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Documentation Index

Fetch the complete documentation index at: https://docs.trynia.ai/llms.txt

Use this file to discover all available pages before exploring further.

Index any PDF — research papers, SEC filings, legal documents, technical manuals — and have intelligent conversations with accurate, section-aware retrieval.
Traditional RAG systems split documents into flat chunks, losing structure and context. Nia invented a different approach:

Document Trees

PDFs are parsed into hierarchical structures — sections, subsections, figures, tables — preserving the document’s logical organization.

Section-Level Indexing

Each section is indexed with its position in the hierarchy, enabling precise retrieval that knows where information lives.

Hybrid Signals

Combines vector embeddings with non-vector signals (headers, page numbers, cross-references) for high recall with minimal context bloat.

Hierarchical Traversal

Agents traverse documents as trees instead of scanning flat chunks, finding relevant sections faster and more accurately.
Why it matters: High recall across large PDF corpora without stuffing context windows with irrelevant chunks. Read the technical deep-dive →

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Interactive Papers Playground

Experience PDFs like never before at app.trynia.ai/playground/papers:

PDF Viewer

View documents directly in the browser with a full-featured PDF viewer. Resizable panes let you read and chat side-by-side.

Document Tree

Navigate using an intelligent document tree. Jump to specific sections, figures, or equations instantly.

Session History

Your conversations are saved. Return anytime to continue where you left off, or review past discussions.

LaTeX Rendering

Full LaTeX support for mathematical expressions. Complex equations render beautifully in chat responses.

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Document Agent & Data Extraction

Once a PDF is indexed, you can go beyond search with two powerful capabilities:

Document Agent

Deploy an autonomous AI agent into your PDF. It plans its own research strategy, uses tools to search, read, and navigate — then delivers cited answers or structured output via JSON schemas.

Data Extraction

Extract structured records from PDFs using JSON schemas. Pull tables, financial data, specifications, and more into clean structured formats.

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How It Works

1

Index a PDF

Provide an arXiv URL, paper ID, or upload directly:
2

Tree Extraction

Nia parses the PDF structure — sections, headers, figures, tables — building a navigable document tree.
3

Chat with Context

Ask questions and get answers grounded in specific sections, with citations pointing to exact locations.

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Use Cases

Research Papers

Index arXiv papers for literature reviews. Search across dozens of papers at once, compare methodologies, understand dense sections.

SEC Filings

Navigate 10-Ks, 10-Qs, and proxy statements. Find specific disclosures, compare across years, extract financial data.

Legal Documents

Index contracts, briefs, and regulations. Search for specific clauses, understand obligations, compare versions.

Technical Manuals

Index product documentation, API specs, or internal wikis. Get precise answers with section references.

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Quick Start

# In your AI coding agent, just say:
"Index https://arxiv.org/abs/2401.12345"

# Then ask questions:
"What problem does this paper solve?"
"Summarize section 3.2"
"What are the limitations mentioned by the authors?"
Or use the Papers Playground for a visual experience.

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Supported Sources

SourceFormatExample
arXivURL or IDhttps://arxiv.org/abs/2401.12345 or 2401.12345
arXiv PDFDirect PDF linkhttps://arxiv.org/pdf/2401.12345
PDF UploadDirect file uploadUpload via the Papers Playground
You can upload PDFs directly in the Papers Playground — just drag and drop or use the upload button.