Build Semantic Search and Chatbot Retrieval Systems with Qdrant on Google Cloud C4A Axion processors

About this Learning Path

Who is this for?

This is an introductory topic for developers, data engineers, and platform engineers who want to build semantic search systems and chatbot retrieval pipelines on Arm64-based Google Cloud C4A Axion processors using the Qdrant vector database.

What will you learn?

Upon completion of this Learning Path, you will be able to:

• Deploy and run the Qdrant vector database on Google Cloud C4A Axion processors
• Generate vector embeddings using transformer models
• Store and index embeddings efficiently using Qdrant
• Perform semantic similarity search using vector queries
• Build a simple chatbot retrieval system powered by vector search

Prerequisites

Before starting, you will need the following:

• A Google Cloud Platform (GCP) account with billing enabled
• Basic familiarity with Python
• Basic understanding of machine learning embeddings
• Familiarity with Linux command-line operations