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GITHUB: https://github.com/ronidas39/LLMtutorial/tree/main/tutorial67 TELEGRAM: https://t.me/ttyoutubediscussion Welcome back to Total Technology Zone! This is tutorial number 67, and today's topic is incredibly exciting. In this video, we'll dive into the development of a RAG-based Help Desk Engineer using the GPT-4o (Omni) model and Langchain framework. In our last tutorial, we explored how to create an L1 support engineer capable of logging tickets on behalf of actual engineers. This AI-driven approach can seamlessly replace physical engineers in ticket logging, saving time and resources. We demonstrated how a user with a specific problem could have it logged as a Jira ticket through this innovative system. **In this tutorial, we will:** 1. Develop a RAG-based Help Desk Engineer. 2. Load a service manual into a vector database. 3. Use Streamlit to create a user-friendly interface. 4. Implement a general RAG workflow to provide contextual and relevant answers to user queries. **Steps Covered:** - **Setting Up the Environment:** We begin by importing necessary libraries such as PDF Loader, Recursive Character Splitter, Chroma (a vector database), and OpenAI embeddings. We load a service manual (in this case, a manual for an Epson printer) and prepare it for processing. - **Creating the Vector Database:** The service manual is split into chunks and loaded into a vector database using Langchain's built-in functionalities. - **Developing the Streamlit App:** We build a Streamlit application to facilitate user interaction. Users can enter their queries, and the system searches the vector database for relevant documents. - **Integrating GPT-4o:** Using GPT-4o, the application refines the context retrieved from the vector database to provide accurate and contextual answers. Throughout the tutorial, we emphasize the importance of automating help desk operations. This automation not only reduces costs but also improves efficiency by eliminating the need for extensive human intervention. **Key Highlights:** - **Code Explanation:** We walk through the code step-by-step, explaining each segment's purpose and functionality. This approach ensures you understand the underlying logic and can replicate the setup for other use cases. - **Real-Time Demonstration:** Watch as we execute the code and interact with the developed system, asking various questions related to the Epson printer manual. The system's ability to provide accurate responses showcases the effectiveness of RAG and GPT-4o in real-world applications. - **User-Friendly Interface:** The Streamlit app offers a clean and intuitive interface, making it easy for end-users to interact with the AI-driven help desk system. **Why This Matters:** Every company with a help desk can benefit from this technology. Traditional help desk operations involve significant ongoing costs for training and maintaining staff. With AI-driven solutions like this, companies can drastically reduce expenses while ensuring consistent and accurate support for users. By the end of this tutorial, you'll have a comprehensive understanding of how to implement a RAG-based Help Desk Engineer using GPT-4o and Langchain. This knowledge can be applied to various domains, enhancing your capability to develop intelligent and efficient support systems. If you find this tutorial valuable, please subscribe to our channel, hit the like button, and share our videos with your family and friends. Your support helps us grow and create more high-quality content. Also, feel free to leave comments with feedback or suggestions for future tutorials. Stay tuned for more interesting videos and use cases in our upcoming series. Until then, take care, have a nice day, and happy learning! **Links and Resources:** - [Source Code](#) - [Previous Tutorial](#) **Tags:** RAG Help Desk, GPT-4o Omni, Langchain Help Desk, AI Help Desk Engineer, GPT-4o for Help Desk, Langchain GPT-4o, RAG Technology, AI in Customer Support, Help Desk Automation, Langchain Tutorial, GPT-4o Demo, AI Customer Service, Tech Support AI, RAG in IT Support, Help Desk Engineering, AI-driven Help Desk, GPT-4o and Langchain, RAG Implementation, Next-Gen Help Desk, GPT-4o Use Cases, Langchain Integration, AI Solutions for Help Desk, RAG and AI, Automated IT Support, Help Desk Innovation, GPT-4o Capabilities, Langchain AI, Advanced Help Desk Tools, AI in IT Service, RAG Help Desk Engineer