From 5efd67919bf4e65f6ff9231e830e5b76567b6371 Mon Sep 17 00:00:00 2001 From: Xubin Ren Date: Wed, 13 May 2026 15:34:03 +0000 Subject: [PATCH] feat(runner): support fallback candidates Resolve fallbackModels as preset references or explicit inline provider configs so failover uses complete model settings without exposing fallback logic to the agent loop. Co-authored-by: Cursor --- docs/configuration.md | 37 ++- nanobot/config/schema.py | 19 +- nanobot/providers/factory.py | 71 +++++- nanobot/providers/fallback_provider.py | 113 ++++++++- tests/agent/test_runner_fallback.py | 321 ++++++++++++++++++++++--- 5 files changed, 502 insertions(+), 59 deletions(-) diff --git a/docs/configuration.md b/docs/configuration.md index 0123017d2..3f7f39709 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -672,7 +672,8 @@ Existing configs do not need to change. If you do not set `modelPresets` or `age "maxTokens": 8192, "contextWindowTokens": 128000, "temperature": 0.1, - "modelPreset": null + "modelPreset": "fast", + "fallbackModels": ["deep"] } }, "modelPresets": { @@ -708,6 +709,40 @@ Existing configs do not need to change. If you do not set `modelPresets` or `age `default` is reserved and always means the implicit preset built from `agents.defaults.*`; do not define `modelPresets.default`. Use `/model default` to switch back to `agents.defaults.*`. +### Model Fallbacks + +`agents.defaults.fallbackModels` defines an ordered failover chain for the active model configuration. The primary model is still selected by `agents.defaults.modelPreset` (or the implicit default config when no preset is active). + +Each fallback candidate can be either: + +- A preset name from `modelPresets`, such as `"deep"`. The preset's full model, provider, generation, and context-window config is used. +- An inline fallback object with at least `provider` and `model`. Optional `maxTokens`, `contextWindowTokens`, and `temperature` fields inherit from the active primary config when omitted. `reasoningEffort` does not inherit; omit it to leave reasoning off for that fallback, or set it explicitly for models that support reasoning. + +```json +{ + "agents": { + "defaults": { + "modelPreset": "fast", + "fallbackModels": [ + "deep", + { + "provider": "deepseek", + "model": "deepseek-v4-pro", + "maxTokens": 4096, + "contextWindowTokens": 262144 + } + ] + } + } +} +``` + +String entries are preset names, not raw model names. If you want to use a model that is not already a preset, use the inline object form. + +Failover only runs when the primary provider returns a retryable model/provider error before any answer text has been streamed. Typical fallback cases include timeouts, connection errors, 5xx server errors, 429 rate limits, overloads, and quota/balance exhaustion. It does not run for malformed requests, authentication/permission errors, content filtering/refusals, or context-length/message-format errors. + +If fallback candidates use smaller `contextWindowTokens` values, nanobot builds context using the smallest window in the active chain so every candidate can receive the same prompt. + Set `agents.defaults.modelPreset` to start with a named preset: ```json diff --git a/nanobot/config/schema.py b/nanobot/config/schema.py index a112b932d..c8556ec9f 100644 --- a/nanobot/config/schema.py +++ b/nanobot/config/schema.py @@ -74,6 +74,20 @@ class DreamConfig(Base): return f"every {hours}h" +class InlineFallbackConfig(Base): + """One inline fallback model configuration.""" + + model: str + provider: str + max_tokens: int | None = None + context_window_tokens: int | None = None + temperature: float | None = None + reasoning_effort: str | None = None + + +FallbackCandidate = str | InlineFallbackConfig + + class ModelPresetConfig(Base): """A named set of model + generation parameters for quick switching.""" @@ -83,7 +97,6 @@ class ModelPresetConfig(Base): context_window_tokens: int = 65_536 temperature: float = 0.1 reasoning_effort: str | None = None - fallback_models: list[str] = Field(default_factory=list) def to_generation_settings(self) -> Any: from nanobot.providers.base import GenerationSettings @@ -107,6 +120,7 @@ class AgentDefaults(Base): context_window_tokens: int = 65_536 context_block_limit: int | None = None temperature: float = 0.1 + fallback_models: list[FallbackCandidate] = Field(default_factory=list) max_tool_iterations: int = 200 max_concurrent_subagents: int = Field(default=1, ge=1) max_tool_result_chars: int = 16_000 @@ -288,6 +302,9 @@ class Config(BaseSettings): name = self.agents.defaults.model_preset if name and name != "default" and name not in self.model_presets: raise ValueError(f"model_preset {name!r} not found in model_presets") + for fallback in self.agents.defaults.fallback_models: + if isinstance(fallback, str) and fallback not in self.model_presets: + raise ValueError(f"fallback_models entry {fallback!r} not found in model_presets") return self def resolve_default_preset(self) -> ModelPresetConfig: diff --git a/nanobot/providers/factory.py b/nanobot/providers/factory.py index e4822b7f8..288611392 100644 --- a/nanobot/providers/factory.py +++ b/nanobot/providers/factory.py @@ -5,7 +5,7 @@ from __future__ import annotations from dataclasses import dataclass from pathlib import Path -from nanobot.config.schema import Config, ModelPresetConfig +from nanobot.config.schema import Config, InlineFallbackConfig, ModelPresetConfig from nanobot.providers.base import LLMProvider from nanobot.providers.fallback_provider import FallbackProvider from nanobot.providers.registry import find_by_name @@ -104,6 +104,36 @@ def _make_provider_core( return provider +def _inline_fallback_preset( + primary: ModelPresetConfig, + fallback: InlineFallbackConfig, +) -> ModelPresetConfig: + return ModelPresetConfig( + model=fallback.model, + provider=fallback.provider, + max_tokens=fallback.max_tokens if fallback.max_tokens is not None else primary.max_tokens, + context_window_tokens=( + fallback.context_window_tokens + if fallback.context_window_tokens is not None + else primary.context_window_tokens + ), + temperature=( + fallback.temperature if fallback.temperature is not None else primary.temperature + ), + reasoning_effort=fallback.reasoning_effort, + ) + + +def _resolve_fallback_presets(config: Config, primary: ModelPresetConfig) -> list[ModelPresetConfig]: + presets: list[ModelPresetConfig] = [] + for fallback in config.agents.defaults.fallback_models: + if isinstance(fallback, str): + presets.append(config.model_presets[fallback]) + else: + presets.append(_inline_fallback_preset(primary, fallback)) + return presets + + def make_provider( config: Config, *, @@ -118,14 +148,14 @@ def make_provider( """ resolved = _resolve_model_preset(config, preset_name=preset_name, preset=preset) provider = _make_provider_core(config, preset_name=preset_name, preset=preset, model=model) + fallback_presets = _resolve_fallback_presets(config, resolved) - if resolved.fallback_models: - fb_preset = resolved.model_copy(update={"provider": "auto", "fallback_models": []}) + if fallback_presets: provider = FallbackProvider( primary=provider, - fallback_models=resolved.fallback_models, - provider_factory=lambda m: _make_provider_core( - config, preset_name=preset_name, preset=fb_preset, model=m + fallback_presets=fallback_presets, + provider_factory=lambda fb: _make_provider_core( + config, preset_name=preset_name, preset=fb ), ) @@ -138,9 +168,29 @@ def provider_signature( preset_name: str | None = None, preset: ModelPresetConfig | None = None, ) -> tuple[object, ...]: - """Return the config fields that affect the primary LLM provider.""" + """Return the config fields that affect the active provider chain.""" resolved = _resolve_model_preset(config, preset_name=preset_name, preset=preset) p = config.get_provider(resolved.model, preset=resolved) + fallback_presets = _resolve_fallback_presets(config, resolved) + + def _fallback_signature(fallback: ModelPresetConfig) -> tuple[object, ...]: + fp = config.get_provider(fallback.model, preset=fallback) + return ( + fallback.model, + fallback.provider, + config.get_provider_name(fallback.model, preset=fallback), + config.get_api_key(fallback.model, preset=fallback), + config.get_api_base(fallback.model, preset=fallback), + fp.extra_headers if fp else None, + fp.extra_body if fp else None, + getattr(fp, "region", None) if fp else None, + getattr(fp, "profile", None) if fp else None, + fallback.max_tokens, + fallback.temperature, + fallback.reasoning_effort, + fallback.context_window_tokens, + ) + return ( resolved.model, resolved.provider, @@ -155,6 +205,7 @@ def provider_signature( resolved.temperature, resolved.reasoning_effort, resolved.context_window_tokens, + tuple(_fallback_signature(fallback) for fallback in fallback_presets), ) @@ -165,10 +216,14 @@ def build_provider_snapshot( preset: ModelPresetConfig | None = None, ) -> ProviderSnapshot: resolved = _resolve_model_preset(config, preset_name=preset_name, preset=preset) + fallback_windows = [ + fallback.context_window_tokens + for fallback in _resolve_fallback_presets(config, resolved) + ] return ProviderSnapshot( provider=make_provider(config, preset=resolved), model=resolved.model, - context_window_tokens=resolved.context_window_tokens, + context_window_tokens=min([resolved.context_window_tokens, *fallback_windows]), signature=provider_signature(config, preset=resolved), ) diff --git a/nanobot/providers/fallback_provider.py b/nanobot/providers/fallback_provider.py index c0b137890..c082c2361 100644 --- a/nanobot/providers/fallback_provider.py +++ b/nanobot/providers/fallback_provider.py @@ -13,6 +13,46 @@ from nanobot.providers.base import LLMProvider, LLMResponse # Circuit breaker tuned to match OpenAICompatProvider's Responses API breaker. _PRIMARY_FAILURE_THRESHOLD = 3 _PRIMARY_COOLDOWN_S = 60 +_MISSING = object() +_FALLBACK_ERROR_KINDS = frozenset({ + "timeout", + "connection", + "server_error", + "rate_limit", + "overloaded", +}) +_NON_FALLBACK_ERROR_KINDS = frozenset({ + "authentication", + "auth", + "permission", + "content_filter", + "refusal", + "context_length", + "invalid_request", +}) +_FALLBACK_ERROR_TOKENS = ( + "rate_limit", + "rate limit", + "too_many_requests", + "too many requests", + "overloaded", + "server_error", + "server error", + "temporarily unavailable", + "timeout", + "timed out", + "connection", + "insufficient_quota", + "insufficient quota", + "quota_exceeded", + "quota exceeded", + "quota_exhausted", + "quota exhausted", + "billing_hard_limit", + "insufficient_balance", + "balance", + "out of credits", +) class FallbackProvider(LLMProvider): @@ -34,13 +74,13 @@ class FallbackProvider(LLMProvider): def __init__( self, primary: LLMProvider, - fallback_models: list[str], - provider_factory: Callable[[str], LLMProvider], + fallback_presets: list[Any], + provider_factory: Callable[[Any], LLMProvider], ): self._primary = primary - self._fallback_models = list(fallback_models) + self._fallback_presets = list(fallback_presets) self._provider_factory = provider_factory - self._has_fallbacks = bool(fallback_models) + self._has_fallbacks = bool(fallback_presets) self._primary_failures = 0 self._primary_tripped_at: float | None = None @@ -55,6 +95,10 @@ class FallbackProvider(LLMProvider): def get_default_model(self) -> str: return self._primary.get_default_model() + @property + def supports_progress_deltas(self) -> bool: + return bool(getattr(self._primary, "supports_progress_deltas", False)) + def _primary_available(self) -> bool: """Return True if the primary provider is not currently tripped.""" if self._primary_tripped_at is None: @@ -110,6 +154,14 @@ class FallbackProvider(LLMProvider): ) return response + if not self._should_fallback(response): + logger.warning( + "Primary model '{}' returned non-fallbackable error: {}", + primary_model, + (response.content or "")[:120], + ) + return response + self._primary_failures += 1 if self._primary_failures >= _PRIMARY_FAILURE_THRESHOLD: self._primary_tripped_at = time.monotonic() @@ -122,7 +174,8 @@ class FallbackProvider(LLMProvider): last_response: LLMResponse | None = None primary_skipped = not self._primary_available() - for idx, fallback_model in enumerate(self._fallback_models): + for idx, fallback in enumerate(self._fallback_presets): + fallback_model = fallback.model if has_streamed is not None and has_streamed[0]: break if idx == 0 and primary_skipped: @@ -138,25 +191,35 @@ class FallbackProvider(LLMProvider): else: logger.info( "Fallback '{}' also failed, trying next fallback '{}'", - self._fallback_models[idx - 1], fallback_model, + self._fallback_presets[idx - 1].model, fallback_model, ) try: - fallback_provider = self._provider_factory(fallback_model) + fallback_provider = self._provider_factory(fallback) except Exception as exc: logger.warning( "Failed to create provider for fallback '{}': {}", fallback_model, exc ) continue - original_model = kwargs.get("model") + original_values = { + name: kwargs.get(name, _MISSING) + for name in ("model", "max_tokens", "temperature", "reasoning_effort") + } kwargs["model"] = fallback_model + kwargs["max_tokens"] = fallback.max_tokens + kwargs["temperature"] = fallback.temperature + if fallback.reasoning_effort is None: + kwargs.pop("reasoning_effort", None) + else: + kwargs["reasoning_effort"] = fallback.reasoning_effort try: fallback_response = await call(fallback_provider, kwargs) finally: - if original_model is not None: - kwargs["model"] = original_model - else: - kwargs.pop("model", None) + for name, value in original_values.items(): + if value is _MISSING: + kwargs.pop(name, None) + else: + kwargs[name] = value if fallback_response.finish_reason != "error": logger.info( @@ -174,7 +237,7 @@ class FallbackProvider(LLMProvider): logger.warning( "All {} fallback model(s) failed", - len(self._fallback_models), + len(self._fallback_presets), ) # Return the last error response we saw (primary or last fallback). if last_response is not None: @@ -184,3 +247,27 @@ class FallbackProvider(LLMProvider): content=f"Primary model '{primary_model}' circuit open and no fallbacks available", finish_reason="error", ) + + @staticmethod + def _should_fallback(response: LLMResponse) -> bool: + if response.error_should_retry is False: + return False + status = response.error_status_code + kind = (response.error_kind or "").lower() + error_type = (response.error_type or "").lower() + code = (response.error_code or "").lower() + text = (response.content or "").lower() + + if status in {400, 401, 403, 404, 422}: + return False + if kind in _NON_FALLBACK_ERROR_KINDS: + return False + if any(token in value for value in (kind, error_type, code) for token in _NON_FALLBACK_ERROR_KINDS): + return False + if response.error_should_retry is True: + return True + if status is not None and (status in {408, 409, 429} or 500 <= status <= 599): + return True + if kind in _FALLBACK_ERROR_KINDS: + return True + return any(token in value for value in (kind, error_type, code, text) for token in _FALLBACK_ERROR_TOKENS) diff --git a/tests/agent/test_runner_fallback.py b/tests/agent/test_runner_fallback.py index 273bd6d6d..0e36fb02a 100644 --- a/tests/agent/test_runner_fallback.py +++ b/tests/agent/test_runner_fallback.py @@ -3,10 +3,11 @@ from __future__ import annotations from typing import Any -from unittest.mock import MagicMock +from unittest.mock import MagicMock, patch import pytest +from nanobot.config.schema import ModelPresetConfig from nanobot.providers.base import LLMProvider, LLMResponse from nanobot.providers.fallback_provider import FallbackProvider @@ -16,14 +17,45 @@ def _make_response( finish_reason: str = "stop", *, error_kind: str | None = None, + error_status_code: int | None = None, + error_type: str | None = None, + error_code: str | None = None, + error_should_retry: bool | None = None, ) -> LLMResponse: - return LLMResponse(content=content, finish_reason=finish_reason, error_kind=error_kind) + return LLMResponse( + content=content, + finish_reason=finish_reason, + error_kind=error_kind, + error_status_code=error_status_code, + error_type=error_type, + error_code=error_code, + error_should_retry=error_should_retry, + ) def _error_response(content: str = "api error") -> LLMResponse: return _make_response(content, finish_reason="error", error_kind="server_error") +def _fallback( + model: str, + provider: str = "custom", + *, + max_tokens: int = 8192, + context_window_tokens: int = 65_536, + temperature: float = 0.1, + reasoning_effort: str | None = None, +) -> ModelPresetConfig: + return ModelPresetConfig( + model=model, + provider=provider, + max_tokens=max_tokens, + context_window_tokens=context_window_tokens, + temperature=temperature, + reasoning_effort=reasoning_effort, + ) + + class _FakeProvider(LLMProvider): """Fake provider for testing.""" @@ -53,24 +85,163 @@ class _FakeProvider(LLMProvider): def test_fallback_models_default_empty() -> None: - from nanobot.config.schema import ModelPresetConfig - p = ModelPresetConfig(model="test/model") - assert p.fallback_models == [] + from nanobot.config.schema import AgentDefaults + + defaults = AgentDefaults() + + assert defaults.fallback_models == [] -def test_fallback_models_accepts_list() -> None: - from nanobot.config.schema import ModelPresetConfig - p = ModelPresetConfig(model="test/primary", fallback_models=["test/a", "test/b"]) - assert p.fallback_models == ["test/a", "test/b"] +def test_fallback_models_accept_preset_refs_and_inline_configs() -> None: + from nanobot.config.schema import Config, InlineFallbackConfig - -def test_fallback_models_from_camel_case() -> None: - from nanobot.config.schema import ModelPresetConfig - p = ModelPresetConfig.model_validate({ - "model": "test/primary", - "fallbackModels": ["test/a"], + config = Config.model_validate({ + "agents": { + "defaults": { + "fallbackModels": [ + "deep", + { + "provider": "openai", + "model": "gpt-4.1", + "maxTokens": 4096, + }, + ] + } + }, + "modelPresets": { + "deep": {"provider": "anthropic", "model": "claude-opus-4-7"} + }, }) - assert p.fallback_models == ["test/a"] + + assert config.agents.defaults.fallback_models[0] == "deep" + assert config.agents.defaults.fallback_models[1] == InlineFallbackConfig( + provider="openai", + model="gpt-4.1", + max_tokens=4096, + ) + + +def test_fallback_model_preset_ref_must_exist() -> None: + from nanobot.config.schema import Config + + with pytest.raises(ValueError, match="fallback_models.*not found"): + Config.model_validate({ + "agents": {"defaults": {"fallbackModels": ["missing"]}}, + "modelPresets": {}, + }) + + +def test_provider_signature_tracks_fallback_presets_and_provider_config() -> None: + from nanobot.config.schema import Config + from nanobot.providers.factory import provider_signature + + base = { + "agents": { + "defaults": { + "modelPreset": "fast", + "fallbackModels": ["deep"], + } + }, + "modelPresets": { + "fast": {"model": "openai/gpt-4.1", "provider": "openai"}, + "deep": {"model": "anthropic/claude-sonnet-4-6", "provider": "anthropic"}, + }, + "providers": { + "openai": {"apiKey": "primary-key"}, + "anthropic": {"apiKey": "fallback-key"}, + }, + } + changed_fallback = { + **base, + "agents": {"defaults": {"modelPreset": "fast", "fallbackModels": ["backup"]}}, + "modelPresets": { + **base["modelPresets"], + "backup": {"model": "deepseek/deepseek-chat", "provider": "deepseek"}, + }, + "providers": { + **base["providers"], + "deepseek": {"apiKey": "deepseek-key"}, + }, + } + changed_key = { + **base, + "providers": { + "openai": {"apiKey": "primary-key"}, + "anthropic": {"apiKey": "new-fallback-key"}, + }, + } + + signature = provider_signature(Config.model_validate(base)) + + assert signature != provider_signature(Config.model_validate(changed_fallback)) + assert signature != provider_signature(Config.model_validate(changed_key)) + + +def test_provider_snapshot_uses_smallest_fallback_context_window() -> None: + from nanobot.config.schema import Config + from nanobot.providers.factory import build_provider_snapshot + + config = Config.model_validate({ + "agents": { + "defaults": { + "modelPreset": "fast", + "fallbackModels": ["deep"], + } + }, + "modelPresets": { + "fast": { + "model": "openai/gpt-4.1", + "provider": "openai", + "contextWindowTokens": 128000, + }, + "deep": { + "model": "deepseek/deepseek-chat", + "provider": "deepseek", + "contextWindowTokens": 64000, + }, + }, + "providers": { + "openai": {"apiKey": "primary-key"}, + "deepseek": {"apiKey": "fallback-key"}, + }, + }) + + with patch("nanobot.providers.openai_compat_provider.AsyncOpenAI"): + snapshot = build_provider_snapshot(config) + + assert snapshot.context_window_tokens == 64000 + + +def test_inline_fallback_reasoning_effort_does_not_inherit_primary() -> None: + from nanobot.config.schema import Config + from nanobot.providers.factory import provider_signature + + config = Config.model_validate({ + "agents": { + "defaults": { + "modelPreset": "fast", + "fallbackModels": [ + {"provider": "openai", "model": "gpt-4.1"} + ], + } + }, + "modelPresets": { + "fast": { + "model": "anthropic/claude-opus-4-5", + "provider": "anthropic", + "reasoningEffort": "high", + } + }, + "providers": { + "anthropic": {"apiKey": "primary-key"}, + "openai": {"apiKey": "fallback-key"}, + }, + }) + + signature = provider_signature(config) + fallback_signatures = signature[-1] + + assert fallback_signatures[0][11] is None # -- FallbackProvider tests -- @@ -83,7 +254,7 @@ class TestNoFallbackWhenPrimarySucceeds: factory = MagicMock() fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a"], + fallback_presets=[_fallback("fallback-a")], provider_factory=factory, ) @@ -102,14 +273,14 @@ class TestFallbackOnPrimaryError: fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a"], + fallback_presets=[_fallback("fallback-a")], provider_factory=factory, ) result = await fb.chat(messages=[{"role": "user", "content": "hi"}], model="primary-model") assert result.content == "fallback ok" assert result.finish_reason == "stop" - factory.assert_called_once_with("fallback-a") + factory.assert_called_once_with(_fallback("fallback-a")) assert primary.chat_calls[0]["model"] == "primary-model" assert fallback.chat_calls[0]["model"] == "fallback-a" @@ -121,7 +292,7 @@ class TestNoFallbackWhenContentStreamed: factory = MagicMock() fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a"], + fallback_presets=[_fallback("fallback-a")], provider_factory=factory, ) @@ -146,14 +317,62 @@ class TestFailoverOnTransientError: factory = MagicMock(return_value=fallback) fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a"], + fallback_presets=[_fallback("fallback-a")], provider_factory=factory, ) result = await fb.chat(messages=[{"role": "user", "content": "hi"}]) assert result.content == "fallback ok" assert result.finish_reason == "stop" - factory.assert_called_once_with("fallback-a") + factory.assert_called_once_with(_fallback("fallback-a")) + + +class TestNoFallbackOnNonRetryableError: + @pytest.mark.asyncio + async def test_bad_request(self) -> None: + primary = _FakeProvider( + "primary", + _make_response( + "invalid request", + finish_reason="error", + error_status_code=400, + error_kind="invalid_request", + ), + ) + factory = MagicMock() + fb = FallbackProvider( + primary=primary, + fallback_presets=[_fallback("fallback-a")], + provider_factory=factory, + ) + + result = await fb.chat(messages=[{"role": "user", "content": "hi"}]) + + assert result.finish_reason == "error" + factory.assert_not_called() + + @pytest.mark.asyncio + async def test_auth_error(self) -> None: + primary = _FakeProvider( + "primary", + _make_response( + "unauthorized", + finish_reason="error", + error_status_code=401, + error_kind="authentication", + ), + ) + factory = MagicMock() + fb = FallbackProvider( + primary=primary, + fallback_presets=[_fallback("fallback-a")], + provider_factory=factory, + ) + + result = await fb.chat(messages=[{"role": "user", "content": "hi"}]) + + assert result.finish_reason == "error" + factory.assert_not_called() @pytest.mark.asyncio async def test_timeout(self) -> None: @@ -165,14 +384,14 @@ class TestFailoverOnTransientError: factory = MagicMock(return_value=fallback) fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a"], + fallback_presets=[_fallback("fallback-a")], provider_factory=factory, ) result = await fb.chat(messages=[{"role": "user", "content": "hi"}]) assert result.content == "fallback ok" assert result.finish_reason == "stop" - factory.assert_called_once_with("fallback-a") + factory.assert_called_once_with(_fallback("fallback-a")) class TestFallbackTriesModelsInOrder: @@ -185,15 +404,15 @@ class TestFallbackTriesModelsInOrder: fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a", "fallback-b"], + fallback_presets=[_fallback("fallback-a"), _fallback("fallback-b")], provider_factory=factory, ) result = await fb.chat(messages=[{"role": "user", "content": "hi"}]) assert result.content == "b ok" assert factory.call_count == 2 - factory.assert_any_call("fallback-a") - factory.assert_any_call("fallback-b") + factory.assert_any_call(_fallback("fallback-a")) + factory.assert_any_call(_fallback("fallback-b")) class TestAllFallbacksFail: @@ -205,7 +424,7 @@ class TestAllFallbacksFail: fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a"], + fallback_presets=[_fallback("fallback-a")], provider_factory=factory, ) @@ -223,7 +442,7 @@ class TestFactoryExceptionSkipsModel: fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a", "fallback-b"], + fallback_presets=[_fallback("fallback-a"), _fallback("fallback-b")], provider_factory=factory, ) @@ -242,13 +461,43 @@ class TestFallbackModelParameter: fb = FallbackProvider( primary=primary, - fallback_models=["fallback-model"], + fallback_presets=[_fallback("fallback-model")], provider_factory=factory, ) await fb.chat(messages=[{"role": "user", "content": "hi"}], model="primary-model") assert fallback.chat_calls[0]["model"] == "fallback-model" + @pytest.mark.asyncio + async def test_uses_fallback_generation_fields(self) -> None: + primary = _FakeProvider("primary", _error_response()) + fallback = _FakeProvider("fallback", _make_response("ok")) + fb = FallbackProvider( + primary=primary, + fallback_presets=[ + _fallback( + "fallback-model", + max_tokens=1234, + temperature=0.4, + reasoning_effort=None, + ) + ], + provider_factory=MagicMock(return_value=fallback), + ) + + await fb.chat( + messages=[{"role": "user", "content": "hi"}], + model="primary-model", + max_tokens=8192, + temperature=0.1, + reasoning_effort="high", + ) + + assert fallback.chat_calls[0]["model"] == "fallback-model" + assert fallback.chat_calls[0]["max_tokens"] == 1234 + assert fallback.chat_calls[0]["temperature"] == 0.4 + assert "reasoning_effort" not in fallback.chat_calls[0] + class TestNoFallbackWhenEmptyList: @pytest.mark.asyncio @@ -258,7 +507,7 @@ class TestNoFallbackWhenEmptyList: fb = FallbackProvider( primary=primary, - fallback_models=[], + fallback_presets=[], provider_factory=factory, ) @@ -277,7 +526,7 @@ class TestChatStreamFailover: fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a"], + fallback_presets=[_fallback("fallback-a")], provider_factory=factory, ) @@ -291,7 +540,7 @@ class TestGetDefaultModel: primary = _FakeProvider("primary") fb = FallbackProvider( primary=primary, - fallback_models=["a"], + fallback_presets=[_fallback("a")], provider_factory=MagicMock(), ) assert fb.get_default_model() == "primary/model" @@ -305,7 +554,7 @@ class TestCircuitBreaker: factory = MagicMock(return_value=fallback) fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a"], + fallback_presets=[_fallback("fallback-a")], provider_factory=factory, ) @@ -329,7 +578,7 @@ class TestCircuitBreaker: factory = MagicMock(return_value=fallback) fb = FallbackProvider( primary=primary, - fallback_models=["fallback-a"], + fallback_presets=[_fallback("fallback-a")], provider_factory=factory, ) @@ -357,7 +606,7 @@ class TestGenerationForwarded: primary.generation = GenerationSettings(temperature=0.5, max_tokens=1024) fb = FallbackProvider( primary=primary, - fallback_models=["a"], + fallback_presets=[_fallback("a")], provider_factory=MagicMock(), ) assert fb.generation.temperature == 0.5