KB: OpenAI

 

Embeddings:


What are embeddings:

OpenAI’s text embeddings measure the relatedness of text strings. Embeddings are commonly used for:
  • Search (where results are ranked by relevance to a query string)
  • Clustering (where text strings are grouped by similarity)
  • Recommendations (where items with related text strings are recommended)
  • Anomaly detection (where outliers with little relatedness are identified)
  • Diversity measurement (where similarity distributions are analyzed)
  • Classification (where text strings are classified by their most similar label)
An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.

What is the difference between embedding and vector database?
Embeddings encode all types of data into vectors that capture the meaning and context of an asset. This allows us to find similar assets by searching for neighboring data points. Vector search methods allow unique experiences like taking a photograph with your smartphone and searching for similar images.

Semantic Search With Embeddings:
The main difference between classical search and semantic search is to use small vectors to represent items. Using embeddings is powerful: it can be used to build systems that can help users find items they like (music, product, videos, recipes, …) using many kinds of queries. It can not only work in explicit search systems (inputting a query in a search bar) but also in implicit ones (relevant products in retailer websites, personalized news in publishers, interesting posts on social platforms).

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