Ziheng Zhang
c627aadd39
* feat: add DeepSeek as a recognized third-party provider Register DeepSeek API (https://api.deepseek.com) with DEEPSEEK_API_KEY env var and add model short names: deepseek-r1 → deepseek-reasoner, deepseek-v3 → deepseek-chat. * feat: add _flatten_message_content utility for list-to-string conversion Extract text from content block lists while skipping thinking/reasoning blocks. This handles the case where LangChain stores assistant messages with content as a list of content blocks instead of a plain string. * fix: flatten list content to strings for OpenAI-compatible providers Add _patch_openai_compat_content() that wraps _generate/_agenerate to sanitize message content before API calls. Apply it for all third-party OpenAI-compat providers and native OpenAI proxies. This fixes "invalid type: sequence, expected a string" errors from strict APIs like DeepSeek that reject list-format content in assistant messages during multi-turn conversations. * feat: add DeepSeek API key validation and integrate into onboarding process test: implement unit tests for content flattening utility in OpenAI-compatible providers --------- Co-authored-by: Xi Zhang <106144707+X-iZhang@users.noreply.github.com> Co-authored-by: X-iZhang <zacharyzhang2022@gmail.com>
793 lines
32 KiB
Python
793 lines
32 KiB
Python
"""Tests for EvoScientist LLM module."""
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from unittest.mock import patch
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from EvoScientist.llm import (
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DEFAULT_MODEL,
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MODELS,
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get_chat_model,
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get_model_info,
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get_models_for_provider,
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list_models,
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)
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from EvoScientist.llm.models import _MODEL_ENTRIES
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# =============================================================================
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# Test MODELS registry
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# =============================================================================
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class TestModelsRegistry:
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def test_models_is_dict(self):
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"""Test that MODELS is a dictionary."""
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assert isinstance(MODELS, dict)
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def test_entries_has_all_providers(self):
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"""Test that _MODEL_ENTRIES covers all registered providers."""
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providers = {p for _, _, p in _MODEL_ENTRIES}
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assert "anthropic" in providers
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assert "openai" in providers
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assert "google-genai" in providers
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assert "minimax" in providers
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assert "nvidia" in providers
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assert "siliconflow" in providers
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assert "openrouter" in providers
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assert "zhipu" in providers
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assert "zhipu-code" in providers
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assert "volcengine" in providers
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assert "dashscope" in providers
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assert "deepseek" in providers
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def test_entries_are_valid_tuples(self):
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"""Test that _MODEL_ENTRIES contains valid (name, model_id, provider) tuples."""
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valid_providers = {
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"anthropic",
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"openai",
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"google-genai",
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"minimax",
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"nvidia",
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"siliconflow",
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"openrouter",
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"zhipu",
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"zhipu-code",
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"volcengine",
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"dashscope",
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"custom-openai",
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"custom-anthropic",
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"deepseek",
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}
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for entry in _MODEL_ENTRIES:
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assert len(entry) == 3, f"Entry {entry} doesn't have 3 elements"
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name, model_id, provider = entry
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assert isinstance(name, str)
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assert isinstance(model_id, str)
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assert provider in valid_providers, (
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f"Unknown provider '{provider}' for '{name}'"
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)
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def test_get_models_for_provider(self):
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"""Test that get_models_for_provider returns correct models."""
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anthropic_models = get_models_for_provider("anthropic")
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assert len(anthropic_models) > 0
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for name, model_id in anthropic_models:
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assert isinstance(name, str)
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assert isinstance(model_id, str)
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# Third-party providers now have registered models
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openrouter_models = get_models_for_provider("openrouter")
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assert len(openrouter_models) > 0
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siliconflow_models = get_models_for_provider("siliconflow")
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assert len(siliconflow_models) > 0
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# =============================================================================
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# Test DEFAULT_MODEL
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# =============================================================================
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class TestDefaultModel:
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def test_default_model_exists_in_registry(self):
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"""Test that DEFAULT_MODEL is a valid model in MODELS."""
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assert DEFAULT_MODEL in MODELS
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def test_default_model_is_anthropic(self):
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"""Test that default model uses Anthropic."""
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_, provider = MODELS[DEFAULT_MODEL]
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assert provider == "anthropic"
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# =============================================================================
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# Test list_models
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# =============================================================================
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class TestListModels:
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def test_returns_list(self):
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"""Test that list_models returns a list."""
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result = list_models()
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assert isinstance(result, list)
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def test_returns_all_model_names(self):
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"""Test that list_models returns all model names."""
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result = list_models()
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assert set(result) == set(MODELS.keys())
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def test_list_is_not_empty(self):
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"""Test that the list is not empty."""
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assert len(list_models()) > 0
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# =============================================================================
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# Test get_model_info
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# =============================================================================
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class TestGetModelInfo:
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def test_returns_tuple_for_valid_model(self):
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"""Test that get_model_info returns tuple for valid model."""
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result = get_model_info("claude-sonnet-4-5")
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assert result is not None
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assert isinstance(result, tuple)
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assert len(result) == 2
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def test_returns_none_for_invalid_model(self):
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"""Test that get_model_info returns None for invalid model."""
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result = get_model_info("nonexistent-model")
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assert result is None
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def test_returns_correct_info(self):
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"""Test that get_model_info returns correct info."""
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model_id, provider = get_model_info("gpt-5-nano")
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assert model_id == "gpt-5-nano-2025-08-07"
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assert provider == "openai"
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# =============================================================================
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# Test get_chat_model
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# =============================================================================
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class TestGetChatModel:
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_uses_default_model_when_none(self, mock_init):
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"""Test that get_chat_model uses default model when model=None."""
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mock_init.return_value = "mock_model"
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get_chat_model()
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mock_init.assert_called_once()
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call_kwargs = mock_init.call_args[1]
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# Default model should be resolved from MODELS
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expected_model_id, expected_provider = MODELS[DEFAULT_MODEL]
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assert call_kwargs["model"] == expected_model_id
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assert call_kwargs["model_provider"] == expected_provider
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_resolves_short_name(self, mock_init):
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"""Test that get_chat_model resolves short names correctly."""
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mock_init.return_value = "mock_model"
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get_chat_model("claude-opus-4-5")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model"] == "claude-opus-4-5"
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assert call_kwargs["model_provider"] == "anthropic"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_resolves_openai_short_name(self, mock_init):
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"""Test that get_chat_model resolves OpenAI short names."""
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mock_init.return_value = "mock_model"
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get_chat_model("gpt-5-mini")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model"] == "gpt-5-mini-2025-08-07"
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assert call_kwargs["model_provider"] == "openai"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_uses_full_model_id(self, mock_init):
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"""Test that get_chat_model accepts full model IDs."""
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mock_init.return_value = "mock_model"
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get_chat_model("claude-3-opus-20240229")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model"] == "claude-3-opus-20240229"
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# Should infer anthropic from the model prefix
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assert call_kwargs["model_provider"] == "anthropic"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_provider_override(self, mock_init):
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"""Test that provider can be overridden."""
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mock_init.return_value = "mock_model"
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get_chat_model("claude-sonnet-4-5", provider="custom_provider")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "custom_provider"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_passes_kwargs(self, mock_init):
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"""Test that additional kwargs are passed through."""
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mock_init.return_value = "mock_model"
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get_chat_model("gpt-5-nano", temperature=0.7, max_tokens=1000)
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["temperature"] == 0.7
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assert call_kwargs["max_tokens"] == 1000
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_infers_openai_from_gpt_prefix(self, mock_init):
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"""Test that OpenAI is inferred from gpt- prefix."""
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mock_init.return_value = "mock_model"
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get_chat_model("gpt-4-turbo-preview")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "openai"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_infers_openai_from_o1_prefix(self, mock_init):
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"""Test that OpenAI is inferred from o1 prefix."""
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mock_init.return_value = "mock_model"
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get_chat_model("o1-preview")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "openai"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_infers_google_from_gemini_prefix(self, mock_init):
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"""Test that google-genai is inferred from gemini prefix."""
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mock_init.return_value = "mock_model"
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get_chat_model("gemini-2.0-flash")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "google-genai"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_defaults_to_anthropic_for_unknown(self, mock_init):
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"""Test that anthropic is default for unknown model prefixes."""
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mock_init.return_value = "mock_model"
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get_chat_model("some-unknown-model")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "anthropic"
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# =============================================================================
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# Test Ollama provider
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# =============================================================================
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class TestOllamaProvider:
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"""Ollama models are not in the static registry (detected dynamically).
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All tests use explicit provider or ollama: prefix."""
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_explicit_provider(self, mock_init):
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"""Test that explicit provider='ollama' routes correctly."""
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mock_init.return_value = "mock_model"
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get_chat_model("llama3.1:8b", provider="ollama")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model"] == "llama3.1:8b"
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assert call_kwargs["model_provider"] == "ollama"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_ollama_base_url_passthrough(self, mock_init, monkeypatch):
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"""Test that OLLAMA_BASE_URL env var is passed to kwargs."""
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mock_init.return_value = "mock_model"
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monkeypatch.setenv("OLLAMA_BASE_URL", "http://gpu-cluster:11434")
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get_chat_model("llama3.1:8b", provider="ollama")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["base_url"] == "http://gpu-cluster:11434"
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assert call_kwargs["model_provider"] == "ollama"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_ollama_no_base_url_when_unset(self, mock_init, monkeypatch):
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"""Test that base_url is not set when OLLAMA_BASE_URL is empty."""
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mock_init.return_value = "mock_model"
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monkeypatch.delenv("OLLAMA_BASE_URL", raising=False)
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get_chat_model("llama3.1:8b", provider="ollama")
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call_kwargs = mock_init.call_args[1]
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assert "base_url" not in call_kwargs
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_reasoning_auto_enabled_for_ollama(self, mock_init, monkeypatch):
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"""Test that reasoning is auto-enabled for Ollama models."""
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mock_init.return_value = "mock_model"
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monkeypatch.delenv("OLLAMA_BASE_URL", raising=False)
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get_chat_model("llama3.1:8b", provider="ollama")
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call_kwargs = mock_init.call_args[1]
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assert "thinking" not in call_kwargs
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assert call_kwargs["reasoning"] is True
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_reasoning_not_overridden_for_ollama(self, mock_init, monkeypatch):
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"""Test that explicit reasoning=False is not overridden for Ollama."""
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mock_init.return_value = "mock_model"
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monkeypatch.delenv("OLLAMA_BASE_URL", raising=False)
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get_chat_model("llama3.1:8b", provider="ollama", reasoning=False)
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["reasoning"] is False
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def test_no_static_registry_entries(self):
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"""Test that Ollama has no static registry entries (models detected dynamically)."""
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ollama_models = get_models_for_provider("ollama")
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assert len(ollama_models) == 0
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_ollama_prefix_inference(self, mock_init, monkeypatch):
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"""Test that ollama: prefix infers ollama provider."""
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mock_init.return_value = "mock_model"
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monkeypatch.delenv("OLLAMA_BASE_URL", raising=False)
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get_chat_model("ollama:phi3:mini")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model"] == "phi3:mini"
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assert call_kwargs["model_provider"] == "ollama"
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# =============================================================================
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# Test slash model ID no longer routes to nvidia
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# =============================================================================
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class TestSlashModelIdFallback:
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_slash_model_id_defaults_to_anthropic(self, mock_init):
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"""Unregistered model IDs containing '/' should NOT route to nvidia.
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They fall through to the default 'anthropic' provider, consistent
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with how all other unknown model IDs are handled.
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"""
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mock_init.return_value = "mock_model"
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get_chat_model("some-org/some-model")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model"] == "some-org/some-model"
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assert call_kwargs["model_provider"] == "anthropic"
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# =============================================================================
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# Test third-party provider routing
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# =============================================================================
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class TestThirdPartyRouting:
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_siliconflow_routes_through_openai(self, mock_init, monkeypatch):
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"""SiliconFlow provider should route through OpenAI with correct base_url."""
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mock_init.return_value = "mock_model"
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monkeypatch.setenv("SILICONFLOW_API_KEY", "sf-key-123")
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get_chat_model("Pro/zai-org/GLM-5", provider="siliconflow")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "openai"
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assert call_kwargs["base_url"] == "https://api.siliconflow.cn/v1"
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assert call_kwargs["api_key"] == "sf-key-123"
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# SiliconFlow should disable thinking
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assert call_kwargs["extra_body"]["enable_thinking"] is False
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_openrouter_routes_through_openai(self, mock_init, monkeypatch):
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"""OpenRouter provider should route through OpenAI with correct base_url."""
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mock_init.return_value = "mock_model"
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monkeypatch.setenv("OPENROUTER_API_KEY", "or-key-456")
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get_chat_model("x-ai/grok-4.1-fast", provider="openrouter")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "openai"
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assert call_kwargs["base_url"] == "https://openrouter.ai/api/v1"
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assert call_kwargs["api_key"] == "or-key-456"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_custom_routes_through_openai(self, mock_init, monkeypatch):
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"""Custom provider should route through OpenAI with env-configured base_url."""
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mock_init.return_value = "mock_model"
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monkeypatch.setenv("CUSTOM_OPENAI_BASE_URL", "https://my-llm.example.com/v1")
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monkeypatch.setenv("CUSTOM_OPENAI_API_KEY", "custom-key-789")
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get_chat_model("my-custom-model", provider="custom-openai")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "openai"
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assert call_kwargs["base_url"] == "https://my-llm.example.com/v1"
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assert call_kwargs["api_key"] == "custom-key-789"
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_anthropic_base_url_override(self, mock_init, monkeypatch):
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"""Anthropic provider should support base_url override (e.g. ccproxy)."""
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mock_init.return_value = "mock_model"
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monkeypatch.setenv("ANTHROPIC_BASE_URL", "http://localhost:8000/api/v1")
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monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-dummy")
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get_chat_model("claude-sonnet-4-6", provider="anthropic")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "anthropic"
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assert call_kwargs["base_url"] == "http://localhost:8000/api/v1"
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assert call_kwargs["api_key"] == "sk-dummy"
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# Proxy mode: thinking skipped for 4-6 models (ccproxy manages it)
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assert "thinking" not in call_kwargs
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_anthropic_no_base_url_when_unset(self, mock_init, monkeypatch):
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"""Anthropic provider should not set base_url when env var is empty."""
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mock_init.return_value = "mock_model"
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monkeypatch.delenv("ANTHROPIC_BASE_URL", raising=False)
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monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-real")
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get_chat_model("claude-sonnet-4-6", provider="anthropic")
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call_kwargs = mock_init.call_args[1]
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assert call_kwargs["model_provider"] == "anthropic"
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assert "base_url" not in call_kwargs
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@patch("EvoScientist.llm.models.init_chat_model")
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def test_third_party_no_reasoning(self, mock_init, monkeypatch):
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"""Third-party providers routed through OpenAI should NOT get auto-reasoning."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
|
|
|
|
get_chat_model("x-ai/grok-4.1-fast", provider="openrouter")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert "reasoning" not in call_kwargs
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_volcengine_routes_through_openai(self, mock_init, monkeypatch):
|
|
"""Volcengine provider should route through OpenAI with correct base_url."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("VOLCENGINE_API_KEY", "ve-key-123")
|
|
|
|
get_chat_model("doubao-seed-1.6", provider="volcengine")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["model_provider"] == "openai"
|
|
assert call_kwargs["base_url"] == "https://ark.cn-beijing.volces.com/api/v3"
|
|
assert call_kwargs["api_key"] == "ve-key-123"
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_dashscope_routes_through_openai(self, mock_init, monkeypatch):
|
|
"""DashScope provider should route through OpenAI with correct base_url."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("DASHSCOPE_API_KEY", "ds-key-456")
|
|
|
|
get_chat_model("qwen-max", provider="dashscope")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["model_provider"] == "openai"
|
|
assert (
|
|
call_kwargs["base_url"]
|
|
== "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
|
)
|
|
assert call_kwargs["api_key"] == "ds-key-456"
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_minimax_routes_through_anthropic(self, mock_init, monkeypatch):
|
|
"""MiniMax provider should route through Anthropic with correct base_url."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("MINIMAX_API_KEY", "mm-key-123")
|
|
|
|
get_chat_model("MiniMax-M2.5", provider="minimax")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["model_provider"] == "anthropic"
|
|
assert call_kwargs["base_url"] == "https://api.minimaxi.com/anthropic"
|
|
assert call_kwargs["api_key"] == "mm-key-123"
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_minimax_gets_thinking(self, mock_init, monkeypatch):
|
|
"""MiniMax provider should get auto-thinking (thinking-capable via Anthropic)."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("MINIMAX_API_KEY", "mm-key")
|
|
|
|
get_chat_model("MiniMax-M2.5", provider="minimax")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert "thinking" in call_kwargs
|
|
assert "reasoning" not in call_kwargs
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_minimax_short_name_resolution(self, mock_init, monkeypatch):
|
|
"""MiniMax short names should resolve to correct model IDs."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("MINIMAX_API_KEY", "mm-key")
|
|
|
|
get_chat_model("minimax-m2.5", provider="minimax")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["model"] == "MiniMax-M2.5"
|
|
assert call_kwargs["model_provider"] == "anthropic"
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_minimax_highspeed_model(self, mock_init, monkeypatch):
|
|
"""MiniMax M2.5-highspeed model should resolve correctly."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("MINIMAX_API_KEY", "mm-key")
|
|
|
|
get_chat_model("minimax-m2.5-highspeed", provider="minimax")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["model"] == "MiniMax-M2.5-highspeed"
|
|
assert call_kwargs["model_provider"] == "anthropic"
|
|
assert call_kwargs["base_url"] == "https://api.minimaxi.com/anthropic"
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_custom_anthropic_via_routed_dict(self, mock_init, monkeypatch):
|
|
"""custom-anthropic should work via _ANTHROPIC_ROUTED_PROVIDERS dict."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("CUSTOM_ANTHROPIC_BASE_URL", "https://my-claude.example.com")
|
|
monkeypatch.setenv("CUSTOM_ANTHROPIC_API_KEY", "ca-key-789")
|
|
|
|
get_chat_model("claude-sonnet-4-6", provider="custom-anthropic")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["model_provider"] == "anthropic"
|
|
assert call_kwargs["base_url"] == "https://my-claude.example.com"
|
|
assert call_kwargs["api_key"] == "ca-key-789"
|
|
# custom-anthropic is NOT thinking-capable → thinking skipped
|
|
assert "thinking" not in call_kwargs
|
|
|
|
|
|
# =============================================================================
|
|
# Test MiniMax provider
|
|
# =============================================================================
|
|
|
|
|
|
class TestMiniMaxProvider:
|
|
def test_minimax_in_anthropic_routed_providers(self):
|
|
"""MiniMax should be registered in _ANTHROPIC_ROUTED_PROVIDERS."""
|
|
from EvoScientist.llm.models import _ANTHROPIC_ROUTED_PROVIDERS
|
|
|
|
assert "minimax" in _ANTHROPIC_ROUTED_PROVIDERS
|
|
base_url, api_key_env = _ANTHROPIC_ROUTED_PROVIDERS["minimax"]
|
|
assert base_url == "https://api.minimaxi.com/anthropic"
|
|
assert api_key_env == "MINIMAX_API_KEY"
|
|
|
|
def test_minimax_not_in_openai_routed_providers(self):
|
|
"""MiniMax should NOT be in _OPENAI_ROUTED_PROVIDERS (moved to Anthropic)."""
|
|
from EvoScientist.llm.models import _OPENAI_ROUTED_PROVIDERS
|
|
|
|
assert "minimax" not in _OPENAI_ROUTED_PROVIDERS
|
|
|
|
def test_minimax_models_registered(self):
|
|
"""MiniMax should have 4 direct model entries in _MODEL_ENTRIES."""
|
|
minimax_models = get_models_for_provider("minimax")
|
|
assert len(minimax_models) == 4
|
|
model_names = {name for name, _ in minimax_models}
|
|
assert "minimax-m2.7" in model_names
|
|
assert "minimax-m2.7-highspeed" in model_names
|
|
assert "minimax-m2.5" in model_names
|
|
assert "minimax-m2.5-highspeed" in model_names
|
|
|
|
def test_minimax_model_ids_correct(self):
|
|
"""MiniMax model IDs should match the official API model names."""
|
|
minimax_models = get_models_for_provider("minimax")
|
|
model_dict = dict(minimax_models)
|
|
assert model_dict["minimax-m2.7"] == "MiniMax-M2.7"
|
|
assert model_dict["minimax-m2.5"] == "MiniMax-M2.5"
|
|
assert model_dict["minimax-m2.5-highspeed"] == "MiniMax-M2.5-highspeed"
|
|
|
|
def test_minimax_short_name_in_models_dict(self):
|
|
"""MiniMax short names should be accessible via the MODELS dict."""
|
|
# Note: MODELS dict uses last-entry-wins, so direct minimax entries
|
|
# may be overridden by nvidia/siliconflow/openrouter entries.
|
|
# Use get_models_for_provider() for provider-specific lookups.
|
|
minimax_models = get_models_for_provider("minimax")
|
|
assert len(minimax_models) > 0
|
|
|
|
|
|
# =============================================================================
|
|
# Test _flatten_message_content
|
|
# =============================================================================
|
|
|
|
|
|
class TestFlattenMessageContent:
|
|
"""Tests for the content-flattening utility used by OpenAI-compatible providers."""
|
|
|
|
def test_string_passthrough(self):
|
|
from EvoScientist.llm.models import _flatten_message_content
|
|
|
|
assert _flatten_message_content("hello") == "hello"
|
|
|
|
def test_non_list_passthrough(self):
|
|
from EvoScientist.llm.models import _flatten_message_content
|
|
|
|
assert _flatten_message_content(42) == 42
|
|
assert _flatten_message_content(None) is None
|
|
|
|
def test_text_blocks(self):
|
|
from EvoScientist.llm.models import _flatten_message_content
|
|
|
|
content = [
|
|
{"type": "text", "text": "Hello"},
|
|
{"type": "text", "text": "World"},
|
|
]
|
|
assert _flatten_message_content(content) == "Hello\n\nWorld"
|
|
|
|
def test_skips_thinking_blocks(self):
|
|
from EvoScientist.llm.models import _flatten_message_content
|
|
|
|
content = [
|
|
{"type": "thinking", "text": "Let me think..."},
|
|
{"type": "text", "text": "The answer is 42"},
|
|
{"type": "reasoning", "text": "internal reasoning"},
|
|
{"type": "reasoning_content", "text": "more reasoning"},
|
|
]
|
|
assert _flatten_message_content(content) == "The answer is 42"
|
|
|
|
def test_string_blocks(self):
|
|
from EvoScientist.llm.models import _flatten_message_content
|
|
|
|
content = ["hello", "world"]
|
|
assert _flatten_message_content(content) == "hello\n\nworld"
|
|
|
|
def test_mixed_blocks(self):
|
|
from EvoScientist.llm.models import _flatten_message_content
|
|
|
|
content = [
|
|
{"type": "thinking", "text": "skip me"},
|
|
"plain string",
|
|
{"type": "text", "text": "dict text"},
|
|
]
|
|
assert _flatten_message_content(content) == "plain string\n\ndict text"
|
|
|
|
def test_empty_list(self):
|
|
from EvoScientist.llm.models import _flatten_message_content
|
|
|
|
assert _flatten_message_content([]) == ""
|
|
|
|
def test_only_thinking_blocks(self):
|
|
from EvoScientist.llm.models import _flatten_message_content
|
|
|
|
content = [{"type": "thinking", "text": "thought"}]
|
|
assert _flatten_message_content(content) == ""
|
|
|
|
|
|
# =============================================================================
|
|
# Test _apply_auto_config
|
|
# =============================================================================
|
|
|
|
|
|
class TestAutoConfig:
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_anthropic_4_5_thinking(self, mock_init, monkeypatch):
|
|
"""Anthropic 4-5 models get enabled thinking with budget."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.delenv("ANTHROPIC_BASE_URL", raising=False)
|
|
|
|
get_chat_model("claude-sonnet-4-5")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["thinking"] == {"type": "enabled", "budget_tokens": 10000}
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_anthropic_4_6_adaptive_thinking(self, mock_init, monkeypatch):
|
|
"""Anthropic 4-6 models get adaptive thinking with max effort."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.delenv("ANTHROPIC_BASE_URL", raising=False)
|
|
|
|
get_chat_model("claude-sonnet-4-6")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["thinking"] == {"type": "adaptive"}
|
|
assert call_kwargs["effort"] == "max"
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_anthropic_4_6_proxy_no_thinking(self, mock_init, monkeypatch):
|
|
"""Anthropic 4-6 models via proxy skip thinking (ccproxy manages it)."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("ANTHROPIC_BASE_URL", "http://127.0.0.1:8000")
|
|
monkeypatch.setenv("ANTHROPIC_API_KEY", "ccproxy-oauth")
|
|
|
|
get_chat_model("claude-sonnet-4-6")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert "thinking" not in call_kwargs
|
|
assert "effort" not in call_kwargs
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_anthropic_4_5_proxy_no_thinking(self, mock_init, monkeypatch):
|
|
"""Anthropic 4-5 models via proxy also skip thinking."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("ANTHROPIC_BASE_URL", "http://127.0.0.1:8000")
|
|
monkeypatch.setenv("ANTHROPIC_API_KEY", "ccproxy-oauth")
|
|
|
|
get_chat_model("claude-sonnet-4-5")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert "thinking" not in call_kwargs
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_anthropic_4_6_no_proxy_no_downgrade(self, mock_init, monkeypatch):
|
|
"""Anthropic 4-6 models without proxy still get adaptive thinking."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("ANTHROPIC_BASE_URL", "https://api.anthropic.com")
|
|
monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-real")
|
|
|
|
get_chat_model("claude-sonnet-4-6")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["thinking"] == {"type": "adaptive"}
|
|
assert call_kwargs["effort"] == "max"
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_anthropic_thinking_not_overridden(self, mock_init):
|
|
"""User-supplied thinking config should not be overridden."""
|
|
mock_init.return_value = "mock_model"
|
|
custom_thinking = {"type": "enabled", "budget_tokens": 500}
|
|
|
|
get_chat_model("claude-sonnet-4-6", thinking=custom_thinking)
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["thinking"] == custom_thinking
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_openai_reasoning(self, mock_init, monkeypatch):
|
|
"""Native OpenAI models get auto-reasoning."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
|
|
|
|
get_chat_model("gpt-5-nano")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["reasoning"] == {"effort": "high", "summary": "auto"}
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_openai_base_url_override(self, mock_init, monkeypatch):
|
|
"""OpenAI provider should support base_url override (e.g. ccproxy Codex)."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.setenv("OPENAI_BASE_URL", "http://127.0.0.1:8000/codex/v1")
|
|
monkeypatch.setenv("OPENAI_API_KEY", "ccproxy-oauth")
|
|
|
|
get_chat_model("gpt-5-nano", provider="openai")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["model_provider"] == "openai"
|
|
assert call_kwargs["base_url"] == "http://127.0.0.1:8000/codex/v1"
|
|
assert call_kwargs["api_key"] == "ccproxy-oauth"
|
|
# Proxy mode: reasoning skipped (triggers Responses API → rs_ 404)
|
|
assert "reasoning" not in call_kwargs
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_openai_no_base_url_when_unset(self, mock_init, monkeypatch):
|
|
"""OpenAI provider should not set base_url when env var is empty."""
|
|
mock_init.return_value = "mock_model"
|
|
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
|
|
monkeypatch.setenv("OPENAI_API_KEY", "sk-real")
|
|
|
|
get_chat_model("gpt-5-nano", provider="openai")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["model_provider"] == "openai"
|
|
assert "base_url" not in call_kwargs
|
|
|
|
@patch("EvoScientist.llm.models.init_chat_model")
|
|
def test_google_thoughts(self, mock_init):
|
|
"""Google GenAI models get include_thoughts=True by default."""
|
|
mock_init.return_value = "mock_model"
|
|
|
|
get_chat_model("gemini-2.5-flash")
|
|
|
|
call_kwargs = mock_init.call_args[1]
|
|
assert call_kwargs["include_thoughts"] is True
|