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https://github.com/open-jarvis/OpenJarvis.git
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* feat: add query complexity analyzer with CLI and UI integration Classify incoming queries by difficulty (trivial→very_complex) to suggest appropriate token budgets for local vs. cloud routing. - Add score_complexity() with weighted signals (length, code, math, reasoning, multi-step, creative) and token tier mapping - Wire into `jarvis ask`: auto-suggest max_tokens when not set by user, show complexity in --profile output, log at DEBUG level - Add complexity metadata to /v1/chat/completions API response - Display complexity tier and score in frontend XRayFooter - Extend RoutingContext with complexity_score, suggested_max_tokens, has_reasoning fields - Update HeuristicRouter to route on complexity_score instead of raw query_length - Remove duplicated regex patterns from router.py (use complexity module as single source of truth) - Add 30 unit tests for complexity module - Fix existing router tests for new complexity-based routing Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: pass temperature and max_tokens from UI settings to backend The settings page stores temperature and max_tokens in the frontend store, but these values were never included in the chat API request. The backend Pydantic model defaults max_tokens to 1024 when the field is absent, which is too low for thinking models like qwen3.5 — they consume all tokens on reasoning and return empty content. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: pass UI settings to backend and auto-bump max_tokens from complexity - Pass temperature and max_tokens from frontend Settings store to the backend API (cherry-picked from fix/ui-max-tokens-passthrough) - Server-side: bump max_tokens when the complexity analyzer suggests a higher budget (e.g. for thinking models on complex queries), never reduce below the client-requested value - Fixes empty responses with thinking models (e.g. qwen3.5) that consumed all tokens on internal reasoning Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: raise token budget tiers to prevent empty responses on thinking models Double all tier budgets (trivial: 512→1024, simple: 1024→2048, etc.) so that thinking models like qwen3.5 have enough headroom for internal chain-of-thought plus visible output, even on simple queries. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: CI lint and test failures - Break long lines in routes.py to satisfy 88-char limit (E501) - Add intelligence.max_tokens and temperature to mocked config in test_ask_router.py so complexity analyzer can compare against int instead of MagicMock Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: line too long in test_complexity.py (E501) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
132 lines
4.3 KiB
Python
132 lines
4.3 KiB
Python
"""Tests for model resolution fallback chain in jarvis ask."""
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from __future__ import annotations
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import importlib
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from unittest import mock
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from click.testing import CliRunner
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from openjarvis.cli import cli
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_ask_mod = importlib.import_module("openjarvis.cli.ask")
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def _mock_engine():
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"""Create a mock engine that returns a simple response."""
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engine = mock.MagicMock()
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engine.engine_id = "mock"
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engine.health.return_value = True
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engine.list_models.return_value = ["test-model"]
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engine.generate.return_value = {
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"content": "Hello!",
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"usage": {"prompt_tokens": 5, "completion_tokens": 3, "total_tokens": 8},
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"model": "test-model",
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"finish_reason": "stop",
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}
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return engine
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def _patch_engine(engine):
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"""Return context managers that patch engine discovery to use our mock."""
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return (
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mock.patch.object(
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_ask_mod,
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"get_engine",
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return_value=("mock", engine),
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),
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mock.patch.object(
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_ask_mod,
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"discover_engines",
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return_value={"mock": engine},
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),
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mock.patch.object(
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_ask_mod,
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"discover_models",
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return_value={"mock": ["test-model"]},
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),
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mock.patch.object(_ask_mod, "register_builtin_models"),
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mock.patch.object(_ask_mod, "merge_discovered_models"),
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mock.patch.object(_ask_mod, "TelemetryStore"),
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)
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class TestAskModelResolution:
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def test_default_model_from_config(self) -> None:
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"""When no -m flag, uses config.intelligence.default_model."""
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engine = _mock_engine()
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patches = _patch_engine(engine)
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with patches[0], patches[1], patches[2], patches[3], patches[4], patches[5]:
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result = CliRunner().invoke(cli, ["ask", "Hello"])
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assert result.exit_code == 0
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assert "Hello!" in result.output
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def test_explicit_model_flag(self) -> None:
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"""The -m flag directly selects a model, bypassing fallback chain."""
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engine = _mock_engine()
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patches = _patch_engine(engine)
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with patches[0], patches[1], patches[2], patches[3], patches[4], patches[5]:
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result = CliRunner().invoke(
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cli,
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["ask", "-m", "test-model", "Hello"],
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)
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assert result.exit_code == 0
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assert "Hello!" in result.output
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def test_fallback_to_engine_models(self) -> None:
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"""When default_model is empty, falls back to first engine model."""
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engine = _mock_engine()
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patches = _patch_engine(engine)
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with (
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patches[0],
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patches[1],
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patches[2],
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patches[3],
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patches[4],
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patches[5],
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mock.patch.object(
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_ask_mod,
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"load_config",
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) as mock_config,
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):
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cfg = mock_config.return_value
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cfg.telemetry.enabled = False
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cfg.intelligence.default_model = ""
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cfg.intelligence.fallback_model = ""
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cfg.intelligence.temperature = 0.7
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cfg.intelligence.max_tokens = 1024
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cfg.agent.context_from_memory = False
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result = CliRunner().invoke(cli, ["ask", "Hello"])
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assert result.exit_code == 0
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def test_fallback_to_fallback_model(self) -> None:
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"""When default_model is empty and no engine models, uses fallback_model."""
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engine = _mock_engine()
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patches = _patch_engine(engine)
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# Override discover_models to return empty list
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with (
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patches[0],
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patches[1],
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mock.patch.object(
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_ask_mod,
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"discover_models",
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return_value={"mock": []},
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),
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patches[3],
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patches[4],
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patches[5],
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mock.patch.object(
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_ask_mod,
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"load_config",
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) as mock_config,
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):
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cfg = mock_config.return_value
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cfg.telemetry.enabled = False
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cfg.intelligence.default_model = ""
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cfg.intelligence.fallback_model = "fallback-model"
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cfg.intelligence.temperature = 0.7
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cfg.intelligence.max_tokens = 1024
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cfg.agent.context_from_memory = False
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result = CliRunner().invoke(cli, ["ask", "Hello"])
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assert result.exit_code == 0
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