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fix: Resolve remaining lint issues (W291, W293, B904)

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- Remove trailing whitespace from examples/prompt_caching_example.py
- Fix exception handling to use 'from e' for proper error chaining
- All lint checks now pass locally

Co-Authored-By: João <joao@crewai.com>
This commit is contained in:
Devin AI
2025-09-18 20:52:18 +00:00
parent 9a0b3e881d
commit 8a2ed4320e
2 changed files with 44 additions and 44 deletions
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82
examples/prompt_caching_example.py
View File

@@ -11,60 +11,60 @@ import asyncio
def create_crew_with_caching():
"""Create a crew with prompt caching enabled."""
llm = LLM(
model="anthropic/claude-3-5-sonnet-20240620",
enable_prompt_caching=True,
temperature=0.1
)
analyst = Agent(
role="Data Analyst",
goal="Analyze data and provide insights",
backstory="""You are an experienced data analyst with expertise in
statistical analysis, data visualization, and business intelligence.
You have worked with various industries including finance, healthcare,
and technology. Your approach is methodical and you always provide
backstory="""You are an experienced data analyst with expertise in
statistical analysis, data visualization, and business intelligence.
You have worked with various industries including finance, healthcare,
and technology. Your approach is methodical and you always provide
actionable insights based on data patterns.""",
llm=llm
)
analysis_task = Task(
description="""Analyze the following dataset: {dataset}
Please provide:
1. Summary statistics
2. Key patterns and trends
3. Actionable recommendations
4. Potential risks or concerns
Be thorough in your analysis and provide specific examples.""",
expected_output="A comprehensive analysis report with statistics, trends, and recommendations",
agent=analyst
)
return Crew(agents=[analyst], tasks=[analysis_task])
def example_kickoff_for_each():
"""Example using kickoff_for_each with prompt caching."""
print("Running kickoff_for_each example with prompt caching...")
crew = create_crew_with_caching()
datasets = [
{"dataset": "Q1 2024 sales data showing 15% growth in mobile segment"},
{"dataset": "Q2 2024 customer satisfaction scores with 4.2/5 average rating"},
{"dataset": "Q3 2024 website traffic data with 25% increase in organic search"},
{"dataset": "Q4 2024 employee engagement survey with 78% satisfaction rate"}
]
results = crew.kickoff_for_each(datasets)
for i, result in enumerate(results, 1):
print(f"\n--- Analysis {i} ---")
print(result.raw)
if crew.usage_metrics:
print(f"\nTotal usage metrics:")
print(f"Total tokens: {crew.usage_metrics.total_tokens}")
@@ -75,21 +75,21 @@ def example_kickoff_for_each():
async def example_kickoff_for_each_async():
"""Example using kickoff_for_each_async with prompt caching."""
print("Running kickoff_for_each_async example with prompt caching...")
crew = create_crew_with_caching()
datasets = [
{"dataset": "Marketing campaign A: 12% CTR, 3.5% conversion rate"},
{"dataset": "Marketing campaign B: 8% CTR, 4.1% conversion rate"},
{"dataset": "Marketing campaign C: 15% CTR, 2.8% conversion rate"}
]
results = await crew.kickoff_for_each_async(datasets)
for i, result in enumerate(results, 1):
print(f"\n--- Async Analysis {i} ---")
print(result.raw)
if crew.usage_metrics:
print(f"\nTotal async usage metrics:")
print(f"Total tokens: {crew.usage_metrics.total_tokens}")
@@ -98,35 +98,35 @@ async def example_kickoff_for_each_async():
def example_bedrock_caching():
"""Example using AWS Bedrock with prompt caching."""
print("Running Bedrock example with prompt caching...")
llm = LLM(
model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
enable_prompt_caching=True
)
agent = Agent(
role="Legal Analyst",
goal="Review legal documents and identify key clauses",
backstory="Expert legal analyst with 10+ years experience in contract review",
llm=llm
)
task = Task(
description="Review this contract section: {contract_section}",
expected_output="Summary of key legal points and potential issues",
agent=agent
)
crew = Crew(agents=[agent], tasks=[task])
contract_sections = [
{"contract_section": "Section 1: Payment terms and conditions"},
{"contract_section": "Section 2: Intellectual property rights"},
{"contract_section": "Section 3: Termination clauses"}
]
results = crew.kickoff_for_each(contract_sections)
for i, result in enumerate(results, 1):
print(f"\n--- Legal Review {i} ---")
print(result.raw)
@@ -135,35 +135,35 @@ def example_bedrock_caching():
def example_openai_caching():
"""Example using OpenAI with prompt caching."""
print("Running OpenAI example with prompt caching...")
llm = LLM(
model="gpt-4o",
enable_prompt_caching=True
)
agent = Agent(
role="Content Writer",
goal="Create engaging content for different audiences",
backstory="Professional content writer with expertise in various writing styles and formats",
llm=llm
)
task = Task(
description="Write a {content_type} about: {topic}",
expected_output="Well-structured and engaging content piece",
agent=agent
)
crew = Crew(agents=[agent], tasks=[task])
content_requests = [
{"content_type": "blog post", "topic": "benefits of renewable energy"},
{"content_type": "social media post", "topic": "importance of cybersecurity"},
{"content_type": "newsletter", "topic": "latest AI developments"}
]
results = crew.kickoff_for_each(content_requests)
for i, result in enumerate(results, 1):
print(f"\n--- Content Piece {i} ---")
print(result.raw)
@@ -171,17 +171,17 @@ def example_openai_caching():
if __name__ == "__main__":
print("=== CrewAI Prompt Caching Examples ===\n")
example_kickoff_for_each()
print("\n" + "="*50 + "\n")
asyncio.run(example_kickoff_for_each_async())
print("\n" + "="*50 + "\n")
example_bedrock_caching()
print("\n" + "="*50 + "\n")
example_openai_caching()

6
src/crewai/llm.py
View File

@@ -740,7 +740,7 @@ class LLM(BaseLLM):
# Catch context window errors from litellm and convert them to our own exception type.
# This exception is handled by CrewAgentExecutor._invoke_loop() which can then
# decide whether to summarize the content or abort based on the respect_context_window flag.
raise LLMContextLengthExceededException(str(e))
raise LLMContextLengthExceededException(str(e)) from e
except Exception as e:
logging.error(f"Error in streaming response: {e!s}")
if full_response.strip():
@@ -762,7 +762,7 @@ class LLM(BaseLLM):
error=str(e), from_task=from_task, from_agent=from_agent
),
)
raise Exception(f"Failed to get streaming response: {e!s}")
raise Exception(f"Failed to get streaming response: {e!s}") from e
def _handle_streaming_tool_calls(
self,
@@ -891,7 +891,7 @@ class LLM(BaseLLM):
except ContextWindowExceededError as e:
# Convert litellm's context window error to our own exception type
# for consistent handling in the rest of the codebase
raise LLMContextLengthExceededException(str(e))
raise LLMContextLengthExceededException(str(e)) from e
# --- 2) Extract response message and content
response_message = cast(Choices, cast(ModelResponse, response).choices)[
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