Legal Summarizer

Summarizing legal documents made easy using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).

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📌 Overview

Legal documents are often dense, complex, and difficult for non-lawyers to understand. This project leverages Information Retrieval and Context Augmentation using Large Language Models (LLMs) to simplify and summarize contracts, agreements, and other legal texts.

Fig: High-level system architecture

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🚨 The Problem: Understanding Legal Documents is Hard

• Legal documents use complex terminologies that require domain expertise.
• They contain long, dense sentences that make key information difficult to extract.
• They rely on statutes, legal citations, and references, assuming prior knowledge.
• The conservative and risk-averse language results in intricate phrasing.
• Misinterpretation can lead to serious consequences, discouraging individuals from handling contracts themselves.

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🤖 The Solution: AI-Powered Legal Summarization

With the advancements in Large Language Models (LLMs), we can now:
✅ Extract key insights from legal documents
✅ Summarize complex clauses into easy-to-read formats
✅ Retrieve relevant information using RAG (Retrieval-Augmented Generation)
✅ Improve accessibility of legal content for non-lawyers

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📌 How Does It Work?

🔍 Retrieval-Augmented Generation (RAG)

RAG enhances the summarization process by first searching for relevant content and then reconstructing it using an LLM.

Step 1: Document Processing

• Processing complex agreements, contracts and other legal documents, extracting information using OCR, transformers, etc and chunking, and tagging them with relevant topics for efficient keyword search.

Step 2: Document Retrieval

• Uses BM25 ranking (keyword-based) or Semantic Search (context-based) to fetch relevant parts of legal documents.

Step 3: Context Augmentation

• The retrieved text is then passed to an LLM to generate a structured and readable summary.

Learn More About RAG 🔗 Exploring the Power of RAG & OpenAI’s Function Calling for Q&A

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🛠 Installation & Setup

1️⃣ Create a Virtual Environment

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

2️⃣ Install Dependencies

$ pip install -r requirements.txt

3️⃣ Run the Application

$ streamlit run summarize.py

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📜 License

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

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💡 Contributing

Contributions are welcome! Feel free to submit an issue or a pull request.

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💡 Need Help?

If you have any questions, feel free to reach out! 🚀