Definition
Contextual embeddings are dynamic vector representations where the numerical encoding of a token changes based on its surrounding sequence, enabling RAG pipelines to resolve polysemy and capture nuanced semantic intent. Unlike static embeddings, these are generated by Transformer models that use self-attention to weight the importance of neighboring words during the encoding process.
Not static Word2Vec/GloVe; these are dynamic, sequence-aware vectors generated in real-time.
"A chameleon changing its color to blend into its environment; the 'color' (vector) of the word changes based on its 'habitat' (surrounding words)."
- Attention Mechanism(Prerequisite)
- Polysemy(Problem Solved)
- Vector Database(Component (Storage))
- Semantic Search(Direct Application)
Conceptual Overview
Contextual embeddings are dynamic vector representations where the numerical encoding of a token changes based on its surrounding sequence, enabling RAG pipelines to resolve polysemy and capture nuanced semantic intent. Unlike static embeddings, these are generated by Transformer models that use self-attention to weight the importance of neighboring words during the encoding process.
Disambiguation
Not static Word2Vec/GloVe; these are dynamic, sequence-aware vectors generated in real-time.
Visual Analog
A chameleon changing its color to blend into its environment; the 'color' (vector) of the word changes based on its 'habitat' (surrounding words).