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OpenJarvis/tests/test_orchestrator_learning/test_policy_model.py
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05f2c02131 feat: Algolia DocSearch + learning subsystem reorganization (#43)
* chore: create learning subdirectory structure (routing, agents, intelligence)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: extract classify_query to routing/_utils.py

Move the classify_query() function and its regex patterns into a shared
utility module so multiple routing policies can import it without
depending on the full trace_policy module.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: move routing files to learning/routing/ subdirectory

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: create LearnedRouterPolicy merging trace-driven + SFT routing

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add conditional Algolia DocSearch integration

Add Algolia DocSearch as an optional search upgrade — native lunr.js
search remains the default until credentials are configured. Includes
CDN assets, Jinja2 conditional config injection, init script with
graceful fallback, light/dark theme CSS, improved search tokenization
for snake_case/dotted identifiers, and search boosts for key pages.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: move agent_evolver and skill_discovery to learning/agents/

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: move learning/orchestrator to learning/intelligence/orchestrator

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: delete removed learning policies, rewrite __init__.py, clean up api_routes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add SFT/GRPO/DSPy/GEPA config dataclasses, update LearningConfig

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add general-purpose SFT trainer (intelligence/sft_trainer.py)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: update stale imports in multi_model_router example

Update imports to use new learning/routing/ paths after the
subdirectory reorganization. Replace BanditRouterPolicy with
LearnedRouterPolicy.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add general-purpose GRPO trainer (intelligence/grpo_trainer.py)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add DSPy agent optimizer (agents/dspy_optimizer.py)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add GEPA agent optimizer (agents/gepa_optimizer.py)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add learning-dspy and learning-gepa optional dependency extras

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: update integration test to check for learned policy instead of grpo

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: clean up stale APIs and unused params in examples

- deep_research: remove system_prompt and max_turns params not accepted
  by Jarvis.ask(), inline system prompt into the query instead
- doc_qa: remove unused --top-k CLI arg that was never passed to the API
- multi_model_router: fix select_model() call to match single-arg signature

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: import SFT/GRPO trainers in intelligence/__init__.py for registry

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* chore: remove .md file changes from PR

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* chore: restore search boost frontmatter for key docs pages

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-12 21:34:31 -07:00

128 lines
4.1 KiB
Python

"""Tests for orchestrator policy model."""
from __future__ import annotations
import pytest
from openjarvis.learning.intelligence.orchestrator.policy_model import (
OrchestratorPolicyModel,
)
from openjarvis.learning.intelligence.orchestrator.types import (
EpisodeState,
OrchestratorAction,
OrchestratorObservation,
)
class TestParseOutput:
"""Test _parse_output without loading a real model."""
def _model(self) -> OrchestratorPolicyModel:
return OrchestratorPolicyModel()
def test_valid_thought_tool_input(self):
m = self._model()
text = (
"THOUGHT: I need to calculate 2+2\n"
"TOOL: calculator\n"
"INPUT: 2+2"
)
po = m._parse_output(text, ["calculator", "think"])
assert po.thought == "I need to calculate 2+2"
assert po.tool_name == "calculator"
assert po.tool_input == "2+2"
assert po.is_final_answer is False
def test_final_answer(self):
m = self._model()
text = (
"THOUGHT: I have the result\n"
"FINAL_ANSWER: 42"
)
po = m._parse_output(text, ["calculator"])
assert po.is_final_answer is True
assert po.tool_input == "42"
def test_final_answer_with_space(self):
m = self._model()
text = "FINAL ANSWER: the result is 7"
po = m._parse_output(text, ["calculator"])
assert po.is_final_answer is True
def test_missing_fields_fallback(self):
m = self._model()
text = "just some random output"
po = m._parse_output(text, ["calculator", "think"])
# Should fallback to first available tool
assert po.tool_name == "calculator"
assert po.thought == "No thought provided"
def test_invalid_tool_name_fallback(self):
m = self._model()
text = "THOUGHT: reason\nTOOL: nonexistent_tool\nINPUT: hello"
po = m._parse_output(text, ["calculator", "think"])
assert po.tool_name == "calculator" # fallback
def test_case_insensitive_tool_match(self):
m = self._model()
text = "THOUGHT: reason\nTOOL: Calculator\nINPUT: 5+5"
po = m._parse_output(text, ["calculator", "think"])
assert po.tool_name == "calculator"
def test_empty_tools_list(self):
m = self._model()
text = "THOUGHT: reason\nTOOL: calc\nINPUT: 1"
po = m._parse_output(text, [])
assert po.tool_name == "unknown"
class TestBuildPrompt:
def test_includes_task(self):
m = OrchestratorPolicyModel()
state = EpisodeState(initial_prompt="What is 2+2?")
prompt = m._build_prompt(state, ["calculator"])
assert "What is 2+2?" in prompt
def test_includes_tools(self):
m = OrchestratorPolicyModel()
state = EpisodeState(initial_prompt="q")
prompt = m._build_prompt(state, ["calculator", "think"])
assert "calculator" in prompt
assert "think" in prompt
def test_includes_history(self):
m = OrchestratorPolicyModel()
state = EpisodeState(initial_prompt="q")
action = OrchestratorAction(
thought="use calc", tool_name="calculator", tool_input="2+2"
)
obs = OrchestratorObservation(content="4")
state.add_turn(action, obs)
prompt = m._build_prompt(state, ["calculator"])
assert "Turn 1:" in prompt
assert "use calc" in prompt
def test_format_instructions(self):
m = OrchestratorPolicyModel()
state = EpisodeState(initial_prompt="q")
prompt = m._build_prompt(state, ["calculator"])
assert "THOUGHT:" in prompt
assert "TOOL:" in prompt
assert "INPUT:" in prompt
class TestPredictActionRequiresModel:
def test_raises_without_model(self):
m = OrchestratorPolicyModel()
state = EpisodeState(initial_prompt="q")
with pytest.raises(RuntimeError, match="Cannot generate"):
m.predict_action(state, ["calculator"])
class TestRepr:
def test_repr(self):
m = OrchestratorPolicyModel()
r = repr(m)
assert "OrchestratorPolicyModel" in r
assert "None" in r