Ontogpt Author Template

Authoring an OntoGPT template

A template is a LinkML YAML file. OntoGPT turns each attribute of the root class into one line of the prompt, sends the text, parses the reply line by line, and grounds every value whose range is a NamedEntity subclass using that class's annotators. The template is the whole contract. Everything the model is told, and everything the grounder is allowed to return, lives in it.

Read references/schema-reference.md for every field OntoGPT reads, and references/ontology-guide.md to pick ontologies and annotator strings. Start from assets/template_skeleton.yaml.

Procedure

1. Write the target as a record. List the fields. For each: single or list, free text or entity, and if entity, what kind. Sketch the YAML you want out before writing the schema. Keep it small: five to eight fields on the root, one level of nesting at most, two at the very most.
2. Find the closest bundled template (ontogpt list-templates, or the catalog in the ontogpt-select-template skill) and copy it. Its prefixes, annotators, and phrasing are tested.
3. Choose ontologies per entity class with references/ontology-guide.md. Write id_prefixes (bare prefixes, e.g. MONDO, HP, CHEBI) and annotators (sqlite:obo:mondo, sqlite:obo:hp) so that the prefixes the annotators return are in id_prefixes. Mismatch here is the most common failure: sqlite:obo:hp returns HP:, not HPO:.
4. Write the prompts. Each attribute's description (or annotations: prompt:) is what the model sees, verbatim, after field_name: <. Say what to extract, from the text only, and how to separate values (semicolon-separated list of ...). Add annotations: prompt.examples: with two to four short examples for anything the model might format oddly. Never mention identifiers or ontologies in prompts. The model produces labels; OAK produces ids.
5. Set one tree_root: true. Nested records are classes whose attributes are themselves entity or string fields; they are extracted by a second SPIRES pass per item. Mark them is_a: CompoundExpression when they represent relations.
6. Validate.

   uv run python skills/ontogpt-author-template/scripts/validate_template.py my_template.yaml --codegen
   

   Fix every ERROR. Read every WARNING. Add --check-annotators once to confirm each annotator opens (this downloads ontologies).
7. Run on one short text that contains known answers:

   ontogpt -v extract -t my_template.yaml -i sample.txt
   

   Check raw_completion_output (did the model answer the fields?), then extracted_object (did parsing keep them?), then the AUTO: values (did grounding fail?). Adjust prompts for the first, separators for the second, annotators and prefixes for the third.
8. Install. A YAML path passed to -t is copied into the package's templates directory and compiled; afterwards its bare name works. To contribute it to OntoGPT, add the YAML under src/ontogpt/templates/, run make to regenerate the pydantic module, and add a docs entry in mkdocs.yml.

Design rules that matter

• One entity class per ontology target. Disease grounds to MONDO; do not reuse it for phenotypes.
• Descriptions are prompts. A description written for a human ("the disease field") produces bad extractions. Write "the name of the disease or condition the patient was diagnosed with, as written in the text".
• Say "semicolon-separated" on every multivalued field. The parser splits on ;. Commas inside values are kept.
• Free text stays range: string. It is never grounded and never nested. Use it for quantities, dates, quoted findings.
• Enums restrict, not suggest. A fixed enum (permissible_values) is appended to the prompt as "Must be one of: ...". A dynamic enum (reachable_from an ontology node) filters grounded ids after the fact and is invisible to the model.
• prompt.skip: "true" hides a field from the prompt. Use it for provenance fields you fill later.
• Two annotators are usually enough. More annotators mean more downloads and more false partial matches. Order them by trust; the first hit wins.
• Test the grounding independently when in doubt: runoak -i sqlite:obo:mondo annotate "type 2 diabetes".

Do not

• Do not put identifiers, URIs, or "use MONDO terms" in prompt text.
• Do not nest deeper than two levels; SPIRES recursion multiplies calls and errors.
• Do not use capital letters or hyphens in the file name; it becomes a Python module name.
• Do not declare a class tree_root and is_a: NamedEntity at once unless the whole document is one entity (see ontology_class).