Start Building with PageIndex
PageIndex works in two steps, and only the first one has a choice to make.
1. Index
PageIndex turns each document into a tree index. This runs either on your own machine or in PageIndex Cloud.
2. Retrieve
An LLM searches that tree and answers the question. This model is always your own, in either index mode.
Build with a coding agent
Paste this into Claude Code, Cursor, or any agent that can read a URL.
Read https://pageindex.ai/docs/SKILL.md and follow it to set up PageIndex in this project.
Quick Start for humans
Install the SDK
pip install -U pageindex
Set up the client
These examples use OpenAI models — see Use different LLMs for other providers.
PageIndex API key — for indexing and storage. Generate one and PageIndex handles parsing, OCR, and image understanding for you.
export PAGEINDEX_API_KEY="your-pageindex-key"
LLM provider API key — bring your own LLM for chat.
export OPENAI_API_KEY="your-openai-key"
Then create the client:
from pageindex import PageIndexClient
client = PageIndexClient(
index="cloud", # index and store in PageIndex Cloud
chat="gpt-5.6-sol", # your own model still answers
)
LLM provider API key — for both indexing and chat. No PageIndex API key is needed, since everything runs against your own model.
export OPENAI_API_KEY="your-openai-key"
Then create the client. Indexing runs on your machine and completes in this same call:
from pageindex import PageIndexClient
client = PageIndexClient(
index="gpt-5.6-luna", # model that builds the tree index
chat="gpt-5.6-sol", # model that searches the tree
)
Submit a document
# wait=True blocks until the document is ready
doc_id = client.submit_document("./2023-annual-report.pdf", wait=True)["doc_id"]
For batch submission and file organization, see the Document Processing reference.
Ask a question
query = "What are the key findings in this document?"
for chunk in client.chat(query, doc_id=doc_id, stream=True):
print(chunk, end="", flush=True)
For multi-document search and citations, see the LLM Integration reference.
Integrations
chat() runs PageIndex's own document-QA agent against your model. You can also take PageIndex's retrieval tools into your own agent, or reach them over MCP.