Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
Vector embeddings convert words, sentences, or entire documents into arrays of floating-point numbers. Texts with similar meanings end up close together in vector space, allowing search systems to find relevant content even when exact keywords differ.
Embedding models are the backbone of semantic search in modern document intelligence. By storing embeddings in a vector database, platforms can retrieve the most contextually relevant passages in milliseconds, regardless of vocabulary mismatch.
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.
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.
Fine-Tuning
The process of further training a pre-trained model on domain-specific data to improve its performance on targeted tasks.
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