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
- 🗂 Data Collection & Preprocessing: GitHub Repo
- 🎨 Gradio Chatbot UI: GitHub Repo
- 🤖 Live Chatbot Demo: HuggingFace
💡 What do you think about AI in agriculture? Drop your thoughts in the comments!
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