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---
title: AI Chatbot with Smart Routing
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
app_port: 7860
---
# 🤖 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](https://huggingface.co/spaces/krinya/smart_routing_with_render_example) ← **Try it here!**
- **💻 Frontend Code**: [`app.py`](https://huggingface.co/spaces/krinya/smart_routing_with_render_example/tree/main) - Gradio interface code
- **🔗 Backend API**: [Deployed on Render](https://gen-ai-demo-rag-bot.onrender.com/docs)
- **📖 Backend API Code**: [GitHub Repository](https://github.com/krinya/gen_ai_demo_rag_bot/tree/main)

## 🎯 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. **Write Backend** (Python + LangChain)
   - FastAPI with smart routing logic
   - RAG pipeline with vector storage
   - Deploy on Render cloud platform

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

3. **Connect & Scale**
   - Backend API serves multiple frontends
   - Docker containerization
   - 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!

	---
	title: AI Chatbot with Smart Routing
	emoji: 🤖
	colorFrom: blue
	colorTo: purple
	sdk: docker
	pinned: false
	license: mit
	app_port: 7860
	---

	# 🤖 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](https://huggingface.co/spaces/krinya/smart_routing_with_render_example) ← Try it here!
	- 💻 Frontend Code: [`app.py`](https://huggingface.co/spaces/krinya/smart_routing_with_render_example/tree/main) - Gradio interface code
	- 🔗 Backend API: [Deployed on Render](https://gen-ai-demo-rag-bot.onrender.com/docs)
	- 📖 Backend API Code: [GitHub Repository](https://github.com/krinya/gen_ai_demo_rag_bot/tree/main)

	## 🎯 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. Write Backend (Python + LangChain)
	- FastAPI with smart routing logic
	- RAG pipeline with vector storage
	- Deploy on Render cloud platform

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

	3. Connect & Scale
	- Backend API serves multiple frontends
	- Docker containerization
	- 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!