Vector databases are the silent backbone of modern AI applications. They make semantic search and retrieval-augmented generation (RAG) practical in the first place. We explain clearly how they work – and why they are the key to reliable AI in the company.
Why classic search reaches its limits
A conventional keyword search only finds what is exactly named. If someone searches for “termination period”, she will not find a hit if the document says “end of contract”. This is where many knowledge databases fail: The knowledge is available, but not findable.
How Vector Databases Work
Instead of storing texts as pure strings, content is stored in so-called Embeddings converted – number vectors that Significance of a text. Similar contents lie close together in the vector space. A vector database finds related passages even if no word exactly matches. Known solutions are pgvector (for PostgreSQL), Qdrant or Milvus.
Use in practice: RAG
With Retrieval-Augmented Generation, the vector database first searches for the most relevant passages from your own data. Only these are given to the AI as context. The result: fact-based answers with references – instead of invented statements. The vector database is the component that decides on quality and speed.
Do you want to use your internal knowledge? Ours AI consulting and RAG systems bring vector databases into your company – GDPR-compliant and on-premise on request. Now discuss.
Would you like to implement this in your company? We support you pragmatically – from the idea to the operation.