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🤖 Create Your Own Local Chatbot with Private Data Using Ollama, OceanBase & Dify In this comprehensive tutorial, learn how to build a powerful local chatbot that can understand and respond to questions about your private data. We'll use three cutting-edge open-source tools: Code: https://github.com/oceanbase-devhub/d... OceanBase Github link: https://github.com/oceanbase/oceanbase OceanBase social account: Twitter: https://x.com/OceanBaseDB Linkedin:   / oceanbase   OceanBase website: https://en.oceanbase.com/ #OceanBase #Ollama #AIChatbot ➡️ Tools Used: Ollama - Free, open-source large language model Dify - Open-source LLM app development platform OceanBase - Enterprise-grade distributed database with vector capabilities 🔑 Key Features: 100% Local Setup - Everything runs on your machine Private Data Integration - Train your chatbot on custom information Consolidated Database - Both SQL & vector storage in one place High Performance - Enterprise-grade distributed system Zero Downtime - Highly available across multiple nodes 💻 System Requirements: Docker installed Minimum 8GB RAM allocated to Docker Compatible with Linux/Mac/Windows 🛠️ Installation Steps Covered: Setting up Ollama with Llama 3.2 model Installing & configuring OceanBase Deploying Dify Creating databases and establishing connections Adding custom knowledge base Testing and verification ⚡ What You'll Learn: Complete setup of a local chatbot environment Database configuration for both structured and vector data Custom data ingestion process RAG (Retrieval Augmented Generation) implementation Integration testing and troubleshooting Timestamp: 0:00 - Chatbot with Private Data Overview 1:17 - Project Introduction & OceanBase Features 2:37 - Three-Step Installation Process 3:10 - Step 1: Installing Olama 3:31 - Step 2: Installing Ocean Base 4:30 - Step 3: Installing Dify 5:52 - Setting Up Dify Interface 6:37 - Testing Chatbot & Adding Custom Data 7:41 - Implementing Context & Testing 8:41 - Final Verification & Conclusion