legal-counter-argument

A powerful LLM-powered pipeline for summarizing legal documents, generating intelligent counterarguments, and enabling context-aware legal question answering. This system is designed to support individuals in understanding legal documents and crafting robust responses to legal accusations.

📌 Table of Contents

• 🧩 Overview

• ⚙️ Features

• 📐 System Architecture

  1. Document Summarization & Counter-Argument Generation Workflow

  2. Document Question Answering Workflow

• 🚀 Quick Start

• 🛠 Tech Stack

• 📌 Future Enhancements

🧩 Overview

This project aims to empower users to generate intelligent legal counterarguments and ask detailed questions about legal documents using large language models (LLMs) like OpenAI’s gpt-3.5-turbo. It supports legal professionals, individuals without legal expertise, and researchers who need automated assistance with legal content.

⚙️ Features

• ✔️ Automatic summarization of lengthy legal documents
• ✔️ Counter-argument generation using LLMs
• ✔️ Multi-format document ingestion (PDF, HTML, TXT)
• ✔️ Semantic search & question-answering with Pinecone vector store
• ✔️ LangChain-powered modular pipelines for flexibility and scalability

📐 System Architecture

🔁 1. Document Summarization & Counter-Argument Generation Workflow:

1. Input Parsing: Load large legal documents from various formats.

2. Chunking: Split into overlapping chunks (~2000 tokens) for contextual coherence.

3. Prompt Design: Use custom prompt templates tailored for legal summarization.

4. LLM Summarization: Generate summary chunks using load_summarize_chain from LangChain.

5. Summary Fusion: Combine individual summaries into a final, cohesive summary.

6. Counter-Argument Generation: Use OpenAI’s GPT models to derive intelligent counterarguments from the final summary.

🔁 2. Document Question Answering Workflow

1. Multi-format Support: Ingest files in PDF, text, or HTML format.

2. Recursive Chunking: Segment using RecursiveCharacterTextSplitter (~1000 tokens with overlap).

3. Embedding: Create dense vector embeddings using OpenAI's text-embedding-ada-002.

4. Vector Store Setup: Store vectors in Pinecone for semantic search.

5. QA Chain: Use LangChain’s load_qa_chain to retrieve relevant content and generate answers from LLM.

🚀 Quick Start

🔧 Prerequisites

• Python 3.9+
• OpenAI API key
• Pinecone API key & environment setup

📦 Installation

git clone https://github.com/yourusername/legal-counter-argument.git
cd legal-counter-argument
pip install -r requirements.txt

⚙️ Configuration

Update your .env file or set environment variables for:

OPENAI_API_KEY=your_openai_api_key
PINECONE_API_KEY=your_pinecone_api_key
PINECONE_ENV=your_pinecone_environment

🏃‍♂️ Running the Pipeline

# Run summarization & counter-argument generation
python summarize_and_counterarg.py --input_dir ./legal_docs

# Run document QA setup
python qa_pipeline.py --input_dir ./legal_docs

# Ask a question
python ask_question.py --question "What are the key charges in the document?"

🛠 Tech Stack

📌 Future Enhancements

• 🔒 Integrate document redaction for sensitive information

• 🌐 Add support for multilingual legal documents

• 🧠 Fine-tune smaller local LLMs for on-premise deployment

• 📊 Visual dashboard for document navigation and QA