Vector Database: What is a Vector Database?

What is a Vector Database?

A vector database is a data management system specifically designed to store, index, and query high-dimensional vectors called embeddings. An embedding is a numerical representation of content (text, image, audio) in the form of a vector with hundreds to thousands of dimensions, where geometric proximity between two vectors reflects the semantic similarity of their original content.

Unlike traditional relational databases that search by exact match (SQL WHERE), vector databases search by similarity: they find the vectors closest to a given query vector, an operation called "nearest neighbor search." This capability enables semantic searches where the query "how to reduce my costs" finds documents discussing "budget optimization" even when no words are shared.

Vector databases have become a critical component of the AI ecosystem with the emergence of RAG (Retrieval-Augmented Generation). In a RAG architecture, enterprise documents are transformed into embeddings and stored in a vector database. When a user asks a question, it is also transformed into an embedding, and the database retrieves the most relevant documents to enrich the LLM's context. For Belgian and European businesses, this technology enables creating AI assistants that access the organization's internal knowledge without ever sending all data to a cloud provider.

Why Vector Databases Matter

Vector databases solve a fundamental problem in modern AI: searching by meaning rather than by words.

• RAG foundation: RAG has become the standard method for giving LLMs access to up-to-date, enterprise-specific data. Without a performant vector database, RAG cannot function effectively.
• Semantic search: traditional keyword searches fail when the user does not know the exact terminology. Vector search understands intent and retrieves relevant results regardless of the vocabulary used.
• Multimodality: embeddings are not limited to text. Images, audio, and even code can be stored and searched in the same vector database, enabling cross-modal searches.
• Performance at scale: specialized indexing algorithms (HNSW, IVF) enable millisecond searches across millions or even billions of vectors, making the technology viable for production applications.
• Data sovereignty: self-hosted solutions (pgvector, Chroma, Qdrant) allow keeping embeddings and associated data entirely on-premise, meeting GDPR requirements and European enterprise security needs.

How It Works

Vector database operation relies on three key steps: ingestion, indexing, and search. During ingestion, documents are split into chunks (segments of a few hundred tokens), each chunk is transformed into an embedding by a specialized model (such as BGE, E5, or OpenAI's embedding model), then the resulting vector is stored with its metadata (source, date, category).

Indexing uses data structures optimized for approximate nearest neighbor (ANN) search. The HNSW (Hierarchical Navigable Small World) algorithm builds a multilayer graph enabling fast navigation to the nearest vectors. The IVF (Inverted File Index) algorithm partitions the vector space into regions, reducing the search space. Quantization (PQ, SQ) compresses vectors to reduce memory footprint while maintaining good search precision.

During search, the query vector is compared to indexed vectors using a distance metric: cosine similarity (angle between vectors), Euclidean distance (geometric distance), or dot product. The database returns the k nearest vectors with their metadata and a similarity score, allowing the RAG system to select the most relevant passages to enrich the LLM's prompt.

Concrete Example

At KERN-IT, KERNLAB uses vector databases as a central component of its RAG solutions. For the A.M.A assistant, the team deployed pgvector (the PostgreSQL extension for vectors) as the vector database, a strategic choice that allows using a single database for relational data and embeddings, simplifying infrastructure and maintenance.

A notable use case: for a legal client, KERNLAB indexed several thousand legal documents (contracts, case law, regulations) in a pgvector database. The intelligent chunking system splits documents respecting logical structure (articles, clauses, paragraphs) rather than cutting arbitrarily at a fixed token count. Embeddings are generated by a multilingual BGE model, enabling searches in French and Dutch. The AI assistant can thus answer precise legal questions while citing exact sources, with search time under 200ms across the entire corpus.

Implementation

1. Choose the vector database: for teams already using PostgreSQL, pgvector is the natural choice (no additional infrastructure). For specialized large-scale needs, evaluate Qdrant, Weaviate, or Pinecone.
2. Select an embedding model: choose a model suited to your language and domain. BGE-M3 and E5-Mistral excel at multilingual, OpenAI and Cohere models offer good generalist performance.
3. Define the chunking strategy: split documents into 256-1024 token segments with 10-20% overlap. Adapt splitting to document structure (sections, paragraphs).
4. Index and enrich metadata: store with each embedding relevant metadata (source, date, author, category) to enable hybrid filtering (vector + metadata).
5. Optimize search: configure indexing algorithms (HNSW with proper ef_construction and M parameters), implement hybrid search (vector + BM25) to combine semantic and lexical search.
6. Monitor and reindex: set up an incremental update pipeline for new documents and periodically reindex to maintain search quality.

Associated Technologies and Tools

• Dedicated vector databases: Pinecone (managed cloud), Qdrant (open source, Rust), Weaviate (open source, Go), Chroma (open source, Python), Milvus (open source, large scale)
• Existing database extensions: pgvector (PostgreSQL), Atlas Vector Search (MongoDB), Elasticsearch kNN — vector integration into databases already in place
• Embedding models: BGE, E5, OpenAI text-embedding-3, Cohere embed-v3, Voyage AI for vector generation
• RAG orchestrators: LangChain, LlamaIndex, Haystack for building RAG pipelines around the vector database
• Evaluation tools: RAGAS, LangSmith, Phoenix for measuring vector search and RAG quality

Conclusion

Vector databases are the foundational building block that enables LLMs to access enterprise-specific knowledge through RAG. KERN-IT, through KERNLAB, favors pgvector for the majority of its deployments, combining the power of vector search with PostgreSQL's robustness in a unified infrastructure. This pragmatic approach enables Belgian and European businesses to deploy performant RAG solutions while retaining control over their data and minimizing operational complexity.