This project, titled "Query-Based Advanced RAG (Rule and Analytics Generator) Utilizing Gemini Pro Model," is a Python application designed to facilitate querying and analysis of past Indian law data. The tool accepts PDF files containing legal documents as input, leveraging advanced Natural Language Processing (NLP) techniques to extract and analyze relevant legal information. Built upon the Gemini Pro Model, it integrates with Gemini's API for enhanced querying capabilities, allowing users to retrieve detailed insights and summaries from historical legal documents. Whether for legal research, compliance analysis, or academic study, this tool offers a robust framework for accessing and interpreting complex legal data efficiently.
Python 3.x: The programming language used for the project.
Python Libraries:
dotenv: For loading environment variables from a .env file. llama_index: Custom package containing the Gemini Pro Model for legal document analysis. requests: For making HTTP requests to the Gemini API. Gemini API Key: Obtain an API key from Gemini for accessing their services.
Environment Setup:
Create a .env file in the project root directory. Add your Gemini API key in the .env file using the format GOOGLE_API_KEY=your_api_key.