Embedding
Embeddings convert text into numerical vectors that capture meaning - enabling AI agents to find relevant information even when exact keywords do not match.
An embedding is a numerical representation of text (or other data) as a vector of numbers that captures its semantic meaning - enabling AI to measure similarity, search by concept, and cluster related content.
Semantic Representation
Text is converted into vectors of 1,536+ dimensions. Similar concepts end up as nearby vectors - "invoice" and "bill" are close; "invoice" and "weather" are far apart.
Similarity Search
Find content by meaning. A customer asking "How do I get a refund?" matches documentation about "return and reimbursement process" even without shared words.
Knowledge Base Indexing
Embed your company documents, FAQs, and policies. The AI agent searches this index to provide accurate, grounded answers.
Content Classification
Classify emails, tickets, and messages by comparing their embeddings to category examples - no explicit rules needed.
Clustering
Group similar content automatically - finding themes in customer feedback, categorizing support tickets, or identifying duplicate entries.
Efficient Retrieval
Vector similarity search is fast - millisecond lookups across millions of documents using approximate nearest neighbor algorithms.
FAQ
Do embeddings work with Arabic text?
Yes. Modern embedding models handle Arabic, including mixed Arabic-English content. Semantic similarity works across languages - an Arabic question can match an English document.
How often do embeddings need updating?
Embeddings are computed once per document. When documents change, only the modified documents need re-embedding. New documents are embedded and indexed when added.
What is the cost of embedding?
Very low. Embedding a million words costs roughly $0.10 with current models. The one-time embedding cost is negligible compared to ongoing inference costs.
Build semantic search for your business
Learn how embeddings power AI knowledge bases.