Legal Document Analysis System

Project Overview

The Legal Document Analysis System is a comprehensive platform designed to assist legal professionals and organizations in processing and understanding large volumes of legal documents. The system leverages advanced Natural Language Processing (NLP) and AI techniques to automatically analyze, extract, and summarize key information from legal documents such as contracts, agreements, and filings.

By automating the extraction of clauses, identifying risk factors, detecting potential fraud or inconsistencies, and generating plain-English summaries, this system aims to reduce manual effort, minimize errors, and accelerate legal workflows.

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Key Features

• Upload and manage legal documents in multiple formats (PDF, DOCX, TXT)
• Automated extraction of key clauses and legal entities
• AI-powered document summarization for quick understanding
• Detection of potential risks, fraud indicators, and compliance issues
• Identification of grammatical and formatting issues
• Export structured analysis results in JSON or other formats for integration
• Support for multi-year and multi-type legal documents with adaptive processing

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Technology Stack

• Backend: Node.js with Express.js framework
• Database: MongoDB for storing documents and analysis metadata
• File Handling: Multer for secure file uploads, native file system operations for storage
• AI & NLP: Integration with custom or third-party AI models (e.g., Hugging Face transformers, OpenAI GPT models)
• Deployment: Suitable for containerization and cloud deployment (Docker, Kubernetes, AWS, etc.)

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Getting Started

Prerequisites

• Node.js (v14 or above)
• npm or yarn package manager
• MongoDB instance (local or cloud)
• Access credentials or API keys for AI/NLP services (if applicable)

Installation Steps

1. Clone the repository:

   git clone https://github.com/your-username/legal-document-analysis.git
   cd legal-document-analysis

2. Install dependencies:

   ```bash
   npm install
   ```

3. Create a `.env` file in the root directory and add necessary environment variables:

   ```
   PORT=5000
   MONGODB_URI=your_mongodb_connection_string
   AI_API_KEY=your_ai_service_api_key  # Optional, if using third-party AI APIs
   ```

4. Start the development server:

   ```bash
   npm run dev
   ```

5. The API server will be running on `http://localhost:5000` by default.

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## Project Structure

```
/uploads          # Directory for storing uploaded documents
/controllers      # Express route controllers handling API logic
/models           # Mongoose schema definitions for MongoDB collections
/routes           # API endpoint route definitions
/services         # Business logic and utility functions (e.g., file processing, AI integration)
/config           # Configuration files, including database and environment setup
/middleware       # Custom middleware for authentication, error handling, etc.
```

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## API Documentation

| HTTP Method | Endpoint             | Description                                                    |
| ----------- | -------------------- | -------------------------------------------------------------- |
| POST        | `/api/documents`     | Upload a new legal document for analysis                       |
| GET         | `/api/documents/:id` | Retrieve analysis results and metadata for a specific document |
| DELETE      | `/api/documents/:id` | Remove a document and its associated data                      |

### Example: Upload Document Request

* **Endpoint:** `POST /api/documents`
* **Request Type:** Multipart/form-data
* **Parameters:**

  * `file` (required) – The legal document file to upload (PDF, DOCX, TXT)
* **Response:** JSON object containing document metadata and initial analysis status

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## Usage Notes

* The system supports large-scale batch processing and can handle documents spanning multiple years.
* AI/NLP services should be configured with valid API keys if using external models.
* Uploaded documents are stored securely on the server’s filesystem or cloud storage, and sensitive information must be handled according to compliance requirements.
* Error handling and validation are implemented to ensure robust and secure operations.

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## Contribution Guidelines

Contributions are welcome to improve features, fix bugs, or enhance documentation. Please follow these steps to contribute:

1. Fork the repository
2. Create a feature branch (`git checkout -b feature-name`)
3. Commit your changes (`git commit -m 'Add some feature'`)
4. Push to the branch (`git push origin feature-name`)
5. Open a Pull Request explaining your changes

Please ensure code quality with proper testing and adhere to the existing project style.