初始化提交
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"""Tests for the parameter handler mechanism (Task 4 extension)."""
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from __future__ import annotations
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import pytest
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from app.ai_provider.config.models import ModelConfig, ParameterMapping, ProviderConfig
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from app.ai_provider.pipeline import PipelineContext, PipelineFactory
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from app.ai_provider.pipeline_handlers import ParameterHandlerRegistry
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class TestParameterHandlerRegistry:
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"""Test the handler registry itself."""
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def test_register_and_get(self):
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@ParameterHandlerRegistry.register("test_handler_1")
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def my_handler(_ctx, _result, _param_key=None):
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pass
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handler = ParameterHandlerRegistry.get("test_handler_1")
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assert handler is my_handler
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def test_get_unknown_handler_raises(self):
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with pytest.raises(KeyError, match="Unknown parameter handler"):
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ParameterHandlerRegistry.get("nonexistent_handler")
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def test_list_handlers(self):
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handlers = ParameterHandlerRegistry.list()
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assert "anthropic_thinking" in handlers
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assert "validate_max_tokens" in handlers
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assert "strip_unsupported_capability_params" in handlers
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class TestAnthropicThinkingHandler:
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"""Test the anthropic_thinking handler via the pipeline."""
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def test_thinking_true_converts_to_dict(self):
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"""标量 thinking=True → {type: enabled}"""
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provider = ProviderConfig(
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name="anthropic",
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format="anthropic",
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base_url_env="ANTHROPIC_BASE_URL",
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api_key_env="ANTHROPIC_API_KEY",
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parameter_mappings={
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"thinking": ParameterMapping(
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provider_param="thinking",
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handler="anthropic_thinking",
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),
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"max_tokens": ParameterMapping(provider_param="max_tokens"),
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},
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)
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pipeline = PipelineFactory.create(provider)
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ctx = PipelineContext(params={"thinking": True, "max_tokens": 4096})
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result = pipeline.run(ctx)
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assert result.params["thinking"] == {"type": "enabled"}
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assert result.params["max_tokens"] == 4096
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def test_thinking_budget_enforces_max_tokens(self):
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"""thinking.budget_tokens 确保 max_tokens > budget_tokens"""
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provider = ProviderConfig(
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name="anthropic",
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format="anthropic",
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base_url_env="X",
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api_key_env="Y",
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parameter_mappings={
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"thinking": ParameterMapping(
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provider_param="thinking",
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handler="anthropic_thinking",
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),
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"max_tokens": ParameterMapping(provider_param="max_tokens"),
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},
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)
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pipeline = PipelineFactory.create(provider)
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# budget=8192, max_tokens=4096 → 不满足，应被抬到 8193
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ctx = PipelineContext(params={"thinking": {"budget_tokens": 8192}, "max_tokens": 4096})
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result = pipeline.run(ctx)
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assert result.params["max_tokens"] >= 8193
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def test_thinking_removes_temperature(self):
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"""开启 thinking 时 temperature 被移除"""
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provider = ProviderConfig(
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name="anthropic",
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format="anthropic",
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base_url_env="X",
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api_key_env="Y",
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parameter_mappings={
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"thinking": ParameterMapping(
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provider_param="thinking",
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handler="anthropic_thinking",
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),
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"temperature": ParameterMapping(
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provider_param="temperature",
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min=0,
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max=1,
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),
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"max_tokens": ParameterMapping(provider_param="max_tokens"),
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},
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)
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pipeline = PipelineFactory.create(provider)
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ctx = PipelineContext(params={"thinking": True, "temperature": 0.7, "max_tokens": 4096})
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result = pipeline.run(ctx)
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assert "temperature" not in result.params
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class TestValidateMaxTokensHandler:
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"""Test the validate_max_tokens handler."""
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def test_clamps_to_model_limit(self):
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"""max_tokens 应被裁剪到模型上限"""
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model = ModelConfig(
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name="test-model",
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provider="anthropic",
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model="claude-sonnet-4-6",
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format="anthropic",
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max_output_tokens=16000,
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)
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provider = ProviderConfig(
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name="anthropic",
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format="anthropic",
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base_url_env="X",
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api_key_env="Y",
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parameter_mappings={
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"max_tokens": ParameterMapping(provider_param="max_tokens"),
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},
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parameter_handlers=["validate_max_tokens"],
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)
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pipeline = PipelineFactory.create(provider)
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ctx = PipelineContext(
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params={"max_tokens": 999999},
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model_config=model,
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provider_config=provider,
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)
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result = pipeline.run(ctx)
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assert result.params["max_tokens"] == 16000
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def test_within_limit_unchanged(self):
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"""max_tokens 在合理范围内时不变"""
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model = ModelConfig(
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name="test-model",
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provider="anthropic",
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model="claude-sonnet-4-6",
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format="anthropic",
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max_output_tokens=16000,
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)
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provider = ProviderConfig(
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name="anthropic",
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format="anthropic",
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base_url_env="X",
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api_key_env="Y",
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parameter_mappings={
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"max_tokens": ParameterMapping(provider_param="max_tokens"),
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},
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parameter_handlers=["validate_max_tokens"],
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)
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pipeline = PipelineFactory.create(provider)
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ctx = PipelineContext(
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params={"max_tokens": 8000},
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model_config=model,
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provider_config=provider,
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)
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result = pipeline.run(ctx)
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assert result.params["max_tokens"] == 8000
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class TestStripUnsupportedCapabilityParams:
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"""Test the strip_unsupported_capability_params handler."""
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def test_strips_image_params_when_no_vision(self):
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"""模型不支持 vision 时移除 image_url"""
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model = ModelConfig(
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name="test-model",
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provider="openai",
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model="gpt-4o",
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format="openai",
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supports_vision=False,
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)
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provider = ProviderConfig(
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name="openai",
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format="openai",
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base_url_env="X",
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api_key_env="Y",
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parameter_mappings={
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"max_tokens": ParameterMapping(provider_param="max_tokens"),
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"image_url": ParameterMapping(provider_param="image_url"),
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},
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parameter_handlers=["strip_unsupported_capability_params"],
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)
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pipeline = PipelineFactory.create(provider)
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ctx = PipelineContext(
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params={"max_tokens": 100, "image_url": "http://example.com/img.png"},
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model_config=model,
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provider_config=provider,
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)
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result = pipeline.run(ctx)
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assert "image_url" not in result.params
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assert "max_tokens" in result.params
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def test_keeps_image_params_when_vision_supported(self):
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"""模型支持 vision 时保留 image_url"""
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model = ModelConfig(
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name="test-model",
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provider="openai",
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model="gpt-4o",
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format="openai",
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supports_vision=True,
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)
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provider = ProviderConfig(
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name="openai",
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format="openai",
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base_url_env="X",
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api_key_env="Y",
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parameter_mappings={
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"max_tokens": ParameterMapping(provider_param="max_tokens"),
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"image_url": ParameterMapping(provider_param="image_url"),
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},
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parameter_handlers=["strip_unsupported_capability_params"],
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)
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pipeline = PipelineFactory.create(provider)
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ctx = PipelineContext(
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params={"max_tokens": 100, "image_url": "http://example.com/img.png"},
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model_config=model,
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provider_config=provider,
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)
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result = pipeline.run(ctx)
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assert "image_url" in result.params
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def test_strips_thinking_when_not_supported(self):
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"""模型不支持 thinking 时移除 thinking 参数"""
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model = ModelConfig(
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name="test-model",
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provider="openai",
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model="gpt-4o",
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format="openai",
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supports_thinking=False,
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)
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provider = ProviderConfig(
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name="openai",
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format="openai",
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base_url_env="X",
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api_key_env="Y",
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parameter_mappings={
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"thinking": ParameterMapping(provider_param="thinking"),
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},
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parameter_handlers=["strip_unsupported_capability_params"],
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)
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pipeline = PipelineFactory.create(provider)
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ctx = PipelineContext(
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params={"thinking": True},
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model_config=model,
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provider_config=provider,
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)
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result = pipeline.run(ctx)
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assert "thinking" not in result.params
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class TestHandlerIntegration:
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"""Test full pipeline integration with handlers."""
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def test_anthropic_pipeline_end_to_end(self):
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"""模拟完整的 Anthropic pipeline 流程"""
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provider = ProviderConfig(
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name="anthropic",
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format="anthropic",
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base_url_env="ANTHROPIC_BASE_URL",
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api_key_env="ANTHROPIC_API_KEY",
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parameter_mappings={
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"max_tokens": ParameterMapping(provider_param="max_tokens", default=4096),
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"thinking": ParameterMapping(
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provider_param="thinking", handler="anthropic_thinking"
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),
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"temperature": ParameterMapping(
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provider_param="temperature", min=0, max=1, default=1.0
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),
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},
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parameter_handlers=[
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"strip_unsupported_capability_params",
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"validate_max_tokens",
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],
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)
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model = ModelConfig(
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name="claude-sonnet",
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provider="anthropic",
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model="claude-sonnet-4-6",
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format="anthropic",
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supports_vision=True,
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supports_thinking=True,
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max_output_tokens=16000,
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)
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pipeline = PipelineFactory.create(provider)
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ctx = PipelineContext(
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params={
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"thinking": {"budget_tokens": 8000},
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"temperature": 0.7,
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},
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model_config=model,
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provider_config=provider,
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)
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result = pipeline.run(ctx)
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# thinking → provider_param name
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assert "thinking" in result.params
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# max_tokens injected via default, then lifted by handler
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assert result.params["max_tokens"] >= 8001
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# temperature removed by handler
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assert "temperature" not in result.params
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