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Vector Database
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Vector Database
Core details
Title
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Description
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A Vector Database stores and queries high-dimensional embeddings for similarity search, powering AI features like semantic search or recommendations. Examples: Pinecone, Weaviate, Milvus.
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Usage & Trade-offs
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When to use it
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Choose Vector DB when: - Building RAG or recommendation systems. - Handling unstructured data queries. - Scaling similarity searches. - Integrating with LLMs.
Pros
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- Fast approximate nearest neighbor (ANN) search. - Handles embeddings natively. - Scalable for billions of vectors. - Metadata filtering. - Hybrid with scalar indexes.
Cons
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- High storage for dense vectors. - Indexing time and costs. - Accuracy trade-offs in ANN. - Managed services pricey. - Self-hosting complex.
Notes
Note: Use HNSW for balanced speed/accuracy. Normalize embeddings. Upsert for updates.
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