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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.llm.anthropic_format import AnthropicFormatAdapter
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from app.ai_provider.types.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
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class TestAnthropicMessageConversion:
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"""Conversion of internal message types to Anthropic content-block format."""
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@pytest.fixture
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def adapter(self):
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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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"temperature": ParameterMapping(provider_param="temperature", min=0, max=1),
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"max_tokens": ParameterMapping(provider_param="max_tokens"),
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"top_p": ParameterMapping(provider_param="top_p", min=0, max=1),
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},
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)
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return AnthropicFormatAdapter(provider_config=provider)
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def test_convert_human_message(self, adapter):
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"""HumanMessage becomes user role with a text content block."""
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msg = HumanMessage(content="Hello!")
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result = adapter._convert_message(msg)
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assert result["role"] == "user"
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assert result["content"] == [{"type": "text", "text": "Hello!"}]
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def test_convert_ai_message(self, adapter):
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"""AIMessage becomes assistant role with a text content block."""
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msg = AIMessage(content="Sure, let me help.")
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result = adapter._convert_message(msg)
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assert result["role"] == "assistant"
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assert result["content"] == [{"type": "text", "text": "Sure, let me help."}]
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def test_convert_ai_message_replays_raw_content_blocks(self, adapter):
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blocks = [
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{"type": "thinking", "thinking": "summary", "signature": "sig_1"},
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{"type": "tool_use", "id": "call_1", "name": "read_file", "input": {}},
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]
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msg = AIMessage(content="", raw_content_blocks=blocks)
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assert adapter._convert_message(msg) == {"role": "assistant", "content": blocks}
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def test_convert_ai_message_with_tool_calls(self, adapter):
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"""AIMessage with tool_calls produces tool_use content blocks."""
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msg = AIMessage(
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content="",
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tool_calls=[
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{"name": "read_file", "args": {"path": "x.txt"}, "id": "call_1"},
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],
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)
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result = adapter._convert_message(msg)
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assert result["role"] == "assistant"
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assert result["content"] == [
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{"type": "tool_use", "id": "call_1", "name": "read_file", "input": {"path": "x.txt"}},
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]
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def test_convert_ai_message_with_text_and_tool_calls(self, adapter):
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"""AIMessage with both text and tool_calls produces multiple content blocks."""
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msg = AIMessage(
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content="I will check that file.",
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tool_calls=[
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{"name": "read_file", "args": {"path": "x.txt"}, "id": "call_1"},
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{"name": "list_dir", "args": {"path": "/tmp"}, "id": "call_2"},
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],
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)
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result = adapter._convert_message(msg)
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assert result["role"] == "assistant"
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assert len(result["content"]) == 3
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assert result["content"][0] == {"type": "text", "text": "I will check that file."}
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assert result["content"][1] == {
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"type": "tool_use",
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"id": "call_1",
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"name": "read_file",
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"input": {"path": "x.txt"},
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}
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assert result["content"][2] == {
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"type": "tool_use",
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"id": "call_2",
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"name": "list_dir",
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"input": {"path": "/tmp"},
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}
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def test_convert_tool_message(self, adapter):
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"""ToolMessage becomes user role with a tool_result content block."""
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msg = ToolMessage(content="File contents", tool_call_id="call_1")
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result = adapter._convert_message(msg)
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assert result["role"] == "user"
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assert result["content"] == [
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{"type": "tool_result", "tool_use_id": "call_1", "content": "File contents"},
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]
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class TestAnthropicRequestBuilding:
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"""Building the full request body for the Anthropic API."""
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@pytest.fixture
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def adapter(self):
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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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"temperature": ParameterMapping(provider_param="temperature", min=0, max=1),
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"max_tokens": ParameterMapping(provider_param="max_tokens"),
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},
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)
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return AnthropicFormatAdapter(provider_config=provider)
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@pytest.fixture
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def model(self):
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return ModelConfig(
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name="claude-sonnet-4",
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provider="anthropic",
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model="claude-sonnet-4-20250514",
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format="anthropic",
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max_output_tokens=8192,
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)
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def test_build_request_body(self, adapter, model):
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"""Request body includes model, messages, and pipeline-processed params."""
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messages = [HumanMessage(content="Hi")]
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body = adapter.build_request(
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model=model,
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messages=messages,
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params={"temperature": 0.7, "max_tokens": 4096},
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)
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assert body["model"] == "claude-sonnet-4-20250514"
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assert body["temperature"] == 0.7
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assert body["max_tokens"] == 4096
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assert len(body["messages"]) == 1
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assert body["messages"][0]["role"] == "user"
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def test_build_request_with_system_message(self, adapter, model):
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"""System message is placed in top-level 'system' field, not in messages."""
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messages = [
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SystemMessage(content="You are a helpful assistant."),
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HumanMessage(content="Hi"),
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]
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body = adapter.build_request(model=model, messages=messages)
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assert body["system"] == "You are a helpful assistant."
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# system message is removed from the messages array
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assert len(body["messages"]) == 1
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assert body["messages"][0]["role"] == "user"
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def test_build_request_with_multiple_system_messages(self, adapter, model):
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"""Multiple system messages are concatenated with newlines."""
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messages = [
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SystemMessage(content="Be concise."),
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SystemMessage(content="Use Chinese."),
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HumanMessage(content="Hi"),
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]
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body = adapter.build_request(model=model, messages=messages)
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assert body["system"] == "Be concise.\nUse Chinese."
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assert len(body["messages"]) == 1
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def test_build_request_sets_default_max_tokens(self, adapter, model):
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"""max_tokens defaults to model.max_output_tokens when not in params."""
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messages = [HumanMessage(content="Hi")]
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body = adapter.build_request(model=model, messages=messages, params={})
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assert body["max_tokens"] == 8192
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def test_build_request_with_tools(self, adapter, model):
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"""Tools are converted to Anthropic format in the request."""
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messages = [HumanMessage(content="Read file x.txt")]
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openai_tools = [
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{
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"type": "function",
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"function": {
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"name": "read_file",
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"description": "Read a file's contents",
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"parameters": {
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"type": "object",
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"properties": {"path": {"type": "string"}},
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"required": ["path"],
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},
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},
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},
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]
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body = adapter.build_request(
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model=model,
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messages=messages,
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tools=openai_tools,
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tool_choice="auto",
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)
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assert "tools" in body
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assert len(body["tools"]) == 1
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anthropic_tool = body["tools"][0]
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assert anthropic_tool["name"] == "read_file"
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assert anthropic_tool["description"] == "Read a file's contents"
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assert anthropic_tool["input_schema"] == {
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"type": "object",
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"properties": {"path": {"type": "string"}},
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"required": ["path"],
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}
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assert body["tool_choice"] == "auto"
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def test_build_request_streaming(self, adapter, model):
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"""Streaming flag is passed through in the request."""
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messages = [HumanMessage(content="Hi")]
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body = adapter.build_request(model=model, messages=messages, stream=True)
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assert body["stream"] is True
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class TestAnthropicToolConversion:
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"""Converting tools between OpenAI and Anthropic formats."""
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def test_convert_tools_standard(self):
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"""OpenAI format tools are converted to Anthropic format."""
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tools = [
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{
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"type": "function",
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"function": {
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"name": "read_file",
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"description": "Read a file",
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"parameters": {"type": "object"},
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},
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},
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]
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result = AnthropicFormatAdapter._convert_tools(tools)
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assert len(result) == 1
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assert result[0] == {
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"name": "read_file",
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"description": "Read a file",
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"input_schema": {"type": "object"},
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}
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def test_convert_tools_already_anthropic(self):
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"""Tools already in Anthropic format pass through correctly."""
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tools = [
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{
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"name": "read_file",
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"description": "Read a file",
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"input_schema": {"type": "object"},
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},
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]
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result = AnthropicFormatAdapter._convert_tools(tools)
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assert len(result) == 1
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assert result[0] == tools[0]
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class TestAnthropicResponseParsing:
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"""Parsing Anthropic API responses (non-streaming)."""
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@pytest.fixture
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def adapter(self):
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return AnthropicFormatAdapter(
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provider_config=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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)
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)
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def test_parse_nonstreaming_text(self, adapter):
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"""Simple text response is parsed correctly."""
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raw = {
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"id": "msg_123",
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"type": "message",
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"role": "assistant",
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"content": [{"type": "text", "text": "Hello, world!"}],
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"model": "claude-sonnet-4-20250514",
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"stop_reason": "end_turn",
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"usage": {"input_tokens": 10, "output_tokens": 5},
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}
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result = adapter.parse_nonstreaming_response(raw, "claude-sonnet-4")
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assert result.content == "Hello, world!"
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assert result.model_name == "claude-sonnet-4"
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assert result.usage is not None
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assert result.usage.prompt_tokens == 10
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assert result.usage.completion_tokens == 5
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assert result.usage.total_tokens == 15
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def test_parse_nonstreaming_with_tool_calls(self, adapter):
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"""Response with tool_use blocks is parsed with tool_calls."""
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raw = {
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"id": "msg_456",
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"type": "message",
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"role": "assistant",
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"content": [
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{"type": "text", "text": "I will read the file."},
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{
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"type": "tool_use",
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"id": "tu_123",
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"name": "read_file",
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"input": {"path": "x.txt"},
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},
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],
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"model": "claude-sonnet-4-20250514",
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"stop_reason": "tool_use",
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"usage": {"input_tokens": 20, "output_tokens": 15},
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}
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result = adapter.parse_nonstreaming_response(raw, "claude-sonnet-4")
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assert result.content == "I will read the file."
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assert len(result.tool_calls) == 1
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assert result.tool_calls[0] == {
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"name": "read_file",
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"args": {"path": "x.txt"},
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"id": "tu_123",
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}
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def test_parse_nonstreaming_with_reasoning(self, adapter):
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"""Response with thinking blocks populates reasoning field."""
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raw = {
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"id": "msg_789",
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"type": "message",
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"role": "assistant",
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"content": [
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{"type": "thinking", "thinking": "Let me think step by step..."},
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{"type": "text", "text": "Here is the answer."},
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{"type": "signature", "signature": "sig_12345"},
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],
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"model": "claude-sonnet-4-20250514",
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"stop_reason": "end_turn",
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"usage": {"input_tokens": 5, "output_tokens": 10},
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}
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result = adapter.parse_nonstreaming_response(raw, "claude-sonnet-4")
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assert result.content == "Here is the answer."
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# signature 块是加密签名，不进入 reasoning
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assert result.reasoning == "Let me think step by step..."
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def test_parse_nonstreaming_no_usage(self, adapter):
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"""Response without usage data still parses correctly."""
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raw = {
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"id": "msg_000",
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"type": "message",
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"role": "assistant",
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"content": [{"type": "text", "text": "No usage info."}],
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"model": "claude-sonnet-4-20250514",
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"stop_reason": "end_turn",
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}
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result = adapter.parse_nonstreaming_response(raw, "claude-sonnet-4")
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assert result.content == "No usage info."
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assert result.usage is None
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def test_parse_content_blocks_empty(self):
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"""Empty content blocks produce empty text and no tool calls."""
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raw = {"content": []}
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text, tools, reasoning = AnthropicFormatAdapter._parse_content_blocks(raw)
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assert text == ""
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assert tools == []
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assert reasoning == ""
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def test_bind_tools(self):
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"""bind_tools adds converted tools to the request body."""
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adapter = AnthropicFormatAdapter(
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provider_config=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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)
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)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "read_file",
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"description": "Read a file",
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"parameters": {"type": "object"},
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},
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},
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]
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body = {}
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adapter.bind_tools(body, tools, tool_choice="auto")
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assert body["tools"] == [
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{
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"name": "read_file",
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"description": "Read a file",
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"input_schema": {"type": "object"},
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},
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]
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assert body["tool_choice"] == "auto"
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class TestAnthropicStreamParsing:
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"""Parsing Anthropic SSE stream events."""
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def test_parse_message_start(self):
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"""message_start event sets initial token usage."""
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data = {
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"type": "message_start",
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"message": {
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"id": "msg_123",
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"usage": {"input_tokens": 15, "output_tokens": 0},
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},
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}
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chunk = AnthropicFormatAdapter._parse_stream_event("message_start", data)
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assert chunk.usage is not None
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assert chunk.usage.prompt_tokens == 15
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assert chunk.usage.completion_tokens == 0
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assert chunk.delta_content == ""
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def test_parse_content_block_start_text(self):
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"""content_block_start for text sets delta_content."""
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data = {
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"type": "content_block_start",
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"index": 0,
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"content_block": {"type": "text", "text": "Hello"},
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}
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chunk = AnthropicFormatAdapter._parse_stream_event("content_block_start", data)
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assert chunk.delta_content == "Hello"
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def test_parse_content_block_start_thinking(self):
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"""content_block_start for thinking sets delta_reasoning."""
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data = {
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"type": "content_block_start",
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"index": 0,
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"content_block": {"type": "thinking", "thinking": "Let me think..."},
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}
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chunk = AnthropicFormatAdapter._parse_stream_event("content_block_start", data)
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assert chunk.delta_reasoning == "Let me think..."
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def test_parse_content_block_start_tool_use(self):
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"""content_block_start for tool_use creates a tool call chunk stub."""
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data = {
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"type": "content_block_start",
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"index": 1,
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"content_block": {
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"type": "tool_use",
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"id": "tu_123",
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"name": "read_file",
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"input": {},
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},
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}
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chunk = AnthropicFormatAdapter._parse_stream_event("content_block_start", data)
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assert len(chunk.tool_call_chunks) == 1
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assert chunk.tool_call_chunks[0]["index"] == 1
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assert chunk.tool_call_chunks[0]["id"] == "tu_123"
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assert chunk.tool_call_chunks[0]["name"] == "read_file"
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assert chunk.tool_call_chunks[0]["args"] == ""
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def test_parse_content_block_delta_text(self):
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"""content_block_delta for text_delta appends content."""
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data = {
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"type": "content_block_delta",
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"index": 0,
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"delta": {"type": "text_delta", "text": " world"},
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}
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chunk = AnthropicFormatAdapter._parse_stream_event("content_block_delta", data)
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assert chunk.delta_content == " world"
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def test_parse_content_block_delta_thinking(self):
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"""content_block_delta for thinking_delta appends reasoning."""
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data = {
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"type": "content_block_delta",
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"index": 0,
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"delta": {"type": "thinking_delta", "thinking": " more..."},
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}
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chunk = AnthropicFormatAdapter._parse_stream_event("content_block_delta", data)
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assert chunk.delta_reasoning == " more..."
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|
||||
def test_parse_content_block_delta_signature(self):
|
||||
"""content_block_delta for signature_delta is ignored (not reasoning)."""
|
||||
data = {
|
||||
"type": "content_block_delta",
|
||||
"index": 0,
|
||||
"delta": {"type": "signature_delta", "signature": "sig_abc"},
|
||||
}
|
||||
chunk = AnthropicFormatAdapter._parse_stream_event("content_block_delta", data)
|
||||
# signature 不展示为 reasoning，但保留原始 delta 供 Agent 下一轮回放。
|
||||
assert chunk.delta_reasoning == ""
|
||||
assert chunk.content_block_index == 0
|
||||
assert chunk.content_block_delta == data["delta"]
|
||||
|
||||
def test_parse_content_block_delta_input_json(self):
|
||||
"""content_block_delta for input_json_delta creates a tool call arg chunk."""
|
||||
data = {
|
||||
"type": "content_block_delta",
|
||||
"index": 0,
|
||||
"delta": {"type": "input_json_delta", "partial_json": '{"path": "'},
|
||||
}
|
||||
chunk = AnthropicFormatAdapter._parse_stream_event("content_block_delta", data)
|
||||
assert len(chunk.tool_call_chunks) == 1
|
||||
assert chunk.tool_call_chunks[0]["args"] == '{"path": "'
|
||||
|
||||
def test_parse_message_delta_stop(self):
|
||||
"""message_delta with end_turn sets finish_reason to 'stop'."""
|
||||
data = {
|
||||
"type": "message_delta",
|
||||
"delta": {"stop_reason": "end_turn", "stop_sequence": None},
|
||||
"usage": {"output_tokens": 42},
|
||||
}
|
||||
chunk = AnthropicFormatAdapter._parse_stream_event("message_delta", data)
|
||||
assert chunk.finish_reason == "stop"
|
||||
assert chunk.usage is not None
|
||||
assert chunk.usage.completion_tokens == 42
|
||||
|
||||
def test_parse_message_delta_tool_use(self):
|
||||
"""message_delta with tool_use sets finish_reason to 'tool_calls'."""
|
||||
data = {
|
||||
"type": "message_delta",
|
||||
"delta": {"stop_reason": "tool_use"},
|
||||
"usage": {"output_tokens": 10},
|
||||
}
|
||||
chunk = AnthropicFormatAdapter._parse_stream_event("message_delta", data)
|
||||
assert chunk.finish_reason == "tool_calls"
|
||||
|
||||
def test_parse_message_delta_max_tokens(self):
|
||||
"""message_delta with max_tokens stop reason."""
|
||||
data = {
|
||||
"type": "message_delta",
|
||||
"delta": {"stop_reason": "max_tokens"},
|
||||
"usage": {"output_tokens": 5},
|
||||
}
|
||||
chunk = AnthropicFormatAdapter._parse_stream_event("message_delta", data)
|
||||
assert chunk.finish_reason == "length"
|
||||
|
||||
def test_parse_content_block_stop_noop(self):
|
||||
"""content_block_stop produces an empty chunk."""
|
||||
data = {"type": "content_block_stop", "index": 0}
|
||||
chunk = AnthropicFormatAdapter._parse_stream_event("content_block_stop", data)
|
||||
assert chunk.delta_content == ""
|
||||
assert chunk.delta_reasoning == ""
|
||||
assert chunk.usage is None
|
||||
|
||||
|
||||
class TestAnthropicAuthHeaders:
|
||||
"""Anthropic-specific authentication headers."""
|
||||
|
||||
def test_headers_contain_api_key_and_version(self):
|
||||
"""Headers include x-api-key, anthropic-version, and content type."""
|
||||
adapter = AnthropicFormatAdapter(
|
||||
provider_config=ProviderConfig(
|
||||
name="anthropic",
|
||||
format="anthropic",
|
||||
base_url_env="ANTHROPIC_BASE_URL",
|
||||
api_key_env="ANTHROPIC_API_KEY",
|
||||
)
|
||||
)
|
||||
headers = adapter._headers("sk-ant-12345")
|
||||
assert headers["x-api-key"] == "sk-ant-12345"
|
||||
assert headers["anthropic-version"] == "2023-06-01"
|
||||
assert headers["Content-Type"] == "application/json"
|
||||
Reference in new issue
Block a user