Learn how to prepare your existing content for AI using LangChain and store it in OpenSearch®, so it can be used with an LLM in the Retrieval Augmented Generation (RAG) pattern.
This workshop is 1,5 hours long.
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We’ll work together to generate embeddings for podcast transcriptions and load that data into OpenSearch. Then we’ll search the documents using similarity search and use those results to improve our responses from an LLM (Large Language Model). Along the way we’ll explain the Retrieval Augmented Generation (RAG) pattern and show how it’s possible to try different LLMs without having to completely rewrite your code.
This workshop is particularly useful for determining what would be required to make your data usable with the RAG pattern.
You will learn how to:
Staff Product Advocate, Aiven
Jay Miller is a Staff Product Advocate and long term advocate for building developer communities. Jay has been a contributor to the Python Community ecosystem and has participated in many user groups and conferences.
Live and interactive sessions to upgrade your skills with expert guidance covering a range of open source technologies.
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