Embeddings 2024 - All You Need To Know: Step by Step

#embedding #openai #openaiapi
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Embeddings 2024 - All You Need To Know: Step by Step
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In this this tutorial we really take a deep-dive in to Embeddings. Embeddings is how AI (Artificial Intelligence) understands text. We look under the hood of how "talk to pdf" type applications work, we go in to
- How to extract data from PDF documents
- How to split data in to chunks, organise it or transform it
- How to Embed the transformed data, line by line using bulk embed arrays
- How to store embeddings (In a Dataframe) - not Vector Database
- How to search the Dataframe with a Query
- How to get a response from ChatCompletions with the response from DataFrame

We basically break down the process in to chunks and go STEP by STEP in helping you understand the concepts. By the end of this tutorial you would have developed your own python code to create and query a set of PDF documents using embeddings - Manually.

This video covers the manual process, no third party libraries involved.

Useful links:
Embeddings Documentation: https://platform.openai.com/docs/guid...
Embeddings Blog: https://openai.com/blog/new-embedding...

Timestamps
00:05:14 What we will cover in this video
00:07:15 High level look at embeddings
00:24:42 Explaining how the embedding DataFrame looks like and how the search will work
00:28:18 Importing all the libraries we will need
00:37:43 Stripping text from PDF documents and organising it in to sentences
00:49:04 Creating an embedding DataFrame of the PDF documents, then saving it as a CSV file
01:00:33 Retrieving the saved CSV file and converting it in to a Data Frame
01:06:36 Performing a search over the DataFrame
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#chatgpt #chatgptcoding #openai #openaiapi