LegalRAG AI is a production-focused Legal AI system built using Retrieval-Augmented Generation (RAG), local LLMs via Ollama, semantic search, vector databases, and grounded legal reasoning pipelines.
This project was built to create a domain-specialized AI assistant capable of answering legal questions using grounded legal documents instead of hallucinated responses.
Unlike normal AI chatbots, this system retrieves relevant legal context from trained legal datasets before generating responses.
Most general AI chatbots:
- hallucinate legal information
- provide ungrounded responses
- lack legal domain specialization
- cannot retrieve real legal acts and sections
LegalRAG AI solves this by:
- training on legal documents
- retrieving relevant legal chunks
- grounding answers in actual legal context
- filtering non-legal questions
- supporting scalable legal ingestion pipelines
LegalRAG AI can be useful for:
- law students
- legal researchers
- compliance teams
- startups
- legal-tech platforms
- cyber law analysts
- corporate legal teams
- AI legal assistant experimentation
- RAG engineering research
- Production-grade Legal RAG pipeline
- PGVector semantic retrieval
- Hybrid legal search
- Semantic chunking
- Contextual compression
- Conversation memory
- Legal query guardrails
- Streaming responses
- Local LLM inference using Ollama
- Domain-specialized legal embeddings
- FastAPI backend
- Modular AI pipeline architecture
- Python
- FastAPI
- AsyncIO
- Uvicorn
- Ollama
- LangChain
- Sentence Transformers
- PGVector
- PostgreSQL
- Semantic Embeddings
- Hybrid Retrieval
- Contextual Compression
- phi3:mini
- qwen2.5:1.5b
- llama3
- mistral
- qwen2.5
The system was trained and structured for:
- Indian Constitution
- IPC
- CRPC
- Cyber Law
- IT Act
- Company Law
- GST / Taxation
- Contracts
- Labor Law
- Compliance Documents
The architecture supports ingesting thousands of additional legal PDFs and documents.
User Query
↓
Legal Guardrails
↓
Retriever
↓
PGVector Semantic Search
↓
Context Compression
↓
Conversation Memory
↓
LLM Generation (Ollama)
↓
Grounded Legal Response
I built:
- a modular production-style RAG architecture
- custom ingestion pipelines
- legal document semantic retrieval
- vector search pipelines
- grounded legal reasoning system
- streaming AI responses
- domain-specific AI guardrails
- scalable legal AI backend APIs
This project was built to deeply understand:
- AI engineering
- LLM systems
- RAG pipelines
- vector databases
- production AI architecture
- retrieval systems
- local inference systems
During development, several real-world AI engineering challenges were encountered:
- Ollama CUDA runtime instability
- GPU memory allocation issues
- streaming inference crashes
- async inference debugging
- Windows GPU compatibility issues
- empty model responses
- vector indexing optimization
- retrieval tuning
- semantic chunking strategies
These challenges helped in understanding real production AI infrastructure debugging.
Local GPU inference may require environment-specific CUDA configuration depending on hardware and Ollama runtime compatibility.
Some Windows + NVIDIA GPU environments may require:
- VRAM optimization
- reduced context windows
- CPU fallback
- Ollama runtime tuning
This project helped me learn:
- production RAG architecture
- vector databases
- semantic search
- LLM inference pipelines
- Ollama runtime systems
- async FastAPI engineering
- streaming AI systems
- grounding AI responses
- prompt engineering
- AI infrastructure debugging
- memory optimization
- legal domain specialization
Explain Article 21 of the Indian Constitution.
What is the punishment under IPC Section 420?
Explain the IT Act related to cybercrime.
What are Fundamental Rights in India?
What is the legal procedure for filing an FIR?
Use this JSON body inside FastAPI Swagger Docs (/docs) or Postman to test the LegalRAG AI system.
{
"question": "Explain Article 21 of the Indian Constitution and the Right to Life.",
"jurisdiction": "india"
}{
"question": "What are the Fundamental Rights guaranteed under the Constitution of India?",
"jurisdiction": "india"
}{
"question": "Explain IPC Section 420 related to cheating and fraud.",
"jurisdiction": "india"
}{
"question": "What does the IT Act say about cybercrime in India?",
"jurisdiction": "india"
}{
"question": "What is the legal process to file an FIR in India?",
"jurisdiction": "india"
}{
"question": "Who won the IPL yesterday?",
"jurisdiction": "india"
}Expected behavior: The system should reject non-legal questions using legal guardrails.
git clone <your-repo-url>
cd nexhire-ragpython -m venv venvvenv\Scripts\activatesource venv/bin/activatepip install -r requirements.txtInstall Ollama:
Pull model:
ollama pull phi3:miniVerify:
ollama listCreate .env
OLLAMA_MODEL=phi3:mini
OLLAMA_BASE_URL=http://localhost:11434uvicorn app.api.main:app --reloadhttp://127.0.0.1:8000/docs
- Multi-agent legal workflows
- Legal citation generation
- Court judgement retrieval
- Legal PDF summarization
- Reranking models
- Fine-tuned legal embeddings
- Agentic legal reasoning
- Docker deployment
- Kubernetes scaling
- Redis caching
- Authentication system
- Legal analytics dashboard
You can continuously improve the system by ingesting:
- latest legal acts
- court judgements
- compliance documents
- government circulars
- updated cyber laws
- new taxation laws
The architecture is designed to scale with additional legal datasets.
This system is intended for educational, research, and AI engineering purposes only.
It should not be considered a substitute for professional legal advice.
Uzain Mohid S