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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>
219 lines
6.6 KiB
Python
219 lines
6.6 KiB
Python
"""Tests for router behavior with the extended model catalog."""
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from __future__ import annotations
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import pytest
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from openjarvis.intelligence.model_catalog import register_builtin_models
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from openjarvis.learning._stubs import RoutingContext
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from openjarvis.learning.routing.router import (
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HeuristicRouter,
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build_routing_context,
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)
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# New local model keys for testing
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NEW_LOCAL_MODELS = [
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"gpt-oss:120b", # 117B total, 5.1B active, MoE
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"qwen3:8b", # 8.2B, dense
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"glm-4.7-flash", # 30B total, 3.0B active, MoE
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"trinity-mini", # 26B total, 3.0B active, MoE
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]
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# Cloud model keys
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CLOUD_MODELS = [
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"gpt-5-mini",
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"claude-opus-4-6",
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"claude-sonnet-4-6",
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"claude-haiku-4-5",
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"gemini-2.5-pro",
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"gemini-3-flash",
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]
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def _setup_models() -> None:
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"""Register builtin models needed for the tests."""
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register_builtin_models()
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class TestRouterWithNewModels:
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"""Router behavior when using the new local models."""
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def test_short_query_routes_to_smallest(self) -> None:
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_setup_models()
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router = HeuristicRouter(
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available_models=NEW_LOCAL_MODELS,
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)
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ctx = RoutingContext(query="hi", query_length=2)
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selected = router.select_model(ctx)
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assert selected == "qwen3:8b"
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def test_code_query_routes_to_largest(self) -> None:
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_setup_models()
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router = HeuristicRouter(
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available_models=NEW_LOCAL_MODELS,
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)
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ctx = RoutingContext(
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query="def merge_sort(arr):",
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query_length=22,
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has_code=True,
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)
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selected = router.select_model(ctx)
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assert selected == "gpt-oss:120b"
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def test_code_query_with_coder_available(self) -> None:
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_setup_models()
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models = NEW_LOCAL_MODELS + ["deepseek-coder-v2:16b"]
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router = HeuristicRouter(available_models=models)
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ctx = RoutingContext(
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query="import numpy as np",
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query_length=18,
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has_code=True,
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)
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selected = router.select_model(ctx)
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assert selected == "deepseek-coder-v2:16b"
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def test_math_query_routes_to_largest(self) -> None:
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_setup_models()
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router = HeuristicRouter(
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available_models=NEW_LOCAL_MODELS,
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)
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ctx = RoutingContext(
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query="solve the integral of x^2 dx",
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query_length=29,
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has_math=True,
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)
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selected = router.select_model(ctx)
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assert selected == "gpt-oss:120b"
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def test_long_context_routes_to_largest(self) -> None:
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_setup_models()
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router = HeuristicRouter(
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available_models=NEW_LOCAL_MODELS,
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)
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ctx = RoutingContext(query="x" * 501, query_length=501)
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selected = router.select_model(ctx)
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assert selected == "gpt-oss:120b"
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def test_high_urgency_routes_to_smallest(self) -> None:
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_setup_models()
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router = HeuristicRouter(
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available_models=NEW_LOCAL_MODELS,
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)
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ctx = RoutingContext(
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query="solve the integral of x^2",
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query_length=25,
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has_math=True,
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urgency=0.9,
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)
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selected = router.select_model(ctx)
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assert selected == "qwen3:8b"
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def test_reasoning_query_routes_to_largest(self) -> None:
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_setup_models()
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router = HeuristicRouter(
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available_models=NEW_LOCAL_MODELS,
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)
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ctx = build_routing_context(
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"Please explain step by step how neural networks"
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" learn"
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)
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selected = router.select_model(ctx)
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assert selected == "gpt-oss:120b"
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def test_medium_query_uses_default(self) -> None:
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_setup_models()
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router = HeuristicRouter(
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available_models=NEW_LOCAL_MODELS,
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default_model="glm-4.7-flash",
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)
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ctx = RoutingContext(
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query="Tell me about the weather today",
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query_length=60,
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)
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selected = router.select_model(ctx)
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assert selected == "glm-4.7-flash"
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class TestRouterCloudFallback:
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"""Router behavior when only cloud models are available."""
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def test_no_local_falls_to_cloud(self) -> None:
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_setup_models()
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router = HeuristicRouter(available_models=CLOUD_MODELS)
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ctx = RoutingContext(query="hi", query_length=2)
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selected = router.select_model(ctx)
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assert selected in CLOUD_MODELS
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def test_cloud_model_selection_with_math(self) -> None:
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_setup_models()
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router = HeuristicRouter(available_models=CLOUD_MODELS)
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ctx = RoutingContext(
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query="solve x", query_length=7, has_math=True,
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)
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selected = router.select_model(ctx)
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assert selected in CLOUD_MODELS
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def test_empty_models_returns_fallback(self) -> None:
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router = HeuristicRouter(
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available_models=[],
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fallback_model="gpt-5-mini",
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)
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ctx = RoutingContext(query="hello", query_length=5)
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assert router.select_model(ctx) == "gpt-5-mini"
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class TestRouterParameterized:
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"""Parametrized tests for model/query combinations."""
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@pytest.mark.parametrize(
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"query,expected_is_largest",
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[
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("hi", False),
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("solve the integral of sin(x)", True),
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("def foo(): pass", True),
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("x" * 501, True),
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],
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)
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def test_query_type_selects_expected_size(
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self,
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query: str,
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expected_is_largest: bool,
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) -> None:
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_setup_models()
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router = HeuristicRouter(
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available_models=NEW_LOCAL_MODELS,
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)
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ctx = build_routing_context(query)
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selected = router.select_model(ctx)
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if expected_is_largest:
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assert selected == "gpt-oss:120b"
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else:
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assert selected == "qwen3:8b"
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@pytest.mark.parametrize("model_id", NEW_LOCAL_MODELS)
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def test_single_model_always_returns_it(
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self, model_id: str,
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) -> None:
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_setup_models()
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router = HeuristicRouter(available_models=[model_id])
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ctx = RoutingContext(
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query="hello world", query_length=11,
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)
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assert router.select_model(ctx) == model_id
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@pytest.mark.parametrize("urgency", [0.85, 0.9, 1.0])
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def test_high_urgency_always_smallest(
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self, urgency: float,
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) -> None:
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_setup_models()
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router = HeuristicRouter(
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available_models=NEW_LOCAL_MODELS,
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)
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ctx = RoutingContext(
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query="complex reasoning task",
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query_length=23,
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has_math=True,
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urgency=urgency,
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)
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assert router.select_model(ctx) == "qwen3:8b"
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