Practical overview
When it fits
Use PageIndex when a retrieval workflow needs to reason over document structure and context rather than rely on vector similarity alone, especially for long or complex professional documents.
Who it is for
- Developers building document question-answering or RAG applications.
- Teams working with long, structured professional documents.
- Researchers creating document navigation or knowledge-assistant workflows.
- Developers integrating retrieval tools into agent frameworks.
Documented uses
- Index long documents into a hierarchical tree.
- Retrieve relevant context through LLM reasoning.
- Answer questions about indexed PDFs.
- Create local or cloud-based document retrieval workflows.
- Connect PageIndex tools to supported agent SDKs or other frameworks.
Requirements & limits
The repository is public and MIT-licensed. Local mode uses a user-provided LLM key; the documented cloud configuration uses a PageIndex Cloud API key.
- Whether a tree index and reasoning-based search fit the document structure better than vector retrieval.
- Whether local processing or PageIndex Cloud is the more suitable operating model.
- The choice of indexing and chat models, since the README recommends a basic model for indexing and the best affordable model for retrieval.
- Whether traceable references and context-aware retrieval are requirements.
- Whether the documented Python SDK and agent integrations fit the application stack.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up a local document question-answering workflow
Install PageIndex, configure an LLM key, index a PDF, and ask a question through the SDK.
Before you start
- Python environment with the PageIndex package installed.
- A user-provided OpenAI API key.
- A PDF document such as report.pdf.
Steps
Install or update the package with pip install -U pageindex.
Set the OPENAI_API_KEY environment variable.
Create a PageIndexClient with separate index and chat models.
Submit the document and retain the returned doc_id.
Call client.chat with a question and the document ID.
Expected outcome
The SDK returns an answer to a question grounded in the indexed document.
Guide limitations
- The documented example uses an OpenAI API key and named models.
- Indexing and chat costs depend on the selected models.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 30 September 2026
- Original website title
- VectifyAI/PageIndex · Official homepage title · retained in its source language