In this workshop 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:

• Find and generate embeddings for existing content
• Ingest that content and its embeddings into OpenSearch
• Use OpenSearch and LangChain to implement the RAG (Retrieval Augmented Generation) pattern with an LLM (Large Language Model)

📚 RESOURCES

• Create your free Aiven account: https://go.aiven.io/signup-opensearch-langchain-workshop
• Here’s the GitHub repository we’ll be using: https://github.com/Aiven-Labs/preparing-data-for-opensearch-and-rag

AIVEN DATA PLATFORM
The Aiven Platform is more than a collection of open source services for streaming, storing and analyzing data. The platform ensures that all services run reliably and securely in the clouds of your choice, are observable, and can easily be integrated with each other and with external 3rd party tools.

CONNECT WITH US
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