As someone passionate about problem-solving, I noticed a significant gap in the Sri Lankan tea industry:

Tea estate owners struggle to access and manage essential documents and information.

So, I built an AI-powered Tea Plantation Assistant—a chatbot that simplifies information retrieval for tea industry stakeholders. 🚀

In this blog, I'll walk you through how I went from analyzing raw data to a fully functional AI chatbot.

Whether you're a developer or tea industry professional, this step-by-step guide will help you understand the impact of AI in agriculture.

🌱 Step 1: Data Collection & Preprocessing

The foundation of this assistant is structured data from official tea industry documents.

To make the chatbot useful, I needed a clean dataset. I built a pipeline that:

✅ Scrapes and processes relevant documents

✅ Cleans and structures the text for efficient retrieval

Check out the GitHub repo:

🔗 ai-assistant-tri (Data Preprocessing)

Even if you're not a coder, imagine this as organizing thousands of tea reports into a well-labeled library. 📚

🎨 Step 2: Designing the Chatbot UI with Gradio

Once the data was ready, I built a user-friendly chatbot UI using Gradio.

Why Gradio?

✅ Quick to set up

✅ Clean, minimalistic interface

✅ Great for AI-powered applications

I customized the UI to align with a tea plantation theme, making it accessible to non-tech users.

🔗 Chatbot UI Repo (Gradio)

🧠 Step 3: Adding AI & Knowledge Retrieval

The core of the assistant is retrieval-augmented generation (RAG) using:

  • LangChain for intelligent query processing
  • Pinecone for storing and retrieving relevant documents

This enables the chatbot to:

✅ Understand tea-related questions using Google Generative AI

✅ Retrieve the most relevant tea industry information

✅ Generate clear and useful responses

For developers: This is a LangChain-powered RetrievalQA chain optimized for real-world tea industry applications. 🍃

🚀 Step 4: Deploying the Working Demo

The final step was making this assistant publicly available for testing.

I deployed the chatbot on HuggingFace Spaces:

🔗 Live Demo: Sri Lankan Tea Chatbot

Now, anyone can ask industry-related questions and receive instant AI-generated answers!

🎯 Conclusion

This project started with a real-world problem:

Stakeholders in the Sri Lankan tea industry struggle to access critical information.

By leveraging AI, LangChain, and Gradio, I built a chatbot that:

✅ Makes information instantly accessible

✅ Bridges the gap between technology & traditional industries

✅ Showcases how AI can revolutionize agriculture 🌍

If you're curious about AI in agriculture, explore the code and try the demo!

Let’s innovate together. 🚀

📌 Resources & Links

💡 What do you think about AI in agriculture? Drop your thoughts in the comments!