What is an Embedding? - Definition & Meaning

Definition

An embedding is a numerical representation (vector) of text, images, or other data in a high-dimensional space. Embeddings capture the semantic meaning of content, so conceptually similar items lie close together in vector space even if they use different words.

Technical explanation

Embeddings are generated by neural networks that have learned to encode semantic relationships between words, sentences, or documents into dense vectors of typically 256 to 3,072 dimensions. Popular embedding models include OpenAI text-embedding-3-small/large, Cohere Embed, and open-source models like E5, BGE, and GTE. The process works as follows: text is passed through the model and the output of a specific layer is taken as the embedding vector. Comparison between embeddings uses cosine similarity (angle between vectors), dot product, or Euclidean distance. Embeddings are stored and searched in vector databases (pgvector, Pinecone, Weaviate, Chroma, Qdrant) optimized for approximate nearest neighbor (ANN) search algorithms like HNSW and IVF. Applications include semantic search, RAG systems, recommendation engines, duplicate detection, clustering, and anomaly detection.

How OpenClaw Installeren applies this

OpenClaw Installeren uses embeddings as the core of the RAG system in your AI assistant. When you upload documents to your knowledge base, they are automatically split into chunks and converted to embeddings stored in a vector database on your VPS. For every user question, an embedding is generated and compared against the knowledge base to retrieve the most relevant information.

Practical examples

• A RAG system converting the question "How can I cancel my subscription?" into an embedding vector and retrieving the most relevant passages from the FAQ database, even if those passages don't literally contain the word "cancel."
• A product recommendation engine comparing embeddings of product descriptions to show "similar products" based on semantic similarity rather than simple keyword matching.
• A duplicate detection system comparing embeddings of customer service tickets to automatically group similar open tickets and merge duplicates.

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