Loading page…
Pitch
Description
Comments
Project
Income Tax Act Knowledge Graph + RAG System
Intelligent querying of the Indian Income Tax Act through Knowledge Graphs.
0likes0comments
Project details
About this project
This project integrates Knowledge Graphs with a Retrieval-Augmented Generation system for smarter interactions with the Indian Income Tax Act. It addresses common limitations in legal document querying by emphasizing the interconnectedness of tax laws, making it easier to navigate complex regulations.
The Income Tax Act Knowledge Graph + RAG System is a sophisticated hybrid solution that leverages Knowledge Graph technology alongside Retrieval-Augmented Generation (RAG) to facilitate intelligent querying of the Indian Income Tax Act. This innovative approach significantly enhances the querying capabilities for complex legal documents, addressing the challenges that traditional RAG systems face when dealing with interconnected legalities.
Key Features
Addressing Specific Challenges
Legal documents often comprise intricate connections and references among various sections. The system excels where traditional RAG fails, specifically in answering queries such as:
- "What sections reference Section 80C?"
- "Show me all exemptions available for senior citizens."
- "What penalties apply if I violate Section 44AD?"
- "How does Section 10 relate to Section 80?"
Knowledge Graph Advantages
This system incorporates a Knowledge Graph that efficiently handles:
- Cross-references between sections
- Definitions that appear in multiple contexts
- Conditions and thresholds such as income slabs and age limits
- Exemptions coupled with specific eligibility criteria
Architecture Overview
The architecture comprises distinct modules working in tandem, outlined as follows:
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Tax Act Text │───▶│ Parser Module │───▶│ Knowledge Graph│
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │
▼ │
┌──────────────────┐ │
│ Vector Store │ │
│ (RAG) │ │
└──────────────────┘ │
│ │
▼ ▼
┌──────────────────────────────────────┐
│ Hybrid Query System │
│ • KG Queries (relationships) │
│ • RAG Queries (content) │
│ • Hybrid Queries (both) │
└──────────────────────────────────────┘
Comprehensive Usage Examples
The system facilitates various query types:
Knowledge Graph Queries (Relationships)
# What sections reference Section 80C?
response = query_system.query("What sections reference Section 80C?")
# Returns: Sections that reference Section 80C:
# • Section 80TTB: Deduction in respect of interest on deposits...
Hybrid Queries (Structure + Content)
# Eligibility questions
response = query_system.query("What exemptions are available for senior citizens?")
# Combines: KG to find exemption sections + RAG for senior citizen content
RAG Queries (Content-Based)
# Detailed explanations
response = query_system.query("Explain Section 44AD for presumptive taxation")
# Specific information
response = query_system.query("What is the penalty for not filing returns?")
Efficient Project Structure
The repository is organized for optimal usability:
ita-kg/
├── tax_parser.py # Income Tax Act text parser
├── knowledge_graph.py # Neo4j Knowledge Graph builder
├── hybrid_query_system.py # Query routing and processing
├── main.py # Interactive system
├── demo.py # Capabilities demonstration
├── sample_income_tax_act.txt # Sample tax act data
├── requirements.txt # Python dependencies
├── .env.example # Environment template
└── README.md # Documentation file
Advantages of the System
This system offers several key advantages:
- Cross-Reference Navigation: Effortlessly explore the intricate web of legal references.
- Structured Categorization: Quickly access all applicable exemptions and deductions.
- Impact Analysis: Understand the implications when changes occur within specific sections.
- Context-Aware Responses: Deliver comprehensive answers through the integration of structured and content data.
- Scalable Design: Capable of accommodating additional legal documents within the same graph framework.
Comments
0Start the conversation
Share the first comment.