Best Free Tools for Building Chatbots with NLP

We have all used chat gpt, but what if I tell you to make a bot which works on your given knowledge base? That means you have some data, maybe proprietary data for your business, and you want a chatbot trained on it. How will you make it? Well, in today's digital world, chatbots are becoming super important for businesses to talk with customers, automate tasks, and provide instant support. Large language models, or llms, have made these bots smarter than ever. But building one, especially one that understands your specific data, might seem complex or expensive. Good news is, there are some fantastic free tools out there that make this possible, even if you're just getting started. This is arguably the cheapest and the most efficient way to get started with llms for your own business.

Understanding the Magic: Retrieval Augmented Generation (RAG)

Before we dive into the tools, let's back up a little. A key technique that makes chatting with your own data work is called retrieval augmented generation, or RAG for short. It's a fancy way of saying we take chunks of your specific data – maybe from PDFs, websites, or documents – and chuck them into the prompt we send to a large language model. The llm then uses this provided context to answer questions. This way, the bot isn't just relying on its general knowledge; it's using *your* knowledge base, making the answers relevant and specific to your needs. You can't always give vast amounts of data directly to something like chatgpt because there are limits, token limits, and it would take a lot of time. RAG offers a smarter way.

Top Free Tools for Your NLP Chatbot Journey

Alright, let's look at some tools that help you implement techniques like RAG and build powerful NLP chatbots without breaking the bank.

1. LangChain: The Flexible Framework for LLM Apps

If you want flexibility and control over how your chatbot works, LangChain is a seriously powerful tool. It's an open-source framework designed to help developers build applications powered by large language models. Think of it as the glue that connects different pieces together.

To get started, there's a bunch of dependencies you might need to import, mainly from LangChain itself, but maybe a little streamlit for the user interface or other libraries for specific tasks. Don't stress though, you typically import them as you need them.

Building Blocks with LangChain

LangChain lets you chain together different components:

• LLMs: You can easily connect to various large language models. For instance, you might use models available through platforms like IBM's watsonx.ai. You'd typically need credentials, like an API key and a service URL. You can pass through decoding parameters to control how the llm generates responses and specify things like a project ID if needed. Using state-of-the-art llms that don't use your data for training is a big plus, especially for businesses.
• Prompt Templates: Structure the input you send to the llm, incorporating user questions and the retrieved context (your data chunks).
• Document Loaders & Text Splitters: These help you load your data (like PDFs using a `load PDF` function) and break it down into manageable chunks suitable for retrieval.
• Embeddings & Vector Stores: To find the relevant chunks of your data quickly, LangChain integrates with embedding models and vector databases (like Chroma DB). It chunks up your document, creates vector representations (embeddings) of these chunks, and loads them into a vector database. This allows for efficient searching based on semantic similarity.
• Retrievers: These are responsible for fetching the relevant data chunks from your vector store based on the user's query.
• Chains: LangChain's core concept. You can create chains like the `RetrievalQA` chain, which combines a retriever and an llm to perform question-answering based on your documents. You basically swap out a base llm call for this specialized chain. Using something like `chain.run` then executes the process.

Why LangChain?

LangChain is great because it's modular and versatile. It's not a simple drag-and-drop tool, but it gives you the power to build sophisticated RAG pipelines tailored exactly to your needs. You can handle chat history, integrate different data sources, and control the entire logic. You might use a tool like Streamlit alongside it to build the user interface – adding chat input components, displaying user messages and llm responses using chat message components (maybe differentiating roles like 'user' and 'assistant'), and even using session state variables to maintain conversation history. Remember to save your app! It's easy to forget. Properly managing history means looping through saved messages and displaying them each time the app reruns.

Caching resources, like loading the vector store index using Streamlit's `@st.cache_resource`, can make your app a whole heap faster by avoiding reloading data unnecessarily.

2. Botpress: The Easy-to-Use Visual Chatbot Platform

Now, if you're looking for something more visual and perhaps easier to get started with, especially if coding isn't your main thing, Botpress is a fantastic option. The best thing I found about this platform was that it is very easy to make a bot. You can make a chatbot by using this, and you can also put those chatbots on your website.

Getting Started with Botpress

Signing up is very straightforward; you can often continue with Google or GitHub. Once you're in, you'll likely see a dashboard where you can manage your bots. You can start from scratch or use a template. They often recommend starting with their "Learn Botpress" template to get familiar with the platform.

Visual Workflow and Knowledge Bases

Botpress shines with its visual interface. You build your bot's logic using a canvas where you connect nodes and cards.

• Nodes & Cards: You start with a 'Start' node and typically end with an 'End' node. In between, you add standard nodes and various cards to handle different actions. You might add a 'Raw Input' card to capture what the user types, prompting them with something like "What do you want to ask?".
• Knowledge Answering: This is where Botpress makes RAG easy. You can enable 'Knowledge Answering' on an input card. Then, you add a knowledge base. You can create a new one or use a default. Inside the knowledge base, you add sources. This is super flexible – you can add web pages (just provide the URL like `https://www.ndtv.com`), do a web search, upload text documents (like `.txt`, `.pdf`, `.html`), or even just type in text directly. Botpress will index this information. When a user asks a question, the bot first tries to find the answer within this configured knowledge base. It will even tell you which source it used! You can add multiple pages or documents. For example, if you add a news website URL, it might parse several pages from that site to build its knowledge. It might not parse millions of pages from a huge domain, but you can add specific pages you care about. This is great for business documents – hold your business documents and ask questions about your business.
• AI Tasks / Connecting to LLMs: What if your knowledge base doesn't have the answer? You can configure your bot to fall back to an external AI, like ChatGPT. You can add an 'AI Generate Text' card (sometimes called AI Task). You can capture the user's input into a variable (e.g., `harry_input`) and pass that variable to the AI task. The AI's response can be saved to another variable (e.g., `harry_output`), and then you display that output variable to the user using a standard 'Text' card.
• Conditional Logic (Expressions): You don't always want the bot to call the external AI. Maybe only if the knowledge base fails. Botpress allows this using Expressions. You can add an 'Expression' card and write a condition like `turn.knowledgeAgent.responded`. By adding a 'not' condition (`!turn.knowledgeAgent.responded`), you can direct the flow to the AI Task *only* if the knowledge agent did *not* find an answer. Otherwise, you can direct the flow perhaps to the end or back to the input. This prevents unnecessary calls to the AI when your own data has the answer.
• Rich Content: You can add more than just text. Add cards for images, audio (you could upload an MP3 file to play a sound or message), video, and more.

Why Botpress?

Botpress makes chatbot building accessible. The visual editor, easy knowledge base integration, and built-in AI capabilities mean you can create a useful bot very quickly. You don't need deep coding knowledge to get started. You can train a bot on your data, like NDTV.com or your business docs, without the hassle of complex setups. Plus, you can publish your bot and easily get a shareable link or embed it on your website. Their documentation is often quite good, allowing you to learn quickly.

Choosing the Right Tool for You

So, LangChain or Botpress? Or maybe another tool?

• Choose LangChain if: You need maximum flexibility, want deep control over the RAG process, are comfortable with Python programming, and plan to build complex, custom LLM applications.
• Choose Botpress if: You prefer a visual interface, want a very easy way to integrate knowledge bases (websites, docs), need quick deployment, and perhaps have less coding experience. It's great for building support bots, FAQ bots, or website assistants rapidly.
• Other Mentions: Tools like Rasa offer open-source control similar to LangChain but with more of a dedicated chatbot framework structure. Google's Dialogflow ES has a generous free tier and integrates well with Google Cloud. Microsoft Bot Framework Composer offers a visual approach integrated with Azure services. These are also worth exploring depending on your specific ecosystem and needs.

Key Features to Look For in Free Tools

When evaluating free chatbot tools, consider these aspects based on what we've seen:

• Ease of Use: How quickly can you get started? Is there a visual builder (like Botpress) or is it code-heavy (like LangChain)?
• Knowledge Base Integration: How easy is it to add your own data (PDFs, URLs, text)? Does it support RAG effectively?
• NLP Capabilities: Does the tool handle natural language understanding well? Can it extract intent and entities?
• LLM/AI Integration: Can you connect to powerful LLMs (like Llama 2, GPT models)? Is it easy to configure fallback AI responses?
• Customization & Control: How much can you customize the workflow, logic (using expressions or code), and responses?
• Deployment Options: Can you easily deploy the bot to your website or other channels (like Messenger)?
• Free Tier Limits: What are the limitations of the free plan (e.g., number of bots, monthly messages, knowledge base size)?
• Community & Documentation: Is there good documentation and an active community to help if you get stuck? Botpress documentation, for example, can be quite helpful.

A Basic Workflow for Building Your Bot

Regardless of the tool, the general steps often look like this:

1. Sign Up / Setup: Create an account or set up your development environment (installing libraries like LangChain, Streamlit).
2. Plan the Flow: Outline the conversation logic. What should happen first? How should the bot respond? When should it search the knowledge base? When should it call an AI?
3. Build the Core Logic: Use the visual builder (Botpress) or code (LangChain) to create the nodes, cards, or chains that represent your flow. Add input prompts and basic responses.
4. Integrate Knowledge: Add your data sources (URLs, documents) to the knowledge base or set up the document loading, embedding, and vector store pipeline.
5. Connect AI (Optional but common): Configure the connection to an LLM, either for core responses or as a fallback. Set up API keys and parameters.
6. Handle State/History: Ensure the bot remembers previous parts of the conversation if needed (using session state or built-in features).
7. Test Thoroughly: Start new conversations, ask questions that should be in the knowledge base, ask questions that require the AI, and see how it behaves. Debug and refine.
8. Publish & Deploy: Make your bot live! Get a shareable link or embed code for your website.

Start Building Today!

Building a chatbot that understands your specific data is more accessible than ever, thanks to powerful and free tools like LangChain and Botpress. Whether you need the deep customization offered by a framework like LangChain or the user-friendly visual approach of Botpress, you can get started without significant investment. You can train a bot on your business data, website content, or personal documents, asking it questions like "How can I improve?", "Which products should I sell?", or specific factual queries based on the content. The sky is the limit!

You have to try it out. As soon as you try it out, you will know about its powers. Explore these platforms, experiment with their features, and see how NLP chatbots can work for you. If you make a good bot, share it! It's amazing to see what can be created.