Vector Databases for Memory
Master how AI agents use vector databases to store, search, and retrieve embeddings for semantic memory
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Review and check off these essential concepts from the Vector Databases module. Complete all items to finish this module and unlock the next one!
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Vector databases enable semantic search based on meaning, not just keyword matching
Embeddings are dense numerical representations (vectors) that capture semantic meaning
High-dimensional vectors (typically 384-1536 dimensions) represent text, images, or other data
Similar concepts produce similar vectors, clustering together in vector space
Cosine similarity, dot product, and Euclidean distance are the three primary similarity metrics
Cosine similarity is most common for text/embeddings as it measures angle, not magnitude
Nearest neighbor search finds the K most similar vectors to a query vector
Specialized indexes (HNSW, IVF) enable fast approximate search over millions of vectors
HNSW (Hierarchical Navigable Small World) offers the best balance of speed and accuracy
Popular vector databases include Pinecone (managed), Weaviate, Qdrant, and Chroma (open source)
Vector databases are essential for RAG (Retrieval-Augmented Generation) in AI agents
Combine vector search with metadata filtering to narrow results (e.g., by timestamp or category)
Always use the same embedding model for encoding data and queries—mixing models breaks similarity
Trade-offs exist between speed and accuracy: exact search is slow, approximate search is fast
Vector databases enable long-term semantic memory for AI agents, supporting experience-based learning