AI Glossary
Embeddings
Understanding AI Terminology
Numerical representations that capture the meaning of text for AI processing.
What It Means
Embeddings are dense vector representations of text that capture semantic meaning in a format AI systems can process mathematically. Similar texts have similar embeddings, enabling semantic search, clustering, and comparison. Embeddings power features like document search, recommendation systems, and RAG implementations.
Examples
- Converting documents to embeddings for search
- Finding similar questions in a support database
- Clustering customer feedback by topic
How This Applies to ARKA-AI
ARKA-AI uses embeddings internally for intelligent routing and semantic understanding of your requests.
Frequently Asked Questions
Common questions about Embeddings
Embeddings convert text to vectors where similar meanings are nearby in vector space. Searching means finding vectors closest to your query's embedding, returning semantically similar results.
A vector database is specialized storage for embeddings that enables fast similarity searches across millions of vectors. Popular options include Pinecone, Weaviate, and Chroma.
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