01 — Problem
What was the objective?
The goal was to provide agricultural guidance using relevant domain knowledge while keeping responses specific to the user's context.
ProjectGenerative AI
AgroFarm AI
A RAG-based agriculture advisor that retrieves relevant knowledge and generates context-aware responses through a Streamlit application.
Problem & Solution
From a defined problem to a practical solution.
02 — Solution
What was built?
Built a RAG-based agronomy assistant that retrieves relevant knowledge and generates context-aware responses through a Streamlit application.
Approach / Workflow
How the project moves from input to outcome.
01
Knowledge Preparation
02
Information Retrieval
03
Context Assembly
04
Response Generation
05
Streamlit Application
Tools & Technologies
Technologies used to build the project.
PythonGeminiChromaDBRAGStreamlit
Key Work
What went into building the project.
01
Knowledge preparation
02
Text processing
03
Vector storage
04
Information retrieval
05
Context assembly
06
LLM integration
07
RAG pipeline
08
Streamlit application
Project Preview
A closer look at the application.
Project Links
Explore the source code and live application.
HAVE A PROJECT?
Let's build something useful.
Have a problem, dataset, or application idea? Let's discuss the right approach.