PageIndex

Reasoning-based RAG for complex, long-form documents. Unlike vector search, PageIndex builds a hierarchical tree index and uses LLM reasoning to navigate it — delivering significantly better accuracy on financial reports, legal filings, technical manuals, and research papers.

Uses the PageIndex cloud API — no vector database or embedding pipeline needed.

Quick Start

import { Agent, openai } from "@agentium/core";
import { PageIndexToolkit } from "@agentium/core/toolkits";

const pageindex = new PageIndexToolkit({
  apiKey: process.env.PAGEINDEX_API_KEY,
});

const agent = new Agent({
  name: "document-analyst",
  model: openai(process.env.OPENAI_MODEL ?? "gpt-6.1-sol"),
  instructions: "Analyze uploaded documents. Answer questions accurately with citations.",
  tools: [...pageindex.getTools()],
});

const result = await agent.run(
  "Submit https://example.com/annual-report.pdf and then summarize the revenue breakdown by segment."
);

Config

string

required

PageIndex API key. Falls back to PAGEINDEX_API_KEY env var. Get yours at dash.pageindex.ai.

string

default:"https://api.pageindex.ai"

API base URL. Override for self-hosted PageIndex deployments.

number

default:"120000"

Request timeout in milliseconds. PDF processing can take time — the default is 2 minutes.

number

default:"50000"

Max response characters returned per tool call.

Tools

Tool	Description
`pageindex_submit`	Submit a PDF document for tree indexing. Returns a `doc_id` for subsequent operations.
`pageindex_status`	Check document processing status — returns tree structure when complete.
`pageindex_tree`	Get the hierarchical tree structure of a processed document (semantic table of contents).
`pageindex_list`	List all documents with IDs, names, statuses, and page counts.
`pageindex_chat`	Ask questions about documents using reasoning-based RAG with optional citations.
`pageindex_retrieve`	Retrieve specific sections from a document using tree-based search.
`pageindex_delete`	Delete a document and all associated data.

How It Works

PageIndex takes a fundamentally different approach from traditional vector RAG:

Tree Indexing — Documents are parsed into a hierarchical tree of sections, subsections, and paragraphs with summaries at each level
LLM Tree Search — At query time, an LLM navigates the tree from root to relevant leaves, using reasoning instead of embedding similarity
No Vectors Needed — No embedding model, no vector database, no chunking strategy to tune

This approach excels on documents where structure matters: financial reports with complex tables, legal contracts with nested clauses, and technical specs with cross-references.

Use Cases

Document Q&A

// After submitting a document
await agent.run("What were the total operating expenses in Q3 2024?");

Multi-Document Analysis

await agent.run(
  "Compare the risk factors section between the 2023 and 2024 annual reports."
);

Structured Extraction

const agent = new Agent({
  name: "extractor",
  model: openai(process.env.OPENAI_MODEL ?? "gpt-6.1-sol"),
  instructions: "Extract structured data from documents with page citations.",
  tools: [...pageindex.getTools()],
  outputType: z.object({
    items: z.array(z.object({
      field: z.string(),
      value: z.string(),
      page: z.number(),
    })),
  }),
});

Environment Variables

Variable	Description
`PAGEINDEX_API_KEY`	PageIndex API key from dash.pageindex.ai

Combining with Agentium Knowledge

PageIndex works best for complex professional documents. For simpler content or when you need a fully local pipeline, combine it with Agentium’s built-in vector knowledge base: Use KnowledgeBase with a configured vector store, retrieve evidence, and pass it to the Agent or expose retrieval as a tool. AgentConfig has no knowledge field. Follow the RAG example, and attach PageIndex tools alongside your retrieval tool.

PageIndex is ideal for complex, structured documents (100+ pages). For short text snippets and FAQ-style retrieval, the built-in vector knowledge base is faster and cheaper.

Find an integration

Voice and call adapters

Sandbox and telemetry

Providers and services

Protocols and devices

Quick Start

Config

Tools

How It Works

Use Cases

Document Q&A

Multi-Document Analysis

Structured Extraction

Environment Variables

Combining with Agentium Knowledge

​Quick Start

​Config

​Tools

​How It Works

​Use Cases

​Document Q&A

​Multi-Document Analysis

​Structured Extraction

​Environment Variables

​Combining with Agentium Knowledge

Quick Start

Config

Tools

How It Works

Use Cases

Document Q&A

Multi-Document Analysis

Structured Extraction

Environment Variables

Combining with Agentium Knowledge