Vector Databases for Memory
Master how AI agents use vector databases to store, search, and retrieve embeddings for semantic memory
Your Progress
0 / 5 completedBeyond Keyword Search
Traditional databases excel at exact matching—finding records where a field equals a specific value. But AI agents need semantic search: finding information based onmeaning, not just keywords.
Vector databases solve this by storing data as embeddings (numerical representations of meaning) and enabling similarity search. Instead of "WHERE name = 'iPhone'", agents ask "FIND SIMILAR TO this concept."
This is the infrastructure powering semantic memory, RAG (Retrieval-Augmented Generation), and intelligent memory systems.
Interactive: Traditional vs Vector Search
Toggle between search methods to see how they handle the query: "iPhone"
Traditional Keyword Search
Exact string matching: finds "iPhone" only where it appears literally.
🎯 Why Agents Need Vector DBs
- •Semantic Memory: Store and recall knowledge by meaning, not keywords
- •RAG Systems: Retrieve relevant context for LLM queries
- •Long-Term Memory: Store unlimited conversations with semantic recall
- •Pattern Matching: Find similar past situations to inform decisions
🏗️ Core Components
- •Embeddings: Numerical representations of text/data (vectors)
- •Similarity Metrics: Cosine, dot product, Euclidean distance
- •Indexes: HNSW, IVF, FAISS for fast nearest neighbor search
- •Metadata Filtering: Combine semantic search with attribute filters