https://github.com/prayag2003/across-protocol-discord-bot
R.O.S.S - Discord Bot for Across Protocol
https://github.com/prayag2003/across-protocol-discord-bot
discord-bot discord-py feedback-learning fine-tuning openai python q-learning rag reinforcement-learning
Last synced: 29 days ago
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R.O.S.S - Discord Bot for Across Protocol
- Host: GitHub
- URL: https://github.com/prayag2003/across-protocol-discord-bot
- Owner: Prayag2003
- Created: 2024-10-20T17:48:25.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-14T17:12:45.000Z (about 1 year ago)
- Last Synced: 2025-04-14T18:26:16.686Z (about 1 year ago)
- Topics: discord-bot, discord-py, feedback-learning, fine-tuning, openai, python, q-learning, rag, reinforcement-learning
- Language: Python
- Homepage:
- Size: 21.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Ross - AI-Powered Assistant for Across Protocol
## Architecture Diagram

## Workflow Overview

## **Priority 1: Key Functionality** (Estimated Time: 2 weeks)
### **1.1 Accessibility through Wallet Connectivity & Financial Assistance**
- **Web3 Wallet Integration**: Enable users to connect their wallets and access balances and transaction histories.
- **Blockchain Data Insights**: Provide real-time token prices, transaction histories, and other financial data.
### **1.2 Personalized User Experience**
- **User-Specific Insights**: Tailor responses based on user expertise (community members, developers, investors, traders) to ensure relevance and enhance engagement.
- **Developer Support**: Offer specialized guidance for developers on smart contracts and Across Protocol navigation.
### **1.3 Information Hub Development**
- **Blockchain Data Analytics**: Deliver insights into trading trends, protocol metrics, liquidity positions, and token activity for informed investment and trading decisions.
- **Docs Explainer**: Simplify and explain Across Protocol documentation to support community understanding.
### **1.4 Data Management & Learning**
- **Daily Learning Cycles**: Ross will analyze and learn from messages written by trusted community members every 24 hours to provide accurate and up-to-date information.
- **Selective Memory**: Ross will prioritize learning from specific roles (e.g., Admins, Developers, Bridge Guardians) to improve the accuracy of its responses.
### **1.5 Data Interactions and Transaction Support**
- **Detailed Transaction Breakdown**: Provide clear explanations of on-chain transactions and their components to enhance user comprehension.
- **Transaction Status Queries**: Enable users to check the status of their bridge transactions in real time, promoting transparency.
- **Liquidity & Delay Updates**: Notify users about route availability, liquidity, and delays in bridge transactions to facilitate informed decision-making.
### **1.6 Bridge Transaction Support**
- **Transaction Status Queries**: Allow users to check the status of their bridge transactions in real time.
- **Liquidity & Delay Updates**: Notify users about route availability, liquidity, and delays in bridge transactions.
### **1.7 Transaction Decoding**
- **Detailed Transaction Breakdown**: Provide detailed explanations of individual components of on-chain transactions.
- **Guidance on On-Chain Data**: Help users navigate transaction data, liquidity positions, fees, and other on-chain interactions.
---
## **Priority 2: Customization & User Experience** (Estimated Time: 3-4 days)
### **2.1 Custom Responses for User Groups**
- **User-Specific Insights**: Provide personalized responses based on the user’s expertise (community members, developers, investors, traders).
- **Developer Support**: Assist developers with guidance on smart contracts, migration, and the Across Protocol.
### **2.2 Interactive Feedback System**
- **Feedback on Responses**: Allow admins and other trusted users to review Ross's responses and provide feedback for improvements.
- **Human Moderation**: Enable human oversight of Ross’s learning process to ensure high-quality answers and maintain community trust.
---
## **Priority 3: System & Efficiency Features** (Estimated Time: 1-2 days)
### **3.1 Credit System**
- **Rate-Limiting Access**: Implement a credit-based system that regulates how many queries a user can make within a certain timeframe. Users earn additional credits through community engagement.
### **3.2 Integration with Notion**
- **Knowledge Base Integration**: Connect Ross with Notion to store FAQs, summarized interactions, and learnings from community discussions for easy access by users.
---
## **Additional Information Required from Across Protocol**
1. **Across Protocol Documentation**: Access to all technical guides, smart contracts, and migration documents for the Docs Explainer feature.
2. **Bridge Transaction Data**: Access to APIs or data sources that track bridge transactions and provide liquidity updates.
3. **Roles and Permissions**: Define which community members (Admins, Devs, Bridge Guardians, etc.) should be prioritized for Ross’s learning process.
4. **Credit System Framework**: Determine how users earn and spend credits when interacting with Ross.
5. **Data Analytics Requirements**: Define the key metrics users want to track for trading, liquidity, and token-related activities.
6. **Access to Discord Server**: Grant permissions to integrate Ross into the Discord server and manage its interactions with different roles and channels.
7. **Learning Cycle Configuration:** Channels and messages to monitor for daily learning cycles and parameters for selective memory.
8. **User Roles and Permissions:** Definitions and access levels for different user roles (e.g., Admins, Developers, Community Members).
9. **Feedback System Design:** Guidelines for feedback submission, review process, and human moderation. Like who can moderate what exactly??
10. **Frequency of Doc updatation:** How frequently the documentation needs to be updated and how should Ross pull the updates.
---
## **APIs and Resources Needed**
### **1. Blockchain & Wallet Management**
- **Ethers.js / Web3.js**: For blockchain interactions and wallet management.
- **Alchemy / Infura**: For accessing Ethereum nodes.
- **Etherscan API**: For fetching transaction histories and on-chain data.
### **2. Token Price & Market Data**
- **CoinGecko API**: For token prices and real-time market data.
- **CoinMarketCap API**: For tracking token prices across different chains.
### **3. Across Protocol / DeFi-Specific APIs**
- **The Graph API**: For querying Across Protocol data.
- **Zapper API**: For tracking DeFi portfolios, liquidity positions, and more.
### **4. Transaction Decoding & Analytics**
- **Tenderly API**: For simulating transactions and decoding smart contract interactions.
- **Zapper API**: For providing detailed insights on transactions.
### **5. Natural Language Processing (NLP)**
- **OpenAI GPT API**: For natural language generation, summarizing documents, and generating conversational responses.
### **6. Feedback System & Knowledge Base**
- **Notion API**: For storing and retrieving FAQs and knowledge base articles.
---
## **Questions for Client**
1. **Financial Assistance**: What exactly should Ross assist with after wallet connection? Do you want Ross to provide suggestions based on transaction history, or just present the data?
2. **Prioritizing Input**: What specific types of input from trusted members should be prioritized? Is it purely based on reactions like thumbs up/thumbs down?
3. **Personal Assistance**: Should we use a RAG (Retrieval-Augmented Generation) model to implement personalized responses and role-based notifications?
4. **DAO Integration**: Could you clarify what you mean by Ross being "callable" in DAOs? How should this feature work?
5. **Document Updates**: How should Ross pull documentation updates? Should we connect to a specific repository or content management system for this?
6. **Credit System Rules**: How should credits be allocated and spent? For example, how many credits should each query cost, and how do users earn additional credits?
7. **Selective Memory**: What is the best approach to implementing selective memory? Should it only be based on specific roles, or should there be other factors (e.g., recency, accuracy)?
---