Understanding Embeddings in RAG and How to use them - Llama-Index - YouTube
Retrieval Augmented Generation (RAG) and Semantic Search for GPTs | OpenAI Help Center
Vector embeddings - OpenAI API
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.
Visit pricing page to learn about Embeddings pricing.
Requests are billed based on the number of tokens in the input.
