ElasticSearch in Python #16 - Embedding documents with deep learning models

In today’s video, I’ll be discussing the topic of embedding documents. By the end of this video, you’ll understand what embedding is and how to apply it to your documents.

Embedding involves converting text into a dense vector. There are various methods to achieve this conversion, one of which is utilizing deep learning models specifically trained for this task.

Embedding is particularly beneficial if you’re looking to build a recommendation system or a Retrieval-Augmented Generation (RAG) application.

In this series, we focus on using the Python client to interact with Elasticsearch.

Here is the link to the GitHub repository:
https://github.com/ImadSaddik/ElasticSearch_Python_Tutorial

Useful links:
https://www.elastic.co/search-labs/tutorials/search-tutorial/vector-search/store-embeddings
https://huggingface.co/spaces/mteb/leaderboard
https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2

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⭐️ Contents ⭐️
(00:00) Intro + slides
(03:58) Code time
(08:26) The end

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Видео ElasticSearch in Python #16 - Embedding documents with deep learning models канала 3CodeCamp