AI Architecture & Integration · Knowledge search
Vector Databases: Data Search in the Age of Generative AI
Employees search in their own words, rarely with the exact term used in the document. Vector databases find content by meaning, which makes them the foundation of many AI assistants that answer from company documents. This article explains when a dedicated vector database pays off and how it fits into your system landscape.
Krzysztof Udycz
Key takeaways
- Vector databases store embeddings, numerical vectors that represent the meaning of text, images or audio, and find similar content.
- They are the basis for semantic search and for RAG assistants that answer from your own documents.
- A dedicated system is not always needed: PostgreSQL with pgvector or an existing Elasticsearch installation can be enough.
- Cost depends mainly on data volume, update frequency, hosting model and requirements for response time and availability.
What a vector database is
Classic search compares words. Searching for “notice period” will not find a contract that says “the term ends three months after notice is given”. Vector search works differently:
- Create embeddings: a model turns text, images or audio into numerical vectors. Content with similar meaning sits close together in vector space.
- Store and index: the vector database stores these vectors together with metadata such as document type, date or access rights.
- Find similar content: the query is turned into a vector too, and the database returns the most similar content, filtered by metadata.
For generative AI this is central: a language model does not know internal documents. The vector database finds the matching passages, and the model writes the answer from them. This method is called retrieval augmented generation (RAG).
What companies use vector databases for
- Knowledge search and Q&A: employees or customers ask in their own words and get answers from manuals, policies or product documentation.
- Semantic search: documents, tickets or products are found by meaning, even with different wording or typos.
- Recommendations: suggesting similar products, articles or cases.
- Duplicates and similarity: spotting similar requests, contracts or images.
- Anomalies: finding entries that resemble no known pattern.
In practice
In practice: apoQlar
The MedTech company’s support and operations teams searched hundreds of product documents by hand. An assistant with a language model and a vector database now answers questions in seconds and names the document for every answer. For security questionnaires, a second system uses ChromaDB and cut processing time by 75%.
In practice: technical inspection authority
The citizen chatbot with eleven domain agents uses Qdrant as its vector database to match everyday questions with technical documentation. It runs on Microsoft Azure.
When a dedicated vector database pays off
Not every project needs an additional system. Often the simplest solution is the one that fits into existing infrastructure:
| Situation | Obvious choice |
|---|---|
| PostgreSQL already in use, moderate data volume | the pgvector extension in the existing database |
| Elasticsearch or OpenSearch already in use | add vector search there and combine it with keyword search |
| Prototype or small team | a lightweight open source database such as Chroma |
| Large data volumes, many filters, high load | a specialised vector database such as Qdrant, Weaviate or Milvus |
| No in-house operation wanted | a managed service, such as Pinecone or the cloud offerings of the open source databases |
Preparation often matters more than the product: how documents are split into sections, which embedding model suits the language and terminology, and which metadata is stored for filters and access rights.
What drives cost
- Data volume: the number of documents and sections determines storage and compute.
- Embeddings: converting all content initially and every update costs compute or API fees.
- Updates: content that changes daily needs a different pipeline than a fixed document set.
- Hosting model: self-hosted on your own servers, in your own cloud or as a managed service.
- Requirements: response time, availability, tenant separation and access rights.
For many knowledge assistants the vector database is not the largest item. Most effort usually goes into data preparation, integration and quality checks.
Connecting to existing systems
- Connect sources: SharePoint, Confluence, file shares, ticketing or custom systems are read regularly.
- Prepare: extract text, split it into sections, carry over metadata and permissions.
- Create embeddings: with a model that suits the language of the documents, in the cloud or locally.
- Keep it current: pick up new, changed and deleted documents automatically.
- Respect permissions: filter search results by the permissions of the person asking.
How this becomes a reliable assistant is covered in our article How enterprises build reliable RAG systems.
Making your documents searchable?
We look at which sources and questions are suitable, whether a dedicated vector database is needed and how it connects to your systems.
Request a process analysisFrequently asked questions
A database that stores embeddings, numerical vectors representing the meaning of text, images or audio. It finds content similar in meaning to a query, even when different words are used.
For retrieval augmented generation: the vector database finds the matching passages in company documents, and the language model writes an answer from them, with the source.
Not always. For moderate data volumes, PostgreSQL with pgvector or an existing Elasticsearch installation is often enough. Specialised systems such as Qdrant, Weaviate or Milvus pay off for large data volumes, many filters or high load.
It depends on data volume, update frequency, hosting model and requirements for response time and availability. In many projects the bigger effort lies in data preparation, integration and quality checks.
Krzysztof builds AI systems for companies at theBlue.ai. His focus is how language models reliably reach company knowledge: embeddings, vector databases and retrieval that keeps working with large document collections in day-to-day operation.
In his articles he explains the technology behind these systems in a way decision-makers can put into context.