Skrevet av: Tonsbergbekhterev / Publisert: 01.08.2024 / Oppdatert: 27.11.2024

A fully functional ChatBot in 10 mins by Rajdeep Biswas

A Dialogflow agent is a virtual agent that handles conversations with your end-users. It is a natural language understanding module that understands the nuances of human language. WhatsApp is the most popular OTT app in many parts of the world. Thanks to WhatsApp chatbots you can provide your customers with support on a platform they use and answer their questions immediately.

And that is how you build your own AI chatbot with the ChatGPT API. Now, you can ask any question you want and get answers in a jiffy. In addition to ChatGPT alternatives, you can use your own chatbot instead of the official website. You can build a ChatGPT chatbot on any platform, whether Windows, macOS, Linux, or ChromeOS. In this article, I am using Windows 11, but the steps are nearly identical for other platforms. Some of the best chatbots available include Microsoft XiaoIce, Google Meena, and OpenAI’s GPT 3.

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Chatbots automate a majority of the customer service process,  single-handedly reducing the customer service workload. They utilize a variety of techniques backed by artificial intelligence, machine learning and data science. This tutorial will focus on enhancing our chatbot, Scoopsie, an ice-cream assistant, by connecting it to an external API. You can think of an API as an accessible way to extract and share data within and across programs.

Having a good understanding of how to read the API will not only make you a better developer, but it will allow you to build whatever type of Discord bot that you want. A bot has now been created and is attached to the application. We are going to need to create a brand new Discord server, or “guild” as the API likes to call it, so that we can drop the bot in to mess around with it. Before getting into the code, we need to create a “Discord application.” This is essentially an application that holds a bot. Remember how I said at the beginning that there was a better place to pass in dynamic instructions and data?

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Shiny for Python adds chat component for generative AI chatbots “Ooh, shiny! ” indeed—use the LLM back end of your choice to spin up chatbots with ease. Open Terminal and run the “app.py” file in a similar fashion as you did above. If a server is already running, press “Ctrl + C” to stop it. You will have to restart the server after every change you make to the “app.py” file.

If the user message includes a keyword reflective of an endpoint of our fictional store’s API, the application will trigger the APIChain. If not, we assume it is a general ice-cream related ChatGPT query, and trigger the LLMChain. This is a simple use-case, but for more complex use-cases, you might need to write more elaborate logic to ensure the correct chain is triggered.

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The set_question event handler is a built-in implicitly defined event handler. Learn more in the events docs under the Setters section. So this is how you can build your own AI chatbot with ChatGPT 3.5. In addition, you can personalize the “gpt-3.5-turbo” model with your own roles.

This will enable our chatbot to send requests to and receive responses from an external API, broadening its functionality. A Python chatbot is an artificial intelligence-based program that mimics human speech. Python is an effective and simple programming language for building chatbots and frameworks like ChatterBot.

Open “stories.md” file and this new custom action “action_check_weather” as part of happy path flow. If you do “ls -la” in a terminal, you can see a list of files which are created by Rasa. After installing miniconda, Follow below commands to create a virtual environment in conda.

• Click the API button on the llama-2–70b-chat model’s navigation bar.
• Now Re-train your Rasa Chatbot using following command.
• Where Weka struggles compared to its Python-based rivals is in its lack of support and its status as more of a plug and play machine learning solution.
• Each message that is sent on the Discord side will trigger this function and send a Message object that contains a lot of information about the message that was sent.

The prompt will ask you to name your function, provide a location and a version of Python. Follow the steps as required and wait until your Azure function has been created. You should be able to find it in the Azure Functions tab, once again right click on the function and select Deploy to Function App. Once you are in the folder, run the below command, and it will start installing all the packages and dependencies.

Step 4: Modify the code for your Function App

PrivateGPT does not have a web interface yet, so you will have to use it in the command-line interface for now. Also, it currently does not take advantage of the GPU, which is a bummer. Once GPU support is introduced, the performance ChatGPT App will get much better. Finally, to load up the PrivateGPT AI chatbot, simply run python privateGPT.py if you have not added new documents to the source folder. Here, you can add all kinds of documents to train the custom AI chatbot.

Llama 2 is an open-source large language model (LLM) developed by Meta. It is a competent open-source large language model, arguably better than some closed models like GPT-3.5 and PaLM 2. It consists of three pre-trained and fine-tuned generative text model sizes, including the 7 billion, 13 billion, and 70 billion parameter models. That works, but we can get a much better interface by using the chat bot UI shown below.

We can also find the installation instructions on Rasa Open Source. All the code used in the article can be found in the GitHub repository. Normal Python for loops don’t work for iterating over state vars because these values can change and aren’t known at compile time. Instead, we use the foreach component to iterate over the chat history. For each function above, jsonify() is used to turn Python dictionaries into JSON format, which is then returned with a 200 status code for successful queries. These lines import Discord’s API, create the Client object that allows us to dictate what the bot can do, and lastly run the bot with our token.

For ChromeOS, you can use the excellent Caret app (Download) to edit the code. We are almost done setting up the software environment, and it’s time to get the OpenAI API key. This is meant for creating a simple UI to interact with the trained AI chatbot. For this project, you will use unsupervised learning to group how to make chatbot in python your customers into clusters based on individual aspects such as age, gender, region and interests. K-means clustering or hierarchical clustering are suitable here, but you can also experiment with fuzzy clustering or density-based clustering methods. You can use the Mall_Customers data set as sample data.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Click on the llama-2–70b-chat model to view the Llama 2 API endpoints. Click the API button on the llama-2–70b-chat model’s navigation bar. On the right side of the page, click on the Python button.