CriticNode

from fibonacci import CriticNode

node = CriticNode(
    id="quality_check",
    model="claude-sonnet-4-5-20250929",
    target_node="writer",
    criteria=[
        "Content is factually accurate",
        "Tone is professional",
        "Length is appropriate"
    ],
    min_score=7
)

from fibonacci import Workflow, LLMNode, CriticNode

workflow = Workflow(name="quality-assured")

# Content generator
writer = LLMNode(
    id="writer",
    model="claude-sonnet-4-5-20250929",
    prompt="Write an email about: {{input.topic}}"
)

# Quality evaluator
critic = CriticNode(
    id="critic",
    model="claude-sonnet-4-5-20250929",
    target_node="writer",
    criteria=[
        "Professional tone throughout",
        "Clear and concise language",
        "Proper grammar and spelling",
        "Includes appropriate greeting and closing"
    ],
    min_score=8
)

workflow.add_node(writer)
workflow.add_node(critic)

result = workflow.execute(inputs={"topic": "project update"})

print(result["critic"])
# {
#     "passed": True,
#     "overall_score": 8.5,
#     "criteria_scores": {
#         "Professional tone throughout": 9,
#         "Clear and concise language": 8,
#         "Proper grammar and spelling": 9,
#         "Includes appropriate greeting and closing": 8
#     },
#     "feedback": "The email is professional and well-structured...",
#     "iterations": 1,
#     "final_output": "Dear Team,\n\n..."
# }

critic = CriticNode(
    id="critic",
    model="claude-sonnet-4-5-20250929",
    target_node="writer",
    criteria=[
        "Engaging opening hook",
        "Clear value proposition",
        "Strong call to action"
    ],
    min_score=8,
    max_iterations=3,  # Try up to 3 refinements
    improvement_prompt="""
    Improve the content based on this feedback:
    {{feedback}}
    
    Original content:
    {{writer}}
    
    Focus on addressing the low-scoring criteria.
    """
)

critic = CriticNode(
    id="code_reviewer",
    model="claude-sonnet-4-5-20250929",
    target_node="coder",
    criteria=[
        "Code is syntactically correct",
        "Follows best practices",
        "Includes error handling",
        "Is well-documented"
    ],
    min_score=8,
    max_iterations=3,
    improvement_prompt="""
    Improve the code based on this review:
    
    **Feedback:**
    {{feedback}}
    
    **Criteria Scores:**
    {{criteria_scores}}
    
    **Original Code:**

Focus on the lowest-scoring criteria. Return only the improved code.
"""
)

criteria = [
    "Response directly addresses the user's question",
    "Information is accurate and verifiable",
    "Tone is appropriate for the target audience",
    "Response is complete but not overly verbose",
    "Technical terms are explained when used"
]

criteria = [
    "Response is between 100-200 words",
    "Includes at least 3 specific examples",
    "Contains no grammatical errors",
    "All claims are supported by evidence",
    "Follows the requested format exactly"
]

critic = CriticNode(
    id="weighted_critic",
    model="claude-sonnet-4-5-20250929",
    target_node="writer",
    criteria=[
        {"criterion": "Factual accuracy", "weight": 3},
        {"criterion": "Writing quality", "weight": 2},
        {"criterion": "Engagement", "weight": 1}
    ],
    min_score=7
)

critic = CriticNode(
    id="comprehensive_critic",
    model="claude-sonnet-4-5-20250929",
    target_node="article",
    criteria=[
        # Content quality
        "Information is accurate and well-researched",
        "Arguments are logical and well-supported",
        "Topic is covered comprehensively",
        
        # Writing quality
        "Grammar and spelling are correct",
        "Sentences flow smoothly",
        "Vocabulary is appropriate",
        
        # Structure
        "Has clear introduction",
        "Body is well-organized",
        "Conclusion summarizes key points"
    ],
    min_score=7
)

critic = CriticNode(
    id="conditional_critic",
    model="claude-sonnet-4-5-20250929",
    target_node="writer",
    criteria=["Professional quality"],
    min_score=8,
    condition={
        "field": "{{input.require_review}}",
        "operator": "equals",
        "value": True
    }
)

result = workflow.execute(inputs=data)

evaluation = result["critic"]

# Check if passed
if evaluation["passed"]:
    print("Quality check passed!")
    final_content = evaluation["final_output"]
else:
    print(f"Failed after {evaluation['iterations']} iterations")
    print(f"Best score: {evaluation['overall_score']}")

# Review individual criteria
for criterion, score in evaluation["criteria_scores"].items():
    status = "✓" if score >= 7 else "✗"
    print(f"{status} {criterion}: {score}/10")

# Get improvement feedback
if not evaluation["passed"]:
    print(f"Feedback: {evaluation['feedback']}")

from fibonacci import Workflow, LLMNode, CriticNode

workflow = Workflow(name="blog-writer")

# Generate blog post
writer = LLMNode(
    id="writer",
    model="claude-sonnet-4-5-20250929",
    prompt="""Write a blog post about {{input.topic}}.
    
    Target audience: {{input.audience}}
    Desired length: {{input.length}} words
    Tone: {{input.tone}}
    """
)

# Quality assurance
critic = CriticNode(
    id="quality_assurance",
    model="claude-opus-4-5-20251101",  # Use best model for evaluation
    target_node="writer",
    criteria=[
        "Content is engaging from the first paragraph",
        "Information is accurate and well-researched",
        "Writing style matches the target audience",
        "Post is close to the requested length",
        "Tone is consistent throughout",
        "Includes actionable takeaways",
        "Has a compelling conclusion"
    ],
    min_score=8,
    max_iterations=3,
    improvement_prompt="""
    Improve this blog post based on the editorial feedback:
    
    **Editorial Feedback:**
    {{feedback}}
    
    **Scores by Criterion:**
    {{criteria_scores}}
    
    **Current Draft:**
    {{writer}}
    
    **Requirements:**
    - Topic: {{input.topic}}
    - Audience: {{input.audience}}
    - Length: {{input.length}} words
    - Tone: {{input.tone}}
    
    Revise the post to address the feedback while maintaining the requirements.
    """
)

workflow.add_node(writer)
workflow.add_node(critic)

# Execute
result = workflow.execute(inputs={
    "topic": "Introduction to Machine Learning",
    "audience": "business professionals",
    "length": 800,
    "tone": "informative yet approachable"
})

# Output
if result["quality_assurance"]["passed"]:
    print("Final blog post:")
    print(result["quality_assurance"]["final_output"])
else:
    print("Could not achieve quality threshold")
    print(f"Best score: {result['quality_assurance']['overall_score']}")