Practical workshop: Semantic search system with Mistral and Pinecone
In this practical workshop, we’ll bridge the gap between theory and implementation. You’ll start by setting up your development environment, connecting to the Mistral API, and generating embeddings for real data. From there, we’ll calculate semantic similarities, identify the closest matches, and then scale the process using Pinecone as a vector database.
Along the way, you’ll see how the concepts we covered (vector representation, similarity metrics, and visualization) translate into working code. By the end, you’ll have a fully functioning semantic search prototype and the know-how to adapt it for your own AI-driven projects.
Step 1: Environment setup
Before we dive into our practical exercises, let’s take a moment to clone the code from GitHub and introduce Google Colab, the platform we’ll be using for our hands-on work with embeddings.
