Text Embeddings
Text embeddings are numerical representations of discrete objects (words, sentences, documents) in a continuous vector space. They map high-dimensional, sparse data into low-dimensional, dense vectors, preserving semantic relationships.
Core Concepts
- Semantic Similarity: Vectors with similar meanings are closer in Euclidean or Cosine distance.
- Dense vs. Sparse: Embeddings are dense vectors, unlike traditional Bag-of-Words or TF-IDF which are sparse.
- Dimensionality: Typical dimensions range from 128 to 1536, balancing granularity and computational cost.
Applications
- Search & Retrieval: Semantic search surpasses keyword matching by understanding intent.
- Clustering: Grouping similar documents or topics automatically.
- Recommendation Systems: Matching user preferences with item attributes via vector proximity.
- Input for LLMs: Often used as the first step in RAG (Retrieval-Augmented Generation) pipelines.
Related Resources
- Vector Embeddings: Semantic Representation for NLP and AI
- Source: Thu Vu’s “Learn Vector Embeddings in 20 Minutes”
- Key Insight: Foundational overview of how numerical representations convert text into machine-readable formats.
See Also
- Cosine Similarity
- Word2Vec
- bert
- High-Dimensional Space