> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/vectifyai/pageindex/llms.txt
> Use this file to discover all available pages before exploring further.
# Tree Structure
> Understanding PageIndex's hierarchical tree structure for document indexing
PageIndex transforms lengthy PDF documents into a semantic **tree structure**, similar to a "table of contents" but optimized for use with Large Language Models (LLMs). This hierarchical representation enables reasoning-based retrieval by organizing documents into naturally structured sections.
## Structure Overview
The PageIndex tree structure organizes documents into a hierarchy of nodes, where each node represents a section or subsection of the document. This mirrors how humans naturally organize and navigate complex documents.
### Node Properties
Each node in the tree contains the following key properties:
* **`title`**: The section heading or title extracted from the document
* **`node_id`**: A unique identifier for the node (e.g., "0001", "0002")
* **`start_index`**: The page number where the section begins
* **`end_index`**: The page number where the section ends
* **`nodes`**: An array of child nodes (subsections) if the section contains nested content
* **`summary`** (optional): An AI-generated summary of the section content
The `start_index` and `end_index` are 1-based page numbers that correspond to the physical page numbers in the PDF document.
## Real Example: Federal Reserve Annual Report
Here's an actual tree structure generated from the Federal Reserve's 2023 Annual Report:
```json theme={null}
{
"doc_name": "2023-annual-report.pdf",
"structure": [
{
"title": "Preface",
"start_index": 1,
"end_index": 4,
"node_id": "0000"
},
{
"title": "Financial Stability",
"start_index": 21,
"end_index": 21,
"nodes": [
{
"title": "Monitoring Financial Vulnerabilities",
"start_index": 22,
"end_index": 28,
"node_id": "0007"
},
{
"title": "Domestic and International Cooperation and Coordination",
"start_index": 28,
"end_index": 31,
"node_id": "0008"
}
],
"node_id": "0006"
},
{
"title": "Supervision and Regulation",
"start_index": 31,
"end_index": 31,
"nodes": [
{
"title": "Supervised and Regulated Institutions",
"start_index": 32,
"end_index": 35,
"node_id": "0010"
},
{
"title": "Supervisory Developments",
"start_index": 35,
"end_index": 54,
"node_id": "0011"
}
],
"node_id": "0009"
}
]
}
```
## Hierarchical Organization
The tree structure supports multiple levels of nesting, allowing for complex document hierarchies:
```json theme={null}
{
"title": "Parent Section",
"start_index": 1,
"end_index": 10,
"node_id": "0001",
"nodes": [
{
"title": "Child Section 1",
"start_index": 1,
"end_index": 5,
"node_id": "0002",
"nodes": [
{
"title": "Grandchild Section",
"start_index": 2,
"end_index": 3,
"node_id": "0003"
}
]
},
{
"title": "Child Section 2",
"start_index": 6,
"end_index": 10,
"node_id": "0004"
}
]
}
```
The hierarchical structure enables efficient tree search algorithms to navigate documents, similar to how human experts would scan a table of contents to find relevant information.
## Document Description
When enabled, PageIndex can generate a high-level description of the entire document:
```json theme={null}
{
"doc_name": "q1-fy25-earnings.pdf",
"doc_description": "A comprehensive financial report detailing The Walt Disney Company's first-quarter fiscal 2025 performance, including revenue growth, segment highlights, guidance for fiscal 2025, and key financial metrics such as adjusted EPS, operating income, and cash flow.",
"structure": [...]
}
```
## Node Summaries
Each node can include an AI-generated summary that captures the key information in that section:
```json theme={null}
{
"title": "Financial Results for the Quarter",
"start_index": 1,
"end_index": 1,
"node_id": "0001",
"summary": "The Walt Disney Company's financial performance for Q1 fiscal 2025: Revenue increased 5% to $24.7 billion, income before taxes rose 27% to $3.7 billion, and diluted EPS grew 35% to $1.40. Total segment operating income increased 31% to $5.1 billion."
}
```
Summaries are generated by LLMs analyzing the actual content of each section, providing context-rich metadata that aids in retrieval and understanding.
## Tree Generation Process
PageIndex generates tree structures through multiple approaches depending on the document:
1. **With Table of Contents**: If the document has a TOC with page numbers, PageIndex extracts and validates it
2. **Without Page Numbers**: If the TOC lacks page numbers, PageIndex matches section titles to page content
3. **No Table of Contents**: PageIndex generates the structure by analyzing document hierarchy directly from content
## Use Cases
The tree structure is ideal for:
* **Financial reports** and regulatory filings (10-Ks, annual reports)
* **Academic textbooks** and research papers
* **Legal documents** and technical manuals
* **Policy documents** and government reports
* Any document that **exceeds LLM context limits**
## Benefits Over Chunking
Sections follow the document's natural structure, not arbitrary token limits
The hierarchical relationship between sections is maintained
Each node maps directly to specific page ranges in the original document
LLMs can reason about section relevance using titles and summaries
## Next Steps
Learn how PageIndex uses tree structures for intelligent retrieval
Start generating tree structures from your documents