What is Vector Database?
A vector database is a specialized database designed to store, index, and query high-dimensional vector embeddings, enabling efficient similarity search and retrieval of unstructured data like text, images, and audio.
Quick Facts
| Created | Concept emerged in 2010s, popularized with LLMs in 2022-2023 |
|---|---|
| Specification | Official Specification |
How It Works
Vector databases store embeddings and retrieve nearby vectors using distance metrics such as cosine similarity, Euclidean distance, or dot product. Their indexes, including HNSW, IVF, and product quantization, trade recall, latency, memory, build time, and update behavior rather than making exact search universally fast. A production design combines retrieval with metadata filters, access controls, source storage, observability, and evaluation. Dedicated products are one option; PostgreSQL with pgvector, search engines, or exact in-process search can be better when they fit the scale and operating model. A <a href="https://qubittool.com/blog/llm-semantic-caching-production-guide">semantic cache</a> is a separate response-reuse layer and must preserve tenant, model, prompt, and source-version boundaries.
Key Characteristics
- Optimized for high-dimensional vector storage and similarity search
- Uses approximate nearest neighbor (ANN) algorithms for fast retrieval
- Supports various distance metrics: cosine, Euclidean, dot product
- Can distribute large vector collections, with latency depending on index, filters, hardware, and recall target
- Often includes metadata filtering alongside vector search
- Integrates with embedding models from OpenAI, Cohere, and others
Common Use Cases
- Semantic search: Find documents by meaning rather than keywords
- RAG systems: Retrieve relevant context for LLM responses
- Recommendation engines: Find similar products, content, or users
- Image search: Find visually similar images in large collections
- Anomaly detection: Identify outliers in high-dimensional data
Example
Loading code...Frequently Asked Questions
What is the difference between a vector database and a traditional database?
Traditional databases store structured data and perform exact matches on values. Vector databases store high-dimensional embeddings and find similar items using distance calculations. While SQL databases excel at filtering and joining tables, vector databases excel at semantic similarity search where the goal is finding conceptually related items rather than exact matches.
What are the most popular vector databases?
Popular dedicated vector databases include Pinecone, Weaviate, Milvus, Qdrant, and Chroma. Traditional databases with vector extensions include PostgreSQL with pgvector, Elasticsearch, and Redis. Cloud providers offer managed solutions like AWS OpenSearch, Google Vertex AI Vector Search, and Azure Cognitive Search.
How do vector databases achieve fast similarity search?
Vector databases use Approximate Nearest Neighbor (ANN) algorithms that trade exact recall for lower search cost. Common approaches include HNSW, IVF, and product quantization. Achievable latency and scale depend on filters, recall target, hardware, replicas, index parameters, and workload concurrency, so they must be benchmarked.
What embedding dimensions should I use?
Embedding dimensions depend on your model and use case. OpenAI's text-embedding-3-small uses 1536 dimensions, while text-embedding-3-large uses 3072. Higher dimensions capture more nuance but require more storage and compute. Many applications work well with 384-1536 dimensions. Some vector databases support dimension reduction for cost optimization.
How do I choose the right distance metric?
Cosine similarity is most common for text embeddings as it measures angle between vectors regardless of magnitude. Euclidean distance works well when vector magnitude matters. Dot product is fastest computationally and works when vectors are normalized. Most embedding models are trained with cosine similarity, making it the default choice.