 HTTP API (DONE)
2. ✅ Hybrid Fusion (DONE)
3. 🔲 Stemming (DE/EN) – Next
4. 🔲 AQL Integration – After Stemming

Medium Priority (v3): 5. 🔲 Phrase Search 6. 🔲 Query Highlighting 7. 🔲 Advanced Analyzers (N-Grams, Synonyms)

Low Priority (v4+): 8. 🔲 Learned Fusion 9. 🔲 Multi-Stage Reranking 10. 🔲 Query Expansion

Testing Strategy

Unit Tests:

• Stemmer: token → stem mappings for DE/EN
• AQL Parser: BM25(doc) function parsing
• Query Engine: Score context propagation

Integration Tests:

• End-to-end AQL queries with FULLTEXT + SORT BM25
• Stemming: Query "running" matches docs with "run"
• Phrase search: Quoted vs. unquoted queries

Performance Tests:

• BM25 latency: 100k docs, 5-token queries (target: <50ms)
• Fusion overhead: Text+Vector vs. separate (target: <2× slowdown)
• Stemming impact: Index size increase (expect: +10-20%)

Documentation TODOs

• AQL Syntax Guide: FULLTEXT operator, BM25(doc) function ✅ COMPLETE

  • Dokumentiert in docs/aql_syntax.md (Zeilen 172-195, 491-577)
  • FULLTEXT operator vollständig dokumentiert mit Beispielen
  • BM25(doc) Funktion für Score-Zugriff dokumentiert
  • Hybrid Search (FULLTEXT + AND) dokumentiert

• Index Configuration: Stemming options, language codes ✅ COMPLETE

  • Dokumentiert in docs/search/fulltext_api.md (Zeilen 1-150)
  • Stemming: stemming_enabled, language (en/de/none)
  • Stopwords: stopwords_enabled, custom stopwords array
  • Umlaut-Normalisierung: normalize_umlauts für DE
  • Vollständige API-Beispiele mit Konfiguration

• Performance Tuning Guide ✅ COMPLETE (07.11.2025)

  • Neu erstellt: docs/search/performance_tuning.md
  • BM25 Parameter Tuning (k1, b) mit Use-Case-Matrix
  • efSearch für Vector-Queries (20-200 mit Recall/Latency trade-offs)
  • k_rrf für Hybrid Search Fusion (20-100 Empfehlungen)
  • weight_text/weight_vector für Weighted Fusion
  • Index Rebuild Strategy & Maintenance
  • Performance Benchmarks und Monitoring
  • Production Checklist

• Migration Guide: v1 → v2 ✅ COMPLETE (07.11.2025)

  • Neu erstellt: docs/search/migration_guide.md
  • Zero-Downtime Migration Strategy (Dual Index)
  • Maintenance Window Strategy (In-Place)
  • Incremental Migration für große Datasets (>10M docs)
  • Rollback Procedures mit Timelines
  • Backward Compatibility Matrix
  • Testing Checklist (Pre/During/Post-Migration)
  • Migration Examples: Stemming, Umlaut-Norm, Vector-Dim-Change
  • Performance Impact & Monitoring
  • FAQ & Troubleshooting

References

• Snowball Stemmer: https://snowballstem.org/
• Okapi BM25: Robertson & Zaragoza (2009)
• RRF: Cormack, Clarke, Büttcher. SIGIR 2009
• LambdaMART: Burges (2010)

Implementation Status (November 2025)

✅ Completed Features

1. BM25 Fulltext Search - Production-ready

   • HTTP API: POST /search/fulltext mit Score-Ranking
   • Index API: POST /index/create mit config options
   • Query semantics: AND-logic, optional limit

2. Stemming & Normalization - Production-ready

   • Languages: EN (Porter subset), DE (suffix stemming)
   • Stopwords: Built-in lists + custom stopwords
   • Umlaut normalization: ä→a, ö→o, ü→u, ß→ss (optional)

3. Phrase Search - Production-ready (v1)

   • Quoted phrases: "exact match" queries
   • Case-insensitive substring matching
   • Works with normalize_umlauts

4. AQL Integration - Production-ready (v1.3)

   • FILTER FULLTEXT(field, query [, limit])
   • SORT BM25(doc) DESC/ASC
   • RETURN {doc, score: BM25(doc)}
   • Hybrid: FULLTEXT + AND predicates
   • OR combinations: FULLTEXT(...) OR ...

5. Hybrid Search (Text + Vector) - Production-ready

   • RRF fusion (Reciprocal Rank Fusion)
   • Weighted fusion (configurable text/vector balance)
   • HTTP API: POST /search/hybrid

🟡 Planned Enhancements

Near-term (Q1 2026):

• Highlighting: Mark matched terms in response
• Performance tuning guide with benchmarks ✅ IMPLEMENTED → siehe docs/search/performance_tuning.md
• Migration guide for index rebuilds ✅ IMPLEMENTED → siehe docs/search/migration_guide.md

Long-term (Q2+ 2026):

• Position-based phrase search (faster than substring)
• Advanced analyzers: n-grams, phonetic matching
• Query expansion with synonyms
• LambdaMART learning-to-rank

Nächste sinnvolle Schritte

1. Umlaut-/ß-Normalisierung ✅ IMPLEMENTED
2. Phrase Queries ✅ IMPLEMENTED (v1 substring-based)
3. AQL-Integration: FULLTEXT-Operator + BM25 ✅ IMPLEMENTED (v1.3)
4. Highlighting für matched terms (v2 planned)
5. Performance Tuning Guide mit Benchmarks ✅ IMPLEMENTED → docs/search/performance_tuning.md