Contract Review System

Overview

Contract Review System is a Retrieval-Augmented Generation (RAG)-based legal document analysis platform designed to automate contract understanding, compliance checking, clause summarization, and risk assessment.

The system processes uploaded PDF contracts, retrieves relevant clauses using semantic search with FAISS embeddings, and uses a fine-tuned Mistral Large Language Model (LLM) to generate structured outputs.

The project was developed as part of my major project focusing on explainable and modular AI-assisted contract analysis.

---

Features

1. Contract Summarization

• Extracts key contract sections
• Generates concise clause summaries
• Uses retrieval-based contextual summarization

2. Compliance Checking

• Rule-based compliance evaluation
• Semantic retrieval using FAISS
• Multi-stage keyword validation pipeline
• Evidence-based clause extraction

3. Risk Analysis

• Business risk assessment based on compliance results

• Generates:

  • Risk level
  • Business impact
  • Recommendations

4. PDF Report Generation

• Generates structured downloadable PDF reports

• Includes:

  • Clause summaries
  • Compliance results
  • Risk analysis

---

System Architecture

User Uploads PDF
        ↓
PDF Processing & Chunking
        ↓
Embedding Generation (MiniLM)
        ↓
FAISS Vector Storage
        ↓
Semantic Retrieval
        ↓
Fine-Tuned Mistral LLM
        ↓
Summary / Compliance / Risk Analysis
        ↓
PDF Report Generation

---

Tech Stack

Component	Technology
Frontend	Streamlit
LLM	Fine-Tuned Mistral 7B
Embeddings	all-MiniLM-L6-v2
Vector Database	FAISS
PDF Processing	PyMuPDF
Report Generation	ReportLab
Inference Hosting	Google Colab + Ngrok
Training Framework	Hugging Face + LoRA/QLoRA

---

Project Structure

Contract-Review-System/
│
├── app.py
├── requirements.txt
├── README.md
├── LICENSE
├── .gitignore
├── .env.example
│
└── src/
    │
    ├── compliance_checker.py
    ├── key_clause_summarizer.py
    ├── llm_client.py
    ├── pdf_processor.py
    ├── report_generator.py
    ├── risk_and_reco.py
    └── vector_store.py

---

# Excluded Folders

The following folders are excluded from the repository:

| Folder        | Reason                                         |
| ------------- | ---------------------------------------------- |
| `models/`     | Large GGUF model files                         |
| `data/`       | Generated vector indexes and metadata          |
| `logs/`       | Runtime logs                                   |
| `config/`     | Environment-specific configuration             |
| `trainingV2/` | Training datasets and generated training files |

---

# Training Process (Summary)

The LLM used in this project was fine-tuned using LoRA/QLoRA techniques on legal-domain datasets.

## Datasets Used

* CUAD (Contract Understanding Atticus Dataset)
* ACORD Dataset
* Custom compliance-rule datasets

## Training Objectives

* Clause extraction
* Compliance-oriented reasoning
* Legal summarization
* Risk-related analysis

## Training Stack

* Hugging Face Transformers
* Unsloth
* PEFT (LoRA/QLoRA)
* GGUF conversion for inference

The final model is served remotely through Google Colab and exposed to the application using Ngrok.

---

# Retrieval Pipeline

The system follows a Retrieval-Augmented Generation (RAG) architecture:

1. PDF text extraction
2. Chunk creation with overlap
3. Embedding generation using MiniLM
4. Vector storage in FAISS
5. Semantic retrieval using rule-based queries
6. LLM-based analysis on retrieved chunks

---

# Compliance Checking Workflow

```text
Rule Selection
      ↓
Query Generation
      ↓
FAISS Retrieval
      ↓
Keyword Validation
      ↓
LLM Clause Extraction
      ↓
Evidence Verification
      ↓
COMPLIANT / NON-COMPLIANT

The compliance system uses a hybrid retrieval-validation approach combining semantic retrieval with strict keyword-based verification.

---

Installation

1. Clone Repository

git clone <repository-url>
cd <repository-name>

2. Install Dependencies

pip install -r requirements.txt

---

Running the Application

Start Streamlit App

streamlit run app.py

---

Remote LLM Setup

The application expects a running remote inference server exposed through Ngrok.

Update the remote endpoint inside:

config/model_config.py

---

Key Research Contributions

• Hybrid RAG-based legal analysis pipeline
• Rule-driven compliance evaluation
• Multi-stage evidence verification
• Modular explainable architecture
• Integration of retrieval and legal reasoning workflows

---

Limitations

• Compliance logic is primarily keyword-guided
• Requires active remote inference server
• Performance depends on retrieval quality
• Does not replace professional legal review

---

Future Improvements

• Full logical compliance reasoning
• Multi-contract comparison
• Clause highlighting in PDFs
• Persistent analysis caching
• Improved legal reasoning datasets
• Multi-agent legal workflows

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Disclaimer

This project is intended for research and educational purposes only. It does not provide legal advice. It is under MIT License