An intelligent, context-aware AI assistant built with Streamlit and Python, designed to onboard users and provide precise, real-time answers regarding Apyx, Digital Credit, and onchain preferred equity ($STRC & $SATA) on Solana.
The Apyx AI Assistant bridges the gap between complex institutional credit mechanics and everyday DeFi users. Trained strictly on official project documentation, whitepapers, and verifiable protocol parameters, the bot delivers accurate, hallucination-free explanations of:
- Digital Credit & Preferred Equity: Understanding yield backed by publicly traded equities like Strategy's $STRC and Strive's $SATA.
- apyUSD Dynamics: Yield comparisons against traditional zero-yield stablecoins ($310B+ idle capital problem) and standard DeFi protocols.
- Risk & Solvency Analysis: Practical explanations supported by underlying empirical research and statistical risk models.
- Strict Context Grounding: Configured with a dedicated system prompt and knowledge retrieval pipeline to ensure answers align with official docs.
- Interactive Web Interface: Clean, responsive, and lightweight UI powered by Streamlit for instant community queries.
- Decentralized Research Backing: Directly integrated with an underlying academic research paper hosted on IPFS.
- Fast & Lightweight Deployment: Ready for one-click deployment on Streamlit Community Cloud or local environments.
- Frontend/Application Framework: Streamlit
- Core Language: Python 3.9+
- LLM Integration: Direct API connection with optimized system prompting and token constraints.
- Underlying Research: Statistical modeling and variance/risk assessment conducted via SPSS.
Ensure you have Python 3.9 or higher installed: python --version
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Clone the repository: git clone https://github.com/YourUsername/apyx-ai-assistant.git cd apyx-ai-assistant
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Create and activate a virtual environment (optional but recommended): python -m venv venv source venv/bin/activate # On Windows use: venv\Scripts\activate
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Install dependencies: pip install -r requirements.txt
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Set up API Keys: Create a .env file or configure your secrets in .streamlit/secrets.toml: API_KEY = "your-llm-api-key-here"
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Run the Streamlit application: streamlit run app.py
This agent's logic is derived from an academic-grade risk assessment and structural comparative study on Solana's Digital Credit architecture:
- Decentralized Paper (IPFS): Available on decentralized storage via Pinata.
- Methodology: Applied descriptive and regression statistical tools (SPSS) to evaluate dividend coverage ratios and structural drawdown limits.
This project is licensed under the MIT License - see the LICENSE file for details.