What is Vector Database

A vector database is a specialized database optimized for storing and querying vector representations of data. Vectors represent data objects like documents, images, or user profiles as numeric arrays of hundreds or thousands of dimensions. This allows running mathematical similarity operations between objects.

Vector databases are well-suited for AI applications like search, recommendations, and natural language processing where finding related pieces of data is key. The vector representations can be easily indexed for fast similarity lookups and retrievals. Vector databases provide much higher performance and scalability over traditional relational databases for vector storage and operations.

What does it do/how does Vector Database work?

A vector database ingests vectors and associated metadata, then indexes the vectors for efficient distance calculations like cosine similarity. This allows fast retrieval of vectors similar to a query vector.

They utilize indexing algorithms tailored for vector data, like graph-based indexes, tree-based indexes, to optimize storage and retrieval. Purpose-built vector serialization formats like Apache Arrow are used to store vectors efficiently.

Why is it important? Where is it used?

Vector databases unlock large-scale ML use cases like visual search, document classification, product recommendations that rely on fast nearest neighbor retrieval across large vector datasets.

Use cases include image search based on extracted feature vectors, product recommendation models using customer vector embeddings, chatbots using vectored text corpora. Vector databases are key for productionalizing ML models by integrating them with graph databases and document stores.

FAQ

How are vector databases different from other databases?

Unlike generic NoSQL databases, vector databases are specialized for storing machine learning vectors and run vector-oriented operations like similarity search, clustering, classification needed for ML model serving.

• Optimized for storing vectors, not generic documents or objects.
• Index structures designed for fast similarity calculations.
• Custom serialization formats for efficient vector storage.
• APIs centered around vectors like search, clustering.

When should you use a vector database?

Vector databases excel at fast similarity searches across vector datasets and are ideal for:

• Building recommendation systems using vector embeddings.
• Image search using extracted visual feature vectors.
• Document retrieval and classification using vectored text corpora.
• Accelerating production ML applications relying on vectors.

What are key challenges with vector databases?

However, vector databases come with their own complexities around scale, tuning, integrations:

• Choosing optimal indexing structures for different vector types and operations.
• Performance tuning indexing and query parameters.
• Scaling to large vector corpora with billions of vectors.
• Lack of native integrations with some ML frameworks.
• Query latency spikes with unoptimized vectors.

What are some popular vector database tools and vendors?

• Pinecone
• Weaviate
• Milvus
• Choroma
• Vald

References

• [List, Ranking] DB-Engines Ranking of Vector DBMS
• [Post] A Comprehensive Guide to Vector Databases
• [Post] What is a vector database?  | Microsoft
• [Post] Explaining Vector Databases in 3 Levels of Difficulty