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e4b7ccf717
This fixes GitHub issue #4046 where certain Bedrock models don't support the stopSequences field. The fix adds support for drop_params and additional_drop_params parameters in BedrockCompletion._get_inference_config() to allow users to drop unsupported parameters from the inference config. Example usage: llm = LLM( model="bedrock/openai.gpt-oss-safeguard-120b", drop_params=True, additional_drop_params=["stopSequences"] ) This follows the same pattern as the Azure provider implementation. Co-Authored-By: João <joao@crewai.com>
943 lines
31 KiB
Python
943 lines
31 KiB
Python
import os
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import sys
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import types
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from unittest.mock import patch, MagicMock
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import pytest
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from crewai.llm import LLM
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from crewai.crew import Crew
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from crewai.agent import Agent
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from crewai.task import Task
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@pytest.fixture(autouse=True)
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def mock_aws_credentials():
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"""Automatically mock AWS credentials and boto3 Session for all tests in this module."""
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with patch.dict(os.environ, {
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"AWS_ACCESS_KEY_ID": "test-access-key",
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"AWS_SECRET_ACCESS_KEY": "test-secret-key",
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"AWS_DEFAULT_REGION": "us-east-1"
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}):
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# Mock boto3 Session to prevent actual AWS connections
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with patch('crewai.llms.providers.bedrock.completion.Session') as mock_session_class:
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# Create mock session instance
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mock_session_instance = MagicMock()
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mock_client = MagicMock()
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# Set up default mock responses to prevent hanging
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default_response = {
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'output': {
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'message': {
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'role': 'assistant',
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'content': [
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{'text': 'Test response'}
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]
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}
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},
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'usage': {
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'inputTokens': 10,
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'outputTokens': 5,
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'totalTokens': 15
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}
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}
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mock_client.converse.return_value = default_response
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mock_client.converse_stream.return_value = {'stream': []}
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# Configure the mock session instance to return the mock client
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mock_session_instance.client.return_value = mock_client
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# Configure the mock Session class to return the mock session instance
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mock_session_class.return_value = mock_session_instance
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yield mock_session_class, mock_client
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def test_bedrock_completion_is_used_when_bedrock_provider():
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"""
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Test that BedrockCompletion from completion.py is used when LLM uses provider 'bedrock'
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"""
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llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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assert llm.__class__.__name__ == "BedrockCompletion"
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assert llm.provider == "bedrock"
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assert llm.model == "anthropic.claude-3-5-sonnet-20241022-v2:0"
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def test_bedrock_completion_module_is_imported():
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"""
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Test that the completion module is properly imported when using Bedrock provider
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"""
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module_name = "crewai.llms.providers.bedrock.completion"
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# Remove module from cache if it exists
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if module_name in sys.modules:
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del sys.modules[module_name]
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# Create LLM instance - this should trigger the import
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LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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# Verify the module was imported
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assert module_name in sys.modules
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completion_mod = sys.modules[module_name]
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assert isinstance(completion_mod, types.ModuleType)
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# Verify the class exists in the module
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assert hasattr(completion_mod, 'BedrockCompletion')
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def test_native_bedrock_raises_error_when_initialization_fails():
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"""
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Test that LLM raises ImportError when native Bedrock completion fails.
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With the new behavior, when a native provider is in SUPPORTED_NATIVE_PROVIDERS
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but fails to instantiate, we raise an ImportError instead of silently falling back.
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This provides clearer error messages to users about missing dependencies.
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"""
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# Mock the _get_native_provider to return a failing class
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with patch('crewai.llm.LLM._get_native_provider') as mock_get_provider:
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class FailingCompletion:
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def __init__(self, *args, **kwargs):
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raise Exception("Native AWS Bedrock SDK failed")
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mock_get_provider.return_value = FailingCompletion
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# This should raise ImportError with clear message
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with pytest.raises(ImportError) as excinfo:
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LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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# Verify the error message is helpful
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assert "Error importing native provider" in str(excinfo.value)
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assert "Native AWS Bedrock SDK failed" in str(excinfo.value)
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def test_bedrock_completion_initialization_parameters():
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"""
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Test that BedrockCompletion is initialized with correct parameters
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"""
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llm = LLM(
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model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
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temperature=0.7,
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max_tokens=2000,
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top_p=0.9,
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top_k=40,
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region_name="us-west-2"
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)
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from crewai.llms.providers.bedrock.completion import BedrockCompletion
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assert isinstance(llm, BedrockCompletion)
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assert llm.model == "anthropic.claude-3-5-sonnet-20241022-v2:0"
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assert llm.temperature == 0.7
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assert llm.max_tokens == 2000
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assert llm.top_p == 0.9
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assert llm.top_k == 40
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assert llm.region_name == "us-west-2"
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def test_bedrock_specific_parameters():
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"""
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Test Bedrock-specific parameters like stop_sequences and streaming
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"""
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llm = LLM(
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model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
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stop_sequences=["Human:", "Assistant:"],
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stream=True,
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region_name="us-east-1"
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)
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from crewai.llms.providers.bedrock.completion import BedrockCompletion
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assert isinstance(llm, BedrockCompletion)
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assert llm.stop_sequences == ["Human:", "Assistant:"]
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assert llm.stream == True
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assert llm.region_name == "us-east-1"
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def test_bedrock_completion_call():
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"""
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Test that BedrockCompletion call method works
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"""
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llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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# Mock the call method on the instance
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with patch.object(llm, 'call', return_value="Hello! I'm Claude on Bedrock, ready to help.") as mock_call:
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result = llm.call("Hello, how are you?")
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assert result == "Hello! I'm Claude on Bedrock, ready to help."
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mock_call.assert_called_once_with("Hello, how are you?")
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def test_bedrock_completion_called_during_crew_execution():
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"""
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Test that BedrockCompletion.call is actually invoked when running a crew
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"""
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# Create the LLM instance first
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bedrock_llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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# Mock the call method on the specific instance
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with patch.object(bedrock_llm, 'call', return_value="Tokyo has 14 million people.") as mock_call:
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# Create agent with explicit LLM configuration
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agent = Agent(
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role="Research Assistant",
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goal="Find population info",
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backstory="You research populations.",
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llm=bedrock_llm,
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)
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task = Task(
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description="Find Tokyo population",
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expected_output="Population number",
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agent=agent,
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)
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crew = Crew(agents=[agent], tasks=[task])
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result = crew.kickoff()
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# Verify mock was called
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assert mock_call.called
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assert "14 million" in str(result)
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@pytest.mark.skip(reason="Crew execution test - may hang, needs investigation")
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def test_bedrock_completion_call_arguments():
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"""
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Test that BedrockCompletion.call is invoked with correct arguments
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"""
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# Create LLM instance first
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bedrock_llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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# Mock the instance method
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with patch.object(bedrock_llm, 'call') as mock_call:
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mock_call.return_value = "Task completed successfully."
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agent = Agent(
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role="Test Agent",
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goal="Complete a simple task",
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backstory="You are a test agent.",
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llm=bedrock_llm # Use same instance
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)
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task = Task(
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description="Say hello world",
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expected_output="Hello world",
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agent=agent,
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)
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crew = Crew(agents=[agent], tasks=[task])
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crew.kickoff()
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# Verify call was made
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assert mock_call.called
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# Check the arguments passed to the call method
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call_args = mock_call.call_args
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assert call_args is not None
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# The first argument should be the messages
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messages = call_args[0][0] # First positional argument
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assert isinstance(messages, (str, list))
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# Verify that the task description appears in the messages
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if isinstance(messages, str):
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assert "hello world" in messages.lower()
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elif isinstance(messages, list):
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message_content = str(messages).lower()
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assert "hello world" in message_content
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def test_multiple_bedrock_calls_in_crew():
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"""
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Test that BedrockCompletion.call is invoked multiple times for multiple tasks
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"""
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# Create LLM instance first
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bedrock_llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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# Mock the instance method
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with patch.object(bedrock_llm, 'call') as mock_call:
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mock_call.return_value = "Task completed."
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agent = Agent(
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role="Multi-task Agent",
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goal="Complete multiple tasks",
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backstory="You can handle multiple tasks.",
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llm=bedrock_llm # Use same instance
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)
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task1 = Task(
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description="First task",
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expected_output="First result",
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agent=agent,
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)
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task2 = Task(
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description="Second task",
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expected_output="Second result",
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agent=agent,
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)
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crew = Crew(
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agents=[agent],
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tasks=[task1, task2]
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)
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crew.kickoff()
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# Verify multiple calls were made
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assert mock_call.call_count >= 2 # At least one call per task
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# Verify each call had proper arguments
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for call in mock_call.call_args_list:
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assert len(call[0]) > 0 # Has positional arguments
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messages = call[0][0]
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assert messages is not None
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def test_bedrock_completion_with_tools():
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"""
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Test that BedrockCompletion.call is invoked with tools when agent has tools
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"""
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from crewai.tools import tool
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@tool
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def sample_tool(query: str) -> str:
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"""A sample tool for testing"""
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return f"Tool result for: {query}"
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# Create LLM instance first
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bedrock_llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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# Mock the instance method
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with patch.object(bedrock_llm, 'call') as mock_call:
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mock_call.return_value = "Task completed with tools."
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agent = Agent(
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role="Tool User",
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goal="Use tools to complete tasks",
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backstory="You can use tools.",
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llm=bedrock_llm, # Use same instance
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tools=[sample_tool]
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)
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task = Task(
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description="Use the sample tool",
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expected_output="Tool usage result",
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agent=agent,
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)
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crew = Crew(agents=[agent], tasks=[task])
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crew.kickoff()
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assert mock_call.called
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call_args = mock_call.call_args
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call_kwargs = call_args[1] if len(call_args) > 1 else {}
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if 'tools' in call_kwargs:
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assert call_kwargs['tools'] is not None
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assert len(call_kwargs['tools']) > 0
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def test_bedrock_raises_error_when_model_not_found(mock_aws_credentials):
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"""Test that BedrockCompletion raises appropriate error when model not found"""
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from botocore.exceptions import ClientError
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# Get the mock client from the fixture
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_, mock_client = mock_aws_credentials
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error_response = {
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'Error': {
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'Code': 'ResourceNotFoundException',
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'Message': 'Could not resolve the foundation model from the model identifier'
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}
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}
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mock_client.converse.side_effect = ClientError(error_response, 'converse')
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llm = LLM(model="bedrock/model-doesnt-exist")
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with pytest.raises(Exception): # Should raise some error for unsupported model
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llm.call("Hello")
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def test_bedrock_aws_credentials_configuration():
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"""
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Test that AWS credentials configuration works properly
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"""
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# Test with environment variables
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with patch.dict(os.environ, {
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"AWS_ACCESS_KEY_ID": "test-access-key",
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"AWS_SECRET_ACCESS_KEY": "test-secret-key",
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"AWS_DEFAULT_REGION": "us-east-1"
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}):
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llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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from crewai.llms.providers.bedrock.completion import BedrockCompletion
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assert isinstance(llm, BedrockCompletion)
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assert llm.region_name == "us-east-1"
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# Test with explicit credentials
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llm_explicit = LLM(
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model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
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aws_access_key_id="explicit-key",
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aws_secret_access_key="explicit-secret",
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region_name="us-west-2"
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)
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assert isinstance(llm_explicit, BedrockCompletion)
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assert llm_explicit.region_name == "us-west-2"
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def test_bedrock_model_capabilities():
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"""
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Test that model capabilities are correctly identified
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"""
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# Test Claude model
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llm_claude = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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from crewai.llms.providers.bedrock.completion import BedrockCompletion
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assert isinstance(llm_claude, BedrockCompletion)
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assert llm_claude.is_claude_model == True
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assert llm_claude.supports_tools == True
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# Test other Bedrock model
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llm_titan = LLM(model="bedrock/amazon.titan-text-express-v1")
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assert isinstance(llm_titan, BedrockCompletion)
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assert llm_titan.supports_tools == True
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def test_bedrock_inference_config():
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"""
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Test that inference config is properly prepared
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"""
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llm = LLM(
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model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
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temperature=0.7,
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top_p=0.9,
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top_k=40,
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max_tokens=1000
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)
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from crewai.llms.providers.bedrock.completion import BedrockCompletion
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assert isinstance(llm, BedrockCompletion)
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# Test config preparation
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config = llm._get_inference_config()
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# Verify config has the expected parameters
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assert 'temperature' in config
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assert config['temperature'] == 0.7
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assert 'topP' in config
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assert config['topP'] == 0.9
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assert 'maxTokens' in config
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assert config['maxTokens'] == 1000
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assert 'topK' in config
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assert config['topK'] == 40
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def test_bedrock_model_detection():
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"""
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Test that various Bedrock model formats are properly detected
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"""
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# Test Bedrock model naming patterns
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bedrock_test_cases = [
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"bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
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"bedrock/anthropic.claude-3-haiku-20240307-v1:0",
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"bedrock/amazon.titan-text-express-v1",
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"bedrock/meta.llama3-70b-instruct-v1:0"
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]
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for model_name in bedrock_test_cases:
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llm = LLM(model=model_name)
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from crewai.llms.providers.bedrock.completion import BedrockCompletion
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assert isinstance(llm, BedrockCompletion), f"Failed for model: {model_name}"
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def test_bedrock_supports_stop_words():
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"""
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Test that Bedrock models support stop sequences
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"""
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llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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assert llm.supports_stop_words() == True
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def test_bedrock_context_window_size():
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"""
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Test that Bedrock models return correct context window sizes
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"""
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# Test Claude 3.5 Sonnet
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llm_claude = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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context_size_claude = llm_claude.get_context_window_size()
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assert context_size_claude > 150000 # Should be substantial (200K tokens with ratio)
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# Test Titan
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llm_titan = LLM(model="bedrock/amazon.titan-text-express-v1")
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context_size_titan = llm_titan.get_context_window_size()
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assert context_size_titan > 5000 # Should have 8K context window
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def test_bedrock_message_formatting():
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"""
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Test that messages are properly formatted for Bedrock Converse API
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"""
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llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
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# Test message formatting
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test_messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello"},
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{"role": "assistant", "content": "Hi there!"},
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{"role": "user", "content": "How are you?"}
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]
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formatted_messages, system_message = llm._format_messages_for_converse(test_messages)
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# System message should be extracted
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assert system_message == "You are a helpful assistant."
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# Remaining messages should be in Converse format
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assert len(formatted_messages) >= 3 # Should have user, assistant, user messages
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# First message should be user role
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assert formatted_messages[0]["role"] == "user"
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# Second should be assistant
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assert formatted_messages[1]["role"] == "assistant"
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# Messages should have content array with text
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assert isinstance(formatted_messages[0]["content"], list)
|
|
assert "text" in formatted_messages[0]["content"][0]
|
|
|
|
|
|
def test_bedrock_streaming_parameter():
|
|
"""
|
|
Test that streaming parameter is properly handled
|
|
"""
|
|
# Test non-streaming
|
|
llm_no_stream = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", stream=False)
|
|
assert llm_no_stream.stream == False
|
|
|
|
# Test streaming
|
|
llm_stream = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", stream=True)
|
|
assert llm_stream.stream == True
|
|
|
|
|
|
def test_bedrock_tool_conversion():
|
|
"""
|
|
Test that tools are properly converted to Bedrock Converse format
|
|
"""
|
|
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
|
|
|
|
# Mock tool in CrewAI format
|
|
crewai_tools = [{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "test_tool",
|
|
"description": "A test tool",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"query": {"type": "string", "description": "Search query"}
|
|
},
|
|
"required": ["query"]
|
|
}
|
|
}
|
|
}]
|
|
|
|
# Test tool conversion
|
|
bedrock_tools = llm._format_tools_for_converse(crewai_tools)
|
|
|
|
assert len(bedrock_tools) == 1
|
|
# Bedrock tools should have toolSpec structure
|
|
assert "toolSpec" in bedrock_tools[0]
|
|
assert bedrock_tools[0]["toolSpec"]["name"] == "test_tool"
|
|
assert bedrock_tools[0]["toolSpec"]["description"] == "A test tool"
|
|
assert "inputSchema" in bedrock_tools[0]["toolSpec"]
|
|
|
|
|
|
def test_bedrock_environment_variable_credentials(mock_aws_credentials):
|
|
"""
|
|
Test that AWS credentials are properly loaded from environment
|
|
"""
|
|
mock_session_class, _ = mock_aws_credentials
|
|
|
|
# Reset the mock to clear any previous calls
|
|
mock_session_class.reset_mock()
|
|
|
|
with patch.dict(os.environ, {
|
|
"AWS_ACCESS_KEY_ID": "test-access-key-123",
|
|
"AWS_SECRET_ACCESS_KEY": "test-secret-key-456"
|
|
}):
|
|
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
|
|
|
|
# Verify Session was called with environment credentials
|
|
assert mock_session_class.called
|
|
# Get the most recent call - Session is called as Session(...)
|
|
call_kwargs = mock_session_class.call_args[1] if mock_session_class.call_args else {}
|
|
assert call_kwargs.get('aws_access_key_id') == "test-access-key-123"
|
|
assert call_kwargs.get('aws_secret_access_key') == "test-secret-key-456"
|
|
|
|
|
|
def test_bedrock_token_usage_tracking():
|
|
"""
|
|
Test that token usage is properly tracked for Bedrock responses
|
|
"""
|
|
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
|
|
|
|
# Mock the Bedrock response with usage information
|
|
with patch.object(llm.client, 'converse') as mock_converse:
|
|
mock_response = {
|
|
'output': {
|
|
'message': {
|
|
'role': 'assistant',
|
|
'content': [
|
|
{'text': 'test response'}
|
|
]
|
|
}
|
|
},
|
|
'usage': {
|
|
'inputTokens': 50,
|
|
'outputTokens': 25,
|
|
'totalTokens': 75
|
|
}
|
|
}
|
|
mock_converse.return_value = mock_response
|
|
|
|
result = llm.call("Hello")
|
|
|
|
# Verify the response
|
|
assert result == "test response"
|
|
|
|
# Verify token usage was tracked
|
|
assert llm._token_usage['prompt_tokens'] == 50
|
|
assert llm._token_usage['completion_tokens'] == 25
|
|
assert llm._token_usage['total_tokens'] == 75
|
|
|
|
|
|
def test_bedrock_tool_use_conversation_flow():
|
|
"""
|
|
Test that the Bedrock completion properly handles tool use conversation flow
|
|
"""
|
|
from unittest.mock import Mock
|
|
|
|
# Create BedrockCompletion instance
|
|
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
|
|
|
|
# Mock tool function
|
|
def mock_weather_tool(location: str) -> str:
|
|
return f"The weather in {location} is sunny and 75°F"
|
|
|
|
available_functions = {"get_weather": mock_weather_tool}
|
|
|
|
# Mock the Bedrock client responses
|
|
with patch.object(llm.client, 'converse') as mock_converse:
|
|
# First response: tool use request
|
|
tool_use_response = {
|
|
'output': {
|
|
'message': {
|
|
'role': 'assistant',
|
|
'content': [
|
|
{
|
|
'toolUse': {
|
|
'toolUseId': 'tool-123',
|
|
'name': 'get_weather',
|
|
'input': {'location': 'San Francisco'}
|
|
}
|
|
}
|
|
]
|
|
}
|
|
},
|
|
'usage': {
|
|
'inputTokens': 100,
|
|
'outputTokens': 50,
|
|
'totalTokens': 150
|
|
}
|
|
}
|
|
|
|
# Second response: final answer after tool execution
|
|
final_response = {
|
|
'output': {
|
|
'message': {
|
|
'role': 'assistant',
|
|
'content': [
|
|
{'text': 'Based on the weather data, it is sunny and 75°F in San Francisco.'}
|
|
]
|
|
}
|
|
},
|
|
'usage': {
|
|
'inputTokens': 120,
|
|
'outputTokens': 30,
|
|
'totalTokens': 150
|
|
}
|
|
}
|
|
|
|
# Configure mock to return different responses on successive calls
|
|
mock_converse.side_effect = [tool_use_response, final_response]
|
|
|
|
# Test the call
|
|
messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}]
|
|
result = llm.call(
|
|
messages=messages,
|
|
available_functions=available_functions
|
|
)
|
|
|
|
# Verify the final response contains the weather information
|
|
assert "sunny" in result.lower() or "75" in result
|
|
|
|
# Verify that the API was called twice (once for tool use, once for final answer)
|
|
assert mock_converse.call_count == 2
|
|
|
|
|
|
def test_bedrock_handles_cohere_conversation_requirements():
|
|
"""
|
|
Test that Bedrock properly handles Cohere model's requirement for user message at end
|
|
"""
|
|
llm = LLM(model="bedrock/cohere.command-r-plus-v1:0")
|
|
|
|
# Test message formatting with conversation ending in assistant message
|
|
test_messages = [
|
|
{"role": "user", "content": "Hello"},
|
|
{"role": "assistant", "content": "Hi there!"}
|
|
]
|
|
|
|
formatted_messages, system_message = llm._format_messages_for_converse(test_messages)
|
|
|
|
# For Cohere models, should add a user message at the end
|
|
assert formatted_messages[-1]["role"] == "user"
|
|
assert "continue" in formatted_messages[-1]["content"][0]["text"].lower()
|
|
|
|
|
|
def test_bedrock_client_error_handling():
|
|
"""
|
|
Test that Bedrock properly handles various AWS client errors
|
|
"""
|
|
from botocore.exceptions import ClientError
|
|
|
|
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
|
|
|
|
# Test ValidationException
|
|
with patch.object(llm.client, 'converse') as mock_converse:
|
|
error_response = {
|
|
'Error': {
|
|
'Code': 'ValidationException',
|
|
'Message': 'Invalid request format'
|
|
}
|
|
}
|
|
mock_converse.side_effect = ClientError(error_response, 'converse')
|
|
|
|
with pytest.raises(ValueError) as exc_info:
|
|
llm.call("Hello")
|
|
assert "validation" in str(exc_info.value).lower()
|
|
|
|
# Test ThrottlingException
|
|
with patch.object(llm.client, 'converse') as mock_converse:
|
|
error_response = {
|
|
'Error': {
|
|
'Code': 'ThrottlingException',
|
|
'Message': 'Rate limit exceeded'
|
|
}
|
|
}
|
|
mock_converse.side_effect = ClientError(error_response, 'converse')
|
|
|
|
with pytest.raises(RuntimeError) as exc_info:
|
|
llm.call("Hello")
|
|
assert "throttled" in str(exc_info.value).lower()
|
|
|
|
|
|
def test_bedrock_stop_sequences_sync():
|
|
"""Test that stop and stop_sequences attributes stay synchronized."""
|
|
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
|
|
|
|
# Test setting stop as a list
|
|
llm.stop = ["\nObservation:", "\nThought:"]
|
|
assert list(llm.stop_sequences) == ["\nObservation:", "\nThought:"]
|
|
assert llm.stop == ["\nObservation:", "\nThought:"]
|
|
|
|
# Test setting stop as a string
|
|
llm.stop = "\nFinal Answer:"
|
|
assert list(llm.stop_sequences) == ["\nFinal Answer:"]
|
|
assert llm.stop == ["\nFinal Answer:"]
|
|
|
|
# Test setting stop as None
|
|
llm.stop = None
|
|
assert list(llm.stop_sequences) == []
|
|
assert llm.stop == []
|
|
|
|
|
|
def test_bedrock_stop_sequences_sent_to_api():
|
|
"""Test that stop_sequences are properly sent to the Bedrock API."""
|
|
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
|
|
|
|
# Set stop sequences via the stop attribute (simulating CrewAgentExecutor)
|
|
llm.stop = ["\nObservation:", "\nThought:"]
|
|
|
|
# Patch the API call to capture parameters without making real call
|
|
with patch.object(llm.client, 'converse') as mock_converse:
|
|
mock_response = {
|
|
'output': {
|
|
'message': {
|
|
'role': 'assistant',
|
|
'content': [{'text': 'Hello'}]
|
|
}
|
|
},
|
|
'usage': {
|
|
'inputTokens': 10,
|
|
'outputTokens': 5,
|
|
'totalTokens': 15
|
|
}
|
|
}
|
|
mock_converse.return_value = mock_response
|
|
|
|
llm.call("Say hello in one word")
|
|
|
|
# Verify stop_sequences were passed to the API in the inference config
|
|
call_kwargs = mock_converse.call_args[1]
|
|
assert "inferenceConfig" in call_kwargs
|
|
assert "stopSequences" in call_kwargs["inferenceConfig"]
|
|
assert call_kwargs["inferenceConfig"]["stopSequences"] == ["\nObservation:", "\nThought:"]
|
|
|
|
|
|
def test_bedrock_drop_params_removes_stop_sequences():
|
|
"""Test that drop_params with additional_drop_params removes stopSequences from inference config.
|
|
|
|
This tests the fix for GitHub issue #4046 where certain Bedrock models
|
|
don't support the stopSequences field.
|
|
"""
|
|
llm = LLM(
|
|
model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
|
|
stop_sequences=["\nObservation:", "\nThought:"],
|
|
drop_params=True,
|
|
additional_drop_params=["stopSequences"],
|
|
)
|
|
|
|
from crewai.llms.providers.bedrock.completion import BedrockCompletion
|
|
assert isinstance(llm, BedrockCompletion)
|
|
|
|
# Get the inference config
|
|
config = llm._get_inference_config()
|
|
|
|
# Verify stopSequences is NOT in the config when drop_params is enabled
|
|
assert "stopSequences" not in config
|
|
|
|
|
|
def test_bedrock_drop_params_preserves_other_params():
|
|
"""Test that drop_params only removes specified parameters, not others."""
|
|
llm = LLM(
|
|
model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
|
|
temperature=0.7,
|
|
max_tokens=1000,
|
|
top_p=0.9,
|
|
stop_sequences=["\nObservation:"],
|
|
drop_params=True,
|
|
additional_drop_params=["stopSequences"],
|
|
)
|
|
|
|
from crewai.llms.providers.bedrock.completion import BedrockCompletion
|
|
assert isinstance(llm, BedrockCompletion)
|
|
|
|
# Get the inference config
|
|
config = llm._get_inference_config()
|
|
|
|
# Verify stopSequences is removed
|
|
assert "stopSequences" not in config
|
|
|
|
# Verify other parameters are preserved
|
|
assert "temperature" in config
|
|
assert config["temperature"] == 0.7
|
|
assert "maxTokens" in config
|
|
assert config["maxTokens"] == 1000
|
|
assert "topP" in config
|
|
assert config["topP"] == 0.9
|
|
|
|
|
|
def test_bedrock_without_drop_params_keeps_stop_sequences():
|
|
"""Test that without drop_params, stopSequences is included in inference config."""
|
|
llm = LLM(
|
|
model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
|
|
stop_sequences=["\nObservation:", "\nThought:"],
|
|
)
|
|
|
|
from crewai.llms.providers.bedrock.completion import BedrockCompletion
|
|
assert isinstance(llm, BedrockCompletion)
|
|
|
|
# Get the inference config
|
|
config = llm._get_inference_config()
|
|
|
|
# Verify stopSequences IS in the config when drop_params is not set
|
|
assert "stopSequences" in config
|
|
assert config["stopSequences"] == ["\nObservation:", "\nThought:"]
|
|
|
|
|
|
def test_bedrock_drop_params_false_keeps_stop_sequences():
|
|
"""Test that with drop_params=False, stopSequences is included even with additional_drop_params."""
|
|
llm = LLM(
|
|
model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
|
|
stop_sequences=["\nObservation:"],
|
|
drop_params=False,
|
|
additional_drop_params=["stopSequences"],
|
|
)
|
|
|
|
from crewai.llms.providers.bedrock.completion import BedrockCompletion
|
|
assert isinstance(llm, BedrockCompletion)
|
|
|
|
# Get the inference config
|
|
config = llm._get_inference_config()
|
|
|
|
# Verify stopSequences IS in the config when drop_params is False
|
|
assert "stopSequences" in config
|
|
assert config["stopSequences"] == ["\nObservation:"]
|
|
|
|
|
|
def test_bedrock_drop_params_multiple_params():
|
|
"""Test that drop_params can remove multiple parameters."""
|
|
llm = LLM(
|
|
model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
|
|
temperature=0.7,
|
|
top_p=0.9,
|
|
stop_sequences=["\nObservation:"],
|
|
drop_params=True,
|
|
additional_drop_params=["stopSequences", "topP"],
|
|
)
|
|
|
|
from crewai.llms.providers.bedrock.completion import BedrockCompletion
|
|
assert isinstance(llm, BedrockCompletion)
|
|
|
|
# Get the inference config
|
|
config = llm._get_inference_config()
|
|
|
|
# Verify both stopSequences and topP are removed
|
|
assert "stopSequences" not in config
|
|
assert "topP" not in config
|
|
|
|
# Verify temperature is preserved
|
|
assert "temperature" in config
|
|
assert config["temperature"] == 0.7
|
|
|
|
|
|
def test_bedrock_drop_params_api_call():
|
|
"""Test that drop_params affects the actual API call parameters."""
|
|
llm = LLM(
|
|
model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
|
|
stop_sequences=["\nObservation:"],
|
|
drop_params=True,
|
|
additional_drop_params=["stopSequences"],
|
|
)
|
|
|
|
# Patch the API call to capture parameters without making real call
|
|
with patch.object(llm.client, 'converse') as mock_converse:
|
|
mock_response = {
|
|
'output': {
|
|
'message': {
|
|
'role': 'assistant',
|
|
'content': [{'text': 'Hello'}]
|
|
}
|
|
},
|
|
'usage': {
|
|
'inputTokens': 10,
|
|
'outputTokens': 5,
|
|
'totalTokens': 15
|
|
}
|
|
}
|
|
mock_converse.return_value = mock_response
|
|
|
|
llm.call("Say hello")
|
|
|
|
# Verify stopSequences is NOT in the inference config sent to the API
|
|
call_kwargs = mock_converse.call_args[1]
|
|
assert "inferenceConfig" in call_kwargs
|
|
assert "stopSequences" not in call_kwargs["inferenceConfig"]
|