In this hands-on project, you'll apply your embedding knowledge to build a fully local semantic log search app using Qdrant, FastAPI, and Streamlit. You'll create a fast, private, and extensible system for searching logs using natural language. No cloud, no APIs, no LLMs.

• Storing and querying vector embeddings in Qdrant
• Building a minimal API backend to serve search results
• Creating a Streamlit-based UI for semantic search and exploration
• Structuring and chunking log data for high-quality results
• Running the entire app locally with Docker for full control and portability

Start the course: https://learndataengineering.com/courses/enrolled/2823291