How to Create a Specialist Chatbot with OpenAIs Assistant API and Streamlit by Alan Jones

Pyrogram is a Python framework that allows developers to interact with the Telegram Bot API. It simplifies the process of building a bot by providing a range of tools and features. With these tools, developers can create custom commands, handle user inputs, and integrate the ChatGPT API to generate responses.

Such LLMs were originally huge and mostly catered to enterprises that have the funds and resources to provision GPUs and train models on large volumes of data. A chatbot is an AI you can have a conversation with, while an AI assistant is a chatbot that can use tools. A tool can be things like web browsing, a calculator, a Python interpreter, or anything else that expands the capabilities of a chatbot [1]. For those looking for a quick and easy way to create an awesome user interface for web apps, the Streamlit library is a solid option.

The easiest way to try out the chatbot is by using the command rasa shell from one terminal, and running the command rasa run actions in another. Here, we demonstrate how Streamlit can be used to build decent user interfaces for LLM applications with just a few lines of code. For the APIChain class, we need the external API’s documentation in string format to access endpoint details.

Add the Bot into the Server

Thus, its applications are wide-ranging and cover a variety of fields, such as customer service, content creation, language translation, or code generation. In our earlier article, we demonstrated how to build an AI chatbot with the ChatGPT API and assign a role to personalize it. But what if you want to train the AI on your own data?

At the outset, we should define the remote interface that determines the remote invocable methods for each node. On the one hand, we have methods that return relevant information for debugging purposes (log() or getIP()). On the other hand, there are those in charge of obtaining remote references to other nodes and registering them into the local hierarchy as an ascending or descending node, using a ChatGPT name that we will assume unique for each node. Additionally, it has two other primitives intended to receive an incoming query from another node (receiveMessage()) and to send a solved query to the API (sendMessagePython()), only executed in the root node. At first, we must determine what constitutes a client, in particular, what tools or interfaces the user will require to interact with the system.

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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. Now, it’s time to install the OpenAI library, which will allow us to interact with ChatGPT through their API. In the Terminal, run the below command to install the OpenAI library using Pip.

You can foun additiona information about ai customer service and artificial intelligence and NLP. You’ll need to obtain an API key from OpenAI to use the API. Once you have your API key, you can use the Requests library to send a text input to the API and receive a response. You’ll need to parse the response and send it back to the user via Telegram. While the OpenAI API is a powerful tool, it does have its limitations. For example, it may not always generate the exact responses you want, and it may require a significant amount of data to train effectively. It’s also important to note that the API is not a magic solution to all problems – it’s a tool that can help you achieve your goals, but it requires careful use and management.

That is exactly the experience I want to create in this article. Test your bot with different input messages to see how it responds. Keep in mind that the responses will be generated by the OpenAI API, so they may not always be perfect. You can experiment with different values for the max_tokens and temperature parameters in the generate_response method to adjust the quality and style of the generated responses. Now that your bot is connected to Telegram, you’ll need to handle user inputs.

If you are getting started there are plenty of tutorials around, especially on Medium. And Stackoverflow is also a great resource for answering questions and understanding issues (your author is often spotted there to try helping fellow developers out 🤓). You can either code in Jupyter Notebook or VSCode or another of your favorite editors. To use the OpenAI API, we first need to create an account on openai.com and create an API key. Remember to copy the key and save it somewhere for later use.

However, we can also emulate the functionality of the API with a custom Kotlin intermediate component, using ordinary TCP Android sockets for communication. Sockets are relatively easy to use, require a bit of effort to manage, ensure everything works correctly, and provide a decent level of control over the code. However, choosing a model for a system should not be based solely on the number of parameters it has, since its architecture denotes the amount of knowledge it can model.

After every answer, it will also display four sources from where it has got the context. Finally, run PrivateGPT by executing the below command. Next, hit Enter, and you will move to the privateGPT-main folder. Now, right-click on the “privateGPT-main” folder and choose “Copy as path“. First, you need to install Python 3.10 or later on your Windows, macOS, or Linux computer.

• So this is how you can build your own AI chatbot with ChatGPT 3.5.
• It allows users to interact with your bot via text messages and provides a range of features for customisation.
• Rasa X and Rasa run actions should run in 2 different terminals.
• In a few days, I am leading a keynote on Generative AI at the upcoming Cascadia Data Science conference.
• If you are using Windows, open Windows Terminal or Command Prompt.

Tools represent distinct components designed for specific tasks, such as fetching information from external sources or processing data. Apart from the OpenAI GPT series, you can choose from many other available models, although most of them require an authentication token to be inserted in the script. For example, recently modern models have been released, optimized in terms of occupied space and time required for a query to go through ChatGPT App the entire inference pipeline. Llama3 is one of them, with small versions of 8B parameters, and large-scale versions of 70B. As can be seen in the script, the pipeline instance allows us to select the LLM model that will be executed at the hosted node. This provides us with access to all those uploaded to the Huggingface website, with very diverse options such as code generation models, chat, general response generation, etc.

Step 6: Deploy your Function App to Azure

The course includes programming-related assignments and practical activities to help students learn more effectively. We can send a message and get a response once the chatbot Python has been trained. Creating a function that analyses user input and uses the chatbot’s knowledge store to produce appropriate responses will be necessary.

It moves on to the next action i.e. to execute a Python REPL command (which is to work interactively with the Python interpreter) that calculates the ratio of survived passengers to total passengers. We will now make the csv agent with just a few lines of code, which is explained line-by-line. This line creates a pandas DataFrame from the historical dividend data extracted from the API response. The ‘historical’ key in the data dictionary contains a list of dictionaries, where each dictionary represents historical dividend data for a specific date.

After installing miniconda, Follow below commands to create a virtual environment in conda. Telegram Bot can work with a Pull or with a Push mechanism (see further Webhooks). The pull mechanism is where the bot (your code) is checking regularly for new available messages on the server. It is possible to create a Poll directly in the Telegram application (without coding) but here we will explore how to develop from scratch a Telegram Chatbot quiz using the Python Telegram Bot library. To stop the custom-trained AI chatbot, press “Ctrl + C” in the Terminal window. Make sure the “docs” folder and “app.py” are in the same location, as shown in the screenshot below.

Keep in mind, the local URL will be the same, but the public URL will change after every server restart. In this section, we are fetching historical dividend data for a specific stock, AAPL (Apple Inc.), using an API provided by FinancialModelingPrep (FMP). We first specify our API key, then construct a URL with the how to make a chatbot in python appropriate endpoint and query parameters. After sending a GET request to the URL, we retrieve the response and convert it to a JSON format for further processing. It represents a model architecture blending features of both retrieval-based and generation-based approaches in natural language processing (NLP).

JavaScript contains a number of libraries, as outlined here for demonstration purposes, while Java lovers can rely on ML packages such as Weka. 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. This is great for small data sets and more simple analyses, but Python’s libraries are much more practical. NLTK is not only a good bet for fairly simple chatbots, but also if you are looking for something more advanced.

As you can see, it first uses getRemoteNode() to retrieve the parent node, and once it has the reference, assigns it to a local variable for each node instance. Afterwards it calls on the connectChild(), which appends to the descendant list the remote node from which it was invoked. In case the parent node does not exist, it will try to call a function on a null object, raising an exception.

• In the left side, you can try to chat with your bot and on the right side you can see, which intent and reply is getting responded.
• Test your bot with different input messages to see how it responds.
• To restart the AI chatbot server, simply move to the Desktop location again and run the below command.
• With these tools, developers can create custom commands, handle user inputs, and integrate the ChatGPT API to generate responses.
• To showcase this capability I served the chatbot through a Shiny for Python web application.

Notable Points Before You Train AI with Your Own Data1. You can train the AI chatbot on any platform, whether Windows, macOS, Linux, or ChromeOS. In this article, I’m using Windows 11, but the steps are nearly identical for other platforms. The guide is meant for general users, and the instructions are explained in simple language. So even if you have a cursory knowledge of computers and don’t know how to code, you can easily train and create a Q&A AI chatbot in a few minutes. If you followed our previous ChatGPT bot article, it would be even easier to understand the process.3.