Contributors

Dhanashree B

Product Marketing Manager

Updated on

May 8, 2025

How to Build an AI Chatbot Using Python and NLP (With and Without Code)

Since the launch of GPTs by OpenAI, AI chatbots have taken over a wide range of tasks—from helping you plan meals to assisting customer support teams at large enterprises. These chatbots are rapidly transforming how both individuals and businesses interact with technology.

In this guide, we’ll walk you through two approaches to building conversational AI chatbots:

No-code approach using Alltius
Code-based approach using Python, NLP libraries like spaCy and ChatterBot, and advanced methods like RAG and Rasa

What Is an AI Chatbot?

An AI chatbot is a software program that simulates human-like conversation using artificial intelligence, especially Natural Language Processing (NLP). These bots can:

Understand user inputs in text or voice
Respond contextually
Learn and improve over time

They’re widely used in industries like:

Customer support
E-commerce
Banking and insurance
Healthcare

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 AI 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 No-Code Approach: Build an 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 conversational AI assistants using Alltius.

Step 1: Create Your Assistant

Sign up at Alltius
Go to Coach Assistants > +Create New

Alltius platform : How to create an AI assistant?

Step 2: Train Your Assistant

Add sources like PDFs, videos, Excel files, webpages, APIs, etc.
Or connect to platforms like Slack, Zendesk, or Intercom via integrations

Training AI assistant on Alltius platform.

Step 3: Test the Assistant

Use the Playground to test responses in real-time

Testing AI assistant on Alltius platform

Step 4: Deploy Anywhere

Go to Channels > Add New Widget
Deploy to your website, Slack, support tools, or product dashboard

Deploying AI assistant on multiple channels using Alltius API

💡 Alltius is ideal for businesses looking to automate support, onboarding, internal knowledge retrieval, and more.

The Code-Based Approach: Build a Python NLP Chatbot

Step 1: Install Required Libraries

Bash

pip install chatterbot

pip install chatterbot_corpus

Step 2: Import Key Libraries

Python

from chatterbot import ChatBot

from chatterbot.trainers import ChatterBotCorpusTrainer

Step 3: Create Your Chatbot

Python

chatbot = ChatBot("Alltius")

Step 4: Train with Built-in Corpus

Python

trainer = ChatterBotCorpusTrainer(chatbot)

trainer.train("chatterbot.corpus.english")

Step 5: Test the Bot

Python

response = chatbot.get_response("Hi there!")

print(response)

Step 6: Train with Custom Data

Python

from chatterbot.trainers import ListTrainer

trainer = ListTrainer(chatbot)

trainer.train(["Hi", "Hello", "How are you?", "I'm good, thanks."])

Step 7: Add a Web Interface Using Flask

Bash

pip install flask

You can create a Flask app to expose the chatbot through a web interface.

Advanced Setup: RAG + Rasa for Knowledge-Retrieval Chatbots

For more robust, production-grade bots, combine Retrieval-Augmented Generation (RAG) with Rasa.

Step 1: Preprocess Documents with spaCy

Use spaCy to clean, tokenize, and lemmatize your documents:

Python

import spacy

nlp = spacy.load("en_core_web_sm")

doc = nlp("Your raw help doc text here.")

Step 2: Train a RAG Model

Use Hugging Face’s Transformers (facebook/bart-base)
Fine-tune with your help documentation

Python

from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration

Step 3: Set Up Rasa Framework

Bash

pip install rasa

rasa init

Step 4: Define Intents, Entities, Actions, and Stories

Use the Rasa project structure to:

Train custom NLU
Define actions (e.g., fetching data from APIs)
Handle contextual and multi-turn conversations

Step 5: Test & Deploy

Use:

Bash

rasa shell

rasa test

Deploy with:

Rasa X or Docker
Cloud platforms like Heroku

Real-World Use Cases for AI Chatbots

Use Case	Description
Customer Support	Reduce wait times and answer 24/7
Loan Processing	Help customers with loan eligibility or application steps
HR Onboarding	Answer employee FAQs, explain policies
Sales Enablement	Provide product info, recommend features
Internal Help Desk	Guide employees to internal documentation

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.

Final Thoughts

Whether you're a developer building from scratch or a business user looking for a fast, no-code solution, there's an AI chatbot framework out there for you.

Use Python + NLP for flexibility and control
Use Alltius if you want to deploy a secure, enterprise-ready chatbot in minutes
Use RAG + Rasa for knowledge-intensive and highly customizable solutions

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.

Related Resources:

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AI Chatbot FAQ Accordion

FAQ

What is the best way to build an AI chatbot using Python?

The best way to build an AI chatbot using Python is by combining libraries like ChatterBot for rule-based responses or using frameworks like Rasa for more complex, NLP-based conversations. You can also integrate transformer models like BART for retrieval-augmented generation (RAG) to handle dynamic queries.

What’s the difference between retrieval-based and generative chatbots?

Retrieval-based chatbots select responses from a pre-defined set using similarity scoring or rules, while generative chatbots like those using GPT or BERT can generate new, context-aware responses from scratch. Retrieval is faster and more reliable; generation is more flexible but needs stronger models.

How does RAG (retrieval augmented generation) work in chatbots?

RAG combines the strength of retrieval and generation. The chatbot first retrieves relevant documents or snippets from a knowledge base, then uses a generative model like GPT or BART to create a response using those documents as context. It improves accuracy and relevance, especially for domain-specific questions.

Can I train a chatbot on my own data?

Yes! You can fine-tune a model or set up a retrieval system with your own documents (PDFs, websites, transcripts) and connect it with a generative model for accurate answers. Tools like LangChain, LlamaIndex, and Vector DBs (Pinecone, Weaviate, FAISS) make this possible.

Is it necessary to use a vector database for chatbot development?

While not always necessary, vector databases are highly recommended for chatbots using semantic search or RAG. They allow you to store and query high-dimensional embeddings of your data for fast and accurate retrieval, which improves the quality of responses from LLMs.

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