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Vector Databases for Memory

Master how AI agents use vector databases to store, search, and retrieve embeddings for semantic memory

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Indexing & Performance

With millions or billions of vectors, brute-force search (comparing query to every vector) becomes impractical. Vector databases use indexes to enable fast approximate nearest neighbor (ANN) search.

Indexes trade 100% accuracy for speed—returning "good enough" results in milliseconds instead of seconds or minutes.

Interactive: Index Performance Simulator

Adjust dataset size and see how different index types scale.

Estimated Search Time
1.0 ms
Algorithm: Brute-force (compare to every vector)
Complexity: O(n) - Linear time
Accuracy: 100% (exact search)
Limitation: Slow with large datasets. Only viable for <10K vectors.

🗄️ Popular Vector Databases

Pinecone

Managed Cloud
  • •Fully managed
  • •Auto-scaling
  • •Real-time indexing
  • •Hybrid search
Best for: Production-ready, hands-off infrastructure

Weaviate

Open Source + Cloud
  • •GraphQL API
  • •Hybrid search
  • •Multi-modal
  • •Flexible schemas
Best for: Complex queries, knowledge graphs

Qdrant

Open Source + Cloud
  • •Rust-based (fast)
  • •Rich filtering
  • •Payload storage
  • •Snapshots
Best for: High-performance, self-hosted

Chroma

Open Source
  • •Embedded mode
  • •Simple API
  • •Local-first
  • •Python-native
Best for: Prototypes, local development

🏗️ Vector Database Architecture

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Storage Layer: Persistent storage for vectors and metadata (disk + memory caching)
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Index Layer: HNSW/IVF/etc. for fast ANN search (built and maintained automatically)
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Query Engine: Processes search requests, applies filters, ranks results
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API Layer: REST/gRPC interfaces for insert, update, delete, search operations
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Metadata Filtering: Combine vector search with traditional filters (WHERE clauses, tags, timestamps)

💡 Implementation Best Practices

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Batch Upserts: Insert/update vectors in batches (100-1000) for efficiency, not one at a time.
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Consistent Model: Always use the same embedding model for encoding data and queries. Mixing models breaks similarity.
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Store Metadata: Include original text, timestamps, user IDs alongside vectors for filtering and debugging.
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Monitor Performance: Track query latency, index build times, and accuracy. Tune parameters as data grows.
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Version Control: Keep track of embedding model versions. Reindex if you upgrade to a new model.
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