Glossary · Definition
Embeddings
Embeddings are lists of numbers that represent the meaning of text, so a computer can find passages that mean similar things even when they use different words.
By Nythrex EngineeringUpdated 1 min read
In plain words
An embedding model turns a sentence or paragraph into a vector — a point in a high-dimensional space. Texts with similar meaning end up close together. That’s what makes semantic search possible: “cancel my plan” can find a document titled “subscription termination”.
When it matters to you
- Building semantic search or RAG
- Grouping similar tickets, reviews or documents
- Finding duplicates that aren’t exact copies
Common pitfalls
- Relying on embeddings alone and missing exact terms like product codes
- Mixing embeddings from different models in one index
- Not planning for re-embedding when you change models
Keep reading
GlossaryVector databaseA vector database stores embeddings and finds the most similar ones quickly, which is how RAG systems locate relevant passages among millions.GlossaryRAG (retrieval-augmented generation)RAG is a technique where an AI system first searches your documents or data for relevant passages and then lets a language model answer using them, with cita…ComparisonVector databases comparedVector stores for RAG: pgvector, Pinecone, Qdrant, Azure AI Search and OpenSearch compared on ops, hybrid search, filtering, scale and cost.
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