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Vector Search Architecture Patterns for Large-Scale Applications

Exploring embedding strategies, indexing approaches, and retrieval optimization techniques.

December 20, 2024
14 min read
Vector SearchEmbeddingsSearchArchitecture

Vector Search Fundamentals

Vector search has become essential for modern AI applications, enabling semantic similarity search over large collections of documents, images, and other data types. Understanding architecture patterns helps build scalable, performant systems.

Embedding Strategies

Model Selection

Choose embedding models based on your use case:

  • General-purpose: OpenAI text-embedding-ada-002, Cohere embed-english-v3.0
  • Domain-specific: Fine-tuned models for specialized domains
  • Multilingual: Models trained on multiple languages
  • Multimodal: Models handling text, images, and other modalities

Embedding Dimensions

Balance between:

  • Higher dimensions: Better representation, more storage
  • Lower dimensions: Faster search, less storage, potential quality loss

Common dimensions: 384, 768, 1536

Indexing Approaches

Exact Search

Brute-force comparison works for small datasets but doesn't scale:

# O(n) complexity - fine for < 10K vectors
def exact_search(query_vector, vectors):
    similarities = [cosine_similarity(query_vector, v) for v in vectors]
    return sorted(similarities, reverse=True)[:k]

Approximate Nearest Neighbor (ANN)

For large-scale search, use ANN algorithms:

  • HNSW (Hierarchical Navigable Small World): Fast, good accuracy
  • IVF (Inverted File Index): Good for very large datasets
  • LSH (Locality-Sensitive Hashing): Fast but lower accuracy

Vector Database Architecture

Managed Services

Consider managed vector databases:

  • Pinecone: Fully managed, easy to use
  • Weaviate: Open-source, self-hostable
  • Qdrant: High performance, Rust-based
  • Milvus: Scalable, feature-rich

Self-Hosted Solutions

For control and cost optimization:

  • Deploy on Kubernetes
  • Use distributed architectures
  • Implement replication for availability

Hybrid Search Patterns

Combine vector search with traditional methods:

Vector + Keyword

def hybrid_search(query, vector_db, keyword_index):
    # Vector search for semantic similarity
    vector_results = vector_db.search(query_embedding, top_k=20)
    
    # Keyword search for exact matches
    keyword_results = keyword_index.search(query, top_k=20)
    
    # Combine and re-rank
    return rerank(vector_results, keyword_results)

Vector + Metadata Filtering

Use metadata to narrow search space before vector search:

  • Filter by date range
  • Filter by category
  • Filter by user permissions

Performance Optimization

Indexing Strategies

  • Batch indexing for bulk updates
  • Incremental indexing for real-time updates
  • Index partitioning for large datasets

Caching

Cache frequent queries and their results:

  • Query result caching
  • Embedding caching
  • Popular item caching

Scaling Patterns

Horizontal Scaling

Distribute vectors across multiple nodes:

  • Sharding by hash or range
  • Replication for availability
  • Load balancing across nodes

Multi-Tenancy

Support multiple customers or use cases:

  • Namespace isolation
  • Resource quotas
  • Tenant-specific indexes

Best Practices

  • Normalize embeddings for consistent similarity calculations
  • Monitor index quality and rebuild periodically
  • Implement proper error handling and fallbacks
  • Test with production-like data volumes
  • Document your search strategy and parameters

Conclusion

Effective vector search architecture balances accuracy, performance, and cost. By understanding these patterns and choosing the right approach for your use case, you can build scalable semantic search systems.

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