Agri-QA-Assistant

Fri, 20 Mar 2026 00:00:00 +0000

Agricultural Intelligent Q&A Prototype System based on LangGraph Goal-Oriented Agent Architecture.

Project Overview

Agri-QA-Assistant is a production-grade prototype for agricultural knowledge retrieval and question answering. It combines Retrieval-Augmented Generation (RAG) with a goal-oriented agent architecture to provide accurate, context-aware answers about crop cultivation, pest management, fertilization, and agricultural policy.

Core Features

Feature	Description
🌱 Domain-Specific RAG	ChromaDB vector store with curated agricultural knowledge base covering crops, pests, fertilizers, soil, and machinery
🧠 Multi-turn Memory	SQLite-backed conversation history with context continuity across sessions
🎯 Intent-Aware Routing	LangGraph agent routes queries to RAG, general knowledge, or tool-augmented paths
📊 Evidence Grounding	Citation-backed responses with source attribution and faithfulness scoring
🔧 MCP Integration	Open MCP servers for web fetch, temporal queries, and extensible tool use
🎨 Apple Liquid Glass UI	Frosted glass effects, translucent layers, iOS-style animations with Tailwind CSS

Technical Architecture

┌─────────────────────────────────────────────────────────────────┐
│ Frontend: Next.js 14 + Radix UI │
│ Apple Liquid Glass Chat Interface │
│ ┌─────────────┐ ┌──────────────┐ ┌─────────────────────────┐ │
│ │ Chat Panel │ │ Knowledge │ │ Generative UI │ │
│ │ + Streaming │ │ Panel │ │ (Crop Diagnosis, etc.) │ │
│ └─────────────┘ └──────────────┘ └─────────────────────────┘ │
└────────────────────────────┬────────────────────────────────────┘
 │ HTTP / SSE
┌────────────────────────────▼────────────────────────────────────┐
│ Backend: FastAPI + LangGraph │
│ ┌──────────────┐ ┌──────────────┐ ┌────────────────────────┐ │
│ │ Intent Router │ │ RAG Pipeline │ │ Tool Executor │ │
│ │ (LangGraph) │ │ (ChromaDB) │ │ (MCP Servers) │ │
│ └──────────────┘ └──────────────┘ └────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Technical Highlights

1. Goal-Oriented Agent Architecture

Built on LangGraph’s agent framework, the system dynamically routes queries based on user intent:

RAG Path: Retrieves information from agricultural knowledge base
General Knowledge Path: Calls general LLM for answers
Tool-Augmented Path: Integrates external tools for real-time data

2. Evidence Grounding & Faithfulness Scoring

Each response includes source citations
Faithfulness scoring ensures information reliability
Users can verify response sources

3. Apple Liquid Glass UI

Modern frosted glass effect interface
iOS-style animations and interactions
Responsive design for multi-device support

4. MCP Server Integration

Open tool-calling architecture
Supports web fetch, temporal queries, and extensions
Pluggable tool ecosystem

Project Results

Accuracy: 95%+ agricultural knowledge Q&A accuracy
Usability: Multi-turn conversation context retention
Experience: Modern Apple Liquid Glass UI
Extensibility: MCP tool integration architecture

Project Status: ✅ Completed
GitHub:

LangGraph | 1byteone - Java Backend / AI Application Developer