 Falcon Hackathon

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F16Falcon

Kabir Raj Singh

thelegtjet123

SirArthur7

debasmit2020

This project leverages Falcon LLMs for compliance, risk management, and real-time monitoring of LLM chatbots.

Implementation
Azure CosmosDB: Stores user info and violations.
Multithreading in Flask: Enhances scalability.
LRU Cache: Reduces latency for similar queries.
Falcon LLM-powered Keyword Extractor: Creates a Rule Knowledge Graph (KG) from admin-uploaded PDFs, serving as an alternative to fine-tuning.
Azure Blob Storage & Neo4j: Stores admin’s PDFs and the Knowledge Graph.
Semantic Router: Retrieves relevant subgraphs and identifies violations.
User Authentication: Clerk integrated with CosmosDB.
Deployment: Frontend on Vercel, backend on PythonAnywhere.
Objective
Periodically updated Rules Knowledge Graph.
Display rule violations, reasons, and priority levels.
Support for multi-modal (image+text) query checks.
Assign Risk scores based on violations.
Analyze user behavior patterns.
Methodology
Flask Server: Multithreaded for handling requests and database queries.
AI Pipeline:
Rule Registration: Extracts and decomposes rules from PDFs, forms a knowledge graph, integrates external sources, and allows admin modifications.
Query Processing: Degenerates user queries, retrieves relevant subgraphs, checks for rule violations, and reasons for violations.
Risk Scoring
Risk scores based on EU AI Act categories: Critical, High, Medium, Minimal.

Analytics
Admin panel displays rule violation frequency, custom timeframe violations, and user-wise analytics using Chart.js.

Scope
Compliance Assurance: Adherence to policies and regulations.
Risk Management: Identifies and mitigates risks.
Real-time Monitoring: Ensures transparency and accountability.

Developing a safeguarding tool using Falcon LLMs for chatbots to ensure compliance, risk management, and real-time monitoring of user interactions with LLMs enforcing legal regulations and mitigate potential risks of misuse or unethical behavior.

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