Document Agent

Builder Feature - Document Agent is included with Builder, Team, and Business plans. Free plan users can also use Document Agent with purchased credits.

Overview

Document Agent is an autonomous AI agent that researches your indexed PDFs and documents. It doesn’t just retrieve chunks — it has access to specialized tools (search, read sections, read pages, navigate document trees) and uses them iteratively, planning its own research strategy to build comprehensive answers. Think of it as deploying an agent into your document. It decides what to search for, which sections to read, navigates the hierarchy, follows cross-references, and synthesizes findings — all autonomously. It supports structured output via JSON schemas, extended thinking for complex reasoning, and real-time streaming. Use Document Agent when you need an agent that can deeply research a specific document — contracts, filings, research papers, technical manuals — with full citation traceability.

Key Capabilities

Inline Citations

Every claim in the response is backed by citations pointing to specific pages, sections, and content from the source document.

Structured Output

Provide a JSON Schema and receive structured data extracted from the document — perfect for pipelines, automation, and data processing.

Extended Thinking

The agent uses extended thinking to reason through complex queries, plan its tool usage, and synthesize multi-part answers with configurable token budgets.

Streaming

Stream responses as Server-Sent Events for real-time UI updates. Watch the agent work through the document as it builds its answer.

Model Selection

Choose the model that fits your task — from fast and efficient to maximum capability with 1M token context windows.

Autonomous Tool Use

The agent autonomously plans its research strategy — deciding which tools to call, what to search for, which sections to read next — rather than relying on a single retrieval pass.

How It Works

Index Your Document

Upload or provide a URL to your PDF. Nia parses it into a hierarchical document tree — sections, subsections, figures, tables — preserving structure and context.

Deploy the Agent

Send a query to the Document Agent along with the source_id of your indexed document. Optionally provide a JSON schema for structured output.

Autonomous Research

The agent plans its research strategy using extended thinking, then autonomously calls tools in a loop — searching across sections, reading specific pages, navigating the document tree, following cross-references — until it has gathered all relevant information. You can watch this happen in real-time via streaming.

Synthesized Report

The agent produces a comprehensive answer with inline citations. Each citation links back to the exact page, section, and content in the source document. If a JSON schema was provided, the response also includes structured output conforming to your schema.

API Usage

Basic Query

Ask a question about an indexed document and receive a cited answer:

curl -X POST https://apigcp.trynia.ai/v2/document/agent \
  -H "Authorization: Bearer $NIA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "source_id": "src_abc123",
    "query": "What are the key risk factors disclosed in this filing?",
    "model": "claude-opus-4-7",
    "thinking_enabled": true,
    "thinking_budget": 10000
  }'

Response:

{
  "answer": "The filing identifies several key risk factors: (1) Market volatility affecting portfolio valuations [p. 23], (2) Regulatory changes in key operating jurisdictions [p. 24-25], and (3) Cybersecurity threats to customer data [p. 27]...",
  "citations": [
    {
      "content": "Our business is subject to market volatility which may adversely affect...",
      "page_number": 23,
      "section_id": "sec_risk_factors_market",
      "section_title": "Market Risk",
      "section_path": "Risk Factors > Market Risk",
      "tool_source": "read_section"
    },
    {
      "content": "Changes in regulatory frameworks across jurisdictions in which we operate...",
      "page_number": 24,
      "section_id": "sec_risk_factors_regulatory",
      "section_title": "Regulatory Risk",
      "section_path": "Risk Factors > Regulatory Risk",
      "tool_source": "search"
    }
  ],
  "model": "claude-opus-4-7",
  "usage": {
    "input_tokens": 15230,
    "output_tokens": 1847,
    "thinking_tokens": 8500
  }
}

Structured Output Query

Extract structured data from documents by providing a JSON schema:

curl -X POST https://apigcp.trynia.ai/v2/document/agent \
  -H "Authorization: Bearer $NIA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "source_id": "src_abc123",
    "query": "Extract all parties, effective date, and termination clauses from this contract.",
    "json_schema": {
      "type": "object",
      "properties": {
        "parties": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "name": { "type": "string" },
              "role": { "type": "string" }
            }
          }
        },
        "effective_date": { "type": "string" },
        "termination_clauses": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "clause_id": { "type": "string" },
              "description": { "type": "string" },
              "notice_period_days": { "type": "integer" }
            }
          }
        }
      },
      "required": ["parties", "effective_date", "termination_clauses"]
    },
    "model": "claude-opus-4-7",
    "thinking_enabled": true,
    "thinking_budget": 15000
  }'

Response:

{
  "answer": "The contract is between Acme Corp (Provider) and GlobalTech Inc (Client), effective January 15, 2025. It contains two termination clauses...",
  "citations": [
    {
      "content": "This Agreement is entered into by and between Acme Corp ('Provider') and GlobalTech Inc ('Client')...",
      "page_number": 1,
      "section_id": "sec_preamble",
      "section_title": "Preamble",
      "section_path": "Preamble",
      "tool_source": "read_page"
    }
  ],
  "structured_output": {
    "parties": [
      { "name": "Acme Corp", "role": "Provider" },
      { "name": "GlobalTech Inc", "role": "Client" }
    ],
    "effective_date": "2025-01-15",
    "termination_clauses": [
      {
        "clause_id": "8.1",
        "description": "Either party may terminate for convenience with written notice",
        "notice_period_days": 90
      },
      {
        "clause_id": "8.2",
        "description": "Immediate termination for material breach after cure period",
        "notice_period_days": 30
      }
    ]
  },
  "model": "claude-opus-4-7",
  "usage": {
    "input_tokens": 22100,
    "output_tokens": 2340,
    "thinking_tokens": 12000
  }
}

Streaming

Stream the response as Server-Sent Events for real-time updates:

curl -N -X POST https://apigcp.trynia.ai/v2/document/agent \
  -H "Authorization: Bearer $NIA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "source_id": "src_abc123",
    "query": "Summarize the methodology section and its limitations.",
    "stream": true
  }'

SSE events arrive as the agent works through the document, enabling responsive UIs that show progress in real time.

Parameters

Parameter	Type	Required	Default	Description
`source_id`	string	Yes	—	Data source ID of the indexed document
`query`	string	Yes	—	Question to ask about the document
`json_schema`	object	No	—	JSON Schema for structured output extraction
`model`	string	No	`claude-opus-4-7`	Model to use for the agent
`thinking_enabled`	boolean	No	`true`	Enable extended thinking for complex reasoning
`thinking_budget`	integer	No	`10000`	Token budget for thinking (1,000 - 50,000)
`stream`	boolean	No	`false`	Stream response as Server-Sent Events

Model Selection Guide

Choose the right model based on your task requirements:

Model	Context Window	Best For	Speed	Cost
`claude-opus-4-7`	1M tokens	Complex reasoning over long documents, multi-step analysis, structured extraction from dense filings	Slower	Highest
`claude-sonnet-4-20250514`	200K tokens	Balanced performance for most document queries, good reasoning with faster responses	Moderate	Moderate
`claude-haiku-35-20241022`	200K tokens	Quick lookups, simple fact extraction, high-volume processing	Fastest	Lowest

Start with the default. claude-opus-4-7 with its 1M context window handles even the longest documents. Scale down to Sonnet or Haiku when you need faster responses or lower costs for simpler queries.

Use Cases

Legal Documents

Extract parties, obligations, termination clauses, and liability caps from contracts. Use structured output to feed data directly into case management systems.

Financial Filings

Query 10-Ks, 10-Qs, and annual reports. Extract risk factors, revenue breakdowns, and forward-looking statements with page-level citations for audit trails.

Technical Manuals

Ask about specifications, procedures, and safety requirements. The agent navigates complex hierarchical documents to find precise answers across sections.

Research Papers

Interrogate methodology, results, and conclusions. Compare findings across sections. Use structured output to extract experimental parameters and metrics into tables.

Structured Output Patterns

Financial Data Extraction

Query: “Extract quarterly revenue figures and year-over-year growth rates.”Schema:

{
  "type": "object",
  "properties": {
    "quarters": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "period": { "type": "string" },
          "revenue_millions": { "type": "number" },
          "yoy_growth_pct": { "type": "number" }
        }
      }
    },
    "fiscal_year": { "type": "string" },
    "currency": { "type": "string" }
  }
}

The agent reads the financial statements, locates revenue tables, and returns clean structured data ready for analysis.

Research Paper Metadata

Query: “Extract the paper’s key details: authors, abstract, datasets used, and reported metrics.”Schema:

{
  "type": "object",
  "properties": {
    "title": { "type": "string" },
    "authors": { "type": "array", "items": { "type": "string" } },
    "abstract": { "type": "string" },
    "datasets": { "type": "array", "items": { "type": "string" } },
    "metrics": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "value": { "type": "number" },
          "dataset": { "type": "string" }
        }
      }
    }
  }
}

The agent extracts structured metadata from any research paper, making it easy to build literature review databases.

Compliance Checklist

Query: “Identify all compliance requirements and their current status mentioned in this audit report.”Schema:

{
  "type": "object",
  "properties": {
    "requirements": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "requirement": { "type": "string" },
          "regulation": { "type": "string" },
          "status": { "type": "string", "enum": ["compliant", "non_compliant", "partially_compliant", "not_assessed"] },
          "findings": { "type": "string" },
          "page_reference": { "type": "integer" }
        }
      }
    }
  }
}

Turn unstructured audit reports into actionable compliance checklists with a single API call.

Document Agent vs. Standard Search

Aspect	Document Agent	Nia Search
Approach	Autonomous agent — plans strategy, calls tools in a loop, follows leads	Single-pass retrieval over indexed chunks
Citations	Page, section, and content-level citations	Source-level citations
Structured Output	JSON Schema support for typed extraction	Not available
Thinking	Extended thinking with configurable budgets	Not available
Best For	Complex questions requiring reasoning across sections	Quick factual lookups
Latency	Higher (multi-step agent loop)	Lower (single retrieval)

Use Document Agent when:

Your question requires synthesizing information from multiple sections
You need structured data extracted from documents
You need page-level citation traceability
The question requires reasoning, not just retrieval

Use Nia Search when:

You need fast, simple lookups across many sources
Low latency is critical
The question maps directly to a specific passage

Getting Started

Capabilities

Source Types

Explore

Examples & Guides

Enterprise

Privacy

Overview

Key Capabilities

Inline Citations

Structured Output

Extended Thinking

Streaming

Model Selection

Autonomous Tool Use

How It Works

API Usage

Basic Query

Structured Output Query

Streaming

Parameters

Model Selection Guide

Use Cases

Legal Documents

Financial Filings

Technical Manuals

Research Papers

Structured Output Patterns

Document Agent vs. Standard Search

​Overview

​Key Capabilities

Inline Citations

Structured Output

Extended Thinking

Streaming

Model Selection

Autonomous Tool Use

​How It Works

​API Usage

​Basic Query

​Structured Output Query

​Streaming

​Parameters

​Model Selection Guide

​Use Cases

Legal Documents

Financial Filings

Technical Manuals

Research Papers

​Structured Output Patterns

​Document Agent vs. Standard Search

Overview

Key Capabilities

How It Works

API Usage

Basic Query

Structured Output Query

Streaming

Parameters

Model Selection Guide

Use Cases

Structured Output Patterns

Document Agent vs. Standard Search