mirror of
https://github.com/open-jarvis/OpenJarvis.git
synced 2026-07-30 19:02:16 +00:00
Jon Saad-FalconandClaude Opus 4.6
3040f468b6
Add four new OpenAI-compatible inference engines across the full stack: Rust backend: - Config structs with serde defaults (ExoEngineConfig, NexaEngineConfig, UzuEngineConfig, AppleFmEngineConfig) in openjarvis-core - Factory constructors on OpenAICompatEngine (exo, nexa, uzu, apple_fm) - Engine enum variants with delegate_engine! macro dispatch - Discovery and resolution in get_engine_static() - PyO3 bridge support in PyEngine Python frontend: - Data-driven engine class registration in openai_compat_engines.py - Config dataclasses with backward-compat host properties - Discovery host map entries - DEFAULT_SEARCH_SPACE updated with new engine backends - Apple FM shim (FastAPI wrapper for python-apple-fm-sdk, macOS only) Config & tests: - TOML config sections for all four engines - Unit tests for factory methods, enum variants, and search space Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
"""Engine discovery — probe running engines and aggregate available models."""
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from __future__ import annotations
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from typing import Any, Dict, List, Tuple
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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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# Map registry keys to config host attribute (None = no host arg)
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_HOST_MAP: Dict[str, str | None] = {
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"ollama": "ollama_host",
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"vllm": "vllm_host",
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"llamacpp": "llamacpp_host",
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"sglang": "sglang_host",
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"mlx": "mlx_host",
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"lmstudio": "lmstudio_host",
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"exo": "exo_host",
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"nexa": "nexa_host",
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"uzu": "uzu_host",
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"apple_fm": "apple_fm_host",
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"cloud": None,
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"litellm": None,
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}
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def _make_engine(key: str, config: JarvisConfig) -> InferenceEngine:
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"""Instantiate a registered engine with the appropriate config host."""
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cls = EngineRegistry.get(key)
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host_attr = _HOST_MAP.get(key)
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if host_attr is not None:
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host = getattr(config.engine, host_attr, None)
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if host:
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return cls(host=host)
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return cls()
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def discover_engines(config: JarvisConfig) -> List[Tuple[str, InferenceEngine]]:
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"""Probe registered engines and return ``[(key, instance)]`` for healthy ones.
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Results are sorted with the config default engine first.
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"""
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healthy: List[Tuple[str, InferenceEngine]] = []
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for key in EngineRegistry.keys():
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try:
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engine = _make_engine(key, config)
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if engine.health():
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healthy.append((key, engine))
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except Exception:
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continue
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default_key = config.engine.default
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def sort_key(item: Tuple[str, Any]) -> Tuple[int, str]:
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return (0 if item[0] == default_key else 1, item[0])
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healthy.sort(key=sort_key)
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return healthy
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def discover_models(
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engines: List[Tuple[str, InferenceEngine]],
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) -> Dict[str, List[str]]:
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"""Call ``list_models()`` on each engine and return a dict."""
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result: Dict[str, List[str]] = {}
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for key, engine in engines:
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try:
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result[key] = engine.list_models()
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except Exception:
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result[key] = []
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return result
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def get_engine(
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config: JarvisConfig, engine_key: str | None = None
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) -> Tuple[str, InferenceEngine] | None:
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"""Get a specific engine by key, or the default with fallback.
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Returns ``(key, engine_instance)`` or ``None`` if no engine is available.
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"""
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if engine_key:
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if EngineRegistry.contains(engine_key):
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try:
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engine = _make_engine(engine_key, config)
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if engine.health():
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return (engine_key, engine)
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except Exception:
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pass
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return None
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# Try default first
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default_key = config.engine.default
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if EngineRegistry.contains(default_key):
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try:
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engine = _make_engine(default_key, config)
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if engine.health():
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return (default_key, engine)
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except Exception:
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pass
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# Fallback to any healthy engine
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healthy = discover_engines(config)
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return healthy[0] if healthy else None
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__all__ = ["discover_engines", "discover_models", "get_engine"]
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