In this video, we dive deep into Retrieval Augmented Generation (RAG) and show how to make any data easily searchable using vector databases. Follow along as we break down every step—from converting your documents into vector embeddings with OpenAI’s text-embedding-ada-002 API to automating the entire workflow using Google Docs, Make.com, and Pinecone.
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Helpful Links:
- Make.com
- pinecone.io
The Text parser regex pattern:
[\r\n]+
Make.com json parsing
{
"input": ["{{join(map(10.embeddings; """\""""¤t&""\"""""); """,""")}}"],
"model": "text-embedding-ada-002",
"encoding_format": "float"
}
What You’ll Learn in this Video:
Retrieval Augmented Generation (RAG): Understand the concept and its benefits for enhancing document search.
Vector Databases Explained: Discover how documents are split into chunks and converted into high-dimensional vectors for precise similarity searches.
Automation with Make.com: See how to set up a seamless integration with Google Drive to automatically process your SOPs.
Using Pinecone: Learn how to store and retrieve document embeddings in a scalable, serverless vector database.
Real-World Application: Watch a step-by-step walkthrough of turning a multi-page SOP into searchable content that can be later used to build an AI-powered chatbot.
Whether you’re an AI experimenter, developer, or business leader looking to optimize your document management system, this tutorial provides the insights and practical know-how to get started.
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