PageIndex Agent工作流

[Use PageIndex through the SDK client →](https://docs.pageindex.ai/getting-started)

Configure other models, streaming, multi-document search, citations, and more.

Quickstart

pip install -U pageindex

import os
from pageindex import PageIndexClient

os.environ["OPENAI_API_KEY"] = "your-openai-key"

client = PageIndexClient(
    index="gpt-5.6-luna",               # model to build the tree index
    chat="gpt-5.6-sol",                 # model to search the tree
)
doc_id = client.submit_document("report.pdf")["doc_id"]

answer = client.chat("What was the 2023 operating margin?", doc_id=doc_id)
print(answer)

[Integrate PageIndex with your own agent →](https://docs.pageindex.ai/sdk/agents)

Drop PageIndex tools into the OpenAI Agents SDK, the Claude Agent SDK, or any other framework.

Compare with Vector RAG

It is ideal for financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks, and any other long, complex professional document.

Cost per query vs. native PDF input

The alternative to retrieval is handing the model the whole PDF on every question. That cost grows with the document; PageIndex's does not, because it reads only the nodes its reasoning reaches. On documents where both routes return the same answer, native PDF input costs 2.1× more at 52 pages and 16.6× more at 420 (gpt-5.6-sol, prompt caching excluded) — and at 805 pages the document no longer fits in the context window at all.