Embedding
Industry Definition Set • Entity Resolution Path: /glossary/embedding
Quick Answer / TL;DR
A dense vector representation of text (or other data) that captures semantic meaning, enabling similarity search and retrieval-augmented generation.
Key Takeaways
- Dense vector representations of text or data.
- Similar texts produce similar vectors.
- Enables semantic search and RAG.
- Dimension typically ranges from 384 to 3072.
Definitive Statement: A dense vector representation of text (or other data) that captures semantic meaning, enabling similarity search and retrieval-augmented generation.
Technical Context & Protocol Usage
- Detailed Explanation
- Embeddings map discrete text tokens or documents into continuous high-dimensional vectors. Similar texts produce similar vectors, enabling semantic search. In MCP and RAG systems, embeddings allow agents to find relevant context by meaning rather than exact keyword matches. Models like OpenAI's `text-embedding-3-large` and open-source models like `e5` and `bge` are commonly used.
Format & Payload Metadata
Format: Dense float vectors (typically 384-3072 dimensions)
Latency: Embedding generation: 10-100ms per text chunk
Real-World Implementation Use Case
An MCP RAG server embeds user queries and compares them against embedded document chunks to find the most relevant context.
Cite This Page
MLA Style:
MCPserver.in Engineering. "Embedding." MCPserver.in Knowledge Hub, 20 July 2026, mcpserver.in/glossary/embedding.
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