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What are embeddings in AI?

Quick Answer

Embeddings are mathematical representations that capture the meaning of text, images, or other data as vectors of numbers. They translate human concepts into a format AI can process, where similar meanings are represented by similar vectors. Embeddings power semantic search, RAG systems, recommendation engines, and classification by enabling AI to understand and compare meaning rather than just matching keywords.

Summary

Key takeaways

  • Convert text and data into numerical vectors that capture semantic meaning
  • Enable AI to find similar content based on meaning, not just keywords
  • Essential for RAG, semantic search, and recommendation systems
  • Generated by specialised embedding models from providers like OpenAI and Cohere

How Embeddings Work

Embedding models convert text, images, or other data into vectors of numbers, typically containing 768 to 3,072 dimensions. Each dimension captures some aspect of meaning, and the combination of all dimensions creates a rich representation of the content's semantics. When two pieces of text have similar meanings, their embedding vectors are close together in this high-dimensional space. For example, the embeddings for 'car' and 'automobile' would be very close, while 'car' and 'banana' would be far apart. This mathematical representation of meaning is what makes semantic search possible. Embedding models are trained on enormous text corpora to learn these meaningful representations, and they can then convert any new text into a vector that captures its meaning relative to everything the model has learned.

Practical Applications of Embeddings

Embeddings have numerous practical applications in business AI. In RAG systems, embeddings power the retrieval step, finding the most relevant documents to answer a user's query. In customer support, embeddings match incoming enquiries with the most similar resolved tickets or knowledge base articles. In content management, embeddings automatically categorise and tag documents based on their semantic content. In recruitment, embeddings match job descriptions with CVs based on meaning rather than keyword overlap. In compliance, embeddings can flag documents or communications that are semantically similar to known policy violations. The versatility of embeddings makes them one of the most practically useful AI technologies for business applications.

Related

Related questions

what is vector databasewhat is ragwhat is semantic searchbuild knowledge base for aiwhat is retrieval augmented generation

FAQ

Frequently asked questions

OpenAI's text-embedding-3-small offers a good balance of quality and cost. For higher quality, text-embedding-3-large or Cohere's embed-v3 are strong choices. Open-source options like BGE and E5 are excellent for local deployment.

Embedding generation is inexpensive. OpenAI charges approximately £0.01 per million tokens for text-embedding-3-small. Embedding a typical business document costs fractions of a penny. Open-source models running locally have no per-use cost.

Yes. Modern embedding models are multilingual, supporting dozens of languages. Models like Cohere's embed-v3 and OpenAI's text-embedding-3 handle multiple languages well, enabling cross-lingual search and matching.

Re-generate embeddings when you change your embedding model or when the source content changes. For static documents, embeddings are generated once. For frequently updated content, set up automated re-embedding pipelines. Changing embedding models requires regenerating all embeddings.

Embeddings are not directly reversible to the original text, but sophisticated attacks can sometimes infer properties of the original content. For highly sensitive data, treat embeddings as derived personal data under GDPR and apply appropriate access controls and security measures.

Related Content

Vector Databases Guide

Where embeddings are stored and searched

Semantic Search Explained

How embeddings enable meaning-based search

RAG Implementation Guide

Using embeddings in production RAG systems

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