NyayaSetu

Inspiration Rental disputes are a common problem faced by tenants, especially students and individuals living away from home. Many tenants are unaware of their legal rights, rental agreement terms, security deposit regulations, and the proper procedures for resolving disputes with landlords. Accessing relevant legal information can be confusing and time-consuming. This inspired us to develop the Tenant Rights & Rental Dispute Navigator, an AI-powered platform designed to make tenancy-related legal information more accessible, understandable, and easier to navigate. 🚀 What It Does The project aims to help tenants understand their rights and responsibilities by allowing them to ask questions about rental agreements, tenancy laws, security deposits, eviction procedures, and rental disputes. Using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), the system retrieves relevant information from a curated knowledge base of tenancy legislation and generates contextual responses. Key Features AI-Powered Legal Assistance: Provides understandable answers to common tenancy-related questions. Document-Based Retrieval: Retrieves relevant information from tenancy laws and legal documents. Vector Database Integration: Uses ChromaDB to store and search document embeddings. Rental Dispute Guidance: Helps users understand possible steps for addressing common rental disputes. Accessible Interface: Aims to provide a simple, user-friendly interface through Streamlit. 🛠️ How We Built It We designed the project using a modular architecture that combines document processing, semantic search, and AI-generated responses. Technology Stack: Python: Core programming language. Streamlit: Interactive web application interface. Google Gemini / Groq: Potential LLM providers for generating responses. ChromaDB: Vector database for storing and retrieving document embeddings. LangChain / LlamaIndex: Potential frameworks for implementing the retrieval pipeline. PyPDF: Extracts text from PDF-based legal documents. The planned workflow involves extracting text from tenancy legislation, splitting it into smaller chunks, generating embeddings, and storing them in ChromaDB. When a user submits a question, the system retrieves relevant passages and uses them as context for generating a response. 📚 What We Learned Working on this project helped us explore several important concepts in AI-powered application development: Understanding Large Language Models (LLMs) and their practical applications. Learning the fundamentals of Retrieval-Augmented Generation (RAG). Processing and extracting text from PDF documents using Python. Understanding embeddings and semantic search. Using ChromaDB for vector storage and document retrieval. Integrating AI components into a user-facing application. Managing project files and source code using Git and GitHub. ⚡ Challenges We Faced Developing the project involved several technical challenges, including: Environment Configuration: Setting up Python virtual environments and resolving package installation issues. Dependency Management: Addressing library compatibility problems and import errors. Document Processing: Handling PDF extraction and locating appropriate legal documents. Knowledge Base Preparation: Structuring extracted legal information into meaningful chunks for retrieval. Vector Database Integration: Configuring ChromaDB and understanding how embeddings support semantic search. Reliable Legal Responses: Designing the system to retrieve relevant legal sources and minimize unsupported or misleading answers. These challenges provided valuable hands-on experience in debugging, dependency management, and building AI applications from the ground up. 🔮 What's Next Our next steps are to complete the document retrieval pipeline, integrate the selected LLM, develop the Streamlit interface, and test the system against realistic tenancy-related questions. We also aim to improve source citations, incorporate jurisdiction-specific tenancy legislation, and clearly distinguish general legal information from professional legal advice. 🌟 Our Vision We envision a future where tenants can better understand their rights, make informed decisions, and navigate rental disputes with greater confidence. Tenant Rights & Rental Dispute Navigator — Making tenancy laws easier to understand, one question at a time.