Legal AI Assistant

⚠️ DISCLAIMER: This tool is for educational purposes only. It does NOT constitute legal advice. Always consult a qualified attorney before making any legal or business decisions.

A Retrieval-Augmented Generation (RAG) pipeline that helps you understand contracts by extracting key clauses, flagging risks, detecting internal conflicts, and answering natural-language questions — all grounded in the actual document text.

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What the Tool Does

• Parses PDF and DOCX contract files into structured text with section detection
• Indexes the document into a FAISS vector store for semantic search
• Summarises the contract: parties, type, effective date, duration, key obligations
• Extracts clauses: indemnification, limitation of liability, termination, governing law, IP ownership, confidentiality
• Analyses risks: flags HIGH / MEDIUM / LOW risk patterns with plain-English explanations
• Detects conflicts: surfaces internal contradictions between clauses
• Answers questions: grounded Q&A with mandatory section citations

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Supported Document Types

Format	Extension	Notes
PDF	.pdf	Text-based PDFs only. Scanned PDFs require OCR pre-processing.
Word	.docx	Supports Heading styles for better section detection.

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Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        main.py (CLI)                            │
└──────────────┬──────────────────────────────────────────────────┘
               │
    ┌──────────▼──────────┐
    │  document_parser.py  │  PDF / DOCX → full_text + sections
    └──────────┬──────────┘
               │
    ┌──────────▼──────────┐
    │     indexer.py       │  text → chunks → HuggingFace embeddings → FAISS
    └──────────┬──────────┘
               │
       ┌───────┴────────┐
       │  OpenAI GPT-4  │  (all LLM calls below use this)
       └───────┬────────┘
               │
    ┌──────────▼──────────┐   ┌──────────────────────┐
    │    summarizer.py     │   │  clause_extractor.py  │
    └─────────────────────┘   └──────────┬───────────┘
                                          │
                               ┌──────────▼──────────┐
                               │   risk_analyzer.py   │
                               └──────────┬──────────┘
                                          │
                               ┌──────────▼──────────┐
                               │ conflict_detector.py │
                               └─────────────────────┘
    ┌─────────────────────┐
    │     qa_chain.py      │  FAISS retriever + custom legal prompt
    └─────────────────────┘

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Setup

1. Clone / navigate to the project

cd legal-ai-assistant

2. Create a virtual environment

python -m venv venv
source venv/bin/activate        # macOS/Linux
venv\Scripts\activate           # Windows

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

cp .env.example .env
# Edit .env and add your OpenAI API key

OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4          # or gpt-3.5-turbo for lower cost

5. Add a contract file

Place a PDF or DOCX contract in data/sample_contracts/ or any other path.

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How to Run

Full analysis (default)

python main.py --file data/sample_contracts/service_agreement.pdf

Use a cheaper model (faster, less accurate)

python main.py --file contract.pdf --model gpt-3.5-turbo

Skip risk analysis and conflict detection

python main.py --file contract.pdf --skip-risks --skip-conflicts

Ask a single question and exit

python main.py --file contract.pdf --question "What are my termination rights?"

Interactive Q&A after analysis

python main.py --file contract.pdf --interactive

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Sample Questions to Ask

What are my termination rights?
Who owns IP I create during the contract?
What is the liability cap?
How does auto-renewal work?
What information must I keep confidential and for how long?
Which court has jurisdiction over disputes?
Can the company change the terms without my consent?
What happens to my work if the contract is terminated early?

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Output Sections Explained

Section	What it shows
Executive Summary	Parties, contract type, effective date, duration, key obligations, plain-English overview
Key Clauses	Table of named clause types with their section references and plain-English translations
Risk Analysis	🔴 HIGH / 🟡 MEDIUM / 🟢 LOW risks with explanations and fair alternatives
Conflict Detection	Internal contradictions between clauses (e.g. mismatched notice periods)
Q&A	Grounded answers with mandatory section citations

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Limitations

These are not bugs — they are inherent limitations of the technology.

1. Cannot reliably detect all conflicts. The LLM may miss conflicts requiring deep legal expertise or flag false positives. Every flagged conflict must be manually verified.

2. PDF extraction may miss some formatting. Tables lose column alignment, scanned PDFs produce no text, and footnotes may appear mid-sentence. Complex formatting in PDFs will degrade extraction quality.

3. LLM can misinterpret complex legal language. Highly technical, jurisdiction-specific, or archaic legal terms may be interpreted incorrectly. The model is not a lawyer.

4. Context window limits truncate long contracts. Summary and clause extraction are capped at 8 000–12 000 characters. Very long contracts (100+ pages) will have their later sections underweighted.

5. Embeddings may not capture domain-specific meaning. The all-MiniLM-L6-v2 model was not trained on legal text specifically; niche legal terms may not retrieve optimally.

6. Always verify with a qualified attorney. This tool helps you know WHAT to look for and WHERE to look. It does not replace professional legal review.

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Project Structure

02-legal-ai-assistant/
├── README.md                  ← this file
├── requirements.txt
├── .env.example
├── data/
│   └── sample_contracts/      ← place your PDF/DOCX files here
├── src/
│   ├── __init__.py
│   ├── document_parser.py     ← PDF/DOCX → structured text
│   ├── indexer.py             ← text → FAISS vector index
│   ├── summarizer.py          ← executive summary generation
│   ├── clause_extractor.py    ← named clause extraction
│   ├── risk_analyzer.py       ← HIGH/MEDIUM/LOW risk scoring
│   ├── conflict_detector.py   ← internal contradiction detection
│   └── qa_chain.py            ← RAG Q&A chain
├── prompts/
│   ├── summary_prompt.txt
│   ├── clause_prompt.txt
│   └── risk_prompt.txt
└── main.py                    ← CLI entry point

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Dependencies

Package	Purpose
langchain + langchain-community + langchain-openai	LLM orchestration and RAG chains
faiss-cpu	Local vector similarity search
sentence-transformers	HuggingFace embedding model (runs locally)
pypdf	PDF text extraction
python-docx	DOCX parsing
openai	OpenAI API client
python-dotenv	.env file loading
pydantic	Data validation
rich	Formatted terminal output