krinya/smart_routing_with_render_example

🤖 Financial AI Chatbot with Smart Routing & RAG

A demo GenAI app that demonstrates smart routing using LangChain

🚀 Try It Live

• —🎯 Live Demo: Financial AI Chatbot ← Try it here!
• —💻 Frontend Code: `app.py` - Gradio interface code
• —🔗 Backend API: Deployed on Render
• —📖 Backend API Code: GitHub Repository

🎯 What This Demonstrates

This project shows how to create a complete GenAI product:

1. 🧠 Smart Routing with LangChain

Intelligently routes financial questions about 5 major companies (Apple, Google, Amazon, Tesla, Intel):

• —🔍 FAQ Route: Quick facts (CEO names, founding dates)
• —📚 RAG Route: Financial data from 2024 annual reports (revenue, profits)
• —🧠 LLM Route: General explanations and financial concepts

2. 📊 RAG Implementation

• —Vector Storage: ChromaDB with processed financial documents (full annual reports)
• —Retrieval System: Semantic search for relevant information
• —Smart Fallbacks: Multiple sources with quality scoring

3. 🏗️ Production Architecture

• —Backend: Python FastAPI with LangChain, deployed on Render
• —Frontend: Gradio UI deployed on Hugging Face Spaces
• —Separation: Backend API + Frontend UI for scalability

🛠️ How This Shows GenAI Product Development

Complete workflow: Backend → Deploy → Frontend

1. 1.Write Backend (Python + LangChain)
2. 2.FastAPI with smart routing logic
3. 3.RAG pipeline with vector storage
4. 4.Deploy on Render cloud platform

1. 1.Create Frontend (Gradio + Hugging Face)
2. 2.Interactive chat interface
3. 3.Real-time routing insights
4. 4.Deploy on Hugging Face Spaces

1. 1.Connect & Scale
2. 2.Backend API serves multiple frontends
3. 3.Docker containerization
4. 4.Production-ready architecture

🔧 Tech Stack

• —AI: OpenAI GPT-4o-mini + LangChain orchestration
• —Backend: Python FastAPI deployed on Render
• —Frontend: Gradio deployed on Hugging Face Spaces
• —Storage: ChromaDB vector database
• —Data: 2024 financial reports (Apple, Google, Amazon, Tesla, Intel)

� Example Queries

Try these in the live demo:

• —"Who is the CEO of Tesla?" → FAQ route
• —"What was Apple's revenue in 2024?" → RAG route
• —"How do you calculate P/E ratio?" → LLM route

🎯 Key Learning: This demonstrates the complete GenAI development stack from data processing to production deployment!