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c150f85044
## Summary Implements the `tool_choice` parameter for `ChatCompletionClient` interface as requested in #6696. This allows users to restrict which tools the model can choose from when multiple tools are available. ## Changes ### Core Interface - Core Interface: Added `tool_choice: Tool | Literal["auto", "required", "none"] = "auto"` parameter to `ChatCompletionClient.create()` and `create_stream()` methods - Model Implementations: Updated client implementations to support the new parameter, for now, only the following model clients are supported: - OpenAI - Anthropic - Azure AI - Ollama - `LlamaCppChatCompletionClient` currently not supported Features - "auto" (default): Let the model choose whether to use tools, when there is no tool, it has no effect. - "required": Force the model to use at least one tool - "none": Disable tool usage completely - Tool object: Force the model to use a specific tool --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: ekzhu <320302+ekzhu@users.noreply.github.com> Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
1002 lines
37 KiB
Python
1002 lines
37 KiB
Python
import asyncio
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import logging
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import os
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from typing import List, Sequence
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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from autogen_core import CancellationToken, FunctionCall
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from autogen_core.models import (
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AssistantMessage,
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CreateResult,
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FunctionExecutionResult,
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FunctionExecutionResultMessage,
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ModelInfo,
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SystemMessage,
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UserMessage,
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)
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from autogen_core.models._types import LLMMessage
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from autogen_core.tools import FunctionTool
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from autogen_ext.models.anthropic import (
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AnthropicBedrockChatCompletionClient,
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AnthropicChatCompletionClient,
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BedrockInfo,
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)
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def _pass_function(input: str) -> str:
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"""Simple passthrough function."""
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return f"Processed: {input}"
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def _add_numbers(a: int, b: int) -> int:
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"""Add two numbers together."""
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return a + b
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@pytest.mark.asyncio
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async def test_mock_tool_choice_specific_tool() -> None:
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"""Test tool_choice parameter with a specific tool using mocks."""
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# Create mock client and response
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mock_client = AsyncMock()
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mock_message = MagicMock()
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mock_message.content = [MagicMock(type="tool_use", name="process_text", input={"input": "hello"}, id="call_123")]
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mock_message.usage.input_tokens = 10
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mock_message.usage.output_tokens = 5
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mock_client.messages.create.return_value = mock_message
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# Create real client but patch the underlying Anthropic client
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307",
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api_key="test-key",
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)
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# Define tools
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pass_tool = FunctionTool(_pass_function, description="Process input text", name="process_text")
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add_tool = FunctionTool(_add_numbers, description="Add two numbers together", name="add_numbers")
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messages: List[LLMMessage] = [
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UserMessage(content="Process the text 'hello'.", source="user"),
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]
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with patch.object(client, "_client", mock_client):
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await client.create(
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messages=messages,
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tools=[pass_tool, add_tool],
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tool_choice=pass_tool, # Force use of specific tool
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)
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# Verify the correct API call was made
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mock_client.messages.create.assert_called_once()
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call_args = mock_client.messages.create.call_args
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# Check that tool_choice was set correctly
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assert "tool_choice" in call_args.kwargs
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assert call_args.kwargs["tool_choice"] == {"type": "tool", "name": "process_text"}
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@pytest.mark.asyncio
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async def test_mock_tool_choice_auto() -> None:
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"""Test tool_choice parameter with 'auto' setting using mocks."""
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# Create mock client and response
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mock_client = AsyncMock()
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mock_message = MagicMock()
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mock_message.content = [MagicMock(type="tool_use", name="add_numbers", input={"a": 1, "b": 2}, id="call_123")]
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mock_message.usage.input_tokens = 10
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mock_message.usage.output_tokens = 5
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mock_client.messages.create.return_value = mock_message
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# Create real client but patch the underlying Anthropic client
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307",
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api_key="test-key",
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)
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# Define tools
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pass_tool = FunctionTool(_pass_function, description="Process input text", name="process_text")
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add_tool = FunctionTool(_add_numbers, description="Add two numbers together", name="add_numbers")
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messages: List[LLMMessage] = [
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UserMessage(content="Add 1 and 2.", source="user"),
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]
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with patch.object(client, "_client", mock_client):
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await client.create(
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messages=messages,
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tools=[pass_tool, add_tool],
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tool_choice="auto", # Let model choose
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)
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# Verify the correct API call was made
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mock_client.messages.create.assert_called_once()
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call_args = mock_client.messages.create.call_args
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# Check that tool_choice was set correctly
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assert "tool_choice" in call_args.kwargs
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assert call_args.kwargs["tool_choice"] == {"type": "auto"}
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@pytest.mark.asyncio
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async def test_mock_tool_choice_none() -> None:
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"""Test tool_choice parameter when no tools are provided - tool_choice should not be included."""
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# Create mock client and response
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mock_client = AsyncMock()
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mock_message = MagicMock()
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mock_message.content = [MagicMock(type="text", text="I can help you with that.")]
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mock_message.usage.input_tokens = 10
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mock_message.usage.output_tokens = 5
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mock_client.messages.create.return_value = mock_message
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# Create real client but patch the underlying Anthropic client
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307",
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api_key="test-key",
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)
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messages: List[LLMMessage] = [
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UserMessage(content="Hello there.", source="user"),
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]
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with patch.object(client, "_client", mock_client):
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await client.create(
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messages=messages,
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# No tools provided - tool_choice should not be included in API call
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)
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# Verify the correct API call was made
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mock_client.messages.create.assert_called_once()
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call_args = mock_client.messages.create.call_args
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# Check that tool_choice was not set when no tools are provided
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assert "tool_choice" not in call_args.kwargs
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@pytest.mark.asyncio
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async def test_mock_tool_choice_validation_error() -> None:
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"""Test tool_choice validation with invalid tool reference."""
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307",
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api_key="test-key",
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)
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# Define tools
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pass_tool = FunctionTool(_pass_function, description="Process input text", name="process_text")
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add_tool = FunctionTool(_add_numbers, description="Add two numbers together", name="add_numbers")
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different_tool = FunctionTool(_pass_function, description="Different tool", name="different_tool")
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messages: List[LLMMessage] = [
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UserMessage(content="Hello there.", source="user"),
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]
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# Test with a tool that's not in the tools list
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with pytest.raises(ValueError, match="tool_choice references 'different_tool' but it's not in the available tools"):
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await client.create(
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messages=messages,
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tools=[pass_tool, add_tool],
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tool_choice=different_tool, # This tool is not in the tools list
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)
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@pytest.mark.asyncio
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async def test_mock_serialization_api_key() -> None:
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307", # Use haiku for faster/cheaper testing
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api_key="sk-password",
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temperature=0.0, # Added temperature param to test
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stop_sequences=["STOP"], # Added stop sequence
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)
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assert client
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config = client.dump_component()
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assert config
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assert "sk-password" not in str(config)
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serialized_config = config.model_dump_json()
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assert serialized_config
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assert "sk-password" not in serialized_config
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client2 = AnthropicChatCompletionClient.load_component(config)
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assert client2
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bedrock_client = AnthropicBedrockChatCompletionClient(
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model="claude-3-haiku-20240307", # Use haiku for faster/cheaper testing
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api_key="sk-password",
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model_info=ModelInfo(
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vision=False, function_calling=True, json_output=False, family="unknown", structured_output=True
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),
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bedrock_info=BedrockInfo(
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aws_access_key="<aws_access_key>",
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aws_secret_key="<aws_secret_key>",
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aws_session_token="<aws_session_token>",
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aws_region="<aws_region>",
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),
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)
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assert bedrock_client
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bedrock_config = bedrock_client.dump_component()
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assert bedrock_config
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assert "sk-password" not in str(bedrock_config)
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serialized_bedrock_config = bedrock_config.model_dump_json()
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assert serialized_bedrock_config
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assert "sk-password" not in serialized_bedrock_config
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bedrock_client2 = AnthropicBedrockChatCompletionClient.load_component(bedrock_config)
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assert bedrock_client2
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@pytest.mark.asyncio
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async def test_anthropic_basic_completion(caplog: pytest.LogCaptureFixture) -> None:
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"""Test basic message completion with Claude."""
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307", # Use haiku for faster/cheaper testing
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api_key=api_key,
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temperature=0.0, # Added temperature param to test
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stop_sequences=["STOP"], # Added stop sequence
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)
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# Test basic completion
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with caplog.at_level(logging.INFO):
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result = await client.create(
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messages=[
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SystemMessage(content="You are a helpful assistant."),
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UserMessage(content="What's 2+2? Answer with just the number.", source="user"),
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]
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)
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assert isinstance(result.content, str)
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assert "4" in result.content
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assert result.finish_reason == "stop"
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assert "LLMCall" in caplog.text and result.content in caplog.text
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# Test JSON output - add to existing test
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json_result = await client.create(
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messages=[
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UserMessage(content="Return a JSON with key 'value' set to 42", source="user"),
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],
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json_output=True,
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)
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assert isinstance(json_result.content, str)
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assert "42" in json_result.content
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# Check usage tracking
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usage = client.total_usage()
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assert usage.prompt_tokens > 0
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assert usage.completion_tokens > 0
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@pytest.mark.asyncio
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async def test_anthropic_streaming(caplog: pytest.LogCaptureFixture) -> None:
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"""Test streaming capabilities with Claude."""
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307",
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api_key=api_key,
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)
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# Test streaming completion
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chunks: List[str | CreateResult] = []
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prompt = "Count from 1 to 5. Each number on its own line."
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with caplog.at_level(logging.INFO):
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async for chunk in client.create_stream(
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messages=[
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UserMessage(content=prompt, source="user"),
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]
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):
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chunks.append(chunk)
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# Verify we got multiple chunks
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assert len(chunks) > 1
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# Check final result
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final_result = chunks[-1]
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assert isinstance(final_result, CreateResult)
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assert final_result.finish_reason == "stop"
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assert "LLMStreamStart" in caplog.text
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assert "LLMStreamEnd" in caplog.text
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assert isinstance(final_result.content, str)
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for i in range(1, 6):
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assert str(i) in caplog.text
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assert prompt in caplog.text
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# Check content contains numbers 1-5
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assert isinstance(final_result.content, str)
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combined_content = final_result.content
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for i in range(1, 6):
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assert str(i) in combined_content
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@pytest.mark.asyncio
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async def test_anthropic_tool_calling() -> None:
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"""Test tool calling capabilities with Claude."""
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307",
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api_key=api_key,
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)
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# Define tools
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pass_tool = FunctionTool(_pass_function, description="Process input text", name="process_text")
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add_tool = FunctionTool(_add_numbers, description="Add two numbers together", name="add_numbers")
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# Test tool calling with instruction to use specific tool
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messages: List[LLMMessage] = [
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SystemMessage(content="Use the tools available to help the user."),
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UserMessage(content="Process the text 'hello world' using the process_text tool.", source="user"),
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]
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result = await client.create(messages=messages, tools=[pass_tool, add_tool])
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# Check that we got a tool call
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assert isinstance(result.content, list)
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assert len(result.content) >= 1
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assert isinstance(result.content[0], FunctionCall)
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# Check that the correct tool was called
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function_call = result.content[0]
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assert function_call.name == "process_text"
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# Test tool response handling
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messages.append(AssistantMessage(content=result.content, source="assistant"))
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messages.append(
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FunctionExecutionResultMessage(
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content=[
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FunctionExecutionResult(
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content="Processed: hello world",
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call_id=result.content[0].id,
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is_error=False,
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name=result.content[0].name,
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)
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]
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)
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)
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# Get response after tool execution
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after_tool_result = await client.create(messages=messages)
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# Check we got a text response
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assert isinstance(after_tool_result.content, str)
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# Test multiple tool use
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multi_tool_prompt: List[LLMMessage] = [
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SystemMessage(content="Use the tools as needed to help the user."),
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UserMessage(content="First process the text 'test' and then add 2 and 3.", source="user"),
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]
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multi_tool_result = await client.create(messages=multi_tool_prompt, tools=[pass_tool, add_tool])
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# We just need to verify we get at least one tool call
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assert isinstance(multi_tool_result.content, list)
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assert len(multi_tool_result.content) > 0
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assert isinstance(multi_tool_result.content[0], FunctionCall)
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@pytest.mark.asyncio
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async def test_anthropic_token_counting() -> None:
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"""Test token counting functionality."""
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307",
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api_key=api_key,
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)
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messages: Sequence[LLMMessage] = [
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SystemMessage(content="You are a helpful assistant."),
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UserMessage(content="Hello, how are you?", source="user"),
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]
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# Test token counting
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num_tokens = client.count_tokens(messages)
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assert num_tokens > 0
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# Test remaining token calculation
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remaining = client.remaining_tokens(messages)
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assert remaining > 0
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assert remaining < 200000 # Claude's max context
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# Test token counting with tools
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tools = [
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FunctionTool(_pass_function, description="Process input text", name="process_text"),
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FunctionTool(_add_numbers, description="Add two numbers together", name="add_numbers"),
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]
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tokens_with_tools = client.count_tokens(messages, tools=tools)
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assert tokens_with_tools > num_tokens # Should be more tokens with tools
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@pytest.mark.asyncio
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async def test_anthropic_cancellation() -> None:
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"""Test cancellation of requests."""
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
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client = AnthropicChatCompletionClient(
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model="claude-3-haiku-20240307",
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api_key=api_key,
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)
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# Create a cancellation token
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cancellation_token = CancellationToken()
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# Schedule cancellation after a short delay
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async def cancel_after_delay() -> None:
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await asyncio.sleep(0.5) # Short delay
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cancellation_token.cancel()
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# Start task to cancel request
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asyncio.create_task(cancel_after_delay())
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# Create a request with long output
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with pytest.raises(asyncio.CancelledError):
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await client.create(
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messages=[
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UserMessage(content="Write a detailed 5-page essay on the history of computing.", source="user"),
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],
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cancellation_token=cancellation_token,
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)
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@pytest.mark.asyncio
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async def test_anthropic_multimodal() -> None:
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"""Test multimodal capabilities with Claude."""
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
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# Skip if PIL is not available
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try:
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from autogen_core import Image
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from PIL import Image as PILImage
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except ImportError:
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pytest.skip("PIL or other dependencies not installed")
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client = AnthropicChatCompletionClient(
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model="claude-3-5-sonnet-latest", # Use a model that supports vision
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api_key=api_key,
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)
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# Use a simple test image that's reliable
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# 1. Create a simple colored square image
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width, height = 100, 100
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color = (255, 0, 0) # Red
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pil_image = PILImage.new("RGB", (width, height), color)
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# 2. Convert to autogen_core Image format
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img = Image(pil_image)
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# Test multimodal message
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result = await client.create(
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messages=[
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UserMessage(content=["What color is this square? Answer in one word.", img], source="user"),
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]
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)
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# Verify we got a response describing the image
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assert isinstance(result.content, str)
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assert len(result.content) > 0
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assert "red" in result.content.lower()
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assert result.finish_reason == "stop"
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@pytest.mark.asyncio
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async def test_mock_serialization() -> None:
|
|
"""Test serialization and deserialization of component."""
|
|
|
|
client = AnthropicChatCompletionClient(
|
|
model="claude-3-haiku-20240307",
|
|
api_key="api-key",
|
|
)
|
|
|
|
# Serialize and deserialize
|
|
model_client_config = client.dump_component()
|
|
assert model_client_config is not None
|
|
assert model_client_config.config is not None
|
|
|
|
loaded_model_client = AnthropicChatCompletionClient.load_component(model_client_config)
|
|
assert loaded_model_client is not None
|
|
assert isinstance(loaded_model_client, AnthropicChatCompletionClient)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_anthropic_message_serialization_with_tools(caplog: pytest.LogCaptureFixture) -> None:
|
|
"""Test that complex messages with tool calls are properly serialized in logs."""
|
|
api_key = os.getenv("ANTHROPIC_API_KEY")
|
|
if not api_key:
|
|
pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
|
|
|
|
# Use existing tools from the test file
|
|
pass_tool = FunctionTool(_pass_function, description="Process input text", name="process_text")
|
|
add_tool = FunctionTool(_add_numbers, description="Add two numbers together", name="add_numbers")
|
|
|
|
client = AnthropicChatCompletionClient(
|
|
model="claude-3-haiku-20240307",
|
|
api_key=api_key,
|
|
)
|
|
|
|
# Set up logging capture - capture all loggers
|
|
with caplog.at_level(logging.INFO):
|
|
# Make a request that should trigger a tool call
|
|
await client.create(
|
|
messages=[
|
|
SystemMessage(content="Use the tools available to help the user."),
|
|
UserMessage(content="Process the text 'hello world' using the process_text tool.", source="user"),
|
|
],
|
|
tools=[pass_tool, add_tool],
|
|
)
|
|
|
|
# Look for any log containing serialized messages, not just with 'LLMCallEvent'
|
|
serialized_message_logs = [
|
|
record for record in caplog.records if '"messages":' in str(record.msg) or "messages" in str(record.msg)
|
|
]
|
|
|
|
# Verify we have at least one log with serialized messages
|
|
assert len(serialized_message_logs) > 0, "No logs with serialized messages found"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_anthropic_muliple_system_message() -> None:
|
|
"""Test multiple system messages in a single request."""
|
|
api_key = os.getenv("ANTHROPIC_API_KEY")
|
|
if not api_key:
|
|
pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
|
|
|
|
client = AnthropicChatCompletionClient(
|
|
model="claude-3-haiku-20240307",
|
|
api_key=api_key,
|
|
)
|
|
# Test multiple system messages
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="When you say anything Start with 'FOO'"),
|
|
SystemMessage(content="When you say anything End with 'BAR'"),
|
|
UserMessage(content="Just say '.'", source="user"),
|
|
]
|
|
|
|
result = await client.create(messages=messages)
|
|
result_content = result.content
|
|
assert isinstance(result_content, str)
|
|
result_content = result_content.strip()
|
|
assert result_content[:3] == "FOO"
|
|
assert result_content[-3:] == "BAR"
|
|
|
|
|
|
def test_mock_merge_continuous_system_messages() -> None:
|
|
"""Tests merging of continuous system messages."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="System instruction 1"),
|
|
SystemMessage(content="System instruction 2"),
|
|
UserMessage(content="User question", source="user"),
|
|
]
|
|
|
|
merged_messages = client._merge_system_messages(messages) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
|
|
# 병합 후 2개 메시지만 남아야 함 (시스템 1개, 사용자 1개)
|
|
assert len(merged_messages) == 2
|
|
|
|
# 첫 번째 메시지는 병합된 시스템 메시지여야 함
|
|
assert isinstance(merged_messages[0], SystemMessage)
|
|
assert merged_messages[0].content == "System instruction 1\nSystem instruction 2"
|
|
|
|
# 두 번째 메시지는 사용자 메시지여야 함
|
|
assert isinstance(merged_messages[1], UserMessage)
|
|
assert merged_messages[1].content == "User question"
|
|
|
|
|
|
def test_mock_merge_single_system_message() -> None:
|
|
"""Tests that a single system message remains unchanged."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="Single system instruction"),
|
|
UserMessage(content="User question", source="user"),
|
|
]
|
|
|
|
merged_messages = client._merge_system_messages(messages) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
|
|
# 메시지 개수는 변하지 않아야 함
|
|
assert len(merged_messages) == 2
|
|
|
|
# 시스템 메시지 내용은 변하지 않아야 함
|
|
assert isinstance(merged_messages[0], SystemMessage)
|
|
assert merged_messages[0].content == "Single system instruction"
|
|
|
|
|
|
def test_mock_merge_no_system_messages() -> None:
|
|
"""Tests behavior when there are no system messages."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
messages: List[LLMMessage] = [
|
|
UserMessage(content="User question without system", source="user"),
|
|
]
|
|
|
|
merged_messages = client._merge_system_messages(messages) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
|
|
# 메시지 개수는 변하지 않아야 함
|
|
assert len(merged_messages) == 1
|
|
|
|
# 유일한 메시지는 사용자 메시지여야 함
|
|
assert isinstance(merged_messages[0], UserMessage)
|
|
assert merged_messages[0].content == "User question without system"
|
|
|
|
|
|
def test_mock_merge_non_continuous_system_messages() -> None:
|
|
"""Tests that an error is raised for non-continuous system messages."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="First group 1"),
|
|
SystemMessage(content="First group 2"),
|
|
UserMessage(content="Middle user message", source="user"),
|
|
SystemMessage(content="Second group 1"),
|
|
SystemMessage(content="Second group 2"),
|
|
]
|
|
|
|
# 연속적이지 않은 시스템 메시지는 에러를 발생시켜야 함
|
|
with pytest.raises(ValueError, match="Multiple and Not continuous system messages are not supported"):
|
|
client._merge_system_messages(messages) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
|
|
|
|
def test_mock_merge_system_messages_empty() -> None:
|
|
"""Tests that empty message list is handled properly."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
merged_messages = client._merge_system_messages([]) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
assert len(merged_messages) == 0
|
|
|
|
|
|
def test_mock_merge_system_messages_with_special_characters() -> None:
|
|
"""Tests system message merging with special characters and formatting."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="Line 1\nWith newline"),
|
|
SystemMessage(content="Line 2 with *formatting*"),
|
|
SystemMessage(content="Line 3 with `code`"),
|
|
UserMessage(content="Question", source="user"),
|
|
]
|
|
|
|
merged_messages = client._merge_system_messages(messages) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
assert len(merged_messages) == 2
|
|
|
|
system_message = merged_messages[0]
|
|
assert isinstance(system_message, SystemMessage)
|
|
assert system_message.content == "Line 1\nWith newline\nLine 2 with *formatting*\nLine 3 with `code`"
|
|
|
|
|
|
def test_mock_merge_system_messages_with_whitespace() -> None:
|
|
"""Tests system message merging with extra whitespace."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content=" Message with leading spaces "),
|
|
SystemMessage(content="\nMessage with leading newline\n"),
|
|
UserMessage(content="Question", source="user"),
|
|
]
|
|
|
|
merged_messages = client._merge_system_messages(messages) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
assert len(merged_messages) == 2
|
|
|
|
system_message = merged_messages[0]
|
|
assert isinstance(system_message, SystemMessage)
|
|
# strip()은 내부에서 발생하지 않지만 최종 결과에서는 줄바꿈이 유지됨
|
|
assert system_message.content == " Message with leading spaces \n\nMessage with leading newline"
|
|
|
|
|
|
def test_mock_merge_system_messages_message_order() -> None:
|
|
"""Tests that message order is preserved after merging."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
messages: List[LLMMessage] = [
|
|
UserMessage(content="Question 1", source="user"),
|
|
SystemMessage(content="Instruction 1"),
|
|
SystemMessage(content="Instruction 2"),
|
|
UserMessage(content="Question 2", source="user"),
|
|
AssistantMessage(content="Answer", source="assistant"),
|
|
]
|
|
|
|
merged_messages = client._merge_system_messages(messages) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
assert len(merged_messages) == 4
|
|
|
|
# 첫 번째 메시지는 UserMessage여야 함
|
|
assert isinstance(merged_messages[0], UserMessage)
|
|
assert merged_messages[0].content == "Question 1"
|
|
|
|
# 두 번째 메시지는 병합된 SystemMessage여야 함
|
|
assert isinstance(merged_messages[1], SystemMessage)
|
|
assert merged_messages[1].content == "Instruction 1\nInstruction 2"
|
|
|
|
# 나머지 메시지는 순서대로 유지되어야 함
|
|
assert isinstance(merged_messages[2], UserMessage)
|
|
assert merged_messages[2].content == "Question 2"
|
|
assert isinstance(merged_messages[3], AssistantMessage)
|
|
assert merged_messages[3].content == "Answer"
|
|
|
|
|
|
def test_mock_merge_system_messages_multiple_groups() -> None:
|
|
"""Tests that multiple separate groups of system messages raise an error."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
# 연속되지 않은 시스템 메시지: 사용자 메시지로 분리된 두 그룹
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="Group 1 - message 1"),
|
|
UserMessage(content="Interrupting user message", source="user"),
|
|
SystemMessage(content="Group 2 - message 1"),
|
|
]
|
|
|
|
with pytest.raises(ValueError, match="Multiple and Not continuous system messages are not supported"):
|
|
client._merge_system_messages(messages) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
|
|
|
|
def test_mock_merge_system_messages_no_duplicates() -> None:
|
|
"""Tests that identical system messages are still merged properly."""
|
|
client = AnthropicChatCompletionClient(model="claude-3-haiku-20240307", api_key="fake-api-key")
|
|
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="Same instruction"),
|
|
SystemMessage(content="Same instruction"), # 중복된 내용
|
|
UserMessage(content="Question", source="user"),
|
|
]
|
|
|
|
merged_messages = client._merge_system_messages(messages) # pyright: ignore[reportPrivateUsage]
|
|
# The method is protected, but we need to test it
|
|
assert len(merged_messages) == 2
|
|
|
|
# 첫 번째 메시지는 병합된 시스템 메시지여야 함
|
|
assert isinstance(merged_messages[0], SystemMessage)
|
|
# 중복된 내용도 그대로 병합됨
|
|
assert merged_messages[0].content == "Same instruction\nSame instruction"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_anthropic_empty_assistant_content_string() -> None:
|
|
"""Test that an empty assistant content string is handled correctly."""
|
|
api_key = os.getenv("ANTHROPIC_API_KEY")
|
|
if not api_key:
|
|
pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
|
|
|
|
client = AnthropicChatCompletionClient(
|
|
model="claude-3-haiku-20240307",
|
|
api_key=api_key,
|
|
)
|
|
|
|
# Test empty assistant content string
|
|
result = await client.create(
|
|
messages=[
|
|
UserMessage(content="Say something", source="user"),
|
|
AssistantMessage(content="", source="assistant"),
|
|
]
|
|
)
|
|
|
|
# Verify we got a response
|
|
assert isinstance(result.content, str)
|
|
assert len(result.content) > 0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_anthropic_trailing_whitespace_at_last_assistant_content() -> None:
|
|
"""Test that an empty assistant content string is handled correctly."""
|
|
api_key = os.getenv("ANTHROPIC_API_KEY")
|
|
if not api_key:
|
|
pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
|
|
|
|
client = AnthropicChatCompletionClient(
|
|
model="claude-3-haiku-20240307",
|
|
api_key=api_key,
|
|
)
|
|
|
|
messages: list[LLMMessage] = [
|
|
UserMessage(content="foo", source="user"),
|
|
UserMessage(content="bar", source="user"),
|
|
AssistantMessage(content="foobar ", source="assistant"),
|
|
]
|
|
|
|
result = await client.create(messages=messages)
|
|
assert isinstance(result.content, str)
|
|
|
|
|
|
def test_mock_rstrip_trailing_whitespace_at_last_assistant_content() -> None:
|
|
messages: list[LLMMessage] = [
|
|
UserMessage(content="foo", source="user"),
|
|
UserMessage(content="bar", source="user"),
|
|
AssistantMessage(content="foobar ", source="assistant"),
|
|
]
|
|
|
|
# This will crash if _rstrip_railing_whitespace_at_last_assistant_content is not applied to "content"
|
|
dummy_client = AnthropicChatCompletionClient(model="claude-3-5-haiku-20241022", api_key="dummy-key")
|
|
result = dummy_client._rstrip_last_assistant_message(messages) # pyright: ignore[reportPrivateUsage]
|
|
|
|
assert isinstance(result[-1].content, str)
|
|
assert result[-1].content == "foobar"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_anthropic_tool_choice_with_actual_api() -> None:
|
|
"""Test tool_choice parameter with actual Anthropic API endpoints."""
|
|
api_key = os.getenv("ANTHROPIC_API_KEY")
|
|
if not api_key:
|
|
pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
|
|
|
|
client = AnthropicChatCompletionClient(
|
|
model="claude-3-haiku-20240307",
|
|
api_key=api_key,
|
|
)
|
|
|
|
# Define tools
|
|
pass_tool = FunctionTool(_pass_function, description="Process input text", name="process_text")
|
|
add_tool = FunctionTool(_add_numbers, description="Add two numbers together", name="add_numbers")
|
|
|
|
# Test 1: tool_choice with specific tool
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="Use the tools as needed to help the user."),
|
|
UserMessage(content="Process the text 'hello world' using the process_text tool.", source="user"),
|
|
]
|
|
|
|
result = await client.create(
|
|
messages=messages,
|
|
tools=[pass_tool, add_tool],
|
|
tool_choice=pass_tool, # Force use of specific tool
|
|
)
|
|
|
|
# Verify we got a tool call for the specified tool
|
|
assert isinstance(result.content, list)
|
|
assert len(result.content) >= 1
|
|
assert isinstance(result.content[0], FunctionCall)
|
|
assert result.content[0].name == "process_text"
|
|
|
|
# Test 2: tool_choice="auto" with tools
|
|
auto_messages: List[LLMMessage] = [
|
|
SystemMessage(content="Use the tools as needed to help the user."),
|
|
UserMessage(content="Add the numbers 5 and 3.", source="user"),
|
|
]
|
|
|
|
auto_result = await client.create(
|
|
messages=auto_messages,
|
|
tools=[pass_tool, add_tool],
|
|
tool_choice="auto", # Let model choose
|
|
)
|
|
|
|
# Should get a tool call, likely for add_numbers
|
|
assert isinstance(auto_result.content, list)
|
|
assert len(auto_result.content) >= 1
|
|
assert isinstance(auto_result.content[0], FunctionCall)
|
|
# Model should choose add_numbers for addition task
|
|
assert auto_result.content[0].name == "add_numbers"
|
|
|
|
# Test 3: No tools provided - should not include tool_choice in API call
|
|
no_tools_messages: List[LLMMessage] = [
|
|
UserMessage(content="What is the capital of France?", source="user"),
|
|
]
|
|
|
|
no_tools_result = await client.create(messages=no_tools_messages)
|
|
|
|
# Should get a text response without tool calls
|
|
assert isinstance(no_tools_result.content, str)
|
|
assert "paris" in no_tools_result.content.lower()
|
|
|
|
# Test 4: tool_choice="required" with tools
|
|
required_messages: List[LLMMessage] = [
|
|
SystemMessage(content="You must use one of the available tools to help the user."),
|
|
UserMessage(content="Help me with something.", source="user"),
|
|
]
|
|
|
|
required_result = await client.create(
|
|
messages=required_messages,
|
|
tools=[pass_tool, add_tool],
|
|
tool_choice="required", # Force tool usage
|
|
)
|
|
|
|
# Should get a tool call (model forced to use a tool)
|
|
assert isinstance(required_result.content, list)
|
|
assert len(required_result.content) >= 1
|
|
assert isinstance(required_result.content[0], FunctionCall)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_anthropic_tool_choice_streaming_with_actual_api() -> None:
|
|
"""Test tool_choice parameter with streaming using actual Anthropic API endpoints."""
|
|
api_key = os.getenv("ANTHROPIC_API_KEY")
|
|
if not api_key:
|
|
pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
|
|
|
|
client = AnthropicChatCompletionClient(
|
|
model="claude-3-haiku-20240307",
|
|
api_key=api_key,
|
|
)
|
|
|
|
# Define tools
|
|
pass_tool = FunctionTool(_pass_function, description="Process input text", name="process_text")
|
|
add_tool = FunctionTool(_add_numbers, description="Add two numbers together", name="add_numbers")
|
|
|
|
# Test streaming with tool_choice
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="Use the tools as needed to help the user."),
|
|
UserMessage(content="Process the text 'streaming test' using the process_text tool.", source="user"),
|
|
]
|
|
|
|
chunks: List[str | CreateResult] = []
|
|
async for chunk in client.create_stream(
|
|
messages=messages,
|
|
tools=[pass_tool, add_tool],
|
|
tool_choice=pass_tool, # Force use of specific tool
|
|
):
|
|
chunks.append(chunk)
|
|
|
|
# Verify we got chunks and a final result
|
|
assert len(chunks) > 0
|
|
final_result = chunks[-1]
|
|
assert isinstance(final_result, CreateResult)
|
|
|
|
# Should get a tool call for the specified tool
|
|
assert isinstance(final_result.content, list)
|
|
assert len(final_result.content) >= 1
|
|
assert isinstance(final_result.content[0], FunctionCall)
|
|
assert final_result.content[0].name == "process_text"
|
|
|
|
# Test streaming without tools - should not include tool_choice
|
|
no_tools_messages: List[LLMMessage] = [
|
|
UserMessage(content="Tell me a short fact about cats.", source="user"),
|
|
]
|
|
|
|
no_tools_chunks: List[str | CreateResult] = []
|
|
async for chunk in client.create_stream(messages=no_tools_messages):
|
|
no_tools_chunks.append(chunk)
|
|
|
|
# Should get text response
|
|
assert len(no_tools_chunks) > 0
|
|
final_no_tools_result = no_tools_chunks[-1]
|
|
assert isinstance(final_no_tools_result, CreateResult)
|
|
assert isinstance(final_no_tools_result.content, str)
|
|
assert len(final_no_tools_result.content) > 0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_anthropic_tool_choice_none_value_with_actual_api() -> None:
|
|
"""Test tool_choice="none" with actual Anthropic API endpoints."""
|
|
api_key = os.getenv("ANTHROPIC_API_KEY")
|
|
if not api_key:
|
|
pytest.skip("ANTHROPIC_API_KEY not found in environment variables")
|
|
|
|
client = AnthropicChatCompletionClient(
|
|
model="claude-3-haiku-20240307",
|
|
api_key=api_key,
|
|
)
|
|
|
|
# Define tools
|
|
pass_tool = FunctionTool(_pass_function, description="Process input text", name="process_text")
|
|
add_tool = FunctionTool(_add_numbers, description="Add two numbers together", name="add_numbers")
|
|
|
|
# Test tool_choice="none" - should not use tools even when available
|
|
messages: List[LLMMessage] = [
|
|
SystemMessage(content="Answer the user's question directly without using tools."),
|
|
UserMessage(content="What is 2 + 2?", source="user"),
|
|
]
|
|
|
|
result = await client.create(
|
|
messages=messages,
|
|
tools=[pass_tool, add_tool],
|
|
tool_choice="none", # Disable tool usage
|
|
)
|
|
|
|
# Should get a text response, not tool calls
|
|
assert isinstance(result.content, str)
|