Embeddings
Numeric vector representations of text that capture semantic meaning — the foundation of semantic search, RAG, and most modern NLP applications.
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What is Embeddings?
Numeric vector representations of text that capture semantic meaning — the foundation of semantic search, RAG, and most modern NLP applications.
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An embedding is a list of numbers (typically 384, 768, 1536, or 3072 dimensions) that represents a piece of text in a high-dimensional space. Texts with similar meanings end up near each other in this space, regardless of exact word overlap — "purchased shoes online" and "ordered sneakers from a website" produce nearby embeddings.
Embeddings power semantic search: instead of matching keywords, search becomes "find vectors near this query vector." In Salesforce contexts, this is how RAG systems retrieve relevant Knowledge Articles, prior Case resolutions, or related Opportunities — by comparing the embedding of a new question to a vector database of embedded company content.
Common embedding models: OpenAI's text-embedding-3-small/large, Cohere's embed, open-source alternatives like sentence-transformers. The choice affects search quality, cost, and infrastructure (some embeddings can run on-device, others require API calls).
Related terms
Browse all terms- Vector DatabaseA database optimized for storing and querying high-dimensional vectors (embeddings) — the storage layer that makes semantic search and RAG fast at scale.
- RAG (Retrieval-Augmented Generation)An LLM is given relevant retrieved documents as context before generating a response — grounding outputs in your specific data, not just the model's training.
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