FinRAG - Financial Document Analysis System

FinRAG is an AI-powered financial document analysis system that leverages Retrieval-Augmented Generation (RAG) to provide insights, summaries, and answers from financial reports, earnings calls, and other documents.

Tech Stack

Backend

• Framework: FastAPI (Python)
• Deployment Server: Uvicorn
• AI/LLM: OpenAI (GPT-4), LangChain
• Vector Database: ChromaDB (Semantic Search)
• PDF Processing: PyPDF2, pdfplumber, Camelot (for tables)
• Authentication: Firebase Admin SDK
• Task Management: Tenacity (Retries)
• Utilities: Pandas, NumPy

Frontend

• Framework: React 18 + TypeScript + Vite
• Styling: Tailwind CSS + Vanilla CSS (Glassmorphism Design)
• State Management & Data Fetching: TanStack Query (React Query)
• Routing: React Router DOM v6
• Animations: Framer Motion
• Icons: Lucide React
• Authentication: Firebase Client SDK
• HTTP Client: Axios

Prerequisites

• Python: 3.9+
• Node.js: 18+
• API Keys:
  • OpenAI API Key (or Groq API Key)
  • Firebase Project Configuration
  • (Optional) Render/Vercel for deployment

Interface & Features

1. Smart Chat: Context-aware Q&A with documents.
2. Streaming Answers: Real-time token streaming.
3. AI Summaries: structured executive summaries with sentiment analysis.
4. Financial Dashboards: Auto-extracted metrics and ratios.
5. Multi-Doc Comparison: Side-by-side analysis of multiple reports.
6. Analytics: Usage stats and query performance tracking.
7. Alerts: Price alerts and ticker extraction.
8. Export: Download conversation reports as Markdown.

How to Run Locally

1. Backend Setup

1. Navigate to the project root (or backend/ directory if preferred, but requirements.txt is in root).
2. Create a virtual environment:

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Mac/Linux
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file in backend/ based on .env.example:

   OPENAI_API_KEY=your_key_here
   # ... other config/firebase credentials

5. Run the server:

   cd backend
   python run.py
   # OR
   uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

   The API will be available at http://localhost:8000. API Docs at http://localhost:8000/docs.

2. Frontend Setup

1. Open a new terminal and navigate to frontend/:

   cd frontend

2. Install dependencies:

   npm install

3. Ensure .env (or .env.local) exists with Firebase config (VITE_FIREBASE_*).
4. Run the development server:

   npm run dev

   The application will be available at http://localhost:5173.

Architecture

• Backend (backend/app/): REST API server.
  • api/: Endpoints (Documents, Chat, Auth, Analytics, etc.)
  • services/: Business logic (LLM, Vector Store, PDF Processing)
  • models/: Pydantic schemes.
• Frontend (frontend/src/): SPA Client.
  • components/: Reusable UI components.
  • pages/: Route views.
  • hooks/: Custom React hooks.
  • lib/: API clients and utilities.