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Once a Plangrep job completes, you can retrieve the full normalized result JSON and query the processed entities via the project endpoints. This guide shows how to fetch the result, traverse its structure, and download associated artifacts.

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Fetch the result JSON

Call GET /api/open/v1/jobs/{jobId}/result after the job reaches a terminal status: 
curl https://plangrep.com/api/open/v1/jobs/job_xyz789/result \
  -H "Authorization: Bearer YOUR_API_KEY"
The response is a single normalized JSON object: 
{
  "schemaVersion": "v1",
  "job": { "id": "job_xyz789", "status": "completed" },
  "usage": {
    "pageCount": 42,
    "drawingPageCount": 38,
    "documentPageCount": 4,
    "creditsDebited": 384
  },
  "documents": [
    {
      "documentId": "doc_111",
      "fileName": "floor-plan.pdf",
      "classification": "plans",
      "pageCount": 38,
      "sheets": [],
      "artifacts": []
    }
  ],
  "evidence": [
    {
      "id": "ev_001",
      "type": "region",
      "regionType": "detail",
      "text": "Provide R-19 batt insulation at all exterior stud walls."
    }
  ],
  "warnings": [],
  "errors": [],
  "generatedAt": "2024-01-15T10:05:00Z"
}
Check errors first. A completed_with_errors job may have partial results alongside per-document error entries describing what could not be processed.

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Iterate over documents and sheets

Each entry in the documents array is a ResultDocument that maps back to a file you uploaded. Each document contains a sheets array with extracted sheet metadata: 
for doc in result["documents"]:
    print(f"File: {doc['fileName']} ({doc['classification']})")
    for sheet in doc.get("sheets", []):
        print(
            f"  Sheet {sheet['sheetNumber']}: "
            f"{sheet['sheetName']} (page {sheet['pageNumber']})"
        )
Sheet objects include fields such as sheetNumber, sheetName, pageNumber, drawingType, discipline, and artifacts for associated renderings.

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Use the evidence array

The top-level evidence array aggregates all citable source records extracted from every processed file. Use it to build indexes, feed downstream AI pipelines, or power search UIs: 
for ev in result["evidence"]:
    label = ev.get("label", "")
    snippet = ev.get("text", "")[:100]
    print(f"[{ev['type']}] {label} — {snippet}")
Evidence records carry a type field (for example "region" or "specification") and additional type-specific fields. Each record also includes a sourceCatalogUri you can pass to the source-text endpoint to fetch the full content.

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Download artifacts

Artifacts are authenticated URLs pointing to rendered outputs on your organization’s result snapshot. Fetch an artifact by passing its path as a query parameter: 
curl "https://plangrep.com/api/open/v1/jobs/job_xyz789/artifact?path=PATH_FROM_ARTIFACT_URL" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  --output result.pdf
Plangrep generates several artifact types for each document and sheet:
TypeDescription
fileThe original uploaded file
thumbnailLow-resolution preview image
viewerInteractive viewer URL
renderFull-resolution page rendering
pdfProcessed or normalized PDF
region_cropCropped image of a specific region
region_textExtracted text for a region
region_ocrRaw OCR output for a region

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Query project entities

After processing completes, you can query individual entity types on the project using paginated list endpoints. These endpoints support filtering and are useful for ongoing workflows that span multiple jobs. 
# List all sheets in a project
curl "https://plangrep.com/api/open/v1/projects/proj_abc123/sheets?limit=50" \
  -H "Authorization: Bearer YOUR_API_KEY"

# List regions of type 'detail'
curl "https://plangrep.com/api/open/v1/projects/proj_abc123/regions?regionType=detail&limit=100" \
  -H "Authorization: Bearer YOUR_API_KEY"
Paginate through results using the cursor parameter returned in each response.
Use compact=true on entity list endpoints to strip artifact URL fields and reduce response size for indexing workflows.