ChatterBot: Build a Chatbot With Python

In this case, you will need to pass in a list of statements where the order of each statement is based on its placement in a given conversation. Each statement in the list is a possible response to its predecessor in the list. In this tutorial, we will be using the Chatterbot Python library to build an AI-based Chatbot.

While we can use asynchronous techniques and worker pools in a more production-focused server set-up, that also won’t be enough as the number of simultaneous users grow. Imagine a scenario where the web server also creates the request to the third-party service. Ideally, we could have this worker running on a completely different server, in its own environment, but for now, we will create its own Python environment on our local machine. Ultimately the message received from the clients will be sent to the AI Model, and the response sent back to the client will be the response from the AI Model. While the connection is open, we receive any messages sent by the client with websocket.receive_test() and print them to the terminal for now.

How to Generate a Chat Session Token with UUID

If you recall, the values in the keywords_dict dictionary were formatted with special sequences of meta-characters. More details about Regular Expression and its syntax can be found here. You can add as many key-value pairs to the dictionary as you want to increase the functionality of the chatbot. Once our keywords list is complete, we need to build up a dictionary that matches our keywords to intents. We also need to reformat the keywords in a special syntax that makes them visible to Regular Expression’s search function. We’ll be designing a very simple chatbot for a bank that can respond to greetings (Hi, Hello, etc.) and answer questions about the bank’s hours of operation.

We will ultimately extend this function later with additional token validation. In the websocket_endpoint function, which takes a WebSocket, we add the new websocket to the connection manager and run a while True loop, to ensure that the socket stays open. Lastly, the send_personal_message method will take in a message and the Websocket we want to send the message to and asynchronously send the message. The ConnectionManager class is initialized with an active_connections attribute that is a list of active connections. The test route will return a simple JSON response that tells us the API is online.

Step 6: Test the chatbot

This data can be acquired from different sources such as social media, forums, surveys, web scraping, public datasets or user-generated content. Using the ChatterBot library and the right strategy, you can create chatbots for consumers that are natural and relevant. A Python chatbot is an artificial intelligence-based program that mimics ai chatbot python human speech. Python is an effective and simple programming language for building chatbots and frameworks like ChatterBot. There is extensive coverage of robotics, computer vision, natural language processing, machine learning, and other AI-related topics. It covers both the theoretical underpinnings and practical applications of AI.

Depending on your input data, this may or may not be exactly what you want. For the provided WhatsApp chat export data, this isn’t ideal because not every line represents a question followed by an answer. All of this data would interfere with the output of your chatbot and would certainly make it sound much less conversational.

Natural Language Processing, often abbreviated as NLP, is the cornerstone of any intelligent chatbot. NLP is a subfield of AI that focuses on the interaction between humans and computers using natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human language in a valuable way. If you’re not interested in houseplants, then pick your own chatbot idea with unique data to use for training. Repeat the process that you learned in this tutorial, but clean and use your own data for training.

We have also included another parameter named ‘logic_adapters’ that specifies the adapters utilized to train the chatbot. We will begin building a Python chatbot by importing all the required packages and modules necessary for the project. We will also initialize different variables that we want to use in it. Moreover, we will also be dealing with text data, so we have to perform data preprocessing on the dataset before designing an ML model. ChatterBot is a Python library that is developed to provide automated responses to user inputs. It makes utilization of a combination of Machine Learning algorithms in order to generate multiple types of responses.

It should be ensured that the backend information is accessible to the chatbot. Finally, in the last line (line 13) a response is called out from the chatbot and passes it the user input collected in line 9 which was assigned as a query. Famous fast food chains such as Pizza Hut and KFC have made major investments in chatbots, letting customers place their orders through them. For instance, Taco Bell’s TacoBot is especially designed for this purpose. It cracks jokes, uses emojis, and may even add water to your order.

This means that you must download the latest version of Python (python 3) from its Python official website and have it installed in your computer. Now, let’s create a chat loop that allows you to interact with the chatbot. The loop takes your input, generates a response, and continues until you decide to exit. Python is a popular choice for creating various types of bots due to its versatility and abundant libraries. Whether it’s chatbots, web crawlers, or automation bots, Python’s simplicity, extensive ecosystem, and NLP tools make it well-suited for developing effective and efficient bots. So, now that we have taught our machine about how to link the pattern in a user’s input to a relevant tag, we are all set to test it.

chatbot-nltk

In the next section, we will build our chat web server using FastAPI and Python. Redis is an in-memory key-value store that enables super-fast fetching and storing of JSON-like data. For this tutorial, we will use a managed free Redis storage provided by Redis Enterprise for testing purposes. I’ve carefully divided the project into sections to ensure that you can easily select the phase that is important to you in case you do not wish to code the full application. In addition to all this, you’ll also need to think about the user interface, design and usability of your application, and much more. Openai-whisper-talk is a sample voice conversation application powered by OpenAI technologies such as Whisper, Completions, Embeddings, and the latest Text-to-Speech.

ChatterBot uses the default SQLStorageAdapter and creates a SQLite file database unless you specify a different storage adapter. NLTK will automatically create the directory during the first run of your chatbot. For this tutorial, you’ll use ChatterBot 1.0.4, which also works with newer Python versions on macOS and Linux.

You’ll go through designing the architecture, developing the API services, developing the user interface, and finally deploying your application. A backend API will be able to handle specific responses and requests that the chatbot will need to retrieve. The integration of the chatbot and API can be checked by sending queries and checking chatbot’s responses.

• Your chatbot has increased its range of responses based on the training data that you fed to it.
• The bot uses the information to build a knowledge graph of known input statements and their probable responses.
• When a user inputs a query, or in the case of chatbots with speech-to-text conversion modules, speaks a query, the chatbot replies according to the predefined script within its library.
• You may have to work a little hard in preparing for it but the result will definitely be worth it.

We also highlighted two routes to creating them — the one that involved coding and the other that is no-code. This article explores the code-based approach, which will be scripted in Python. The responses are described in another dictionary with the intent being the key. Here, we first defined a list of words list_words that we will be using as our keywords. We used WordNet to expand our initial list with synonyms of the keywords. As discussed previously, we’ll be using WordNet to build up a dictionary of synonyms to our keywords.

The model we will be using is the GPT-J-6B Model provided by EleutherAI. It’s a generative language model which was trained with 6 Billion parameters. In the next section, we will focus on communicating with the AI model and handling the data transfer between client, server, worker, and the external API. We can store this JSON data in Redis so we don’t lose the chat history once the connection is lost, because our WebSocket does not store state. Next, to run our newly created Producer, update chat.py and the WebSocket /chat endpoint like below.

The consume_stream method pulls a new message from the queue from the message channel, using the xread method provided by aioredis. In server.src.socket.utils.py update the get_token function to check if the token exists in the Redis instance. If it does then we return the token, which means that the socket connection is valid. Now that we have a token being generated and stored, this is a good time to update the get_token dependency in our /chat WebSocket. We do this to check for a valid token before starting the chat session.