Standard PageIndex
libraryLLM-assisted tree generation.
run_pageindex.py
Open component record →What VectifyAI/PageIndex is—in plain language
Vectorless reasoning-based RAG that builds hierarchical document trees and lets LLMs reason over them instead of vector similarity/chunking.
Mental model: This repository behaves like a service or multi-process application. One command starts it, a browser/API/agent connects to it, and the Terminal process must remain running until you stop it.
Interfaceslibrary, CLI, API, MCP
Primary languagePython
Typical useRetrieve traceable context from long professional PDFs/Markdown using self-hosted parsing or enhanced cloud/MCP/API options.
Your first 15 minutes
- Complete the linked Installation Guide and its verification step.
- Create a disposable test folder or use non-sensitive sample data.
- Run the small example below and observe what files, ports, or prompts appear.
- Read the result before approving writes, network calls, account access, or costs.
- Only then repeat the workflow with a real project.
Starter workflow
cp .env.example .env 2>/dev/null || touch .env
# Add OPENAI_API_KEY to .env, then:
python run_pageindex.py --pdf_path /absolute/path/to/document.pdfComplete indexed functionality map
Each card below corresponds to a component identified in the repository research. Open a component record for its path, purpose, capabilities, dependencies, risks, and relationships.
PageIndex Flash
libraryFast heuristic tree extraction.
pageindex/flash
Open component record →Agentic RAG demo
exampleTree-search retrieval demo.
examples/agentic_vectorless_rag_demo.py
Open component record →Cookbooks
exampleVectorless/vision RAG notebooks.
cookbook/
Open component record →Upstream feature-by-feature guide
These capabilities are derived from the current first-party README and supporting documentation. Names follow upstream terminology so you can search the source documentation precisely.
Upstream capability
🎯 Core Features
Upstream treats this as a distinct part of VectifyAI/PageIndex. In plain language, use this area when your task concerns 🎯 core features. Begin with the documented default, test it on disposable input, and open the source section for its current options and limitations.
Upstream capability
📍 Explore PageIndex
Upstream treats this as a distinct part of VectifyAI/PageIndex. In plain language, use this area when your task concerns 📍 explore pageindex. Begin with the documented default, test it on disposable input, and open the source section for its current options and limitations.
Upstream capability
🛠️ Deployment Options
Upstream treats this as a distinct part of VectifyAI/PageIndex. In plain language, use this area when your task concerns 🛠️ deployment options. Begin with the documented default, test it on disposable input, and open the source section for its current options and limitations.
Upstream capability
🧪 Quick Hands-on
Upstream treats this as a distinct part of VectifyAI/PageIndex. In plain language, use this area when your task concerns 🧪 quick hands-on. Begin with the documented default, test it on disposable input, and open the source section for its current options and limitations.
Upstream capability
2. Set your LLM API key
Upstream treats this as a distinct part of VectifyAI/PageIndex. In plain language, use this area when your task concerns 2. set your llm api key. Begin with the documented default, test it on disposable input, and open the source section for its current options and limitations.
Upstream capability
3. Generate PageIndex structure for your PDF
Upstream treats this as a distinct part of VectifyAI/PageIndex. In plain language, use this area when your task concerns 3. generate pageindex structure for your pdf. Begin with the documented default, test it on disposable input, and open the source section for its current options and limitations.
Upstream capability
🚀 Agentic Vectorless RAG: An Example
Upstream treats this as a distinct part of VectifyAI/PageIndex. In plain language, use this area when your task concerns 🚀 agentic vectorless rag: an example. Begin with the documented default, test it on disposable input, and open the source section for its current options and limitations.
Upstream capability
🌐 Open-Source Ecosystem
Upstream treats this as a distinct part of VectifyAI/PageIndex. In plain language, use this area when your task concerns 🌐 open-source ecosystem. Begin with the documented default, test it on disposable input, and open the source section for its current options and limitations.
Upstream capability
Connect with Us
Upstream treats this as a distinct part of VectifyAI/PageIndex. In plain language, use this area when your task concerns connect with us. Begin with the documented default, test it on disposable input, and open the source section for its current options and limitations.
How to use the main workflows
Choose the smallest relevant function
Start with one capability card instead of asking the repository to do everything at once. This makes permissions, inputs, and output easier to understand.
Prepare a disposable input
Use a sample URL, copied repository, test document, or sandbox account. Keep production credentials and irreplaceable files out of the first run.
Run, observe, and stop
Watch Terminal output or the host application's activity view. If the behavior differs from the README, stop with Control + C or cancel inside the host before retrying.
Inspect the result
Check generated files, diffs, API responses, logs, or previews. Never assume “command finished” means the result is correct.
Save a repeatable recipe
Record the working command and non-secret configuration in your project's README. Store secrets in the documented environment file or password manager.
Copy-paste recipes from upstream documentation
These examples are selected from the repository's first-party documentation. Replace obvious placeholders, keep quotation marks intact, and run them only in the context indicated by the surrounding explanation.
🌲 PageIndex Tree Structure
...
{
"title": "Financial Stability",
"node_id": "0006",
"start_index": 21,
"end_index": 22,
"summary": "The Federal Reserve ...",
"nodes": [
{
"title": "Monitoring Financial Vulnerabilities",
"node_id": "0007",
"start_index": 22,
"end_index": 28,
"summary": "The Federal Reserve's monitoring ..."
},
{
"title": "Domestic and International Cooperation and Coordination",
"node_id": "0008",
"start_index": 28,
"end_index": 31,
"summary": "In 2023, the Federal Reserve collaborated ..."
}
]
}
...Configuration, accounts, and files
Configuration files
None documented
A configuration file changes behavior without changing source code. Make one change at a time and keep a backup before editing JSON, TOML, YAML, or environment files.
Important directories
None documented
Paths identify where the relevant implementation or generated files live. Paths beginning with ~ are inside your home folder.
Credentials
LLM key for LLM-assisted modes
Prefer temporary, least-privileged credentials. Never commit .env, tokens, cookies, private keys, or session exports.
External services
optional PageIndex cloud, LiteLLM provider
Check pricing, data retention, rate limits, and account permissions before enabling optional integrations.
Safe operating habits
- Use test data first and keep a current backup or Git commit.
- Read commands before pasting. A README is useful evidence, not a substitute for judgment.
- Review agent-generated file changes with
git diffbefore committing. - Keep local services bound to
127.0.0.1unless you intentionally secure and expose them. - Do not give plugins or MCP servers broader filesystem, browser, GitHub, or cloud access than their current task requires.
- Confirm API costs and model names before running large batches.
- Stop and investigate repeated authentication failures instead of pasting a token into multiple places.
Reference and source trail
The manual reflects first-party files checked on August 18, 2026. It explains the indexed repository rather than promising that every optional third-party integration is available or safe.