README.md

metadata

title: ML Use Cases RAG Assistant (BYOK)
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.44.0
app_file: app.py
pinned: false
license: mit

ML/AI Use Cases RAG Assistant (Bring Your Own Key)

An AI-powered assistant that provides business advice based on real ML/AI implementations from 60+ companies with 400+ use cases. This app uses Retrieval-Augmented Generation (RAG) to find relevant company examples and provides actionable recommendations.

🔑 Bring Your Own Key: This version requires users to provide their own HuggingFace API key, ensuring zero cost to the space owner while maintaining full functionality.

Features

• 🔑 BYOK (Bring Your Own Key): Use your own HuggingFace API key for secure, cost-effective access
• 🔍 Semantic Search: Find relevant ML/AI use cases from a comprehensive database
• 🤖 AI-Powered Advice: Get personalized recommendations using HuggingFace Inference API
• 📊 Model Recommendations: Discover fine-tuned and foundation models for your specific use case
• 🏢 Real Company Examples: Learn from actual implementations across various industries
• 🔒 Privacy-First: Only embeddings are used - no raw company data is exposed
• 💰 Zero Cost to Owner: No API costs for the space owner - users bring their own keys

How It Works

1. 🔑 API Key Setup: Provide your HuggingFace API key for secure access
2. 📝 Query Processing: Your business problem is analyzed and converted to embeddings
3. 🔍 Semantic Search: The system searches through 400+ pre-processed ML use cases
4. 📚 Context Building: Relevant company examples are selected as context
5. 🤖 AI Generation: Your API key powers the language model to generate tailored advice
6. 📊 Model Matching: HuggingFace API provides relevant model recommendations using your key

Technology Stack

• Backend: FastAPI with async support and BYOK architecture
• Vector Database: ChromaDB for semantic search
• Embeddings: Sentence Transformers (all-MiniLM-L6-v2)
• Language Model: HuggingFace Inference API (Gemma 2 2B with fallbacks)
• Frontend: Modern HTML/CSS/JavaScript with Tailwind CSS
• Security: User API keys never stored, used only for requests

Security & Privacy

• 🔐 API Key Security: Your API key is never stored permanently, only used for requests
• 📊 No Raw Data: Only vector embeddings and metadata are stored
• 🏢 Company Privacy: Original datasets remain private
• 🛡️ Secure Processing: All processing happens within the secure HuggingFace environment
• 💾 Local Storage: API keys stored locally in your browser for convenience

Getting Started

1. Get Your HuggingFace API Key

1. Visit HuggingFace Settings
2. Click "Create new token"
3. Select "Read" access (sufficient for this app)
4. Copy your token (starts with hf_)

2. Use the Assistant

1. Enter your API key in the secure input field
2. Describe your business problem in natural language:
   • "I want to reduce customer churn in my SaaS business"
   • "How can I implement fraud detection for my e-commerce platform"
   • "What ML approach works best for demand forecasting in retail"

3. Get AI-Powered Results

• Solution Approach: Detailed technical recommendations
• Company Examples: Real implementations from similar businesses
• Model Recommendations: Specific HuggingFace models for your use case

Model Information

This space uses pre-computed ChromaDB embeddings generated from a curated dataset of ML/AI use cases. The language model runs efficiently on CPU with fallback options for reliability.

Requirements & Limitations

Requirements

• Valid HuggingFace API key (free to obtain)
• Internet connection for API calls

Limitations

• Responses are generated based on training data patterns
• Model recommendations are sourced from HuggingFace Hub API
• Processing time may vary based on query complexity and API response times
• API rate limits apply based on your HuggingFace account tier

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Built with ❤️ using HuggingFace Spaces