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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


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


How It Works

PageIndex takes a fundamentally different approach from traditional vector RAG:
  1. Tree Indexing — Documents are parsed into a hierarchical tree of sections, subsections, and paragraphs with summaries at each level
  2. LLM Tree Search — At query time, an LLM navigates the tree from root to relevant leaves, using reasoning instead of embedding similarity
  3. 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

Multi-Document Analysis

Structured Extraction


Environment Variables


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:
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.