Since the launch of Open AI GPTs, AI chatbots have taken up tasks ranging from suggesting weekly meal plans to handling customer complaints for large businesses. AI chatbots show huge potential in automating tedious tasks in both personal and professional spaces. But how would you build an AI chatbot using Python and NLP?

In this blog, we will go through the step by step process of creating simple conversational AI chatbots using Python & NLP.  

Understanding AI chatbot

An AI chatbot is an advanced software application that simulates human conversation, either through text or voice interactions. 

Using artificial intelligence, particularly natural language processing (NLP), these chatbots understand and respond to user queries in a natural, human-like manner. They are increasingly popular in customer service, e-commerce, and various other industries, providing round-the-clock assistance, handling customer inquiries, and even assisting with sales and marketing strategies. 

AI chatbots are programmed to learn from interactions, enabling them to improve their responses over time and offer personalized experiences to users. Their integration into business operations helps in enhancing customer engagement, reducing operational costs, and streamlining processes.

Different types of AI chatbots 

Before you jump off to create your own AI chatbot, let’s try to understand the broad categories of chatbots in general. 

Rule-based Chatbots 

Rule-based chatbots are based on predefined rules & the entire conversation is scripted. They’re ideal for handling simple tasks, following a set of instructions and providing pre-written answers. They can’t deviate from the rules and are unable to handle nuanced conversations. 

AI-powered conversational Chatbot

Conversational chatbots use generative AI to handle conversations in a human-like manner. AI chatbots learn from previous conversations, can extract knowledge from documentation, can handle multi-lingual conversations and engage customers naturally. They’re useful for handling all kinds of tasks from routing tasks like account QnA to complex product queries. 

Each type of chatbot serves unique purposes, and choosing the right one depends on the specific needs and goals of a business.

The smarter way to build AI chatbot : Using Alltius 

Alltius is a GenAI platform that allows you to create skillful, secure and accurate AI assistants with a no-code user interface. With Alltius, you can create your own AI assistants within minutes using your own documents. 

Alltius’ AI assistants are powerful given it offers the widest variety of data sources to train AI assistants like PDF, videos, emails, images, excel, APIs, webpages, FAQs and more. The AI assistants can be trained to greet, answer queries, extract information from documents, create pitches, draft emails, extract insights and much more. And the AI assistants can be deployed on websites, Slack, Zendesk, Intercom, your product and more. 

Let’s see how easy it is to build AI assistants using Alltius. 

Step 1: Create AI assistant 

Create your free account on Alltius. Once you login, select Coach Assistants from the left menu and select +Create New. Name your new assistant and it will lead you to your next step. 

Alltius platform : How to create an AI assistant?

Step 2 : Train your AI assistant 

Now, let’s add sources to train your AI assistant. Select +Add New Source. You’ll see a dropdown menu listing all the sources you can add. Select your data sources one by one and add the required data. 

In case you need to extract data from your software, go to Integrations from the left menu and install the required integration. 

Training AI assistant on Alltius platform.

Step 3 : Test the AI assistant 

Once you’ve added all the data sources, it’s time to test it out. Go to Playground to interact with your AI assistant before you deploy it. If you face any issues, our team is one call away.

Testing AI assistant on Alltius platform

 

Step 4 : Deploy it 

The next step is to deploy it. Go to Channels and select Add a new Widget. Follow all the instructions to add brand elements to your AI chatbot and deploy it on your website or app of your choice. 

Deploying AI assistant on multiple channels using Alltius API

Using Python to Build an AI chatbot

Let’s start with building our own python AI chatbot. We’ve listed all the important steps for you and while this only shows a basic AI chatbot, you can add multiple functions on top of it to make it suitable for your requirements. 

Step 1: Install libraries 

In this AI chatbot, we will use the ChatterBot library. ChatterBot is an AI-based library that provides necessary tools to build conversational agents which can learn from previous conversations and given inputs. 

To get started, just use the pip install command to add the library.

pip install chatterbot

Step 2: Other libraries 

Now, we will import additional libraries, ChatBot and corpus trainers. 

ChatBot allows us to call a ChatBot instance representing the chatbot itself. It provides an interface for interacting with it. The ChatterBot Corpus has multiple conversational datasets that can be used to train your python AI chatbots in different languages and topics without providing a dataset yourself. 

from chatterbot import ChatBot  from chatterbot.trainers import ChatterBotCorpusTrainer

Step 3: Create your AI chatbot 

Now, as discussed earlier, we are going to call the ChatBot instance. And in the brackets, we can name it whatever we like. We’ll name our chatbot Alltius.  

chatbot = ChatBot(‘Alltius’)

Step 4: Train your python chatbot with corpus trainer 

Now, we will use the ChatterBotCorpusTrainer to train our python chatbot. We will use an English language corpus. Here is a list of all different available corpus. 

trainer = ChatterBotCorpusTrainer(chatbot)  trainer.train("chatterbot.corpus.english")

Step 5: Test your AI chatbot 

Now, let’s test it out. We will see how our chatbot responds to a simple greeting. 

response = chatbot.get_response("Hello, how are you doing today?")  print(response)

Step 6: Train your python chatbot with custom data

What if you want to train using your data? Chatterbot allows you to do that using ListTrainer. Using ListTrainer, you can pass a list of commands where the python AI chatbot will consider every item in the list as a good response for its predecessor in the list.

from chatterbot.trainers import ListTrainer trainer = ListTrainer(bot) trainer.train([ 'How are you?', 'I am good.', 'That is good to hear.', 'Thank you', 'You are welcome.',]

Step 7: Integrate your python chatbot in website application 

Here, you can use Flask to create a front-end for your NLP chatbot. This will allow your users to interact with chatbot using a webpage or a public URL. 

from flask import Flask, render_template, request app = Flask(__name__) @app.route("/") def home(): return render_template("index.html") @app.route("/get") def get_bot_response(): userText = request.args.get('msg') return str(englishBot.get_response(userText))if __name__ == "__main__": app.run()

Using Rasa & Rag models to build an AI chatbot

Step 1 - Prepare your help docs for the RAG Project

Gather and prepare all documents you’ll need to to train your AI chatbot. You’ll need to pre-process the documents which means converting raw textual information into a format suitable for training natural language processing models. In this method, we’ll use spaCy, a powerful and versatile natural language processing library.

With spaCy, we can tokenize the text, removing stop words, and lemmatizing words to obtain their base forms. This not only reduces the dimensionality of the data but also ensures that the model focuses on meaningful information.

Through spaCy's efficient preprocessing capabilities, the help docs become refined and ready for further stages of the chatbot development process. This meticulous preparation lays the foundation for training models, ensuring that the chatbot can effectively understand and respond to user queries based on the enriched and structured information gleaned from the help documentation.

import spacy

# Load spaCy English model

nlp = spacy.load("en_core_web_sm")

# Example help docs

help_docs = """

Your help docs here.

"""

# Tokenize, remove stop words, and lemmatize

def preprocess(text):

doc = nlp(text)

tokens = [token.lemma_ for token in doc if not token.is_stop]

return " ".join(tokens)

preprocessed_help_docs = preprocess(help_docs)

Step 2 - Train the RAG model

In the second step of building a chatbot on help docs, training a RAG (Retrieval-Augmented Generation) Architecture or model is pivotal for enabling the system to understand and generate contextually relevant responses.

Leveraging the preprocessed help docs, the model is trained to grasp the semantic nuances and information contained within the documentation. The choice of the specific model is crucial, and in this instance,we use the facebook/bart-base model from the Transformers library.

from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration

# Tokenize the preprocessed help docs

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")

tokenized_help_docs = tokenizer(preprocessed_help_docs, return_tensors="pt")

# Train the RAG model

model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-base")

model.train()

loss = model(**tokenized_help_docs)

loss.backward()

optimizer.step()

This process involves adjusting model parameters based on the provided training data, optimizing its ability to comprehend and generate responses that align with the context of user queries. The training phase is crucial for ensuring the chatbot's proficiency in delivering accurate and contextually appropriate information derived from the preprocessed help documentation.

Step 3 Create a chatbot interface using the Rasa Framework Library

Rasa is an open-source platform for building conversational AI applications. In the next steps, we will navigate you through the process of setting up, understanding key concepts, creating a chatbot, and deploying it to handle real-world conversational scenarios.

Setting Up Your Rasa Environment

Before delving into chatbot creation, it's crucial to set up your development environment. This involves installing Python, pip, and Rasa. A straightforward pip command ensures the download and installation of the necessary packages, while rasa init initiates the creation of your Rasa project, allowing customization of project name and location.

pip install rasa

rasa init

Understanding Key Rasa Concepts

Familiarizing yourself with essential Rasa concepts lays the foundation for effective chatbot development. Intents represent user goals, entities extract information, actions dictate bot responses, and stories define conversation flows. The directory and file structure of a Rasa project provide a structured framework for organizing intents, actions, and training data.

# domain.yml

intents:

  - greet

  - goodbye

actions:

  - utter_greet

  - utter_goodbye

Step 4 : Creating your chatbot. 

Building a chatbot involves defining intents, creating responses, configuring actions and domain, training the chatbot, and interacting with it through the Rasa shell. The guide illustrates a step-by-step process to ensure a clear understanding of the chatbot creation workflow.

rasa train nlu

rasa shell

Handling Complex Conversations

Real-world conversations often involve structured information gathering, multi-turn interactions, and external integrations. Rasa's capabilities in handling forms, managing multi-turn conversations, and integrating custom actions for external services are explored in detail.

# forms.yml

forms:

  booking_form:

required_slots:

   - origin

   - destination

   - date

Enhancing Natural Language Understanding (NLU)

Improving NLU accuracy is crucial for effective user interactions. The guide provides insights into leveraging machine learning models, handling entities and slots, and deploying strategies to enhance NLU capabilities.

rasa test nlu --nlu data/nlu.md --config config.yml --cross-validation

Deployment Strategies

Deploying a Rasa chatbot to production requires careful planning. Containerization through Docker, utilizing webhooks for external integrations, and exploring chatbot hosting platforms are discussed as viable deployment strategies.

docker-compose up -d

rasa run --endpoints endpoints.yml

Handling Real-World Scenarios

Rasa's flexibility shines in handling dynamic responses with custom actions, maintaining contextual conversations, providing conditional responses, and managing user stories effectively. The guide delves into these advanced techniques to address real-world conversational scenarios.

# actions.py

class ActionCheckWeather(Action):

   def name(self) -> Text:

   return "action_check_weather"

   def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:

   dispatcher.utter_message("Hello World! from custom action")

   return []

Ste 5: Testing and Evaluation

Thorough testing of the chatbot's NLU models and dialogue management is crucial for identifying issues and refining performance. The guide introduces tools like rasa test for NLU unit testing, interactive learning for NLU refinement, and dialogue story testing for evaluating dialogue management.

rasa interactive

rasa test core --stories data/stories.md --config config.yml

Deployment becomes paramount to make the chatbot accessible to users in a production environment. Deploying a Rasa Framework chatbot involves setting up the Rasa Framework server, a user-friendly and efficient solution that simplifies the deployment process. Rasa Framework server streamlines the deployment of the chatbot, making it readily available for users to engage with.

To initiate deployment, developers can opt for the straightforward approach of using the Rasa Framework server, which provides a convenient way to expose the chatbot's functionality through a REST API. This allows users to interact with the chatbot seamlessly, sending queries and receiving responses in real-time.

Alternatively, for those seeking a cloud-based deployment option, platforms like Heroku offer a scalable and accessible solution. Deploying on Heroku involves configuring the chatbot for the platform and leveraging its infrastructure to ensure reliable and consistent performance.

The deployment phase is pivotal for transforming the chatbot from a development environment to a practical and user-facing tool. Whether utilizing the Rasa Framework server or platforms like Heroku, this step ensures that the chatbot is operational, responsive, and ready to assist users by leveraging the insights extracted from the preprocessed help documentation through the trained RAG model.

Step 6: Testing and Production Customization

After deploying the Rasa Framework chatbot, the crucial phase of testing and production customization ensues. Users can now actively engage with the chatbot by sending queries to the Rasa Framework API endpoint, marking the transition from development to real-world application. While the provided example offers a fundamental interaction model, customization becomes imperative to align the chatbot with specific requirements.

Testing plays a pivotal role in this phase, allowing developers to assess the chatbot's performance, identify potential issues, and refine its responses. Rigorous testing ensures that the chatbot comprehensively understands user queries and delivers accurate, contextually relevant information extracted from the preprocessed help documentation via the trained RAG model.

Customization, on the other hand, involves tailoring the chatbot to meet unique demands. This can include refining the natural language understanding (NLU) capabilities, incorporating domain-specific language, and enhancing response generation.

Challenges while building your AI chatbot 

Now that you have information about how to build an AI chatbot, let’s take a look at some of the challenges you might face while making one: 

Understanding Natural Language: One of the biggest challenges is ensuring that the chatbot understands human language. This might include slang, idioms, and various synonyms. You must constantly refine to handle the nuances and complexity of human communication effectively. Alltius’ AI assistants are intelligent enough to understand the nuances of human language and the emotions. 

Context Handling: Maintaining the context of a conversation over multiple interactions is difficult. A chatbot needs to remember past interactions and use this context to make current interactions more relevant and coherent. Alltius’ AI assistants can remember all the past conversations and use the knowledge to provide better customer experiences to every user. 

User Intent Recognition: Identifying what the chatbot user wants (intent) from their input can be challenging, especially when the input is ambiguous. The AI chatbot must be trained on a wide range of possible inputs to accurately discern user intent. Alltius’ AI assistants can interpret user intent with almost 99% accuracy. 

Personalization: Tailoring conversations to individual users, based on their preferences, history, and behavior, is essential for enhanced user experience but is challenging to implement effectively. 

Handling Unexpected Queries: Users may pose questions or use language that the chatbot hasn't been trained on. Building a chatbot that can gracefully handle such unexpected inputs without breaking the flow of conversation is a significant challenge. Alltius’ AI chatbots are trained to answer “I don’t know” instead of giving a random output so as to not irritate the user. 

Scalability and Performance: As the number of users increases, the chatbot should be able to scale accordingly without compromising on response time or accuracy. Alltius’ AI chatbots can handle over 10K+ queries everyday. 

Integration with Multiple Platforms: Ensuring the chatbot functions seamlessly across various platforms (websites, social media, messaging apps) involves dealing with different APIs and interfaces. Alltius integrates with all major platforms. 

Data Privacy and Security: Safeguarding user data and ensuring privacy, especially in sectors like healthcare or finance, is critical and requires adherence to various regulations and standards. Alltius is an extremely secure platform, with SOC2, VAPT, GDPR and ISO certifications. 

Conclusion

We've covered the fundamentals of building an AI chatbot using Python and NLP. Now, you’ve a basic idea about how to create a python AI chatbot. These are basic chatbots, the potential of AI chatbots is huge. 

Keep in mind that artificial intelligence is an ever-evolving field, and staying up-to-date is crucial. To ensure that you're at the forefront of AI advancements, refer to reputable resources like research papers, articles, and blogs. 

In case you’re looking to implement an AI chatbot for your business, Alltius is a good place to start. You can create and implement your own AI chatbot on your website or your app within hours without any external help. We offer a free trial and in case you face any issues, feel free to set up a call with us!  

Note: The code snippets provided in this blog post are for illustrative purposes and may require additional modifications and error handling to suit your specific requirements.

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