How do we deploy and index embeddings at scale?
Efficient vector search powers many AI apps.
How It Works:
Store embedding vectors in a vector database (like Faiss or Pinecone), build indexes (e.g., HNSW), and expose search APIs for real-time similarity queries.
Key Benefits:
- Millisecond retrieval times
- Scalable to millions of vectors
- Supports dynamic updates
Real-World Use Cases:
- Personalized content feeds
- Real-time anomaly detection
FAQs
Which index type to choose?
How handle new vectors?
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