Glossary
Embedding Model
A neural network that converts text, images, or other data into dense vector representations capturing semantic meaning for agent use.
What is Embedding Model?
Embedding models transform high-dimensional data like text into fixed-size vectors where semantically similar content appears closer in vector space. These embeddings power agent capabilities like semantic search, similarity matching, clustering, and retrieval-augmented generation. Quality embeddings capture nuanced meanings, handling synonyms, context, and domain-specific language effectively.
Agents use embeddings to understand user queries, search knowledge bases, and identify relevant context. Embedding quality directly impacts agent performance in tasks requiring semantic understanding. Different embedding models optimize for various qualities like language coverage, domain specificity, or computational efficiency. Models may be general-purpose like OpenAI's text-embedding-3 or specialized for legal, medical, or code domains.
Example
A customer support agent uses an embedding model to convert user questions into vectors, then searches a knowledge base by finding documentation with the closest vector similarity. The question "How do I reset my password?" matches documentation for "Account security and credential recovery" despite different wording.
How Signet addresses this
Signet's Quality dimension evaluates agents' retrieval and understanding capabilities, which often depend on embedding model quality. Agents using high-quality, task-appropriate embeddings demonstrate better semantic understanding and achieve higher quality scores.
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