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47 Commits

Author SHA1 Message Date
Lorenze Jay
a88e910a7a test: Improve flow persistence test cases and logging 2025-02-19 13:40:32 -08:00
Lorenze Jay
73ee7ce1c9 Merge branch 'better/event-emitter' of github.com:crewAIInc/crewAI into better/event-emitter 2025-02-19 13:24:28 -08:00
Lorenze Jay
74727592bc Merge branch 'main' of github.com:crewAIInc/crewAI into better/event-emitter 2025-02-19 13:24:26 -08:00
Lorenze Jay
5a022fe6c0 Merge branch 'main' into better/event-emitter 2025-02-19 13:15:01 -08:00
Lorenze Jay
34f5469490 refactor: clean up and organize imports in llm and flow modules 2025-02-19 08:18:48 -08:00
Lorenze Jay
f68a85a6f5 Merge branch 'main' of github.com:crewAIInc/crewAI into better/event-emitter 2025-02-19 08:13:28 -08:00
Lorenze Jay
390031026a Remove ToolUsageStartedEvent emission in tool usage process
- Remove unnecessary event emission for tool usage start
- Simplify tool usage event handling
- Eliminate redundant event data preparation step
2025-02-18 15:07:23 -08:00
Lorenze Jay
ae4c4cffc4 Improve test_validate_tool_input_invalid_input with mock objects
- Add explicit mock objects for agent and action in test case
- Ensure proper string values for mock agent and action attributes
- Simplify test setup for ToolUsage validation method
2025-02-18 14:50:52 -08:00
Lorenze Jay
b623960a94 Improve type hinting for TaskCompletedEvent handler
- Add explicit type annotation for TaskCompletedEvent in event_listener.py
- Enhance type safety for event handling in EventListener
2025-02-18 14:48:35 -08:00
Lorenze Jay
d368efdeda Update AgentExecutionStartedEvent to use task_prompt
- Modify test_events.py to use task_prompt instead of inputs
- Simplify event input validation in test case
- Align with recent event system refactoring
2025-02-18 14:35:12 -08:00
Lorenze Jay
4dc258a590 Refactor task events to use base CrewEvent
- Move CrewEvent import from crew_events to base_events
- Remove unnecessary blank lines in task_events.py
- Simplify event class structure for task-related events
2025-02-18 14:30:11 -08:00
Lorenze Jay
c64c0698c5 Refactor event system and improve crew testing
- Extract base CrewEvent class to a new base_events.py module
- Update event imports across multiple event-related files
- Modify CrewTestStartedEvent to use eval_llm instead of openai_model_name
- Add LLM creation validation in crew testing method
- Improve type handling and event consistency
2025-02-18 14:29:39 -08:00
Lorenze Jay
6fea26d223 Merge branch 'main' of github.com:crewAIInc/crewAI into better/event-emitter 2025-02-18 14:18:24 -08:00
Lorenze Jay
1b5cc08abe Enhance event handling for tool usage and agent execution
- Add new events for tool usage: ToolSelectionErrorEvent, ToolValidateInputErrorEvent
- Improve error tracking and event emission in ToolUsage and LLM classes
- Update AgentExecutionStartedEvent to use task_prompt instead of inputs
- Add comprehensive test coverage for new event types and error scenarios
2025-02-18 14:13:18 -08:00
Lorenze Jay
e9dc68723f Remove RunType enum and clean up crew events module
- Delete unused RunType enum from crew_events.py
- Simplify crew_events.py by removing unnecessary enum definition
- Improve code clarity by removing unneeded imports
2025-02-18 09:14:47 -08:00
Lorenze Jay
d0f9abaa85 Add FlowPlotEvent and update event bus to support flow plotting
- Introduce FlowPlotEvent to track flow plotting events
- Replace Telemetry method with event bus emission in Flow.plot()
- Update event bus to support new FlowPlotEvent type
- Add test case to validate flow plotting event emission
2025-02-18 09:10:27 -08:00
Lorenze Jay
935da884ed Enhance EventListener with singleton pattern and color configuration
- Implement singleton pattern for EventListener to ensure single instance
- Add default color configuration using EMITTER_COLOR from constants
- Modify log method calls to use default color and remove redundant color parameters
- Improve initialization logic to prevent multiple initializations
2025-02-18 08:53:41 -08:00
Lorenze Jay
64569ce130 Rename event_bus to crewai_event_bus for improved clarity and specificity
- Replace all references to `event_bus` with `crewai_event_bus`
- Update import statements across multiple files
- Remove the old `event_bus.py` file
- Maintain existing event handling functionality
2025-02-18 08:36:21 -08:00
Lorenze Jay
1603a1d9ac Update test_events to validate multiple tool usage events
- Modify test to assert 75 events instead of a single error event
- Remove pytest.raises() check, allowing crew kickoff to complete
- Adjust event validation to support broader event tracking
2025-02-14 16:07:15 -08:00
Lorenze Jay
6d1bcff6d1 Improve AgentOps listener type hints and formatting
- Add string type hints for AgentOps classes to resolve potential import issues
- Clean up unnecessary whitespace and improve code indentation
- Simplify initialization and event handling logic
2025-02-14 16:00:30 -08:00
Lorenze Jay
aa2e7c888e Update test_events to validate tool usage error event handling
- Modify test to assert single error event with correct attributes
- Use pytest.raises() to verify error event generation
- Simplify error event validation in test case
2025-02-14 15:57:38 -08:00
Lorenze Jay
ec048cf6fe Refactor AgentOps event listener for crew-level tracking
- Modify AgentOpsListener to handle crew-level events
- Initialize and end AgentOps session at crew kickoff and completion
- Create agents for each crew member during session initialization
- Improve session management and event recording
- Clean up and simplify event handling logic
2025-02-14 15:49:42 -08:00
Lorenze Jay
18791eadd3 dont forget crew level 2025-02-14 14:50:38 -08:00
Lorenze Jay
6eab0e3d3b moving to dedicated eventlistener 2025-02-14 14:49:34 -08:00
Lorenze Jay
fe7c8b2049 Reorder and clean up event imports in event_listener
- Reorganize imports for tool usage events and other event types
- Maintain consistent import ordering and remove unused imports
- Ensure clean and organized import structure in event_listener module
2025-02-14 09:26:41 -08:00
Lorenze Jay
1c2903abea Add event handling for tool usage events
- Introduce event listeners for ToolUsageFinishedEvent and ToolUsageErrorEvent
- Log tool usage events with descriptive emoji icons ( and )
- Update event_listener to track and log tool usage lifecycle
2025-02-14 09:26:18 -08:00
Lorenze Jay
f4547648b4 Enable test coverage for Flow method execution failure event
- Uncomment pytest.raises() in test_events to verify exception handling
- Ensure test validates MethodExecutionFailedEvent emission during flow kickoff
2025-02-14 09:14:37 -08:00
Lorenze Jay
a557275112 Propagate method execution failures in Flow class
- Modify Flow class to re-raise exceptions after emitting MethodExecutionFailedEvent
- Reorder MethodExecutionFailedEvent import to maintain consistent import style
2025-02-14 09:10:56 -08:00
Lorenze Jay
e17159f877 Merge branch 'main' of github.com:crewAIInc/crewAI into better/event-emitter 2025-02-14 09:06:32 -08:00
Lorenze Jay
7d168d6d61 Add MethodExecutionFailedEvent to handle flow method execution failures
- Introduce new MethodExecutionFailedEvent in flow_events module
- Update Flow class to catch and emit method execution failures
- Add event listener for method execution failure events
- Update event-related imports to include new event type
- Enhance test coverage for method execution failure handling
2025-02-14 09:00:16 -08:00
Lorenze Jay
766422dd5e Update crew test verbose output with improved emoji icons
- Replace task and agent completion icons from 👍 to 
- Enhance readability of test output logging
- Maintain consistent test coverage for crew verbose output
2025-02-14 08:36:29 -08:00
Lorenze Jay
3e3e68ed75 Update crew test to validate verbose output and kickoff_for_each method
- Enhance test_crew_verbose_output to check specific listener log messages
- Modify test_kickoff_for_each_invalid_input to use Pydantic validation error
- Improve test coverage for crew logging and input validation
2025-02-14 08:34:18 -08:00
Lorenze Jay
43064e2a0e Clean up unused imports and event-related code
- Remove unused imports from various event and flow-related files
- Reorder event imports to follow standard conventions
- Remove unnecessary event type references
- Simplify import statements in event and flow modules
2025-02-13 18:07:43 -08:00
Lorenze Jay
184d08e6e7 Remove telemetry and tracing dependencies from Task and Flow classes
- Remove telemetry-related imports and private attributes from Task class
- Remove `_telemetry` attribute from Flow class
- Update event handling to emit events without direct telemetry tracking
- Simplify task and flow execution by removing explicit telemetry spans
- Move telemetry-related event handling to EventListener
2025-02-13 17:54:45 -08:00
Lorenze Jay
00a98cd5c9 Enhance event handling for Crew, Task, and Event classes
- Add crew name to failed event types (CrewKickoffFailedEvent, CrewTrainFailedEvent, CrewTestFailedEvent)
- Update Task events to remove redundant task and context attributes
- Refactor EventListener to use Logger for consistent event logging
- Add new event types for Crew train and test events
- Improve event bus event tracking in test cases
2025-02-13 12:01:18 -08:00
Lorenze Jay
62a20426a5 Refactor Flow and Agent event handling to use event_bus
- Remove `event_emitter` from Flow class and replace with `event_bus.emit()`
- Update Flow and Agent tests to use event_bus event listeners
- Remove redundant event emissions in Flow methods
- Add debug print statements in Flow execution
- Simplify event tracking in test cases
2025-02-13 10:54:58 -08:00
Lorenze Jay
097ed1f0df Fix tool usage and event import handling
- Update tool usage to use `.get()` method when checking tool name
- Remove unnecessary `__all__` export list in events/__init__.py
2025-02-12 16:26:15 -08:00
Lorenze Jay
fa5d7a2e05 Add default model for CrewEvaluator and fix event import order
- Set default model to "gpt-4o-mini" in CrewEvaluator when no model is specified
- Reorder event-related imports in task.py to follow standard import conventions
- Update event bus initialization method return type hint
- Export event_bus in events/__init__.py
2025-02-12 16:23:05 -08:00
Lorenze Jay
779db3c3dd Refactor event classes to improve type safety and naming consistency
- Rename event classes to have explicit 'Event' suffix (e.g., TaskStartedEvent)
- Update import statements and references across multiple files
- Remove deprecated events.py module
- Enhance event type hints and configurations
- Clean up unnecessary event-related code
2025-02-12 16:17:52 -08:00
Lorenze Jay
9debd3a6da Merge branch 'main' of github.com:crewAIInc/crewAI into better/event-emitter 2025-02-12 15:47:50 -08:00
Lorenze Jay
1250388635 Enhance event system type safety and error handling
- Improve type annotations for event bus and event types
- Add null checks for agent and task in event emissions
- Update import paths for base tool and base agent
- Refactor event listener type hints
- Remove unnecessary print statements
- Update test configurations to match new event handling
2025-02-12 15:46:56 -08:00
Lorenze Jay
25453f7cb1 Merge branch 'main' of github.com:crewAIInc/crewAI into better/event-emitter 2025-02-12 10:41:48 -08:00
Lorenze Jay
f70162c064 Refactor event system and add third-party event listeners
- Move event_bus import to correct module paths
- Introduce BaseEventListener abstract base class
- Add AgentOpsListener for third-party event tracking
- Update event listener initialization and setup
- Clean up event-related imports and exports
2025-02-12 10:29:27 -08:00
Lorenze Jay
3a89b9feab Add event emission for agent execution lifecycle
- Emit AgentExecutionStarted and AgentExecutionError events
- Update CrewAgentExecutor to use event_bus for tracking agent execution
- Refactor error handling to include event emission
- Minor code formatting improvements in task.py and crew_agent_executor.py
- Fix a typo in test file
2025-02-11 14:35:55 -08:00
Lorenze Jay
9eb5b361dd Merge branch 'main' of github.com:crewAIInc/crewAI into better/event-emitter 2025-02-11 14:33:08 -08:00
Lorenze Jay
676cabfdd6 Refactor event handling and introduce new event types
- Migrate from global `emit` function to `event_bus.emit`
- Add new event types for task failures, tool usage, and agent execution
- Update event listeners and event bus to support more granular event tracking
- Remove deprecated event emission methods
- Improve event type consistency and add more detailed event information
2025-02-11 14:31:50 -08:00
Lorenze Jay
95bae8bba3 WIP crew events emitter 2025-02-06 11:06:43 -08:00
11 changed files with 3475 additions and 137 deletions

View File

@@ -114,15 +114,10 @@ class CrewAgentExecutorMixin:
prompt = (
"\n\n=====\n"
"## HUMAN FEEDBACK: Provide feedback on the Final Result and Agent's actions.\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"
"Respond with 'looks good' to accept or provide specific improvement requests.\n"
"You can provide multiple rounds of feedback until satisfied.\n"
"=====\n"
)
self._printer.print(content=prompt, color="bold_yellow")
response = input()
if response.strip() != "":
self._printer.print(content="\nProcessing your feedback...", color="cyan")
return response
return input()

View File

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

View File

@@ -548,6 +548,10 @@ 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(
@@ -567,8 +571,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
answer = current_answer
while self.ask_for_human_input:
# If the user provides a blank response, assume they are happy with the result
if feedback.strip() == "":
response = self._get_llm_feedback_response(feedback)
if not self._feedback_requires_changes(response):
self.ask_for_human_input = False
else:
answer = self._process_feedback_iteration(feedback)
@@ -576,6 +581,27 @@ 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

@@ -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
from litellm import Choices, get_supported_openai_params
from litellm.types.utils import ModelResponse
from litellm.utils import get_supported_openai_params, supports_response_schema
from litellm.utils import 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 params is not None and "tools" in params
return "response_format" 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 params is not None and "stop" in params
return "stop" in params
except Exception as e:
logging.error(f"Failed to get supported params: {str(e)}")
return False

View File

@@ -23,6 +23,7 @@
"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

@@ -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) -> BaseModel:
def to_pydantic(self, current_attempt=1):
"""Convert text to pydantic."""
try:
if self.llm.supports_function_calling():
result = self._create_instructor().to_pydantic()
return self._create_instructor().to_pydantic()
else:
response = self.llm.call(
[
@@ -32,40 +32,18 @@ class Converter(OutputConverter):
{"role": "user", "content": self.text},
]
)
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
return self.model.model_validate_json(response)
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 validation error: {e}"
f"Failed to convert text into a Pydantic model due to the following 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 error: {e}"
f"Failed to convert text into a Pydantic model due to the following error: {e}"
)
def to_json(self, current_attempt=1):
@@ -219,15 +197,11 @@ 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\nOutput ONLY the valid JSON and nothing else.\n\n"
f"The JSON must follow this schema exactly:\n```json\n{model_schema}\n```"
f"\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
)
else:
model_description = generate_model_description(model)
instructions += (
f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
f"The JSON must follow this format exactly:\n{model_description}"
)
instructions += f"\n\nThe JSON should follow this format:\n{model_description}"
return instructions

View File

@@ -1,6 +1,7 @@
"""Test Agent creation and execution basic functionality."""
import os
from datetime import UTC, datetime, timezone
from unittest import mock
from unittest.mock import patch
@@ -8,7 +9,7 @@ import pytest
from crewai import Agent, Crew, Task
from crewai.agents.cache import CacheHandler
from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
from crewai.agents.crew_agent_executor import 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
@@ -998,35 +999,23 @@ 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: instruct change
"", # Second feedback: empty string signals acceptance
"Don't say hi, say Hello instead!", # First feedback
"looks good", # Second feedback to exit loop
]
)
def ask_human_input_side_effect(*args, **kwargs):
return next(feedback_responses)
# 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,
):
with patch.object(
CrewAgentExecutor, "_ask_human_input", side_effect=ask_human_input_side_effect
) as mock_human_input:
# Execute the task
output = agent.execute_task(task)
# 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"
# 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'
def test_interpolate_inputs():

View File

@@ -0,0 +1,520 @@
interactions:
- request:
body: !!binary |
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stop \\u003c|end_header_id|\\u003e\\nPARAMETER stop \\u003c|eot_id|\\u003e\\nLICENSE
\\\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version Release Date:
September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions
for use, reproduction, distribution \\nand modification of the Llama Materials
set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications, manuals
and documentation accompanying Llama 3.2\\ndistributed by Meta at https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D
or \u201Cyou\u201D means you, or your employer or any other person or entity
(if you are \\nentering into this Agreement on such person or entity\u2019s
behalf), of the age required under\\napplicable laws, rules or regulations to
provide legal consent and that has legal authority\\nto bind your employer or
such other person or entity if you are entering in this Agreement\\non their
behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language models
and software and algorithms, including\\nmachine-learning model code, trained
model weights, inference-enabling code, training-enabling code,\\nfine-tuning
enabling code and other elements of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama
Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and Documentation
(and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D
or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in
or, \\nif you are an entity, your principal place of business is in the EEA
or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the
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or distributing any portion or element of the Llama Materials,\\nyou agree to
be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
\ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
and royalty-free limited license under Meta\u2019s intellectual property or
other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
distribute, copy, create derivative works \\nof, and make modifications to the
Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If
you distribute or make available the Llama Materials (or any derivative works
thereof), \\nor a product or service (including another AI model) that contains
any of them, you shall (A) provide\\na copy of this Agreement with any such
Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non
a related website, user interface, blogpost, about page, or product documentation.
If you use the\\nLlama Materials or any outputs or results of the Llama Materials
to create, train, fine tune, or\\notherwise improve an AI model, which is distributed
or made available, you shall also include \u201CLlama\u201D\\nat the beginning
of any such AI model name.\\n\\n ii. If you receive Llama Materials,
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
the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
\ iv. Your use of the Llama Materials must comply with applicable laws
and regulations\\n(including trade compliance laws and regulations) and adhere
to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
which is hereby \\nincorporated by reference into this Agreement.\\n \\n2.
Additional Commercial Terms. If, on the Llama 3.2 version release date, the
monthly active users\\nof the products or services made available by or for
Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
active users in the preceding calendar month, you must request \\na license
from Meta, which Meta may grant to you in its sole discretion, and you are not
authorized to\\nexercise any of the rights under this Agreement unless or until
Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS
AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY
OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR
ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
\\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
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
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harm to individuals, including use of Llama 3.2 related to the following:\\n
\ 8. Military, warfare, nuclear industries or applications, espionage, use
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\ 11. Operation of critical infrastructure, transportation technologies, or
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Representing that the use of Llama 3.2 or outputs are human-generated\\n 19.
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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*
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headers:
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Date:
- Wed, 15 Jan 2025 20:47:12 GMT
Transfer-Encoding:
- chunked
http_version: HTTP/1.1
status_code: 200
version: 1

View File

@@ -1,5 +1,4 @@
import json
import os
from typing import Dict, List, Optional
from unittest.mock import MagicMock, Mock, patch
@@ -221,13 +220,10 @@ 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()
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
== f"Please convert the following text into valid JSON.\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
)
assert instructions == expected_instructions
def test_get_conversion_instructions_non_gpt():
@@ -350,17 +346,12 @@ def test_convert_with_instructions():
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"], record_mode="once")
@pytest.mark.vcr(filter_headers=["authorization"])
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,
@@ -368,17 +359,19 @@ 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
@skip_external_api
@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
@pytest.mark.vcr(filter_headers=["authorization"])
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,
@@ -386,19 +379,14 @@ 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")
@@ -575,7 +563,7 @@ def test_converter_with_ambiguous_input():
with pytest.raises(ConverterError) as exc_info:
output = converter.to_pydantic()
assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
assert "validation error" in str(exc_info.value).lower()
# Tests for function calling support