Vector Database
A vector database is a specialized database designed to store, index, and retrieve unstructured data represented as high-dimensional vectors, enabling efficient semantic search, similarity search, and powering applications such as LLM long-term memory, semantic search, and recommendation systems.
About this tool
Vector Database
A vector database (also known as a vector store or vector search engine) is a specialized type of database designed to store, index, and retrieve data represented as high-dimensional vectors. These databases enable efficient semantic and similarity searches, making them essential for modern applications in AI, machine learning, and information retrieval.
Features
- Storage of High-Dimensional Vectors: Capable of storing fixed-length lists of numbers (vectors) representing data such as text, images, audio, and more.
- Approximate Nearest Neighbor (ANN) Search: Implements algorithms to quickly retrieve database records most similar to a given query vector.
- Semantic and Similarity Search: Facilitates searching based on meaning or similarity rather than exact matches.
- Support for Multi-Modal Data: Can handle vectors derived from diverse data types (text, images, audio, etc.).
- Integration with Machine Learning: Feature vectors are often computed using machine learning methods, such as feature extraction, word embeddings, or deep learning networks.
- Use in Retrieval-Augmented Generation (RAG): Supports methods to enhance large language model (LLM) outputs by retrieving relevant context from stored vectors.
- Recommendation Systems: Enables building recommendation engines by finding semantically similar items.
- Techniques for High-Dimensional Search:
- Hierarchical Navigable Small World (HNSW) graphs
- Locality-sensitive Hashing (LSH) and Sketching
- Product Quantization (PQ)
- Inverted Files
- Combinations of these techniques
- Scalability and Performance: Designed for efficient search and retrieval in large-scale, high-dimensional datasets.
Common Use Cases
- Semantic search
- Similarity search
- Multi-modal search
- Recommendation engines
- Long-term memory for large language models (LLMs)
- Object detection
- Retrieval-augmented generation (RAG)
Implementations
Vector databases can be found as standalone products or as features in broader database systems. Examples include:
- Milvus
- Pinecone
- Weaviate
- Qdrant
- Elasticsearch
- OpenSearch
- MongoDB Atlas
- Redis Stack
- Vespa
- Chroma
- PostgreSQL with pgvector extension
- Many others (see Wikipedia article for a comprehensive list)
References
Category
- Concepts & Definitions
Tags
- vector-databases
- definition
- semantic-search
- similarity-search
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