153 lines
4.4 KiB
Python
153 lines
4.4 KiB
Python
from __future__ import annotations
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from skills_learning.models import (
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FlowStep,
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FlowTemplateSkill,
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SkillMetadata,
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skill_embedding_text,
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)
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from skills_learning.store import SkillStore
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from skills_learning.synthesis import extract_tool_calls, synthesize_flow_skill
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def _record(
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action: str,
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args: dict[str, object] | None = None,
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*,
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result: dict[str, object] | None = None,
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purpose: str | None = None,
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expected_outcome: str | None = None,
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) -> dict[str, object]:
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tool_call: dict[str, object] = {
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"action": action,
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"args": args or {},
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}
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if purpose is not None:
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tool_call["purpose"] = purpose
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if expected_outcome is not None:
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tool_call["expected_outcome"] = expected_outcome
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return {
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"tool_call": tool_call,
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"result": result or {},
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}
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def test_extract_tool_calls_filters_read_only_tools_in_order() -> None:
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records = [
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_record("describe_screen"),
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_record("launch_app", {"app_id": "com.example"}),
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_record("screenshot"),
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_record("tap", {"x": 1, "y": 2}),
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_record("find_text", {"query": "Send"}),
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_record("input_text", {"text": "coffee"}),
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]
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steps = extract_tool_calls("", records)
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assert steps == [
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FlowStep("launch_app", {"app_id": "com.example"}),
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FlowStep("tap", {"x": 1, "y": 2}),
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FlowStep("input_text", {"text": "coffee"}),
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]
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def test_first_time_synthesis_has_literal_steps_and_no_parameters() -> None:
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skill = synthesize_flow_skill(
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"search coffee",
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[_record("input_text", {"text": "coffee"})],
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)
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assert skill.name == "search coffee"
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assert skill.steps == [FlowStep("input_text", {"text": "coffee"})]
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assert skill.parameters == {}
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def test_synthesis_preserves_action_metadata_for_reuse_and_embedding() -> None:
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skill = synthesize_flow_skill(
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"open settings",
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[
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_record(
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"tap",
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{"x": 12, "y": 34},
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purpose="Open the settings tab.",
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expected_outcome="The settings page is visible.",
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)
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],
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)
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assert skill.steps == [
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FlowStep(
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"tap",
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{"x": 12, "y": 34},
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purpose="Open the settings tab.",
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expected_outcome="The settings page is visible.",
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)
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]
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assert FlowStep.from_dict(skill.steps[0].to_dict()) == skill.steps[0]
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embedding_text = skill_embedding_text(skill)
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assert "Open the settings tab." in embedding_text
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assert "The settings page is visible." in embedding_text
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def test_second_execution_promotes_differing_argument_to_parameter() -> None:
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store = SkillStore()
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store.create_version(
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FlowTemplateSkill(
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metadata=SkillMetadata(
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name="search",
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description="Learned search",
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originating_goal="search coffee",
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),
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steps=[FlowStep("input_text", {"text": "coffee"})],
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parameters={},
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)
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)
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records = [
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_record(
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"input_text",
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{"text": "tea"},
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result={
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"semantic_scene": {
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"page": "Search",
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"intents": ["search"],
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"widgets": [
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{
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"element_id": "search-input",
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"purpose": "search field",
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}
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],
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}
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},
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)
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]
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skill = synthesize_flow_skill("search tea", records, store=store)
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assert skill.name == "search"
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assert skill.steps == [FlowStep("input_text", {"text": "{search_field}"})]
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assert "search_field" in skill.parameters
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def test_identical_repeat_does_not_add_parameters() -> None:
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store = SkillStore()
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store.create_version(
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FlowTemplateSkill(
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metadata=SkillMetadata(
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name="search",
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description="Learned search",
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originating_goal="search coffee",
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),
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steps=[FlowStep("input_text", {"text": "coffee"})],
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parameters={},
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)
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)
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skill = synthesize_flow_skill(
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"search coffee again",
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[_record("input_text", {"text": "coffee"})],
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store=store,
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)
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assert skill.steps == [FlowStep("input_text", {"text": "coffee"})]
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assert skill.parameters == {}
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