Grant Matcher MVP - LLM-Driven Edition

An intelligent platform that matches startup pitch decks with relevant grant opportunities using LLMs for advanced text understanding, classification, and recommendation.

Architecture Overview

The application follows a modular microservices architecture with the following components:

Frontend

• React-based SPA with Material UI
• State management using Redux
• React Router for navigation
• ChatGPT-powered chatbot for grant inquiries
• LLM-enhanced search bar for natural language queries

Backend Services

1. Deck Analysis Service (LLM-Powered)

   • Handles pitch deck uploads and processing
   • OCR and NLP using an LLM (GPT-4-turbo)
   • Summarization and key concept extraction
   • Built with Python (FastAPI)

2. LLM-Powered Recommendation Engine

   • Matches pitch content with grants
   • Semantic search & embeddings (via OpenAI)
   • Scoring system based on relevancy ranking
   • Built with Python (FastAPI) & LangChain

3. Grant Data Collection Service

   • Automated scraping and API aggregation of grant opportunities
   • Data pre-processing for better LLM comprehension
   • Built with Python (Scrapy, Playwright)

4. User Management Service

   • Authentication and authorization
   • Profile and saved grant matches
   • Built with Node.js/Express

5. Feedback & Iteration Service

   • Tracks user interactions with recommendations
   • Fine-tunes grant matching models based on feedback
   • Allows dynamic prompt engineering adjustments
   • Built with Python (FastAPI) & Vector Database (Pinecone)

Prerequisites

• Node.js >= 18
• Python >= 3.10
• Docker and Docker Compose
• PostgreSQL >= 14
• MongoDB >= 6.0
• Redis >= 7.0

Getting Started

1. Clone the repository:

git clone https://github.com/yourusername/grant-matcher-llm.git
cd grant-matcher-llm

2. Install dependencies (instructions for each service in their respective directories)

3. Set up environment variables:

cp .env.example .env
# Edit .env with your API keys and configuration

4. Start the development environment:

docker-compose up

5. Access the application:

• Frontend: http://localhost:3000
• API Documentation: http://localhost:8000/docs

License

This project is licensed under the MIT License - see the LICENSE file for details.