Verdicto – AI Legal Research Assistant

"Smarter, faster, and more accurate legal research for the modern era."

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Overview

Legal research can be slow, fragmented, and overwhelming.
Lawyers, students, and researchers often spend hours reviewing case laws, statutes, and legal opinions — and many tools still rely on outdated keyword searches.

Verdicto changes that by using AI-powered semantic search combined with Retrieval-Augmented Generation (RAG).
Instead of only listing results, it summarizes, compares, and explains legal information — all with reliable citations you can trust.

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Why Use Verdicto?

• Ask naturally – Use everyday language instead of rigid keywords.
• Accurate summaries – Answers are based on actual legal documents, not AI guesses.
• Upload and search instantly – Query your own case files in seconds.
• Live legal updates – Fetch real-time judgments and precedents via legal APIs.
• Save time – Reduce hours of research while keeping accuracy and compliance.

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How Verdicto Works

1. Upload or Fetch Data – Upload your case files, statutes, or pull data from integrated APIs like Indian Kanoon or CourtListener.
2. Vector Embeddings – Documents are converted into high-dimensional vectors using OpenAI’s text-embedding-3-large.
3. Semantic Search – Your query finds the most relevant legal content in the vector database.
4. RAG Pipeline – LangChain retrieves top matches and sends them to the AI model for summarization.
5. Function Calling – For live updates (e.g., “Latest Supreme Court ruling on XYZ”), the system queries external APIs.
6. AI Response with Citations – You get a clear, legally precise answer with sources you can verify.

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Tech Stack

Backend: Python + FastAPI
AI Orchestration: LangChain
Vector Database: Pinecone / ChromaDB / Weaviate
LLM: OpenAI GPT-4 (or LangChain-compatible models)
Embeddings: OpenAI text-embedding-3-large
Frontend: React (chat-based interface)
Data Sources: Indian Kanoon API, CourtListener API
Deployment: Render (Backend), Vercel/Netlify (Frontend)

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Implementation Plan

1. Backend

• FastAPI for handling API requests.
• LangChain for RAG and API function calls.
• Pinecone/ChromaDB for storing embeddings.
• Live API integration for legal judgments and statutes.

2. Frontend

• React chat interface for user queries.
• Highlight sources directly in responses.
• PDF upload and instant search.

3. Data Pipeline

• Preprocess and split legal documents into chunks.
• Generate embeddings using OpenAI API.
• Store and retrieve via vector database.

4. RAG Setup

• LangChain RetrievalQA with legal-specific prompts.
• Metadata-powered citations for transparency.

5. Testing & Optimization

• Test with real legal queries.
• Fine-tune prompts for accuracy and clarity.
• Gradually expand dataset coverage.

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Installation

# Clone the repository
git clone https://github.com/yourusername/verdicto.git
cd verdicto

# Backend setup
cd backend
python -m venv venv
source venv/bin/activate  # For Windows: venv\Scripts\activate
pip install -r requirements.txt

# Add environment variables
echo "OPENAI_API_KEY=your_openai_api_key" > .env
echo "VECTOR_DB_URL=your_vector_db_url" >> .env
echo "LEGAL_API_KEY=your_legal_api_key" >> .env

# Run backend
uvicorn main:app --reload

# Frontend setup
cd ../frontend
npm install
npm start


###Example Use Cases
- Law students summarizing lengthy case studies.

- Lawyers quickly checking the latest precedents.

- Researchers analyzing multiple judgments at once.

###Future Improvements
- Support for more jurisdictions.

- Offline mode for local legal database search.

- Multi-language legal document processing.