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CLI & Automation

The openmed console script mirrors the Python APIs so you can analyze snippets, inspect registry metadata, and tweak configuration without writing code. It is perfect for demos, notebooks, or CI smoke tests.

Installation

The CLI is installed automatically with the base package:

uv pip install .
# or pip install openmed
openmed --help

Analyze text from the terminal

openmed analyze \
  --model disease_detection_superclinical \
  --text "Imatinib inhibits BCR-ABL in chronic myeloid leukemia." \
  --output-format json \
  --confidence-threshold 0.6 \
  --group-entities

Flags:

  • --model: registry key or HF id.
  • Provide either --text or --input-file.
  • --output-format: dict, json, html, or csv.
  • --group-entities: toggles adjacent span merging.
  • --no-confidence: omit confidence values from output.

Discover models

openmed models list --include-remote   # adds Hugging Face search
openmed models info pharma_detection_superclinical

models list prints registry models (and optionally HF remote ones). models info <key> dumps the curated metadata so you can confirm categories, entity types, recommended confidence, and model sizes.

Manage configuration

openmed config show
openmed config set device cuda
openmed config set default_org OpenMed
openmed config set hf_token xxx --unset    # remove a setting
  • Values are stored in the CLI config file (see openmed/core/config.py for defaults). Use --config-path to point at an alternate location when scripting CI/CD workflows.
  • The CLI reuses OpenMedConfig, so any changes you make here apply to both CLI runs and Python imports (assuming they load the same file).

Automation tips

  • Wrap CLI calls in shell scripts for smoke tests. Example:
openmed models list >/tmp/models.txt
openmed analyze --model disease_detection_superclinical --text "$SAMPLE_NOTE" --output-format json > /tmp/result.json
  • Use uv run or pipx run to avoid polluting system environments when scripting release pipelines.
  • In GitHub Actions, you can execute the CLI after installing .[hf] to ensure models load before publishing.

For more complex workflows (batch jobs, structured logging, streaming), prefer the Python APIs covered in Analyze Text Helper and ModelLoader & Pipelines.