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:- 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