Gringotts

A Python-based multi-agent Retrieval Augmented Generation (RAG) system designed to process and query large volumes of financial documents for M&A due diligence purposes.

Features

• Multi-agent architecture for parallel processing of large financial documents
• Specialized chunking strategies optimized for financial documents (PDFs, Excel, Word, etc.)
• Intelligent financial entity extraction and indexing
• Advanced semantic search with financial term expansion
• Topic modeling for document categorization
• Integration with LLMs for comprehensive analysis and summarization
• Vector database integration for efficient retrieval
• Distributed processing capabilities for handling large document collections
• REST API for interacting with the system

Use Case: M&A Due Diligence

This system is specifically designed to assist financial analysts and investment bankers in the due diligence process for mergers and acquisitions. It can process and analyze:

• Financial statements and annual reports
• Legal contracts and agreements
• Regulatory filings
• Market analysis reports
• Valuation documents
• Tax documents
• Due diligence memos and reports

The system extracts key financial information, identifies risks and opportunities, and provides a comprehensive analysis to support M&A decision-making.

Project Structure

financial-due-diligence-rag/
├── config/                 # Configuration files
├── data/                   # Data storage location
│   └── financial_indices/  # Intelligent indices for financial documents
├── docs/                   # Documentation
└── src/                    # Source code
    ├── agents/             # Multi-agent system components
    ├── api/                # API endpoints
    ├── document_processing/ # Document processors for financial documents
    ├── utils/              # Utility functions
    └── vector_store/       # Vector database integration

Document Processing Pipeline

1. Document Loading: Supports various financial document formats (PDF, DOCX, XLSX, etc.)
2. OCR Processing: Handles scanned documents with OCR capabilities
3. Financial Entity Extraction: Identifies companies, monetary values, dates, percentages, etc.
4. Intelligent Chunking: Splits documents based on semantic boundaries
5. Metadata Extraction: Extracts key financial metrics and document categories
6. Embedding Generation: Creates vector representations of document chunks
7. Intelligent Indexing: Builds specialized indices for financial terms and entities
8. Topic Modeling: Categorizes documents for better organization and retrieval

Setup

1. Clone the repository
2. Create a virtual environment: python -m venv venv
3. Activate the virtual environment: source venv/bin/activate (Unix) or venv\Scripts\activate (Windows)
4. Install dependencies: pip install -r requirements.txt
5. Copy .env.example to .env and add your API keys (especially OpenAI for LLM integration)

Usage

1. Start the system: python src/main.py
2. Use the API to upload financial documents and query the system

API Endpoints

• POST /api/upload: Upload and process a financial document
• POST /api/query: Query the system with financial questions
• POST /api/task/status: Check the status of a document processing task
• GET /api/collections: List all document collections
• GET /api/collections/{collection_name}/stats: Get statistics for a collection

Supported Financial Document Types

The system supports various financial document formats including:

• PDF (text and scanned via OCR)
• Microsoft Word (DOCX)
• Microsoft Excel (XLSX, XLS)
• Microsoft PowerPoint (PPTX, PPT)
• CSV and TSV files
• Plain text files
• HTML and XML documents
• Markdown files
• JSON files

License

MIT