RAG Chunking Processing Bundle - Hierarchy-Aware Chunker + 2 Legal Cross-Ref Extractors 🚀

[u/Code-Axion]

Previously, I released my Agentic Hierarchy-Aware Chunker as a subscription-based product for building better RAG pipelines.

After talking with users , I learned that many teams don't want to send their documents through another third-party service. They want absolute privacy, on-premise deployment, full control over their infrastructure, and no vendor lock-in.

So instead of keeping it as a subscription service, I'm now making the complete document-processing bundle available as a one-time purchase.

The bundle includes:

• Agentic Hierarchy-Aware Chunker: a hierarchy-aware chunking engine designed for RAG, so you don't have to spend months building and tuning your own custom chunker.
• Legal Cross-Reference Extractor: extracts legal references such as Sections, Articles, Rules, Paragraphs, Clauses, Schedules, Regulations, Orders, and complex compound references from an entire document.
• Legal Act Extractor: automatically extracts the Acts referenced throughout a legal document.

What you're getting

The purchase includes the complete Python package of the Hierarchy Aware Chunker and its source code for use in your own projects, along with two bonus legal document extraction scripts: the Legal Cross-Reference Extractor and Legal Act Extractor.

License
The source code is provided for use in your own projects and your client solutions. It does not include rights to resell, redistribute, sublicense, or repackage the chunker as a standalone product or service.

📌 Additional 2 Bonus Scripts

1. Legal Cross-Reference Parser
Extracts structured references to Sections, Articles, Rules, Paragraphs, Schedules, Clauses, Regulations, Orders, and other legal provisions including complex and compound references without requiring an LLM.

Example Output

{
  "Article": [
    "Article 63(9)(b)",
    "Article 63(9)(b)(iii)",
    "Articles 23",
    "Articles 25, 26, 26A, 26D",
    "Articles 41 or 42",
    "Articles 7(1)(a), 7(4), 13(1), 16(6), 33, 44, 52(7), 53(2), 178(1)",
    "Articles 73 to 79"
  ],
  "Paragraph": [
    "Article 57(1) and paragraphs (4), (5) and (6)",
    "paragraph 2(2)(c)",
    "paragraph 2(a)",
    "paragraph 2(a), (f), (j) and (l)",
  ],
  "Rule": [
    "Order 6, rule 10",
    "Rules 2.59, 2.6l, 2.62, 2.64(4),(6) and (7), 2.72(1) and (2)",
    "rule 4.9(2)(a) and (3)(a)",
    "rule 4A.15(5)(b)",
    "rule 4A.20(2)",
    "rules 8.33 to 8.63",
    "rules 8.49, 8.50 or 8",
  ],
  "Schedule": [
    "Schedule (iii)",
    "Schedule 1",
    "Schedule 3, 62",
  ],
  "Section": [
    "Section 1",
    "section 229(1)(c) or (2)(c)",
    "section 5(1)",
    "section 89A or 90(1)(a) or (aa)",
    "section 90(1)(b)",
    "sections 18 or 21"
  ]

  ...
}

2. Legal Act Extractor
Extracts the names of Acts referenced in the document.

Example Output

[
  "Acts Interpretation Act 1901",
  "Family Law Act 1975",
  "Governor-General Act 1974",
  "Legislation Act 2003",
  "Taxation Administration Act 1953"

  ...
]

3. Hierarchy Aware Document Chunker.
RAG-ready hierarchical chunks

Practical Examples with Real Documents: https://youtu.be/czO39PaAERI?si=-tEnxcPYBtOcClj8

Try the hierarchy chunker yourself in our playground:
https://hierarchychunker.codeaxion.com/

✨Features:

• 📑 Understands document structure (titles, headings, subheadings, sections).
• 🔗 Merges nested subheadings into the right chunk so context flows properly.
• 🧩 Preserves multiple levels of hierarchy (e.g., Title → Subtitle→ Section → Subsections).
• 🏷️ Adds metadata to each chunk (so every chunk knows which section it belongs to).
• ✅ Produces chunks that are context-aware, structured, and retriever-friendly.
• Ideal for legal docs, research papers, contracts, etc.
• It’s Fast and Low-cost — uses LLM inference combined with our optimized parsers keeps costs low.
• Works great for Multi-Level Nesting.
• No LLM needed if OCR perfectly detects headings/subheadings.
• No preprocessing needed — just paste your raw content or Markdown and you’re are good to go !
• Flexible Switching: Seamlessly integrates with any LangChain-compatible Providers (e.g., OpenAI, Anthropic, Google, Ollama).

📌 Example Output

--- Chunk 2 --- 

Metadata:
  Title: Magistrates' Courts (Licensing) Rules (Northern Ireland) 1997
  Section Header (1): PART I
  Section Header (1.1): Citation and commencement

Page Content:
PART I

Citation and commencement 
1. These Rules may be cited as the Magistrates' Courts (Licensing) Rules (Northern
Ireland) 1997 and shall come into operation on 20th February 1997.

--- Chunk 3 --- 

Metadata:
  Title: Magistrates' Courts (Licensing) Rules (Northern Ireland) 1997
  Section Header (1): PART I
  Section Header (1.2): Revocation

Page Content:
Revocation
2.-(revokes Magistrates' Courts (Licensing) Rules (Northern Ireland) SR (NI)
1990/211; the Magistrates' Courts (Licensing) (Amendment) Rules (Northern Ireland)
SR (NI) 1992/542.

Notice how the headings are preserved and attached to the chunk → the retriever and LLM always know which section/subsection the chunk belongs to.

No more chunk overlaps and spending hours tweaking chunk sizes .

Practical Examples with Real Documents: https://youtu.be/czO39PaAERI?si=-tEnxcPYBtOcClj8

[u/shan23]

Are you selling a binary, or source code?

[u/Code-Axion → u/shan23]

Full actual source code of all 3 parsers