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Documentation Index

Fetch the complete documentation index at: https://docs.anduril.atlas.arenaphysica.com/llms.txt

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

System metagraph view

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1. Upload documents

Upload PDFs, schematics, specs, or any reference files that describe your system to the Atlas data catalog. Each upload returns a dataset_name — capture it; you’ll pass it to the metagraph update job in the next step. 
curl -X POST "$ATLAS_URL/api/v1/uploads/atlas_data" \
  -H "Authorization: Bearer $ATLAS_TOKEN" \
  -F "file=@system_spec.pdf"
The response includes a dataset_name for each uploaded file: 
{
  "message": "Upload completed: 1 succeeded, 0 failed",
  "total_files": 1,
  "successful": 1,
  "failed": 0,
  "results": [
    {
      "file_name": "system_spec.pdf",
      "success": true,
      "dataset_name": "system_spec.pdf",
      "error": null
    }
  ]
}
Upload multiple files in one request with -F "files=@a.pdf" -F "files=@b.pdf", or in separate requests — each comes back with its own dataset_name. The endpoint is synchronous: when it returns, the bytes are in the catalog and ready to use in step 2.
Using the Atlas web UI instead of the API? The metagraph update form has a built-in file-upload field — drop your files into it and submit. Atlas handles the upload and the context_files reference in one step.

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2. Create a metagraph update job

The job only reads the documents you pass to it via context_files — it does not scan a system’s prior uploads. Pass the dataset_names from step 1. 
curl -X POST "$ATLAS_URL/api/v1/jobs" \
  -H "Authorization: Bearer $ATLAS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "job_type": "metagraph_update",
    "config": {
      "job_type": "metagraph_update",
      "system_id_or_name": "<system_id>",
      "context_files": ["system_spec.pdf"],
      "additional_context": "Focus on the power distribution subsystem and its interfaces."
    }
  }'
Save the uuid from the response: 
export JOB_ID="<uuid from response>"
context_files is the list of dataset_names captured in step 1. If you omit it (or pass an empty list) and don’t supply additional_context, the job completes with no metagraph changes. additional_context is optional — use it to scope or guide the analysis when you have a specific part of the system you want Atlas to model.

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3. Start the job run

curl -X POST "$ATLAS_URL/api/v1/job-runs" \
  -H "Authorization: Bearer $ATLAS_TOKEN" \
  -H "Content-Type: application/json" \
  -d "{\"job_id\": \"$JOB_ID\"}"
Save the uuid from the response: 
export JOB_RUN_ID="<uuid from response>"

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4. Poll for completion

curl "$ATLAS_URL/api/v1/job-runs/$JOB_RUN_ID" \
  -H "Authorization: Bearer $ATLAS_TOKEN"
The status field will transition through pending → running → completed (or failed). Poll until you see a terminal state.

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5. Inspect the metagraph

Once the job completes, fetch the result: 
curl "$ATLAS_URL/api/v1/systems/metagraphs/$SYSTEM_ID" \
  -H "Authorization: Bearer $ATLAS_TOKEN"
If you passed an existing system UUID as system_id_or_name in step 2, $SYSTEM_ID is that same value. If you passed a new name to create a system from scratch, read the resulting UUID from the job-run’s preview.metagraph_system_id field: 
export SYSTEM_ID=$(
  curl -s "$ATLAS_URL/api/v1/job-runs/$JOB_RUN_ID" \
    -H "Authorization: Bearer $ATLAS_TOKEN" \
  | jq -r '.preview.metagraph_system_id'
)
The response is the full nodes-and-edges graph Atlas built from your documents. Pass an optional ?version=<n> query parameter to fetch the metagraph as it existed at a specific applied version.