Embedding
A mathematical representation of text, images, or other data as a series of numbers (vector), allowing an AI system to understand and compare their meaning.
What is an embedding?
An embedding is a mathematical representation of information, such as text or images, as a series of numbers (a vector). AI models use embeddings to capture the meaning of data. Texts with similar meaning receive vectors that are close together in a multidimensional space, while unrelated texts are far apart. This makes it possible to search by meaning, rather than only by exact word matches.
How do embeddings work?
Embeddings are generated by an AI model trained to understand the meaning of text. When documents or web pages are processed, the model converts each text into a vector of hundreds to thousands of numbers. These vectors are stored in a vector database. For a search query, the query itself is also converted into an embedding, after which the most relevant documents are found by comparing vectors. In Wabber's RAG pipeline, this process runs entirely on proprietary hardware in the Netherlands.
Example
A healthcare organization implements Wabber's AI chat solution to give employees quick answers to questions about internal protocols. All policy documents are converted into embeddings and stored in a vector database. When an employee asks 'What is the protocol during an emergency?', this question is converted into an embedding and compared with all stored documents. The system automatically finds the most relevant protocols, even if the exact words don't match.
Why are embeddings important?
The quality of embeddings directly determines the quality of AI answers. Good embeddings ensure that the AI system finds the right context for every question. Wabber selects embedding models that perform optimally for the language and domain of your organization. For Dutch-language content, this is particularly important, as not all models perform equally well in Dutch.
Related solutions
Frequently asked questions
What is the difference between an embedding and a vector?
A vector is a mathematical series of numbers, while an embedding is a specific type of vector generated by an AI model to capture the meaning of text or data. Every embedding is therefore a vector, but not every vector is an embedding. In the context of AI search systems, the terms are often used interchangeably.
Do embeddings also work for Dutch-language documents?
Yes, but not all embedding models perform equally well in Dutch. Wabber specifically selects and tests models that work optimally for Dutch-language content. This is an important part of every AI implementation, as language support directly affects the quality of search results and AI answers.
Where are embeddings stored?
Embeddings are stored in a specialized vector database optimized for quickly comparing vectors. At Wabber, this database runs on our own AI cluster in the Netherlands, so your data never leaves the country. This is essential for organizations that value data privacy and compliance.
How do embeddings improve the search function of an AI system?
Traditional search methods look for exact word matches, while embeddings search by meaning. This means a query like 'how do I file a complaint' also finds documents about 'objection procedure' or 'complaint procedure', even if those exact words aren't in the query. This makes AI search systems significantly more accurate and user-friendly.
AI-Readiness Scan
Scan in 2 minutes - discover where you stand.
Ready to put your data to work?
Schedule a no-obligation 30-minute session. Discover how private AI and tracking systems measurably improve your operation.