# Introduction to Vector Databases

Vector databases are specialized systems designed to store, retrieve, and search high-dimensional vector embeddings efficiently. These databases are great for applications that require similarity searches, such as recommendation engines, image recognition, and natural language processing. Unlike traditional databases, vector databases handle complex relationships within data by focusing on vector proximity or similarity rather than exact matches.

### Examples of Vector Databases

- **[FAISS (Facebook AI Similarity Search)](https://github.com/facebookresearch/faiss)**
- **[ChromaDB](https://www.trychroma.com/)**
- **[Pinecone](https://www.pinecone.io/)**
- **[MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search)**
- **[Weaviate](https://weaviate.io/)**
- **[Qdrant](https://qdrant.tech/)**
- **[Milvus](https://milvus.io/)**

These databases provide the infrastructure needed to support advanced AI and machine learning applications by enabling efficient vector storage and retrieval.

I have several examples of vector databases, RAG, RAG Fusion, RAPTOR, as well as an overview of Searchable Encryption, Homomorphic Encryption, and Multiparty Computation in AI implementations in my blog at https://becomingahacker.org