Editing Embeddings Vector Dbs (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a scalable embedding and vector search infrastructure:

'''1. Embedding pipeline'''
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Data source (docs, products, articles)
    ↓
[Change detection: hash-based or timestamp comparison]
    ↓
[Chunking: semantic or recursive, 512-1024 tokens]
    ↓
[Embedding generation: batched, GPU-accelerated]
    ↓
[Vector store upsert: id, vector, metadata, document text]
    ↓
[BM25 index update for hybrid search]
</syntaxhighlight>

'''2. Query pipeline'''
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User query
    ↓
[Query preprocessing: lowercase, strip special chars]
    ↓
[Parallel retrieval:
  ├── Dense (ANN): top-50 by cosine similarity
  └── Sparse (BM25): top-50 by keyword relevance]
    ↓
[Reciprocal Rank Fusion: merge and deduplicate]
    ↓
[Cross-encoder reranking: top-10 → top-5]
    ↓
Top-k results with metadata and scores
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'''3. Production considerations'''
* Pre-filter by metadata before ANN search to reduce search space
* Cache frequent query embeddings (TTL-based)
* Use asynchronous indexing to avoid blocking on document ingestion
* Set up monitoring: index size growth, query latency, empty result rates

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:Vector Databases]]
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