8. Query Pipeline¶

8.1. Search Strategy¶

Phonetic search uses a multi-stage approach with graceful fallback:

1. User input: "Springfeld" (typo or variant spelling)
   ↓
2. Language detection (optional):
   - Use UI locale hint
   - Or attempt automatic detection
   - Default: treat as multilingual
   ↓
3. Generate query embedding:
   - Siamese BiLSTM model encodes query string directly
   - Same model used for indexing (consistency)
   - No IPA conversion required at query time
   - Inference latency target: <10ms
   ↓
4. Elasticsearch multi-stage search on toponyms index:

   Stage A: Vector kNN search (primary)
   - Index: toponyms
   - Field: embedding_bilstm
   - k: 100 candidates
   - Similarity: cosine
   
   Stage B: Text search (fallback/boost)
   - Fuzzy match on name field
   - Edge n-gram on name for prefix matching
   - Exact match on name_lower for precise queries
   
   Stage C: Completion suggest (autocomplete)
   - Type-ahead suggestions with language context
   ↓
5. Score combination:
   - Weighted blend: 0.7 × vector_score + 0.3 × text_score
   - Return top-scoring toponym_ids (name@lang)
   ↓
6. Join to places:
   - Query places index for documents referencing matched toponyms
   - Apply geographic/temporal filters if specified
   - Deduplicate and rank by best toponym match score
   ↓
7. Return ranked place results with matched toponym and confidence scores

8.2. Elasticsearch Query Structure¶

8.2.1. Vector Search¶

{
  "knn": {
    "field": "embedding_bilstm",
    "query_vector": [0.12, -0.34, "..."],
    "k": 100,
    "num_candidates": 500
  },
  "_source": ["toponym_id", "name", "lang"]
}

8.2.2. Hybrid Search (Vector + Text)¶

{
  "query": {
    "bool": {
      "should": [
        {
          "knn": {
            "field": "embedding_bilstm",
            "query_vector": [0.12, -0.34, "..."],
            "k": 50,
            "boost": 0.7
          }
        },
        {
          "match": {
            "name": {
              "query": "Springfeld",
              "fuzziness": "AUTO",
              "boost": 0.3
            }
          }
        }
      ]
    }
  }
}

8.2.3. Completion Suggest¶

{
  "suggest": {
    "toponym-suggest": {
      "prefix": "Lond",
      "completion": {
        "field": "suggest",
        "size": 10,
        "contexts": {
          "lang": ["en"]
        }
      }
    }
  }
}

8.3. Error Handling¶

Condition	Response
Embedding generation fails	Fall back to text-only search
No vector results	Expand to fuzzy text search
Elasticsearch timeout	Return partial results with warning
Empty results	Progressively relax filters (language, geography, time)

8.4. Performance Optimisation¶

Query embedding cache: Cache embeddings for frequent queries (LRU, 10k entries)
Pre-computed embeddings: Top 10k toponyms cached in memory
Index optimisation: HNSW parameters tuned for recall/latency tradeoff
Connection pooling: Persistent Elasticsearch connections