GPT 3.5 Embedding for Semantic Search

We'll learn how to use opens a new embedding model text-embedding-ada-002.

We are going to learn how to use the Open Embedding API to generate language integration and then index these embedding vectors.

This is a powerful and common combination for building semantic search, question-answering, threat-detection, and other applications that rely on NLP and search over a large corpus of text data.

Everything will be implemented by opening new GPT 3.5 class embedding model called text-embedding-ada-002; their latest embedding model that is 10x cheaper than earlier embedding models, more performant, and capable of indexing ~10 pages into a single vector embedding.

Colab notebook:
https://colab.research.google.com/github/pinecone-io/examples/blob/master/integrations/openai/semantic_search_openai.ipynb

by: @jamesbriggs