README.md

🎯 Custom LLM Framework for Financial Analysis (Public Version)

Lucas Kemper – First Year MScFin Student at HEC Lausanne, Switzerland

📄 Resume: Access my detailed resume here.

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Inspired by my interests in quantitative finance, AI, and LLMs, I am customizing a fork of Lobe Chat to develop an innovative AI-powered framework for advanced financial analysis. Initiated on October 20, 2024, this project integrates state-of-the-art AI tools with finance-specific features designed to streamline data-driven analysis in finance, investment, and accounting.

🛡️ Note: The code remains private to maintain confidentiality as I refine and enhance this framework.

🌟 Core Features:

1. 📂 File Upload & Knowledge Base
   Personal knowledge repository enabling efficient file upload and search, tailored for financial datasets (upload functionality currently under optimization).

2. 🔄 Multi-Model Support
   Integrated support for OpenAI and Anthropic models to provide versatile AI options.

3. 🚀 Advanced Infrastructure Optimizations:

   • Vercel: Front-end deployment optimized for speed and cost efficiency; transitioning to AWS for enhanced performance and security. ⏳
   • Cloudflare: Added comprehensive security with DNS, CDN, and DDoS protection.✅
   • MongoDB to AWS Migration: Migrated database from MongoDB to AWS PostgreSQL (using Prisma) for enhanced data management efficiency. ✅
   • Clerk Authentication: Implemented secure, multi-provider login through GitHub and Google.✅

🧩 Current Challenges:

• 📉 Model Complexity Constraints: Addressing limitations in context length for complex financial queries.
• ⚙️ File Upload Bug ✅: Finalizing network configurations for seamless file processing.
• 💸 API Cost Optimization: Focusing on cost-efficient API usage.
• 📊 Data Quality Assurance ⏳: Enhancing data input accuracy, essential for quality financial analysis.

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🛤️ Project Roadmap:

🚩 Short-Term (0- 0.5 Months):

• 📑 PDF Generation & Code Execution ✅: Embedding functionalities for on-demand report generation and real-time code execution.
• 📈 OpenBB Integration ⏳: Integrate OpenBB for in-chat quantitative finance tools, facilitating deeper investment insights and the potential for AI-driven trading strategy recommendations.
• 💡 Cost Optimization Measures: Experiment with caching strategies and intelligent API call routing to minimize expenses while maintaining performance.

🔜 Mid-Term (0.5 - 1.5 Months):

• 🧠 Custom Financial LLM Models: Fine-tune local LLMs specifically for financial contexts, reducing reliance on external APIs and enhancing response accuracy.
• 📊 Advanced Analysis Modules: Introduce specialized models for financial forecasting, portfolio risk analysis, and sensitivity analysis.
• 🔍 Data Quality Enhancements: Develop a preprocessing pipeline to ensure consistency and accuracy across various financial data inputs.

📅 Long-Term (1.5 - 6 Months):

• 👥 Enhanced Multi-User Collaboration: Allow multiple users to upload, search, and analyze data collaboratively in real time, supporting finance teams.
• 🚨 Automated Investment Insights & Alerts: Implement alerting mechanisms for notable trends or anomalies in real-time data.
• 🔗 Extended API Partnerships: Explore additional API integrations (e.g., Refinitiv, Bloomberg) to provide enriched data for comprehensive financial analysis.

🌐 Future Innovations (6+ Months):

• 🧩 Integrated Financial Knowledge Graph: Build a knowledge graph to uncover complex relationships between entities, events, and markets.
• 🤖 Adaptive AI: Employ reinforcement learning for the model to continuously improve based on past interactions and evolving financial contexts.
• 🔒 Public/Private User Segmentation: Develop user-access tiers, allowing tailored access to advanced financial insights based on user permissions.

📬 Connect with Me: LinkedIn or email me to collaborate or learn more.