title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned
CRE Knowledge Assistant	🏢	blue	indigo	streamlit	1.27.2	app.py	false

Commercial Real Estate Knowledge Assistant

A sophisticated Retrieval-Augmented Generation (RAG) chatbot that transforms how professionals understand commercial real estate concepts. Built with Azure OpenAI and modern Python technologies, this assistant processes commercial real estate documentation and provides accurate, context-aware answers to your questions.

🚀 Deployments

• Live Demo: Try it on Hugging Face Spaces

🌟 Key Features

• Multi-Document Support: Process and analyze multiple PDF documents simultaneously
• Intelligent PDF Processing: Advanced document analysis and text extraction
• Azure OpenAI Integration: Leveraging GPT-3.5 Turbo for accurate, contextual responses
• Semantic Search: Using Azure OpenAI embeddings for precise context retrieval
• Vector Storage: Efficient document indexing with ChromaDB
• Modern UI: Beautiful chat interface with message history and source tracking
• Enterprise-Ready: Comprehensive logging and error handling

🎯 Use Cases

• Training & Education: Help new CRE professionals understand industry concepts
• Quick Reference: Instant access to definitions and explanations
• Document Analysis: Extract insights from CRE documentation
• Knowledge Base: Build and query your own CRE knowledge repository

🚀 Quick Start

Prerequisites

• Python 3.8+
• Azure OpenAI Service access with:
  • gpt-35-turbo model deployment
  • text-embedding-ada-002 model deployment

Installation

1. Clone the repository:

git clone https://github.com/tony-42069/cre-chatbot-rag.git
cd cre-chatbot-rag

2. Create and activate virtual environment:

python -m venv venv
venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Create .env file with Azure OpenAI credentials:

AZURE_OPENAI_ENDPOINT=your_endpoint_here
AZURE_OPENAI_KEY=your_key_here
AZURE_OPENAI_DEPLOYMENT_NAME=your_gpt_deployment_name
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=text-embedding-ada-002

5. Run the application:

streamlit run app/main.py

🔌 Embedding

To embed this chatbot in your website, use the following HTML code:

<iframe
    src="https://tony-42069-cre-chatbot-rag.hf.space"
    frameborder="0"
    width="850px"
    height="450px"
></iframe>

💡 Features

Modern Chat Interface

• Clean, professional design
• Persistent chat history
• Source context tracking
• Multiple document management
• Real-time processing feedback

Advanced RAG Implementation

• Semantic chunking of documents
• Azure OpenAI embeddings for accurate retrieval
• Context-aware answer generation
• Multi-document knowledge base
• Source attribution for answers

Enterprise Security

• Secure credential management
• Azure OpenAI integration
• Local vector storage with ChromaDB
• Comprehensive error handling
• Detailed logging system

🛠️ Technical Stack

• Frontend: Streamlit
• Language Models: Azure OpenAI (GPT-3.5 Turbo)
• Embeddings: Azure OpenAI (text-embedding-ada-002)
• Vector Store: ChromaDB
• PDF Processing: PyPDF2
• Framework: LangChain

📚 Documentation

• Azure OpenAI Service
• Streamlit
• LangChain
• ChromaDB

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Azure OpenAI team for providing the powerful language models
• LangChain community for the excellent RAG framework
• Streamlit team for the amazing web framework