HOW TO CREATE AN AI WITH OPENAI AND LANGCHAIN IN 3 MINUTES

Do you have tasks that you could solve quickly with AI, but you need to do it fast and without investing a lot of time and money? In this video, we walk you through the process of creating an intelligent application based on the Langchain and OpenAI framework to generate topic-specific tips. This guide is intended for business leaders and decision-makers who want to quickly and efficiently gain valuable insights into various topics, which in turn can contribute to strategic decision making. We use Google Colab as a platform to write and execute our code. This choice allows us to jump straight into development without complicated setup processes. The main problem we tackle is the need to get fast and accurate information on a wide range of topics. Our solution uses artificial intelligence to generate specific useful tips based on user input. The technology behind this solution is OpenAI’s GPT 3.5 turbo model, an advanced natural language processing model. For illustrative purposes, this video uses API keys that are not real to demonstrate the handling of sensitive data. The first part of our code involves installing the necessary packages, langchain, and OpenAI using the command PIP install. This is the foundation for using the function of the GPT 3.5 model. We then define the environment variable for the OpenAI API key, commented out here. This step shows where and how you would insert your own API key, although it is important to emphasize that the key shown here is merely a fictitious example. We then import the necessary components from the lang chain and API key libraries to define the structure of our request and communication with the GPT 3.5 model. The Chat Open AI class is used to create an instance that interacts with API keys, while prompt template and LLM chain are used to define the specific request structure and processing chain. By the way, we would like to introduce you to a special option. For just €1, you can register on crowdcompany.net and develop your own AI solution for your company without having to invest a lot of time or money. This also gives smaller companies to advanced technologies. The next part of the source code defines a template for the queries we make to the model. This template shows the flexibility of the system to respond to different requests. The use of prompt template and LLM chain enables dynamic interaction with the AI model based on user input. In the final step of our program, we use a loop to continuously receive topics from users and generate tips based on them. This illustrates the real-time ability of our application to respond to requests. To summarize, in this video, you learned how to use LangChain and OpenAI to easily and quickly develop an AI-driven application that responds to user input and generates topic-specific tips. This concept can help improve your business decision-making process. If you have any further questions or need support, please do not hesitate to contact us by email. We thank you for your attention and hope you found this video informative. Don’t forget to contact us if you’re interested in further explanations, and we look forward to welcoming you again in the next video.