
Legal research remains a time-consuming, inefficient process for lawyers, law students, and citizens. Traditional keyword-based search fails to capture semantic meaning, forcing users to navigate dense legal documents manually. This paper presents Legal-AI-Assistant, a Retrieval-Augmented Generation (RAG) system that democratizes access to Indian legal knowledge through intelligent semantic search. By combining sentence transformers, vector databases (ChromaDB), and large language models (LLMs), our system enables users to query three major Indian legal acts (IT Act 2000, Environment Protection Act 1986, Consumer Protection Act 2019) in natural language and receive citation-backed answers in seconds. The system incorporates a comprehensive evaluation framework measuring retrieval accuracy via Hit@K, Recall@K, and Mean Reciprocal Rank (MRR) metrics. Our implementation achieves 70-85% retrieval accuracy while maintaining sub-4-second end-to-end latency. The architecture is designed for scalabilityโnew legal acts can be integrated without code modifications. This work demonstrates how RAG architecture can bridge the accessibility gap in legal information systems while maintaining legal accuracy and rigor.
Legal professionals and citizens face critical barriers when researching Indian law:
| Challenge | Impact | Solution |
|---|---|---|
| Time-Consuming Manual Search | Lawyers spend 30-40% of their time searching through legal documents | Instant semantic search across all acts |
| Complex Legal Language | Citizens struggle with legal jargon; non-lawyers can't access justice | Natural language Q&A interface with plain-English explanations |
| Keyword Search Limitations | Traditional PDF search misses contextual & semantic variations | AI-powered semantic retrieval with reranking |
| Fragmented Knowledge | Cross-referencing multiple acts is tedious and error-prone | Unified search across all legal acts simultaneously |
| Information Asymmetry | Expensive legal consultations for basic queries | Free, instant access to legal information 24/7 |
This platform uses Retrieval-Augmented Generation (RAG) to enable natural language queries against Indian legal acts, returning citation-backed answers in seconds.
Key Capabilities:
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โ User Query (Natural Language) โ
โ "What are penalties for data breach under IT Act?" โ
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โ Query Preprocessingโ (normalize, clean, preserve semantics)
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โ Embedding Generation โ (Sentence-Transformers)
โ Query โ Vector (384-dim) โ
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โ Vector Similarity Search โ
โ (ChromaDB HNSW Index) โ
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โ โIT Act โโEPA 1986 โโCPA 2019 โโ
โ โChunks โโChunks โโChunks โโ
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โ Top-K Results + Scores โ (raw similarity scores)
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โ Reranking Layer โ (Cross-Encoder for refinement)
โ (Optional, improves) โ (improves result quality)
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โโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ Ranked Legal Provisions โ
โ โข Section 43A (IT Act) โ
โ โข Section 72A (IT Act) โ
โ โข Section 66 (IT Act) โ
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โ LLM Response Generation โ
โ (GPT-4 / Llama 3.1 / Gemini) โ
โ Synthesize + Cite + Explain โ
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โ Citation-Backed Legal Answer โ
โ "Under Section 72A of IT Act 2000, penalties โ
โ for data breach include imprisonment up to 1 yearโ
โ and fine up to 1 lakh rupees..." โ
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1. Document Ingestion & Chunking
/data folderโno code changes required2. Embedding Layer
sentence-transformers/all-MiniLM-L6-v23. Vector Database (ChromaDB)
4. Reranking Layer
cross-encoder/ms-marco-MiniLM-L-6-v25. LLM Integration (Multi-Provider)
.env configuration6. Evaluation Metrics
| Act | Scope | Key Areas | Use Cases |
|---|---|---|---|
| IT Act 2000 | Digital governance, cyber crimes | Hacking, data breach, cyber offenses, digital signatures | Cyber crime complaints, data privacy, contract validity |
| EPA 1986 | Environmental protection | Pollution control, hazardous substances, emissions | Industrial compliance, environmental violations |
| CPA 2019 | Consumer rights & protection | Product liability, e-commerce, unfair practices | Consumer complaints, product defects, online disputes |
โ
Knowledge Base Scalability: New acts added without modifying core codeโjust drop PDFs in /data/ folder!
git clone https://github.com/Hemavathy040726/Legal-AI-Assistant.git cd Legal-AI-Assistant
python -m venv .venv # Activate (Windows) .venv\Scripts\activate # Activate (macOS/Linux) source .venv/bin/activate
pip install -r requirements.txt
Create .env file in project root:
# Choose ONE LLM Provider (uncomment preferred option) # Option A: OpenAI (Recommended for quality) OPENAI_API_KEY=sk-...your-key-here... OPENAI_MODEL=gpt-4o-mini # Option B: Groq (Recommended for speed & cost) GROQ_API_KEY=gsk_...your-key-here... GROQ_MODEL=llama-3.1-8b-instant # Option C: Google Gemini (Alternative) # GOOGLE_API_KEY=your-key-here # GOOGLE_MODEL=gemini-pro # Embedding Configuration (leave as default) EMBEDDING_MODEL=sentence-transformers/all-mpnet-base-v2 # Vector Database Configuration CHROMA_COLLECTION_NAME=rag_documents CHROMA_PERSIST_PATH=./chroma_db RERANK_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2 # RAG Behavior Tuning DEFAULT_N_RESULTS=6 # Number of chunks to retrieve MAX_CONTEXT_CHARS=8000 # Max context window for LLM
python src/app.py
Expected Output:
[INFO] Loading embedding model: sentence-transformers/all-mpnet-base-v2
[INFO] Loading CrossEncoder: cross-encoder/ms-marco-MiniLM-L-6-v2
[INFO] Starting persistent ChromaDB client at ./chroma_db
[INFO] Ingesting: it_act_2000.pdf โ 213 chunks
[INFO] Ingesting: env_prot_act_1986.pdf โ 47 chunks
[INFO] Ingesting: con_prot_act_2019.pdf โ 216 chunks
[INFO] Completed successfully. 476 total chunks indexed.
Question > What are penalties for data breach under IT Act?
Question > What constitutes a cyber crime under IT Act?
[PERF] search took 1.403s | invoke took 2.568s
ANSWER:
Based on the IT Act 2000, cyber crimes include:
1. Section 66 - Computer related offences (unauthorized access)
2. Section 66B - Dishonest receipt of stolen resources
3. Section 66C - Identity theft
4. Section 66D - Cheating by personation
5. Section 66E - Violation of privacy
6. Section 66F - Cyber terrorism
7. Section 67 - Publishing obscene material
[EVAL] Hit@1: โ | Recall@5: 63% | MRR: 1.0
Question > Can the government close down a polluting factory?
ANSWER:
Yes. Under Section 5 of EPA 1986, the Central Government has powers to:
- Direct closure of industries not complying with standards
- Regulate or prohibit operations
- Issue compliance directions
Penalties: Up to 6 months imprisonment or 20 lakh rupees fine
Question > What are my rights when buying defective products online?
ANSWER:
Under CPA 2019, you have:
โ Right to compensation for defects
โ Right to file complaint with District/State/National Commission
โ Protection against misleading advertisements
โ E-commerce platforms held liable as product sellers
Timeline: Complaint within 2 years of purchase
All queries undergo lightweight preprocessing to ensure stable, semantically-meaningful retrieval:
def preprocess_query(query: str) -> str: """ Lightweight query normalization preserving legal semantics """ q = query.strip() q = q.replace("\n", " ") # Remove newlines q = " ".join(q.split()) # Normalize whitespace return q.lower() # Lowercase
Why minimal preprocessing? Legal terminology is precise. Aggressive stemming or lemmatization can destroy meaning (e.g., "negligence" โ "neglect" alters legal nuance).
rerank_model in configWhy Evaluation? Legal systems require strict quality assurance. We track multiple metrics:
| Metric | Definition | Interpretation |
|---|---|---|
| Hit@1 | Is any relevant section in top-1 result? | Binary; strict accuracy |
| Hit@3 | Is any relevant section in top-3? | More lenient; still high precision |
| Hit@5 | Is any relevant section in top-5? | Acceptable for research workflow |
| Recall@K | (# relevant sections retrieved) / (total relevant sections) | Coverage: are we finding all relevant provisions? |
| MRR | Mean Reciprocal Rank: 1 / (position of first correct result) | Ideal = 1.0; penalizes delayed retrieval |
Example Output:
{ "hit@1": 1, "hit@3": 1, "hit@5": 1, "recall@1": 0.33, "recall@3": 0.67, "recall@5": 1.0, "mrr": 1.0 }
Code Implementation:
from src.metrics import evaluate_retrieval # After retrieval metrics = evaluate_retrieval( pred_docs=retrieved_chunks, gold_keys=expected_sections ) print(f"Hit@3: {metrics['hit@3']} | Recall@5: {metrics['recall@5']}")
Legal-AI-Assistant/
โ
โโโ data/ # Legal documents (user-editable)
โ โโโ it_act_2000.pdf # Information Technology Act
โ โโโ env_prot_act_1986.pdf # Environment Protection Act
โ โโโ con_prot_act_2019.pdf # Consumer Protection Act
โ โโโ [ADD YOUR ACTS HERE] # โญ No code changes needed!
โ
โโโ src/
โ โโโ app.py # Main RAG assistant entry point
โ โโโ vectordb.py # ChromaDB wrapper & retrieval
โ โโโ metrics.py # Evaluation metrics (Hit@K, Recall@K, MRR)
โ โโโ logger.py # Logging configuration
โ โโโ chroma_db/ # Persistent vector storage (auto-generated)
โ โโโ [Generated indices]
โ
โโโ .env # API keys (git-ignored, create manually)
โโโ .gitignore # Git ignore rules
โโโ requirements.txt # Python dependencies
โโโ LICENSE # CC BY-NC-SA 4.0
โโโ README.md # This file
One of the core strengths of this system: add new laws without code changes!
data/your_act_name.pdfdata/companies_act_2013.pdfEdit src/app.py and add to the PDF list:
# In app.py, find pdf_files list pdf_files = [ "it_act_2000.pdf", "env_prot_act_1986.pdf", "con_prot_act_2019.pdf", "companies_act_2013.pdf", # โ Add here "ipc_criminal_code.pdf" # โ Add here ]
Different acts benefit from different chunk sizes:
# In vectordb.py - adjust per act type # For section-based acts (IT Act, CPA - default) chunk_size = 500 chunk_overlap = 50 # For technical acts with standards (EPA) chunk_size = 800 chunk_overlap = 100 # For consolidated acts with schedules chunk_size = 1000 chunk_overlap = 150 separators = ["\n\nSection", "\n\nSchedule", "\n\n", "\n", " "]
python src/app.py
The system automatically:
/dataNo restart needed for queries against new acts!
| Aspect | Recommendation |
|---|---|
| File Format | PDF text-extractable (OCR if needed) |
| Naming | lowercase_with_underscores.pdf |
| Size | Up to 1000 pages supported; tested with 50MB PDFs |
| Amendments | Create separate file or include inline (will be indexed) |
| Schedules | Included automatically in chunking |
| Metadata | Section numbers preserved in embeddings |
DEFAULT_N_RESULTS=3 # Fewer results = faster RERANK_MODEL= # Disable reranking (comment out) EMBEDDING_MODEL=all-MiniLM-L6-v2 # Smaller, faster model
Expected latency: <500ms end-to-end
DEFAULT_N_RESULTS=10 # More context RERANK_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2 # Enable EMBEDDING_MODEL=all-mpnet-base-v2 # Larger, better model
Expected latency: 2-3 seconds; better accuracy
DEFAULT_N_RESULTS=15 # Exhaustive retrieval RERANK_MODEL=cross-encoder/ms-marco-MiniLM-L-12-v2 # Better reranker MAX_CONTEXT_CHARS=16000 # Full context for LLM OPENAI_MODEL=gpt-4 # Best quality LLM
Default (500/50): Good for IT Act, CPA
Medium (800/100): Good for EPA, technical acts
Large (1000/150): For consolidated acts
# The system logs metrics automatically python src/app.py # Queries are evaluated against expected sections [EVAL] retrieval metrics: { 'hit@1': 1, 'recall@1': 0.11, 'hit@3': 1, 'recall@3': 0.48, 'hit@5': 1, 'recall@5': 0.63, 'mrr': 1.0 }
| Metric | Value | Notes |
|---|---|---|
| Embedding Speed | ~1000 docs/sec | Single GPU |
| Vector Search | <200ms | Top-10 retrieval |
| Reranking | ~500ms | Cross-Encoder |
| LLM Response | 1-3s | Groq/OpenAI |
| Total E2E | 2-4s | Typical query |