🧠 PART 08 – Create a Qdrant Vector Database and Connect It to n8n

In this eighth episode, we dive into the vector database layer of our local AI stack. We’ll set up a Qdrant database, perfect for storing AI embeddings (vectors), and integrate it with n8n to automate interactions with that data. 📌 In this episode, you’ll learn: How to launch a Qdrant container using Docker How to create a Qdrant collection (vector database structure) How to connect n8n to Qdrant using a custom HTTP request or API node How to add or retrieve vectors from Qdrant via an n8n workflow How to prep for future use with LLMs/Ollama models 🎯 Goal: Enable your local AI stack to store, query, and work with vector data efficiently and locally! 💬 Join the Discord for docker-compose files, Qdrant API requests, or community support: 👉 https://discord.gg/aFmqMCQz5Y 📫 Contact: [email protected] 🔔 Subscribe to stay updated on the next steps in building your ethical, local AI stack!