KanoonGPT/indian-legal-documents

Indian Legal Documents

Open Indian statutory and legal-document data for AI, search, and legal research.

This dataset is part of the KanoonGPT Open Legal Data Initiative — an effort to make Indian legal data easier to access, structure, and build on for open-source research, legal tech, and production AI systems.

KanoonGPT is building structured Indian legal datasets and data infrastructure for open-source, research, and enterprise AI applications. Learn more at kanoongpt.in.

Repository: KanoonGPT/indian-legal-documents

Related dataset: KanoonGPT/indian-case-laws

Coverage at a glance

This dataset contains 35,851 Indian legal documents prepared for NLP, search, retrieval, analytics, and LLM training/evaluation.

It includes Acts, Rules, Notifications, Circulars, Orders, Regulations, Guidelines, Ordinances, Schemes, Manuals, Policies, and other legal or quasi-legal government documents.

Dataset size

Metric	Value
Documents	35,851
Unique document IDs	35,851
Median text length	9,495 characters
Average text length	44,106 characters
75th percentile text length	35,901 characters
90th percentile text length	96,122 characters
Maximum text length	6,664,304 characters

Major document types

Document type	Documents	Share
Rules	11,069	30.88%
Notification	9,078	25.32%
Act	8,533	23.80%
Legislation	1,756	4.90%
Circular	1,396	3.89%
Order	1,342	3.74%
Legislation (Act)	1,255	3.50%
Guidelines	448	1.25%
Ordinance	217	0.61%
Regulation	166	0.46%

Major jurisdictions

Jurisdiction	Documents	Share
Central	14,946	41.69%
State (Maharashtra)	1,855	5.17%
State (Uttar Pradesh)	1,704	4.75%
State (Haryana)	1,637	4.57%
State (Karnataka)	1,575	4.39%
State (Rajasthan)	1,470	4.10%
State (Bihar)	1,135	3.17%
State (Tamil Nadu)	896	2.50%
State (Punjab)	884	2.47%
State (Delhi)	849	2.37%
State (Jharkhand)	848	2.37%
State (Assam)	758	2.11%
State (Goa)	639	1.78%
State (Kerala)	477	1.33%
State (Odisha)	467	1.30%

Time coverage

The dataset contains documents with parsed issue dates from 1970 to 2026, with some machine-extracted date outliers also present. Around 28,823 documents have parsed dates and 7,028 documents do not have reliably parsed dates.

Recent year distribution includes:

Year	Documents
2026	65
2025	944
2024	1,259
2023	1,379
2022	1,240
2021	1,171
2020	1,422
2019	1,946
2018	1,738
2017	2,042

Date metadata is machine-extracted and should be treated as a useful filter, not as authoritative legal dating.

Why this dataset exists

Indian legal documents are public in principle, but difficult to use at scale.

Acts, rules, notifications, circulars, and regulations are often scattered across government portals, gazettes, PDFs, scanned documents, inconsistent HTML pages, and fragmented archives. This makes them hard to use for modern AI, search, analytics, and legal research workflows.

KanoonGPT packages this data into an AI-ready, query-friendly, tabular format so builders can use it for:

• legal search and retrieval
• statutory RAG pipelines
• Indian legal NLP benchmarks
• document classification
• metadata extraction
• legal summarization
• legal copilots and agents
• downstream model training and evaluation
• public-interest legal research

What this dataset contains

Each row represents one legal document.

The dataset includes minimal public metadata and cleaned document text. Source URLs and internal scraping provenance are intentionally not included in this public release.

The exported files are sorted by:

1. document_type ascending
2. issue_date descending
3. doc_id ascending

The dataset is stored as sharded Parquet files under:

data/train-*.parquet

Schema

Column	Description
doc_id	Stable document identifier
document_title	Extracted document title
document_type	Extracted document category such as Act, Rules, Notification, Circular, Order, Regulation, Guidelines, etc.
document_jurisdiction	Extracted jurisdiction such as Central, State (Maharashtra), State (Delhi), etc.
issuing_authority	Extracted issuing authority, ministry, department, regulator, legislature, or government body
issue_date	Extracted issue/publication/enactment date when available
short_description	Short machine-generated description of the document
text	Cleaned document text converted from markdown

Example row

A row may include:

• a stable doc_id
• document title
• document type such as Act, Rules, Notification, or Circular
• jurisdiction such as Central or State (Maharashtra)
• issuing authority
• issue date when available
• short description
• cleaned full text for search, retrieval, and model training

Loading

from datasets import load_dataset

ds = load_dataset("KanoonGPT/indian-legal-documents")
print(ds["train"][0])

Load with streaming:

from datasets import load_dataset

ds = load_dataset("KanoonGPT/indian-legal-documents", streaming=True)

for row in ds["train"]:
    print(row["document_title"])
    print(row["text"][:500])
    break

Load with pandas:

import pandas as pd

df = pd.read_parquet("hf://datasets/KanoonGPT/indian-legal-documents/data/train-00000.parquet")
print(df.head())

Intended use

This dataset is intended for:

• Indian legal NLP research
• statutory search
• retrieval-augmented generation
• document classification
• legal information extraction
• legal summarization
• legal data analytics
• LLM pre-training, fine-tuning, and evaluation
• open-source legal-tech infrastructure

Design principles

AI-ready, not just archive-ready

This dataset is designed for downstream usage in search, retrieval, classification, analytics, and model-building.

Minimal public schema

Only a compact set of useful public columns is included. The release intentionally avoids exposing internal scraping fields, source URLs, page-level splits, or pipeline metadata.

Text-first

The text field is cleaned from markdown and normalized for easier model training and retrieval. The goal is to preserve document content while avoiding unnecessary page-split artifacts.

Structured metadata

Important metadata such as title, type, jurisdiction, issuing authority, and issue date are exposed as columns so users can filter and analyze the corpus.

Honest about quality

Legal documents at scale are messy. Metadata fields are machine-extracted and may contain errors, inconsistent labels, missing dates, or noisy jurisdiction/type values. Users should filter, inspect, and validate records for high-stakes use.

Known limitations

• Metadata is machine-extracted and may contain mistakes.
• Some document types are noisy or overly specific.
• Some jurisdiction labels may be inconsistent or verbose.
• Some issue dates may be missing, incorrectly parsed, or outliers.
• Text is generated from scraped/OCR/PDF-derived markdown and may contain formatting artifacts.
• This dataset is not legal advice.
• Users should verify important legal facts against official sources before relying on them.

Responsible use

• Verify important legal facts against official government sources before high-stakes use.
• Do not treat model outputs based on this dataset as legal advice.
• Add citations, validation, and human review layers in end-user legal systems.
• Respect applicable laws, privacy norms, and platform terms in downstream applications.
• Be careful when using machine-extracted metadata for legal or compliance-sensitive decisions.

Why KanoonGPT is publishing this

We believe Indian legal AI needs better open infrastructure.

Open-source legal datasets help researchers, startups, students, public-interest builders, and enterprises work from a common foundation instead of repeatedly rebuilding the same ingestion and cleaning layer.

KanoonGPT’s goal is to contribute usable, structured Indian legal data that is easier to explore, benchmark, and build on.

About KanoonGPT

KanoonGPT is building Indian legal datasets and data infrastructure for open-source, research, and enterprise AI applications.

Coverage areas:

• ⚖️ Case law
• 📜 Legal documents and statutes
• 🏷️ Legal metadata
• 🧩 Structured legal corpora
• 🔎 Legal search and retrieval infrastructure

Website: kanoongpt.in

Related dataset: KanoonGPT/indian-case-laws

Licensing

The dataset is released under the Apache License 2.0.

Citation

If you use this dataset, please cite the Hugging Face dataset.

@dataset{kanoongpt_indian_legal_documents,
  author       = {KanoonGPT},
  title        = {Indian Legal Documents},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/KanoonGPT/indian-legal-documents},
  note         = {Company website: https://kanoongpt.in}
}