How to Build a Qdrant Vector Database for AI Agents | RAG Tutorial | No Code

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Learn how to create a Qdrant vector database and integrate it with AI agents for RAG (Retrieval Augmented Generation). This tutorial shows you how to upload documents, split them into chunks, create embeddings, and connect everything to an AI agent that can search your knowledge base and answer questions accurately.

Key Timestamps:
0:00 – Introduction
0:23 – Qdrant Account Setup & Cluster Creation
0:56 – n8n Workflow Setup & Form Trigger
1:43 – Qdrant Vector Store Configuration
2:10 – API Key & Endpoint Connection
2:34 – Collection Setup & Embedding Batch Size
2:55 – OpenAI Embeddings Configuration (text-embedding-3-small)
3:33 – Document Loader Setup
3:59 – Text Splitting Configuration (Chunk Size & Overlap)
4:18 – Uploading Documents to Vector Store
4:36 – AI Agent Setup with Qdrant Tool
5:00 – Tool Description & Search Configuration
5:27 – Embeddings Integration
5:45 – System Message for Knowledge Base Usage
6:16 – Testing RAG: Querying the Knowledge Base
6:41 – Verifying Retrieved Results

Resources:
Qdrant: https://qdrant.tech
Qdrant Cloud: https://cloud.qdrant.io
n8n: https://n8n.io
OpenAI Platform: https://platform.openai.com

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