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- Add AnthropicSamplingHandler in server/sampling/anthropic.py - Consolidate all sampling examples into examples/sampling/ with rich output - Examples: text.py, structured_output.py, tool_use.py, server_fallback.py - Add anthropic optional dependency
238 lines
6.9 KiB
Python
238 lines
6.9 KiB
Python
from unittest.mock import MagicMock
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import pytest
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from anthropic import Anthropic
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from anthropic.types import Message, TextBlock, ToolUseBlock, Usage
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from mcp.types import (
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CreateMessageResult,
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CreateMessageResultWithTools,
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ModelHint,
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ModelPreferences,
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SamplingMessage,
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TextContent,
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ToolUseContent,
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)
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from fastmcp.server.sampling.anthropic import AnthropicSamplingHandler
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def test_convert_sampling_messages_to_anthropic_messages():
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msgs = AnthropicSamplingHandler._convert_to_anthropic_messages(
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messages=[
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SamplingMessage(
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role="user", content=TextContent(type="text", text="hello")
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),
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SamplingMessage(
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role="assistant", content=TextContent(type="text", text="ok")
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),
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],
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)
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assert msgs == [
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{"role": "user", "content": "hello"},
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{"role": "assistant", "content": "ok"},
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]
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def test_convert_to_anthropic_messages_raises_on_non_text():
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from fastmcp.utilities.types import Image
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with pytest.raises(ValueError):
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AnthropicSamplingHandler._convert_to_anthropic_messages(
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messages=[
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SamplingMessage(
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role="user",
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content=Image(data=b"abc").to_image_content(),
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)
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],
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)
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@pytest.mark.parametrize(
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"prefs,expected",
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[
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("claude-3-5-sonnet-20241022", "claude-3-5-sonnet-20241022"),
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(
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ModelPreferences(hints=[ModelHint(name="claude-3-5-sonnet-20241022")]),
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"claude-3-5-sonnet-20241022",
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),
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(["claude-3-5-sonnet-20241022", "other"], "claude-3-5-sonnet-20241022"),
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(None, "fallback-model"),
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(["unknown-model"], "fallback-model"),
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],
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)
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def test_select_model_from_preferences(prefs, expected):
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mock_client = MagicMock(spec=Anthropic)
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handler = AnthropicSamplingHandler(
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default_model="fallback-model", client=mock_client
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)
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assert handler._select_model_from_preferences(prefs) == expected
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def test_message_to_create_message_result():
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mock_client = MagicMock(spec=Anthropic)
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handler = AnthropicSamplingHandler(
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default_model="fallback-model", client=mock_client
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)
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message = Message(
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id="msg_123",
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type="message",
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role="assistant",
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content=[TextBlock(type="text", text="HELPFUL CONTENT FROM A VERY SMART LLM")],
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model="claude-3-5-sonnet-20241022",
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stop_reason="end_turn",
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stop_sequence=None,
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usage=Usage(input_tokens=10, output_tokens=20),
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)
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result: CreateMessageResult = handler._message_to_create_message_result(message)
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assert result == CreateMessageResult(
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content=TextContent(type="text", text="HELPFUL CONTENT FROM A VERY SMART LLM"),
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role="assistant",
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model="claude-3-5-sonnet-20241022",
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)
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def test_message_to_result_with_tools():
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message = Message(
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id="msg_123",
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type="message",
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role="assistant",
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content=[
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TextBlock(type="text", text="I'll help you with that."),
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ToolUseBlock(
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type="tool_use",
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id="toolu_123",
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name="get_weather",
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input={"location": "San Francisco"},
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),
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],
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model="claude-3-5-sonnet-20241022",
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stop_reason="tool_use",
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stop_sequence=None,
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usage=Usage(input_tokens=10, output_tokens=20),
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)
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result: CreateMessageResultWithTools = (
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AnthropicSamplingHandler._message_to_result_with_tools(message)
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)
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assert result.role == "assistant"
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assert result.model == "claude-3-5-sonnet-20241022"
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assert result.stopReason == "toolUse"
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assert len(result.content) == 2
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assert result.content[0] == TextContent(
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type="text", text="I'll help you with that."
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)
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assert result.content[1] == ToolUseContent(
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type="tool_use",
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id="toolu_123",
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name="get_weather",
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input={"location": "San Francisco"},
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)
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def test_convert_tool_choice_auto():
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result = AnthropicSamplingHandler._convert_tool_choice_to_anthropic(
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MagicMock(mode="auto")
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)
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assert result == {"type": "auto"}
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def test_convert_tool_choice_required():
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result = AnthropicSamplingHandler._convert_tool_choice_to_anthropic(
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MagicMock(mode="required")
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)
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assert result == {"type": "any"}
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def test_convert_tool_choice_none():
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result = AnthropicSamplingHandler._convert_tool_choice_to_anthropic(
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MagicMock(mode="none")
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)
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# Anthropic doesn't have "none", falls back to "auto"
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assert result == {"type": "auto"}
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def test_convert_tools_to_anthropic():
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from mcp.types import Tool
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tools = [
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Tool(
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name="get_weather",
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description="Get the current weather",
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inputSchema={
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"type": "object",
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"properties": {"location": {"type": "string"}},
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"required": ["location"],
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},
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)
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]
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result = AnthropicSamplingHandler._convert_tools_to_anthropic(tools)
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assert len(result) == 1
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assert result[0]["name"] == "get_weather"
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assert result[0]["description"] == "Get the current weather"
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assert result[0]["input_schema"] == {
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"type": "object",
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"properties": {"location": {"type": "string"}},
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"required": ["location"],
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}
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def test_convert_messages_with_tool_use_content():
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"""Test converting messages that include tool use content from assistant."""
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msgs = AnthropicSamplingHandler._convert_to_anthropic_messages(
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messages=[
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SamplingMessage(
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role="assistant",
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content=ToolUseContent(
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type="tool_use",
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id="toolu_123",
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name="get_weather",
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input={"location": "NYC"},
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),
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),
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],
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)
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assert len(msgs) == 1
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assert msgs[0]["role"] == "assistant"
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assert msgs[0]["content"] == [
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{
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"type": "tool_use",
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"id": "toolu_123",
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"name": "get_weather",
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"input": {"location": "NYC"},
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}
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]
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def test_convert_messages_with_tool_result_content():
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"""Test converting messages that include tool result content from user."""
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from mcp.types import ToolResultContent
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msgs = AnthropicSamplingHandler._convert_to_anthropic_messages(
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messages=[
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SamplingMessage(
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role="user",
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content=ToolResultContent(
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type="tool_result",
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toolUseId="toolu_123",
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content=[TextContent(type="text", text="72F and sunny")],
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),
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),
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],
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)
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assert len(msgs) == 1
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assert msgs[0]["role"] == "user"
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assert msgs[0]["content"] == [
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{
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"type": "tool_result",
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"tool_use_id": "toolu_123",
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"content": "72F and sunny",
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}
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]
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