Vector Databases for AI Agents - Semantic Search & Storage

What is a Vector Database?

A vector database is a specialized database designed to store, index, and query high-dimensional vectors (embeddings). It enables semantic similarity search - finding items based on meaning rather than exact keyword matches.

Why Vector Databases for Memory?

Vector databases are ideal for agent memory because:

Enable semantic search over memories

Find related content by meaning, not just keywords

Scale to millions of memories

Support fast approximate nearest neighbor search

Handle high-dimensional embedding vectors

How They Work

Vector database workflow:

1. **Embed**: Convert text/data to vectors using embedding models

2. **Index**: Build efficient search structures (HNSW, IVF, etc.)

3. **Store**: Persist vectors with metadata

4. **Query**: Find similar vectors using distance metrics

5. **Return**: Retrieve original content with similarity scores

Popular Vector Databases

Leading vector database options:

**Pinecone**: Fully managed, easy to start

**Weaviate**: Open-source, GraphQL API

**Qdrant**: Open-source, Rust-based performance

**Milvus**: Open-source, highly scalable

**Chroma**: Lightweight, developer-friendly

**pgvector**: PostgreSQL extension

Distance Metrics

Common similarity measures:

**Cosine Similarity**: Angle between vectors

**Euclidean Distance**: Straight-line distance

**Dot Product**: Magnitude-aware similarity

Indexing Algorithms

Efficient search structures:

**HNSW**: Hierarchical Navigable Small World graphs

**IVF**: Inverted File Index

**PQ**: Product Quantization

**Flat**: Brute-force (small datasets)

Memory Use Cases

Vector databases in agent memory:

Retrieving relevant past conversations

Finding similar user questions

Semantic search over knowledge bases

Entity and concept matching

Hybrid search with metadata filters