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* 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>
152 lines
4.9 KiB
Python
152 lines
4.9 KiB
Python
"""Tests for LearnedRouterPolicy (merged trace-driven + SFT routing)."""
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from __future__ import annotations
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import time
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from pathlib import Path
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from openjarvis.core.types import StepType, Trace, TraceStep
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from openjarvis.learning._stubs import RoutingContext
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from openjarvis.learning.routing.learned_router import LearnedRouterPolicy
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from openjarvis.traces.analyzer import TraceAnalyzer
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from openjarvis.traces.store import TraceStore
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def _make_trace(
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query: str = "test",
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model: str = "qwen3:8b",
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outcome: str | None = "success",
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feedback: float | None = 0.8,
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) -> Trace:
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now = time.time()
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return Trace(
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query=query,
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agent="orchestrator",
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model=model,
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engine="ollama",
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result="result",
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outcome=outcome,
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feedback=feedback,
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started_at=now,
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ended_at=now + 0.5,
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total_tokens=100,
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total_latency_seconds=0.5,
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steps=[
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TraceStep(
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step_type=StepType.GENERATE,
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timestamp=now,
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duration_seconds=0.5,
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output={"tokens": 100},
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),
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],
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)
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class TestLearnedRouterPolicy:
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def test_registered_as_learned(self) -> None:
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from openjarvis.core.registry import RouterPolicyRegistry
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from openjarvis.learning.routing.learned_router import ensure_registered
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ensure_registered()
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assert RouterPolicyRegistry.contains("learned")
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def test_fallback_no_traces(self) -> None:
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policy = LearnedRouterPolicy(default_model="qwen3:8b")
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ctx = RoutingContext(query="hello")
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assert policy.select_model(ctx) == "qwen3:8b"
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def test_fallback_chain(self) -> None:
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policy = LearnedRouterPolicy(
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default_model="missing",
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fallback_model="llama3:8b",
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available_models=["llama3:8b"],
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)
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ctx = RoutingContext(query="hello")
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assert policy.select_model(ctx) == "llama3:8b"
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def test_update_from_traces(self, tmp_path: Path) -> None:
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store = TraceStore(tmp_path / "test.db")
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for _ in range(6):
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store.save(_make_trace(
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query="def foo(): pass",
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model="codestral",
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outcome="success",
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feedback=0.9,
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))
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for _ in range(6):
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store.save(_make_trace(
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query="def bar(): return 1",
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model="qwen3:8b",
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outcome="failure",
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feedback=0.3,
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))
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analyzer = TraceAnalyzer(store)
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policy = LearnedRouterPolicy(
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analyzer=analyzer,
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default_model="qwen3:8b",
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)
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policy.min_samples = 3
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result = policy.update_from_traces()
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assert result["updated"] is True
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ctx = RoutingContext(query="import os; def main(): pass")
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assert policy.select_model(ctx) == "codestral"
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store.close()
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def test_policy_map_readable(self, tmp_path: Path) -> None:
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store = TraceStore(tmp_path / "test.db")
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for _ in range(5):
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store.save(_make_trace(
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query="hello", model="small-model",
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outcome="success",
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))
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analyzer = TraceAnalyzer(store)
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policy = LearnedRouterPolicy(analyzer=analyzer, default_model="default")
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policy.min_samples = 3
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policy.update_from_traces()
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pmap = policy.policy_map
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assert isinstance(pmap, dict)
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assert "short" in pmap
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assert pmap["short"] == "small-model"
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store.close()
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def test_observe_online(self) -> None:
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policy = LearnedRouterPolicy(default_model="default")
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policy.min_samples = 3
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policy.observe("hello", "fast-model", "success", 0.9)
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assert policy.policy_map.get("short") == "fast-model"
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def test_batch_update(self) -> None:
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"""Test the batch update() method inherited from SFT routing logic."""
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from unittest.mock import MagicMock
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policy = LearnedRouterPolicy(default_model="default")
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mock_store = MagicMock()
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mock_store.list_traces.return_value = [
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_make_trace(
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query="def foo(): pass", model="code-model",
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outcome="success", feedback=0.9,
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),
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_make_trace(
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query="def bar(): pass", model="code-model",
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outcome="success", feedback=0.85,
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),
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_make_trace(
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query="def baz(): pass", model="code-model",
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outcome="success", feedback=0.88,
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),
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_make_trace(
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query="def qux(): pass", model="code-model",
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outcome="success", feedback=0.92,
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),
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_make_trace(
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query="def quux(): pass", model="code-model",
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outcome="success", feedback=0.87,
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),
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]
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result = policy.update(mock_store)
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assert isinstance(result, dict)
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assert "policy_map" in result
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