Embedding Model
A specialized neural network that converts text into dense vector representations for similarity comparison.
Embedding models are trained to map semantically similar texts to nearby points in vector space. Unlike generative LLMs, they produce fixed-size numerical vectors rather than text output. Popular options include OpenAI ada, Cohere embed, and Google Gecko.
The choice of embedding model affects retrieval quality, vector dimensions, and cost. Smaller models are faster and cheaper but may miss nuanced similarity. Larger models capture more semantic detail but require more storage and compute for similarity search.
Related Terms
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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