Vector Database

A vector database is a specialized database system that stores high-dimensional vectors (embeddings) and enables efficient similarity search – a key technology for AI applications like RAG and semantic search.

With the rise of AI applications and large language models (LLMs) a new type of database has come to the fore: the vector database. It does not store text or numbers in tables but mathematical representations (embeddings) of data in high-dimensional vector spaces. That enables something classic databases cannot do – semantic similarity search. Vector databases are therefore the foundation for Retrieval-Augmented Generation (RAG), recommendation systems and many other AI applications.

What is Vector Database?

A vector database is a database system optimized for storing and querying high-dimensional vectors (typically hundreds to thousands of dimensions). These vectors are produced by embedding models that turn text, images, audio or other data into numerical representations so that semantically similar content lies close together in the vector space. Unlike classic databases that look for exact matches vector databases perform Approximate Nearest Neighbor (ANN) search to find the most similar vectors to a given query vector. Well-known vector databases include Pinecone, Weaviate, Qdrant, Milvus and Chroma. Traditional databases like PostgreSQL (with pgvector) also offer vector search. Vector databases use specialized index structures such as HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) for efficient search over millions of vectors.

How does Vector Database work?

The workflow starts with generating embeddings: an embedding model (e.g. OpenAI text-embedding-3, Cohere Embed or an open-source model) converts text, images or other data into numerical vectors. These vectors are stored and indexed in the vector database together with metadata. For a query the search term is also converted to a vector and the database finds the most similar stored vectors using ANN algorithms. Similarity is measured with cosine similarity or Euclidean distance. In RAG applications the retrieved document chunks are then passed as context to an LLM, which generates an informed answer based on them.

Practical Examples

A company builds an AI knowledge base with RAG where internal documents are stored as embeddings in a vector database and used as context for an LLM when users ask questions.

An e-commerce company implements semantic product search that finds relevant products even for vague queries – e.g. 'protection for smartphone' also returns phone cases.

A streaming service uses vector databases for recommendations by comparing film and user profiles as vectors to suggest similar content.

An image agency enables visual similarity search where users upload an image and the vector database finds similar images by visual features.

A pharma company uses vector databases to search scientific publications semantically and find relevant research for drug development.

Typical Use Cases

Retrieval-Augmented Generation (RAG) for AI chatbots that answer from company documents

Semantic search in knowledge management that considers meaning instead of exact keyword match

Recommendation systems for e-commerce, streaming and content platforms based on embedding similarity

Duplicate and fraud detection by comparing data points in the vector space

Multimodal search linking text, images and other types in a shared vector space

Advantages and Disadvantages

Advantages

Semantic search: Finds semantically similar results even without exact keyword match
AI integration: Straightforward connection to LLMs and embedding models for RAG and other AI use cases
Scalability: Specialized index structures enable fast search over millions to billions of vectors
Versatility: Support for text, images, audio and other types via corresponding embedding models
Metadata filtering: Combine vector search with classic filters for precise, context-aware results

Disadvantages

Dependence on embedding quality: Search quality is only as good as the embedding model
Resource-intensive: Storing and indexing high-dimensional vectors requires significant memory
Young ecosystem: Many vector databases are relatively new and production standards are still evolving
Not a replacement for relational DBs: Vector databases complement but do not replace classic databases for structured data

Frequently Asked Questions about Vector Database

Do I need a separate vector database or is PostgreSQL with pgvector enough?

For smaller datasets (up to a few hundred thousand vectors) pgvector in PostgreSQL is a pragmatic choice since no extra infrastructure is needed. For large scale, low latency and advanced features like hybrid search or automatic sharding dedicated vector databases such as Pinecone, Weaviate or Qdrant are better.

Which vector database should I choose?

Choice depends on requirements: Pinecone offers a fully managed cloud solution; Weaviate and Qdrant are open source and can be self-hosted; Milvus fits very large datasets. For getting started and prototypes Chroma is a lightweight option. Criteria include data volume, hosting preference, performance needs and budget.

How does Retrieval-Augmented Generation (RAG) work with a vector database?

In RAG, company documents are first split into chunks and stored as embeddings in a vector database. When a user asks a question it is also converted to a vector and the most relevant document chunks are retrieved via similarity search. Those chunks are then passed to an LLM together with the question as context, and the LLM generates an informed, source-based answer.

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