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13 Commits

Author SHA1 Message Date
Brandon Hancock
55dbe6cc7d Drop prints 2025-02-20 12:16:06 -05:00
Brandon Hancock (bhancock_ai)
65e102c2a3 Merge branch 'main' into bugfix/support-tool-calling 2025-02-20 12:06:05 -05:00
Brandon Hancock
854b47ac95 Fix issues pointed out by lorenze 2025-02-20 12:03:59 -05:00
Brandon Hancock (bhancock_ai)
14503bc43b imporve HITL (#2169)
* imporve HITL

* fix failing test

* fix failing test part 2

* Drop extra logs that were causing confusion

---------

Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
2025-02-20 12:01:49 -05:00
Brandon Hancock (bhancock_ai)
c06feac175 Merge branch 'main' into bugfix/support-tool-calling 2025-02-20 11:46:47 -05:00
Brandon Hancock
7014e8dc9b fix other test that fails in ci/cd 2025-02-19 11:00:46 -05:00
Brandon Hancock
4d2757a4d6 Update cassettes again 3 2025-02-18 16:52:30 -05:00
Brandon Hancock
00595e8851 Update cassettes again 2 2025-02-18 16:45:46 -05:00
Brandon Hancock
f895af1d32 Update cassettes again 2025-02-18 16:42:13 -05:00
Brandon Hancock
b1021acf02 Update cassettes 2025-02-18 16:35:57 -05:00
Brandon Hancock (bhancock_ai)
eee5a626ff Merge branch 'main' into bugfix/support-tool-calling 2025-02-18 16:30:08 -05:00
Brandon Hancock
6e82c8d38c Fix failing tests 2025-02-18 16:29:48 -05:00
Brandon Hancock
4f06880015 Check the right property 2025-02-18 15:31:12 -05:00
18 changed files with 147 additions and 3551 deletions

View File

@@ -114,10 +114,15 @@ class CrewAgentExecutorMixin:
prompt = (
"\n\n=====\n"
"## HUMAN FEEDBACK: Provide feedback on the Final Result and Agent's actions.\n"
"Respond with 'looks good' to accept or provide specific improvement requests.\n"
"You can provide multiple rounds of feedback until satisfied.\n"
"Please follow these guidelines:\n"
" - If you are happy with the result, simply hit Enter without typing anything.\n"
" - Otherwise, provide specific improvement requests.\n"
" - You can provide multiple rounds of feedback until satisfied.\n"
"=====\n"
)
self._printer.print(content=prompt, color="bold_yellow")
return input()
response = input()
if response.strip() != "":
self._printer.print(content="\nProcessing your feedback...", color="cyan")
return response

View File

@@ -31,11 +31,11 @@ class OutputConverter(BaseModel, ABC):
)
@abstractmethod
def to_pydantic(self, current_attempt=1):
def to_pydantic(self, current_attempt=1) -> BaseModel:
"""Convert text to pydantic."""
pass
@abstractmethod
def to_json(self, current_attempt=1):
def to_json(self, current_attempt=1) -> dict:
"""Convert text to json."""
pass

View File

@@ -548,10 +548,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self, initial_answer: AgentFinish, feedback: str
) -> AgentFinish:
"""Process feedback for training scenarios with single iteration."""
self._printer.print(
content="\nProcessing training feedback.\n",
color="yellow",
)
self._handle_crew_training_output(initial_answer, feedback)
self.messages.append(
self._format_msg(
@@ -571,9 +567,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
answer = current_answer
while self.ask_for_human_input:
response = self._get_llm_feedback_response(feedback)
if not self._feedback_requires_changes(response):
# If the user provides a blank response, assume they are happy with the result
if feedback.strip() == "":
self.ask_for_human_input = False
else:
answer = self._process_feedback_iteration(feedback)
@@ -581,27 +576,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
return answer
def _get_llm_feedback_response(self, feedback: str) -> Optional[str]:
"""Get LLM classification of whether feedback requires changes."""
prompt = self._i18n.slice("human_feedback_classification").format(
feedback=feedback
)
message = self._format_msg(prompt, role="system")
for retry in range(MAX_LLM_RETRY):
try:
response = self.llm.call([message], callbacks=self.callbacks)
return response.strip().lower() if response else None
except Exception as error:
self._log_feedback_error(retry, error)
self._log_max_retries_exceeded()
return None
def _feedback_requires_changes(self, response: Optional[str]) -> bool:
"""Determine if feedback response indicates need for changes."""
return response == "true" if response else False
def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
"""Process a single feedback iteration."""
self.messages.append(

View File

@@ -11,7 +11,6 @@ from typing import Any, Dict, Optional, Union
from pydantic import BaseModel
from crewai.flow.persistence.base import FlowPersistence
from crewai.utilities.datetime_compat import UTC
class SQLiteFlowPersistence(FlowPersistence):
@@ -96,7 +95,7 @@ class SQLiteFlowPersistence(FlowPersistence):
""", (
flow_uuid,
method_name,
datetime.now(UTC).isoformat(),
datetime.utcnow().isoformat(),
json.dumps(state_dict),
))

View File

@@ -26,9 +26,9 @@ from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning)
import litellm
from litellm import Choices, get_supported_openai_params
from litellm import Choices
from litellm.types.utils import ModelResponse
from litellm.utils import supports_response_schema
from litellm.utils import get_supported_openai_params, supports_response_schema
from crewai.traces.unified_trace_controller import trace_llm_call
@@ -449,7 +449,7 @@ class LLM:
def supports_function_calling(self) -> bool:
try:
params = get_supported_openai_params(model=self.model)
return "response_format" in params
return params is not None and "tools" in params
except Exception as e:
logging.error(f"Failed to get supported params: {str(e)}")
return False
@@ -457,7 +457,7 @@ class LLM:
def supports_stop_words(self) -> bool:
try:
params = get_supported_openai_params(model=self.model)
return "stop" in params
return params is not None and "stop" in params
except Exception as e:
logging.error(f"Failed to get supported params: {str(e)}")
return False

View File

@@ -1,7 +1,8 @@
import ast
import datetime
import json
import time
from datetime import datetime
from datetime import UTC
from difflib import SequenceMatcher
from json import JSONDecodeError
from textwrap import dedent
@@ -17,7 +18,6 @@ from crewai.tools import BaseTool
from crewai.tools.structured_tool import CrewStructuredTool
from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
from crewai.utilities import I18N, Converter, ConverterError, Printer
from crewai.utilities.datetime_compat import UTC
from crewai.utilities.events.crewai_event_bus import crewai_event_bus
from crewai.utilities.events.tool_usage_events import (
ToolSelectionErrorEvent,
@@ -158,7 +158,7 @@ class ToolUsage:
self.task.increment_tools_errors()
started_at = time.time()
started_at_trace = datetime.now(UTC)
started_at_trace = datetime.datetime.now(UTC)
from_cache = False
result = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
@@ -506,8 +506,8 @@ class ToolUsage:
event_data = self._prepare_event_data(tool, tool_calling)
event_data.update(
{
"started_at": datetime.fromtimestamp(started_at),
"finished_at": datetime.fromtimestamp(finished_at),
"started_at": datetime.datetime.fromtimestamp(started_at),
"finished_at": datetime.datetime.fromtimestamp(finished_at),
"from_cache": from_cache,
}
)

View File

@@ -1,6 +1,6 @@
import inspect
import os
from datetime import datetime
from datetime import UTC, datetime
from functools import wraps
from typing import Any, Awaitable, Callable, Dict, List, Optional
from uuid import uuid4
@@ -14,7 +14,6 @@ from crewai.traces.models import (
LLMResponse,
ToolCall,
)
from crewai.utilities.datetime_compat import UTC
class UnifiedTraceController:

View File

@@ -23,7 +23,6 @@
"summary": "This is a summary of our conversation so far:\n{merged_summary}",
"manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
"formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
"human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\"",
"conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
"feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary."
},

View File

@@ -1,5 +1,4 @@
from .converter import Converter, ConverterError
from .datetime_compat import UTC
from .file_handler import FileHandler
from .i18n import I18N
from .internal_instructor import InternalInstructor
@@ -23,7 +22,6 @@ __all__ = [
"Printer",
"Prompts",
"RPMController",
"UTC",
"YamlParser",
"LLMContextLengthExceededException",
"EmbeddingConfigurator",

View File

@@ -20,11 +20,11 @@ class ConverterError(Exception):
class Converter(OutputConverter):
"""Class that converts text into either pydantic or json."""
def to_pydantic(self, current_attempt=1):
def to_pydantic(self, current_attempt=1) -> BaseModel:
"""Convert text to pydantic."""
try:
if self.llm.supports_function_calling():
return self._create_instructor().to_pydantic()
result = self._create_instructor().to_pydantic()
else:
response = self.llm.call(
[
@@ -32,18 +32,40 @@ class Converter(OutputConverter):
{"role": "user", "content": self.text},
]
)
return self.model.model_validate_json(response)
try:
# Try to directly validate the response JSON
result = self.model.model_validate_json(response)
except ValidationError:
# If direct validation fails, attempt to extract valid JSON
result = handle_partial_json(response, self.model, False, None)
# Ensure result is a BaseModel instance
if not isinstance(result, BaseModel):
if isinstance(result, dict):
result = self.model.parse_obj(result)
elif isinstance(result, str):
try:
parsed = json.loads(result)
result = self.model.parse_obj(parsed)
except Exception as parse_err:
raise ConverterError(
f"Failed to convert partial JSON result into Pydantic: {parse_err}"
)
else:
raise ConverterError(
"handle_partial_json returned an unexpected type."
)
return result
except ValidationError as e:
if current_attempt < self.max_attempts:
return self.to_pydantic(current_attempt + 1)
raise ConverterError(
f"Failed to convert text into a Pydantic model due to the following validation error: {e}"
f"Failed to convert text into a Pydantic model due to validation error: {e}"
)
except Exception as e:
if current_attempt < self.max_attempts:
return self.to_pydantic(current_attempt + 1)
raise ConverterError(
f"Failed to convert text into a Pydantic model due to the following error: {e}"
f"Failed to convert text into a Pydantic model due to error: {e}"
)
def to_json(self, current_attempt=1):
@@ -197,11 +219,15 @@ def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
if llm.supports_function_calling():
model_schema = PydanticSchemaParser(model=model).get_schema()
instructions += (
f"\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
f"The JSON must follow this schema exactly:\n```json\n{model_schema}\n```"
)
else:
model_description = generate_model_description(model)
instructions += f"\n\nThe JSON should follow this format:\n{model_description}"
instructions += (
f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
f"The JSON must follow this format exactly:\n{model_description}"
)
return instructions

View File

@@ -1,23 +0,0 @@
"""Compatibility module for datetime functionality across Python versions.
This module provides timezone constants that work consistently across different
Python versions, particularly focusing on maintaining compatibility between
Python 3.10 and newer versions.
Notes:
- In Python 3.10, datetime.UTC is not available, so we use timezone.utc
- In Python 3.11+, this provides equivalent functionality to datetime.UTC
- This implementation maintains consistent behavior across versions for
timezone-aware datetime operations
- No known limitations or edge cases between versions
- Safe to use with DST transitions and leap years
- Maintains exact timezone offset (always UTC+00:00)
Example:
>>> from datetime import datetime
>>> from crewai.utilities.datetime_compat import UTC
>>> dt = datetime.now(UTC) # Creates timezone-aware datetime with UTC
"""
from datetime import timezone
UTC = timezone.utc # Equivalent to datetime.UTC (Python 3.11+)

View File

@@ -1,7 +1,6 @@
"""Test Agent creation and execution basic functionality."""
import os
from datetime import datetime, timezone
from unittest import mock
from unittest.mock import patch
@@ -9,7 +8,7 @@ import pytest
from crewai import Agent, Crew, Task
from crewai.agents.cache import CacheHandler
from crewai.agents.crew_agent_executor import CrewAgentExecutor
from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
@@ -18,7 +17,6 @@ from crewai.tools import tool
from crewai.tools.tool_calling import InstructorToolCalling
from crewai.tools.tool_usage import ToolUsage
from crewai.utilities import RPMController
from crewai.utilities.datetime_compat import UTC
from crewai.utilities.events import crewai_event_bus
from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
@@ -917,10 +915,9 @@ def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_tool_usage_information_is_appended_to_agent():
from datetime import datetime, timezone
from datetime import UTC, datetime
from crewai.tools import BaseTool
from crewai.utilities.datetime_compat import UTC
class MyCustomTool(BaseTool):
name: str = "Decide Greetings"
@@ -930,9 +927,8 @@ def test_tool_usage_information_is_appended_to_agent():
return "Howdy!"
fixed_datetime = datetime(2025, 2, 10, 12, 0, 0, tzinfo=UTC)
with patch("crewai.tools.tool_usage.datetime", autospec=True) as mock_datetime:
with patch("datetime.datetime") as mock_datetime:
mock_datetime.now.return_value = fixed_datetime
mock_datetime.fromtimestamp = datetime.fromtimestamp
mock_datetime.side_effect = lambda *args, **kw: datetime(*args, **kw)
agent1 = Agent(
@@ -1002,23 +998,35 @@ def test_agent_human_input():
# Side effect function for _ask_human_input to simulate multiple feedback iterations
feedback_responses = iter(
[
"Don't say hi, say Hello instead!", # First feedback
"looks good", # Second feedback to exit loop
"Don't say hi, say Hello instead!", # First feedback: instruct change
"", # Second feedback: empty string signals acceptance
]
)
def ask_human_input_side_effect(*args, **kwargs):
return next(feedback_responses)
with patch.object(
CrewAgentExecutor, "_ask_human_input", side_effect=ask_human_input_side_effect
) as mock_human_input:
# Patch both _ask_human_input and _invoke_loop to avoid real API/network calls.
with (
patch.object(
CrewAgentExecutor,
"_ask_human_input",
side_effect=ask_human_input_side_effect,
) as mock_human_input,
patch.object(
CrewAgentExecutor,
"_invoke_loop",
return_value=AgentFinish(output="Hello", thought="", text=""),
) as mock_invoke_loop,
):
# Execute the task
output = agent.execute_task(task)
# Assertions to ensure the agent behaves correctly
assert mock_human_input.call_count == 2 # Should have asked for feedback twice
assert output.strip().lower() == "hello" # Final output should be 'Hello'
# Assertions to ensure the agent behaves correctly.
# It should have requested feedback twice.
assert mock_human_input.call_count == 2
# The final result should be processed to "Hello"
assert output.strip().lower() == "hello"
def test_interpolate_inputs():

View File

@@ -1,520 +0,0 @@
interactions:
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@@ -1,5 +1,5 @@
import os
from datetime import datetime
from datetime import UTC, datetime
from unittest.mock import MagicMock, patch
from uuid import UUID
@@ -21,7 +21,6 @@ from crewai.traces.unified_trace_controller import (
trace_flow_step,
trace_llm_call,
)
from crewai.utilities.datetime_compat import UTC
class TestUnifiedTraceController:

View File

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other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
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you distribute or make available the Llama Materials (or any derivative works
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or any derivative works thereof, from a Licensee as part\\nof an integrated
end user product, then Section 2 of this Agreement will not apply to you. \\n\\n
\ iii. You must retain in all copies of the Llama Materials that you distribute
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UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
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IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
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\\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
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owned by or associated with the other or any of its affiliates, \\nexcept as
required for reasonable and customary use in describing and redistributing the
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you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required
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OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
\ a. No trademark licenses are granted under this Agreement, and in connection
with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
owned by or associated with the other or any of its affiliates, \\nexcept as
required for reasonable and customary use in describing and redistributing the
Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required
\\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s
brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/).
All goodwill arising out of your use of the Mark \\nwill inure to the benefit
of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and
derivatives made by or for Meta, with respect to any\\n derivative works
and modifications of the Llama Materials that are made by you, as between you
and Meta,\\n you are and will be the owner of such derivative works and modifications.\\n\\n
\ c. If you institute litigation or other proceedings against Meta or any
entity (including a cross-claim or\\n counterclaim in a lawsuit) alleging
that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n
\ of any of the foregoing, constitutes infringement of intellectual property
or other rights owned or licensable\\n by you, then any licenses granted
to you under this Agreement shall terminate as of the date such litigation or\\n
\ claim is filed or instituted. You will indemnify and hold harmless Meta
from and against any claim by any third\\n party arising out of or related
to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination.
The term of this Agreement will commence upon your acceptance of this Agreement
or access\\nto the Llama Materials and will continue in full force and effect
until terminated in accordance with the terms\\nand conditions herein. Meta
may terminate this Agreement if you are in breach of any term or condition of
this\\nAgreement. Upon termination of this Agreement, you shall delete and cease
use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination
of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will
be governed and construed under the laws of the State of \\nCalifornia without
regard to choice of law principles, and the UN Convention on Contracts for the
International\\nSale of Goods does not apply to this Agreement. The courts of
California shall have exclusive jurisdiction of\\nany dispute arising out of
this Agreement.\\\"\\nLICENSE \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta
is committed to promoting safe and fair use of its tools and features, including
Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use
Policy (\u201C**Policy**\u201D). The most recent copy of this policy can be
found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited
Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You agree
you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate
the law or others\u2019 rights, including to:\\n 1. Engage in, promote, generate,
contribute to, encourage, plan, incite, or further illegal or unlawful activity
or content, such as:\\n 1. Violence or terrorism\\n 2. Exploitation
or harm to children, including the solicitation, creation, acquisition, or dissemination
of child exploitative content or failure to report Child Sexual Abuse Material\\n
\ 3. Human trafficking, exploitation, and sexual violence\\n 4.
The illegal distribution of information or materials to minors, including obscene
materials, or failure to employ legally required age-gating in connection with
such information or materials.\\n 5. Sexual solicitation\\n 6.
Any other criminal activity\\n 1. Engage in, promote, incite, or facilitate
the harassment, abuse, threatening, or bullying of individuals or groups of
individuals\\n 2. Engage in, promote, incite, or facilitate discrimination
or other unlawful or harmful conduct in the provision of employment, employment
benefits, credit, housing, other economic benefits, or other essential goods
and services\\n 3. Engage in the unauthorized or unlicensed practice of any
profession including, but not limited to, financial, legal, medical/health,
or related professional practices\\n 4. Collect, process, disclose, generate,
or infer private or sensitive information about individuals, including information
about individuals\u2019 identity, health, or demographic information, unless
you have obtained the right to do so in accordance with applicable law\\n 5.
Engage in or facilitate any action or generate any content that infringes, misappropriates,
or otherwise violates any third-party rights, including the outputs or results
of any products or services using the Llama Materials\\n 6. Create, generate,
or facilitate the creation of malicious code, malware, computer viruses or do
anything else that could disable, overburden, interfere with or impair the proper
working, integrity, operation or appearance of a website or computer system\\n
\ 7. Engage in any action, or facilitate any action, to intentionally circumvent
or remove usage restrictions or other safety measures, or to enable functionality
disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the
planning or development of activities that present a risk of death or bodily
harm to individuals, including use of Llama 3.2 related to the following:\\n
\ 8. Military, warfare, nuclear industries or applications, espionage, use
for materials or activities that are subject to the International Traffic Arms
Regulations (ITAR) maintained by the United States Department of State or to
the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons
Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including
weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n
\ 11. Operation of critical infrastructure, transportation technologies, or
heavy machinery\\n 12. Self-harm or harm to others, including suicide, cutting,
and eating disorders\\n 13. Any content intended to incite or promote violence,
abuse, or any infliction of bodily harm to an individual\\n3. Intentionally
deceive or mislead others, including use of Llama 3.2 related to the following:\\n
\ 14. Generating, promoting, or furthering fraud or the creation or promotion
of disinformation\\n 15. Generating, promoting, or furthering defamatory
content, including the creation of defamatory statements, images, or other content\\n
\ 16. Generating, promoting, or further distributing spam\\n 17. Impersonating
another individual without consent, authorization, or legal right\\n 18.
Representing that the use of Llama 3.2 or outputs are human-generated\\n 19.
Generating or facilitating false online engagement, including fake reviews and
other means of fake online engagement\\n4. Fail to appropriately disclose to
end users any known dangers of your AI system\\n5. Interact with third party
tools, models, or software designed to generate unlawful content or engage in
unlawful or harmful conduct and/or represent that the outputs of such tools,
models, or software are associated with Meta or Llama 3.2\\n\\nWith respect
to any multimodal models included in Llama 3.2, the rights granted under Section
1(a) of the Llama 3.2 Community License Agreement are not being granted to you
if you are an individual domiciled in, or a company with a principal place of
business in, the European Union. This restriction does not apply to end users
of a product or service that incorporates any such multimodal models.\\n\\nPlease
report any violation of this Policy, software \u201Cbug,\u201D or other problems
that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n*
Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n*
Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n*
Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
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headers:
Content-Type:
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Date:
- Wed, 15 Jan 2025 20:47:12 GMT
Transfer-Encoding:
- chunked
http_version: HTTP/1.1
status_code: 200
- Tue, 18 Feb 2025 21:52:21 GMT
status:
code: 200
message: OK
version: 1

View File

@@ -1,4 +1,5 @@
import json
import os
from typing import Dict, List, Optional
from unittest.mock import MagicMock, Mock, patch
@@ -220,10 +221,13 @@ def test_get_conversion_instructions_gpt():
supports_function_calling.return_value = True
instructions = get_conversion_instructions(SimpleModel, llm)
model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
assert (
instructions
== f"Please convert the following text into valid JSON.\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
expected_instructions = (
"Please convert the following text into valid JSON.\n\n"
"Output ONLY the valid JSON and nothing else.\n\n"
"The JSON must follow this schema exactly:\n```json\n"
f"{model_schema}\n```"
)
assert instructions == expected_instructions
def test_get_conversion_instructions_non_gpt():
@@ -346,12 +350,17 @@ def test_convert_with_instructions():
assert output.age == 30
@pytest.mark.vcr(filter_headers=["authorization"])
# Skip tests that call external APIs when running in CI/CD
skip_external_api = pytest.mark.skipif(
os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
)
@skip_external_api
@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
def test_converter_with_llama3_2_model():
llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
sample_text = "Name: Alice Llama, Age: 30"
instructions = get_conversion_instructions(SimpleModel, llm)
converter = Converter(
llm=llm,
@@ -359,19 +368,17 @@ def test_converter_with_llama3_2_model():
model=SimpleModel,
instructions=instructions,
)
output = converter.to_pydantic()
assert isinstance(output, SimpleModel)
assert output.name == "Alice Llama"
assert output.age == 30
@pytest.mark.vcr(filter_headers=["authorization"])
@skip_external_api
@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
def test_converter_with_llama3_1_model():
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
sample_text = "Name: Alice Llama, Age: 30"
instructions = get_conversion_instructions(SimpleModel, llm)
converter = Converter(
llm=llm,
@@ -379,14 +386,19 @@ def test_converter_with_llama3_1_model():
model=SimpleModel,
instructions=instructions,
)
output = converter.to_pydantic()
assert isinstance(output, SimpleModel)
assert output.name == "Alice Llama"
assert output.age == 30
# Skip tests that call external APIs when running in CI/CD
skip_external_api = pytest.mark.skipif(
os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
)
@skip_external_api
@pytest.mark.vcr(filter_headers=["authorization"])
def test_converter_with_nested_model():
llm = LLM(model="gpt-4o-mini")
@@ -563,7 +575,7 @@ def test_converter_with_ambiguous_input():
with pytest.raises(ConverterError) as exc_info:
output = converter.to_pydantic()
assert "validation error" in str(exc_info.value).lower()
assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
# Tests for function calling support

View File

@@ -1,35 +0,0 @@
"""Test datetime compatibility module."""
from datetime import datetime, timedelta, timezone
from crewai.utilities.datetime_compat import UTC
def test_utc_timezone_compatibility():
"""Test that UTC timezone is compatible with both Python 3.10 and 3.11+"""
assert UTC == timezone.utc
assert UTC.tzname(None) == "UTC"
# Verify it works with datetime.now()
dt = datetime.now(UTC)
assert dt.tzinfo == timezone.utc
def test_utc_timezone_edge_cases():
"""Test UTC timezone handling in edge cases."""
# Test with leap year
leap_date = datetime(2024, 2, 29, tzinfo=UTC)
assert leap_date.tzinfo == timezone.utc
# Test DST transition dates
dst_date = datetime(2024, 3, 10, 2, 0, tzinfo=UTC) # US DST start
assert dst_date.tzinfo == timezone.utc
# Test with minimum/maximum dates
min_date = datetime.min.replace(tzinfo=UTC)
max_date = datetime.max.replace(tzinfo=UTC)
assert min_date.tzinfo == timezone.utc
assert max_date.tzinfo == timezone.utc
# Test timezone offset calculations
dt = datetime(2024, 1, 1, tzinfo=UTC)
offset = dt.utcoffset()
assert offset == timedelta(0) # UTC should always have zero offset