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* feat(engine): add DeepSeek as a first-class cloud provider Adds DEEPSEEK_API_KEY support to the cloud engine, wiring DeepSeek's OpenAI-compatible API (api.deepseek.com/v1) alongside the existing MiniMax, OpenRouter, Anthropic, and Google providers. - Add _DEEPSEEK_MODELS list (deepseek-v4-flash, deepseek-v4-pro) - Add _is_deepseek_model() routing predicate - Init self._deepseek_client from DEEPSEEK_API_KEY in _init_clients() - Add _generate_deepseek() and _stream_deepseek() methods - Wire DeepSeek into generate(), stream(), _stream_full_openai(), list_models(), and health() - Add approximate pricing entries for both models Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(engine): strict cloud model routing + deepseek can_serve branch Builds on the DeepSeek provider (PR #504) with two routing-correctness fixes to CloudEngine._client_for_model: 1. Add the missing DeepSeek branch so can_serve('deepseek-*') agrees with list_models()/health() when only DEEPSEEK_API_KEY is set (mirrors the minimax branch). Without it the engine advertised deepseek models via list_models() but refused to serve them (the #532 can_serve contract). 2. Fix #335: _client_for_model previously fell through to the OpenAI client for ANY unrecognized model name, so an OpenAI key (even a dummy sk-dummy... one) made can_serve('qwen3.5:0.8b') return True. With the local engine transiently down (classic post-Windows-restart Ollama not yet up), model-aware get_engine then mis-selected the cloud engine for a local model and died with "OpenAI client not available". Add a positive _is_openai_model predicate (gpt-/chatgpt-/o1/o3/o4 + _OPENAI_MODELS) and return None for unrecognized names, so can_serve declines them. generate() and stream() keep their OpenAI fall-through, preserving loud failure for an explicitly-requested unknown cloud model. Tests: DeepSeek detection/pricing/health/list_models/generate-routing/ can_serve and a #335 regression (can_serve rejects local names with an OpenAI key; unknown model not served even with all clients set; end-to-end get_engine does not misroute a local model with a dummy OpenAI key). Fixes #335 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: Jen Huls <me@jenhuls.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
344 lines
12 KiB
Python
344 lines
12 KiB
Python
"""Tests for engine discovery."""
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from __future__ import annotations
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from unittest import mock
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from openjarvis.core.config import JarvisConfig
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from openjarvis.core.registry import EngineRegistry
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from openjarvis.engine._base import InferenceEngine
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from openjarvis.engine._discovery import (
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discover_engines,
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discover_models,
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get_engine,
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)
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class _FakeEngine(InferenceEngine):
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engine_id = "fake"
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def __init__(
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self,
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*,
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healthy: bool = True,
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models: list | None = None,
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**kwargs, # noqa: ANN003
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) -> None:
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self._healthy = healthy
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self._models = models or []
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def generate(self, messages, *, model, **kwargs): # noqa: ANN001, ANN003
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return {"content": "ok", "usage": {}}
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async def stream(self, messages, *, model, **kwargs): # noqa: ANN001, ANN003
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yield "ok"
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def list_models(self) -> list:
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return self._models
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def health(self) -> bool:
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return self._healthy
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def _reg(key: str, eid: str) -> None:
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"""Register a fake engine type under *key*."""
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cls = type(key.title(), (_FakeEngine,), {"engine_id": eid})
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EngineRegistry.register_value(key, cls)
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class TestDiscoverEngines:
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def test_only_healthy_returned(self) -> None:
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_reg("healthy", "healthy")
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_reg("sick", "sick")
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cfg = JarvisConfig()
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=lambda k, c: _FakeEngine(healthy=(k == "healthy")),
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):
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result = discover_engines(cfg)
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assert len(result) == 1
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assert result[0][0] == "healthy"
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def test_default_engine_first(self) -> None:
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_reg("a", "a")
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_reg("b", "b")
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cfg = JarvisConfig()
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cfg.engine.default = "b"
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=lambda k, c: _FakeEngine(healthy=True),
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):
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result = discover_engines(cfg)
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assert result[0][0] == "b"
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def test_health_checks_run_concurrently(self) -> None:
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"""Regression for #263 — discovery must probe engines in parallel.
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Each engine's health() does a blocking network probe with its own
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timeout, so serial discovery cost = sum of probe times. With N
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engines each sleeping S, parallel discovery wall-time must stay far
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below N*S (closer to S). We don't measure real time precisely (CI is
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noisy); instead we record concurrency: the max number of health()
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calls in flight simultaneously must exceed 1.
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"""
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import threading
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import time
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n_engines = 6
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sleep_s = 0.15
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for i in range(n_engines):
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_reg(f"slow{i}", f"slow{i}")
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lock = threading.Lock()
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in_flight = 0
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max_in_flight = 0
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class _SlowEngine(_FakeEngine):
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def health(self) -> bool:
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nonlocal in_flight, max_in_flight
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with lock:
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in_flight += 1
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max_in_flight = max(max_in_flight, in_flight)
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time.sleep(sleep_s)
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with lock:
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in_flight -= 1
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return True
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cfg = JarvisConfig()
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=lambda k, c: _SlowEngine(healthy=True),
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):
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start = time.monotonic()
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result = discover_engines(cfg)
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elapsed = time.monotonic() - start
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# All slow engines were discovered (plus any real registered ones).
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assert len([r for r in result if r[0].startswith("slow")]) == n_engines
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# Concurrency actually happened — more than one probe overlapped.
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assert max_in_flight > 1, f"probes ran serially (max_in_flight={max_in_flight})"
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# Wall-time is well under the serial sum (n*sleep), allowing slack.
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assert elapsed < n_engines * sleep_s * 0.7
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class TestDiscoverModels:
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def test_aggregate_models(self) -> None:
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e1 = _FakeEngine(models=["m1", "m2"])
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e2 = _FakeEngine(models=["m3"])
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result = discover_models([("ollama", e1), ("vllm", e2)])
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assert result == {"ollama": ["m1", "m2"], "vllm": ["m3"]}
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class TestGetEngine:
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def test_fallback_when_default_unhealthy(self) -> None:
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_reg("bad", "bad")
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_reg("good", "good")
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cfg = JarvisConfig()
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cfg.engine.default = "bad"
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def _make(k, c): # noqa: ANN001
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return _FakeEngine(healthy=(k == "good"))
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=_make,
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):
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result = get_engine(cfg)
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assert result is not None
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assert result[0] == "good"
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def test_explicit_key_falls_back_to_any_healthy(self) -> None:
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"""When an explicit engine_key fails, fallback to any healthy engine.
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Fixes #73: LM Studio running but not found because get_engine()
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returned None when the explicitly-requested key failed.
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"""
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_reg("requested", "requested")
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_reg("running", "running")
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cfg = JarvisConfig()
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cfg.engine.default = "requested"
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def _make(k, c): # noqa: ANN001
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return _FakeEngine(healthy=(k == "running"))
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=_make,
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):
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# Explicit key "requested" is unhealthy, but "running" is healthy
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result = get_engine(cfg, engine_key="requested")
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assert result is not None
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assert result[0] == "running"
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def test_skips_engine_that_cannot_serve_model(self) -> None:
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"""#532: a healthy engine that can't serve the requested model is
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skipped for one that can — this is what stops the cloud fallback being
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chosen (when the local engine is down) for a model whose provider
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client is missing.
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"""
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_reg("picky", "picky")
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_reg("local", "local")
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class _Picky(_FakeEngine):
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def can_serve(self, model: str) -> bool:
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return model == "servable"
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cfg = JarvisConfig()
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cfg.engine.default = "picky"
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def _make(k, c): # noqa: ANN001
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if k == "picky":
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return _Picky(healthy=True)
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return _FakeEngine(healthy=(k == "local"))
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=_make,
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):
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# "picky" is healthy but cannot serve "other" -> fall back to "local"
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result = get_engine(cfg, model="other")
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assert result is not None
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assert result[0] == "local"
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def test_dummy_openai_key_does_not_misroute_local_model(
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self, monkeypatch: object
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) -> None:
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"""#335: a present-but-dummy OPENAI_API_KEY + a down local engine must
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NOT cause a local Ollama model to be routed to the cloud engine.
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Before the fix, CloudEngine.can_serve('qwen3.5:0.8b') returned True
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whenever any OpenAI client existed (even a junk key), so get_engine
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picked 'cloud' and the request later died with "OpenAI client not
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available". With the strict _client_for_model fall-through it returns
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None for unrecognized names, so get_engine declines cloud and (with the
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local engine down) returns None — surfacing a "start your local engine"
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failure instead.
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"""
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from openjarvis.engine.cloud import CloudEngine
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_reg("ollama", "ollama")
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EngineRegistry.register_value("cloud", CloudEngine)
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cfg = JarvisConfig()
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cfg.engine.default = "ollama"
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def _make(k, c): # noqa: ANN001
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if k == "ollama":
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# Local engine is down (post-restart Ollama not yet up).
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return _FakeEngine(healthy=False, models=["qwen3.5:0.8b"])
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# Real CloudEngine with only a (dummy) OpenAI client wired.
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eng = CloudEngine.__new__(CloudEngine)
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for name in (
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"_openai_client",
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"_anthropic_client",
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"_google_client",
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"_openrouter_client",
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"_minimax_client",
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"_deepseek_client",
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"_codex_client",
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):
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setattr(eng, name, object() if name == "_openai_client" else None)
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return eng
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=_make,
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):
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result = get_engine(cfg, model="qwen3.5:0.8b")
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# Cloud must NOT be selected for a local model name.
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assert result is None
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def test_model_none_preserves_model_agnostic_selection(self) -> None:
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"""model=None keeps the legacy behaviour: first healthy engine wins."""
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_reg("primary", "primary")
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cfg = JarvisConfig()
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cfg.engine.default = "primary"
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=lambda k, c: _FakeEngine(healthy=True), # noqa: ANN001
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):
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result = get_engine(cfg, model=None)
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assert result is not None
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assert result[0] == "primary"
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class TestMiningSidecarEngineHandoff:
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"""Engine discovery picks up (or ignores) a mining sidecar at runtime."""
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def test_engine_discovery_picks_up_mining_sidecar(
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self, written_sidecar, monkeypatch
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) -> None:
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"""When a mining sidecar exists with vllm_endpoint, discovery
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registers a ``vllm-pearl-mining`` engine in the EngineRegistry.
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"""
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from openjarvis.mining import _constants as mining_const
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monkeypatch.setattr(mining_const, "SIDECAR_PATH", written_sidecar)
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cfg = JarvisConfig()
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=lambda k, c: _FakeEngine(healthy=True),
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):
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discover_engines(cfg)
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assert EngineRegistry.contains("vllm-pearl-mining")
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def test_engine_discovery_no_mining_engine_when_sidecar_absent(
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self, tmp_path, monkeypatch
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) -> None:
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"""No mining sidecar → no ``vllm-pearl-mining`` engine registered."""
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from openjarvis.mining import _constants as mining_const
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missing = tmp_path / "no-such-mining.json"
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monkeypatch.setattr(mining_const, "SIDECAR_PATH", missing)
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cfg = JarvisConfig()
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=lambda k, c: _FakeEngine(healthy=True),
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):
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discover_engines(cfg)
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assert not EngineRegistry.contains("vllm-pearl-mining")
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def test_engine_discovery_skips_when_sidecar_missing_vllm_endpoint(
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self, tmp_path, monkeypatch
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) -> None:
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"""Sidecar present but no ``vllm_endpoint`` field → skip registration.
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Data-driven gate: a future cpu-pearl provider writes a sidecar that
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doesn't replace an inference engine (no vllm_endpoint field).
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"""
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import json as _json
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sidecar = tmp_path / "mining.json"
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sidecar.write_text(
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_json.dumps(
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{
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"provider": "cpu-pearl",
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"wallet_address": "prl1q...",
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"started_at": 1234567890,
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# deliberately omit vllm_endpoint
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}
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)
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)
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from openjarvis.mining import _constants as mining_const
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monkeypatch.setattr(mining_const, "SIDECAR_PATH", sidecar)
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cfg = JarvisConfig()
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with mock.patch(
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"openjarvis.engine._discovery._make_engine",
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side_effect=lambda k, c: _FakeEngine(healthy=True),
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):
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discover_engines(cfg)
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assert not EngineRegistry.contains("vllm-pearl-mining")
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