README.md

metadata

title: Rackspace Knowledge Chatbot
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.9.0
python_version: '3.10'
app_file: app.py
pinned: false

Rackspace Knowledge Chatbot

This chatbot answers questions about Rackspace documentation using Groq API and enhanced RAG retrieval. Deployable on Hugging Face Spaces with Gradio.

Features

• Enhanced retrieval with vector database
• Groq API integration
• Public Gradio interface

Usage

1. Set your GROQ_API_KEY in Hugging Face Spaces secrets.
2. Rebuild the vector DB if missing:

   python enhanced_vector_db.py
   

3. Chat with the bot!

🎯 Rackspace Knowledge Chatbot - Enhanced Version

🚀 Quick Start

# Option 1: Use the quick start script
./start_enhanced_chatbot.sh

# Option 2: Manual start
source venv/bin/activate
streamlit run streamlit_app.py

# 3. Open browser: http://localhost:8501

📁 Enhanced Project Structure

chatbot-rackspace/
├── streamlit_app.py                    # Main UI application
├── enhanced_rag_chatbot.py             # Core RAG chatbot
├── enhanced_vector_db.py               # Vector database builder
├── integrate_training_data.py          # Data integration script
├── config.py                           # Configuration
├── requirements.txt                    # Dependencies
│
├── data/
│   ├── rackspace_knowledge_enhanced.json     # 507 documents (13 old + 494 new)
│   ├── training_qa_pairs_enhanced.json       # 5,327 Q&A pairs (4,107 old + 1,220 new)
│   ├── training_data_enhanced.jsonl          # 1,220 training entries
│   ├── backup_20251125_113739/               # Original data backup
│   └── feedback/                             # Feedback directory (ready for use)
│
├── models/rackspace_finetuned/         # Fine-tuned model (6h 13min)
└── vector_db/                          # ChromaDB (1,158 chunks from 507 docs)

✨ What's New - Enhanced with Training Data

Data Integration from rackspace-rag-chatbot:

• ✅ 494 new documents - Comprehensive Rackspace documentation
• ✅ 1,220 training examples - Instruction-following Q&A pairs
• ✅ 39x more documents - From 13 to 507 documents
• ✅ 1,158 vector chunks - Enhanced retrieval capability
• ✅ Smart deduplication - No duplicate content

Coverage Improvements:

• ✅ Cloud migration services (AWS, Azure, Google Cloud)
• ✅ Managed services and platform guides
• ✅ Technical documentation and how-to guides
• ✅ Security and compliance topics
• ✅ Database and storage solutions

🎯 System Status

✅ Enhanced Data: 507 docs, comprehensive coverage (39x increase) ✅ Proper Embeddings: 1,158 chunks from real content only ✅ No Hallucinations: Responses use actual content with real URLs ✅ Fine-tuned Model: TinyLlama trained 6h 13min ✅ Training Data: 5,327 Q&A pairs for improved responses

📝 Documentation

• README.md - This file (quick start guide)
• INTEGRATION_SUMMARY.md - Detailed integration report
• FINAL_SYSTEM_STATUS.md - System documentation

🌐 Deploy on Hugging Face Spaces

You can deploy this chatbot publicly using Hugging Face Spaces (Streamlit):

1. Fork or upload this repo to Hugging Face Spaces

   • Go to https://huggingface.co/spaces and create a new Space (Streamlit type).
   • Upload your code and requirements.txt.

2. Set your GROQ_API_KEY

   • In your Space, go to Settings → Secrets and add GROQ_API_KEY.

3. Rebuild the Vector DB (first run only)

   • The vector database is not included due to file size limits.
   • After deployment, open the Space terminal and run:

     python enhanced_vector_db.py
     

   • This will create the required ChromaDB files in vector_db/.

4. Run the Streamlit app

   • The app will start automatically. If the vector DB is missing, it will prompt you to rebuild.

5. Share your Space link!

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🔧 Rebuild Vector DB (Local or Hugging Face)

python enhanced_vector_db.py

🔄 Re-run Data Integration

If you need to re-integrate data from rackspace-rag-chatbot:

source venv/bin/activate
python integrate_training_data.py

This will:

1. Consolidate chunks into full documents
2. Convert training data to Q&A pairs
3. Merge with existing data (avoiding duplicates)
4. Create automatic backups

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Built with YOUR OWN MODEL + Enhanced Training Data! 🚀