Embeddings
Numeric fingerprints of text or images that let computers measure semantic similarity.
Why it matters
Embeddings are how AI understands meaning rather than just matching keywords. They power semantic search, recommendation engines, and RAG systems. When you search for "affordable laptop" and get results about "budget notebooks," embeddings made that connection.
Understanding embeddings helps you evaluate AI search tools and knowledge bases - better embeddings mean more relevant results.
How it works
4 stepsRelated terms
From the glossaryFrequently asked questions
What is an embedding model?+
A model trained specifically to convert text into vectors that capture semantic meaning. Popular ones include OpenAI text-embedding-3 and open-source models like BGE and E5.
How are embeddings stored?+
In a vector database such as Pinecone, Weaviate, or pgvector. These databases are optimised for the nearest-neighbour search needed to find semantically similar vectors quickly.
Do embeddings work across languages?+
Multilingual embedding models exist that map text from different languages into the same vector space, so a query in English can match documents in French or Spanish.
See the tools that use it.
The fastest way to understand Embeddings is to see it inside real products. Browse hand-reviewed tools that put it to work, each one checked by a person before it was listed.
Browse hand-reviewed AI tools