---
title: "Introduction to Vector Databases"
description: "Overview of vector database concepts and semantic search"
keywords: ["vector database", "embeddings", "similarity search", "semantic search"]
category: "database"
tags: ["vectors", "search", "embeddings"]
---

# Vector Databases

Vector databases store and retrieve data based on semantic similarity rather than exact keyword matches. They use embeddings to represent documents as high-dimensional vectors.

## Key Concepts

### Embeddings
Embeddings are numerical representations of text that capture semantic meaning. Similar concepts have similar vector representations, enabling semantic search.

### Similarity Search
Instead of exact matching, vector databases find documents with similar meanings by calculating distances between vectors in high-dimensional space.

## Common Use Cases

- Semantic search engines
- Recommendation systems
- Question answering systems
- Document clustering and classification

## Popular Vector Databases

- ChromaDB - Open source, Python-native
- Pinecone - Managed service
- Weaviate - GraphQL interface
- Milvus - Scalable and distributed
