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What are Vector Embeddings? - Definition & Meaning

Learn what vector embeddings are, how text and data are converted into numeric vectors for AI, and why they are essential for semantic search and RAG.

Vector embeddings are numeric representations of texts, images or other data. Similar content gets similar vectors. They enable semantic search, clustering and RAG.

What is What are Vector Embeddings? - Definition & Meaning?

Vector embeddings are numeric representations of texts, images or other data. Similar content gets similar vectors. They enable semantic search, clustering and RAG.

How does What are Vector Embeddings? - Definition & Meaning work technically?

Models: OpenAI text-embedding-3, Cohere, open source (sentence-transformers). Dimensions: 384 to 3072. Vector databases: Pinecone, pgvector, Weaviate.

How does MG Software apply What are Vector Embeddings? - Definition & Meaning in practice?

MG Software uses embeddings for semantic search in knowledge bases and RAG systems. We store embeddings in pgvector or dedicated vector DBs.

What are some examples of What are Vector Embeddings? - Definition & Meaning?

  • A search function that finds by meaning instead of exact words.
  • RAG: retrieve embeddings of docs, pass most relevant to LLM.
  • Clustering support tickets by theme.

Related terms

ai agentsmachine learningllm

Further reading

Knowledge BaseWhat is Deep Learning? - Definition & MeaningWhat is an LLM? - Definition & MeaningAI Automation Examples - Smart Solutions with Artificial IntelligenceChatbot Implementation Examples - Inspiration & Best Practices

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Frequently asked questions

Embeddings capture semantics: “dog” and “poodle” are close; keyword search misses that.
From 384 to 3072 dimensions. More dimensions = more nuance, but more storage and compute cost.

Why embeddings instead of keyword search?

Embeddings capture semantics: “dog” and “poodle” are close; keyword search misses that.

How large are embedding models?

From 384 to 3072 dimensions. More dimensions = more nuance, but more storage and compute cost.

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