Models Retrieval
Embedding Model
A machine learning model that converts text or data into numerical vectors to enable semantic search and similarity matching.
An embedding model is a machine learning system that converts text (or other data) into dense numerical vectors (embeddings) that capture semantic meaning. Similar concepts end up with similar vector representations, enabling semantic similarity calculations.
Popular embedding models include OpenAI's text-embedding-ada-002, Cohere Embed, and open-source options like Sentence-BERT. These models are fundamental to AI applications like semantic search, content clustering, and retrieval-augmented generation.
The quality of embeddings determines the quality of similarity matches. Different models have different strengths—some excel at short text, others at long documents, some at specific domains like legal or medical content.
Example: When you use a semantic search tool to find "similar articles," the system converts both your query and all articles into embeddings, then finds articles with vectors closest to your query.
In SeCoFlow: SeCoFlow uses embedding models for features like semantic topic analysis and content clustering, helping identify related content and coverage gaps across your site.