Embedding Model
A neural network that converts raw data (text, images, audio) into dense numerical vectors that capture semantic meaning.
Embedding models are the invisible backbone of every intelligent search, recommendation, and classification system in BasaltHQ. When BASALTECHO processes a new document, the embedding model converts each chunk into a fixed-length vector (typically 768 or 1,536 dimensions). Documents with similar meaning cluster together in this high-dimensional space, enabling instant similarity search. BasaltHQ supports multiple embedding providers and allows enterprises to deploy custom embedding models fine-tuned on their domain vocabulary, ensuring that industry-specific jargon (e.g., medical codes, legal citations) is accurately represented.
Related Concepts
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Vector Database
A specialized database optimized for storing and querying high-dimensional mathematical representations of unstructured data like text, images, and audio.
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Semantic Search
A search methodology that understands the intent and contextual meaning behind a query, rather than matching literal keywords.
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Fine-Tuning
The process of further training a pre-trained LLM on a smaller, domain-specific dataset to specialize its behavior for a particular industry or task.