Search your data using OpenAI Embeddings

In this video, we'll take a look at vector embeddings based semantic search, a powerful technique for finding relevant information in large text datasets. We'll explore the fundamentals of natural language processing, explain how vector embeddings represent words and phrases in high-dimensional space, and demonstrate how semantic search can effectively identify related content by measuring the similarity between these embeddings.

Links:
Blog - https://partee.io/2022/08/11/vector-e...
OpenAI API - https://platform.openai.com/docs/guid...

0:00 - Introduction
1:50 - OpenAI Documentation & common use cases
4:48 - VSCode, loading libraries, utils, basics of vectors
8:03 - Generate embeddings for list of words
9:56 - Cosine similarity function
11:49 - Vectors in 3D space explanation
14:07 - Cosine similarity applied to dataframe
14:35 - Working with longer, realistic documents/text
16:38 - OpenAI synthetic document/knowledge-base generation
17:15 - Semantic search on knowledge-base

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