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Process and analyze documents across multiple pages with context preservation and cross-document correlation. Perfect for medical record processing, legal document review, multi-document workflows, and comprehensive document analysis that requires understanding relationships between different document types.

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Usage Example

For best results, we recommend using the Structured Outputs API to get responses in a structured and validated data format.
The following examples can analyze and triage multiple pages or documents, identify cross-document relationships, extract consistent information across pages, and provide comprehensive analysis with context preservation. The response schema includes page summaries, cross-document connections, and thematic analysis.
from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>", base_url="https://agent.vlm.run/v1")

# Analyze multi-page medical documents
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
          "role": "user",
          "content": [
            {"type": "text", "text": "Analyze this multi-page medical document set. Extract patient referral page, medical insurance card and identification form in 3 separate fields in JSON format."},
            {"type": "file_url", "file_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.agent/multi-document-input-example.pdf", "detail": "auto"}}
          ]
        }
    ]
)

print(response.choices[0].message.content)

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FAQ

  • Cross-Document Correlation: Identify relationships between different document types
  • Data Consistency Checking: Verify data matches across pages and documents
  • Theme Analysis: Track recurring themes and topics across pages
  • Reference Tracking: Follow references and citations across pages
  • Content Flow Analysis: Understand how content flows between pages
  • Medical Records: Referral forms, insurance cards, ID forms, lab reports
  • Legal Documents: Contracts, amendments, exhibits, supporting documents
  • Financial Documents: Invoices, receipts, statements, tax forms
  • Academic Papers: Research papers, appendices, references, figures
  • Business Reports: Executive summaries, detailed sections, appendices
The system identifies connections between different pages/documents by:
  • Data Matching: Finding identical or similar values across documents
  • Reference Tracking: Following explicit references between pages
  • Contextual Analysis: Understanding semantic relationships
  • Confidence Scoring: Providing reliability scores for each connection
The confidence score is a value between 0 and 1 that indicates the reliability of cross-document connections. Higher scores indicate more reliable matches and relationships between pages.
Yes, multi-page analysis can process different document types within a single PDF or across multiple uploaded documents, identifying relationships and correlations between them.