Definition
An embeddings model is a machine learning model that converts input data (like text or images) into numerical vectors (embeddings). These embeddings capture the semantic meaning or characteristics of the input data.
Why it matters (in Poovi’s context)
Fundamental to RAG, as it transforms raw text into a format that can be stored in a vector database and used for similarity comparisons.
Key properties or components
- Input: data (text, images)
- Output: numerical vectors (embeddings)
- Captures semantic meaning
- Used in RAG and vector search
Contradictions or debates
None.