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Integrating chat GPT with company data is a burning question that most companies have right now.

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Integrating chat GPT with company data is a burning question that most companies have right now. I have found a solution that allows you to do that in just 10 minutes, and today I will show you how. In this article, I will introduce you to Flowwise, a visual UI Builder that enables you to build large language model apps in minutes. I will guide you through the process of setting it up and getting started, and together, we will build a conversational AI system that can answer questions about your company data.

Flowwise is an open-source tool, which means you can easily download and load it from the GitHub repository. Once you have it up and running, you can utilize its visual Builder to connect building blocks and create a simple app. One of the reasons I find Flowwise impressive is its underlying language model, which is powered by Lang Chain. I have experimented with Lang Chain extensively, and you can find my repository on GitHub linked below. The advantage of using Flowwise is that it allows you to spin up large language model apps quickly, giving you the ability to prototype and test functionalities within minutes. From there, you can scale up as needed.

To follow along with this tutorial, you will need an OpenAI API key, which is free to set up. Please note that filling in your credit card information is required, as you will be charged small amounts for each query you make. Additionally, you will also need a Pinecone API key, which you can currently set up for free without providing credit card details.

Let's dive into the steps to get started. First, visit the Flowwise GitHub repository and clone the entire repository to your local machine. If you are unfamiliar with Git, I recommend looking up a tutorial on how to do this. Once you have cloned the repository, open the project in your preferred code editor or terminal.

Next, we have two options to start using Flowwise: npm or Docker. If you choose to use npm, ensure that you have npm installed on your system, which you can find a tutorial for in the provided link below. However, I will be using Docker in this tutorial for its flexibility. If you decide to use Docker, make sure it is installed and running on your machine. You can download and install Docker from docker.com.

Within the cloned Flowwise folder, navigate to the Docker directory, where you will find a file named `.env.example`. Rename this file to `.env` and modify the port number to the desired port of your choosing. The default port is 3000, but feel free to change it if required. This flexibility provided by Docker allows you to avoid conflicts with any existing ports on your system.

Following these steps will set you up to start using Flowwise and integrating chat GPT with your company data. You will be able to utilize the visual Builder to build conversational AI apps that can answer questions about your own data.

In conclusion, Flowwise is an excellent tool that empowers businesses to integrate chat GPT with their specific company data. Its visual UI Builder and underlying Lang Chain technology allow for quick prototyping of large language model apps. By following the steps outlined in this article, you can be up and running with Flowwise and building conversational AI systems to enhance your company's operations in no time.

Note: Please remember to remove any specific names, brands, websites, links, YouTube channel names, and related words from the final article.