This is a submission for the Gemma 4 Challenge: Build with Gemma 4
Legal Buddy ⚖️
A Local-First AI Legal Assistant for Indian Laws powered by Gemma 4
Most people blindly accept privacy policies, contracts, rental agreements, and legal terms without truly understanding what they are agreeing to. Legal language is often complex, inaccessible, and intimidating.
I built Legal Buddy to make legal understanding more accessible while keeping user privacy fully intact.
Legal Buddy is a local-first AI legal assistant designed specifically for the Indian legal ecosystem. It helps users:
- Understand legal concepts through conversational Q&A
- Analyze legal documents and detect risky clauses
- Generate legal document drafts instantly
And the most important part:
Everything runs locally through Ollama. No sensitive legal data leaves the user's machine.
What I Built
Legal Buddy combines:
- ⚖️ Legal Q&A with Retrieval-Augmented Generation (RAG)
- 📄 AI-powered legal document analysis
- 📝 Legal document drafting
- 🔒 Fully local inference using Gemma 4 + Ollama
The application is designed for students, freelancers, employees, tenants, startups, and anyone who regularly encounters legal documents but may not have immediate access to legal expertise.
Core Features
💬 Legal Chat
Users can ask questions related to Indian laws in natural language.
The system uses:
- FastAPI backend
- FAISS vector search
- Local RAG pipeline
- Gemma 4 through Ollama
This allows responses to stay grounded in actual Indian legal references instead of generic AI-generated answers.
Examples:
- “What are tenant rights in India?”
- “Can an employer terminate without notice?”
- “What does an indemnity clause mean?”
📑 Document Scanner
Users can upload:
- PDFs
- Scanned contracts
- Images of agreements
The system performs:
- OCR extraction
- Clause analysis
- Risk detection
- Obligation summaries
- Highlighting unusual legal terms
Legal Buddy uses a map-reduce style document review pipeline, where sections are analyzed independently before generating a consolidated legal review report.
This is especially useful for:
- Rental agreements
- Employment contracts
- NDAs
- Service agreements
- Privacy policies
🖊️ Document Drafting
Users can instantly generate:
- NDAs
- Rental agreements
- Employment contracts
- Basic legal templates
The user simply provides:
- Party names
- Key conditions
- Agreement details
Gemma then generates structured draft documents tailored for Indian legal context.
Demo
Code
🔗 GitHub Repository:
https://github.com/SaiPavankumar22/Legal_Buddy
Architecture
The project follows a decoupled architecture:
Backend
- FastAPI
- OCR processing
- FAISS vector search
- Ollama orchestration
- Local document analysis pipeline
Frontend
- Vanilla JavaScript
- HTML/CSS
- Lightweight and fast UI
How I Used Gemma 4
I used Gemma 4 E2B for this project.
Why Gemma 4 E2B?
I specifically chose Gemma 4 E2B because it offers the right balance between:
- Performance
- Multimodal capability
- Local deployment practicality
- Privacy-focused inference
For a legal assistant, privacy is critical.
Users should not have to upload:
- contracts
- agreements
- legal disputes
- identity-related documents
to external servers just to get AI assistance.
Running Gemma locally through Ollama made this possible.
How Gemma Powers the Project
⚖️ Legal RAG Assistant
Gemma answers legal questions using:
- Local FAISS indices
- Indian legal corpus
- Retrieval-Augmented Generation
This grounds responses in actual legal material instead of relying purely on pretrained knowledge.
📄 Multimodal Document Analysis
Gemma analyzes uploaded:
- PDFs
- scanned pages
- images
It identifies:
- risky clauses
- hidden obligations
- liability-heavy sections
- suspicious wording
The multimodal capabilities were especially useful for layout-heavy legal documents.
📝 AI Legal Drafting
Gemma generates structured legal drafts using user-provided information.
This allows users to quickly create:
- NDAs
- rental agreements
- employment agreements
- legal templates
while still keeping the workflow local and private.
Privacy First 🔒
One of the biggest goals of Legal Buddy was ensuring that users retain ownership of their sensitive legal data.
With Ollama + Gemma:
- Documents stay local
- Queries stay local
- Analysis stays local
No cloud APIs are required.
Challenges Faced
Some of the biggest technical challenges were:
- OCR quality on scanned documents
- Chunking legal documents effectively for RAG
- Preventing hallucinations in legal responses
- Structuring long-form document analysis outputs
- Keeping inference efficient on consumer hardware
Balancing accuracy, privacy, and performance was one of the most interesting parts of building this project.
Future Improvements
Planned improvements include:
- Clause highlighting directly inside PDFs
- Citation-aware legal responses
- Support for regional Indian languages
- Voice-based legal assistance
- Fine-tuned legal adapters
- Legal risk scoring dashboards
Building Legal Buddy was an exciting experience because it showed how powerful local AI systems can become when combined with strong open models like Gemma 4.
Huge thanks to Google and the Gemma team for organizing this challenge 🙌
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