LLMNode

from fibonacci import LLMNode

node = LLMNode(
    id="analyzer",
    model="claude-sonnet-4-5-20250929",
    prompt="Analyze the following text: {{input.text}}"
)

node = LLMNode(
    id="writer",
    model="claude-sonnet-4-5-20250929",
    prompt="""
    Write a {{input.style}} email about {{input.topic}}.
    
    Previous analysis: {{analyzer}}
    
    User preferences: {{memory.preferences}}
    """
)

node = LLMNode(
    id="extractor",
    model="claude-sonnet-4-5-20250929",
    prompt="Extract entities from: {{input.text}}",
    output_format="json",
    output_schema={
        "type": "object",
        "required": ["entities"],
        "properties": {
            "entities": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "name": {"type": "string"},
                        "type": {"type": "string"},
                        "confidence": {"type": "number"}
                    }
                }
            }
        }
    }
)

node = LLMNode(
    id="structured",
    model="claude-sonnet-4-5-20250929",
    prompt="Return JSON with sentiment analysis",
    output_format="json",
    output_schema=schema,
    retry_on_validation_error=True,
    max_validation_retries=2
)

node = LLMNode(
    id="assistant",
    model="claude-sonnet-4-5-20250929",
    system_prompt="""You are a helpful customer service assistant.
    - Be concise and friendly
    - Always offer to help further
    - Never make promises you can't keep""",
    prompt="Customer message: {{input.message}}"
)

from fibonacci import Workflow, LLMNode

workflow = Workflow(name="pipeline")

# First node - no dependencies
analyzer = LLMNode(
    id="analyzer",
    model="claude-sonnet-4-5-20250929",
    prompt="Analyze: {{input.text}}"
)

# Second node - depends on analyzer
summarizer = LLMNode(
    id="summarizer",
    model="claude-sonnet-4-5-20250929",
    prompt="Summarize this analysis: {{analyzer}}",
    dependencies=["analyzer"]
)

# Third node - depends on both
reporter = LLMNode(
    id="reporter",
    model="claude-sonnet-4-5-20250929",
    prompt="""
    Create report:
    Analysis: {{analyzer}}
    Summary: {{summarizer}}
    """,
    dependencies=["analyzer", "summarizer"]
)

node = LLMNode(
    id="chat",
    model="claude-sonnet-4-5-20250929",
    prompt="""
    Conversation history:
    {{memory.history}}
    
    User preferences:
    {{memory.preferences}}
    
    User message: {{input.message}}
    """,
    memory_read=["history", "preferences"]
)

node = LLMNode(
    id="summarizer",
    model="claude-sonnet-4-5-20250929",
    prompt="Summarize: {{input.text}}",
    memory_write={
        "key": "last_summary",
        "scope": "user"  # workflow, user, organization, global
    }
)

node = LLMNode(
    id="detailed_analysis",
    model="claude-opus-4-5-20251101",
    prompt="Deep analysis: {{input.text}}",
    condition={
        "field": "{{input.analysis_level}}",
        "operator": "equals",
        "value": "detailed"
    }
)

from fibonacci import LLMNode, RetryConfig

node = LLMNode(
    id="analyzer",
    model="claude-sonnet-4-5-20250929",
    prompt="Analyze: {{input.text}}",
    retry=RetryConfig(
        max_attempts=3,
        delay=1.0,
        backoff="exponential"
    )
)

# Primary with expensive model
primary = LLMNode(
    id="primary",
    model="claude-opus-4-5-20251101",
    prompt="Complex analysis: {{input.text}}",
    timeout=60,
    fallback="fallback"
)

# Fallback with faster model
fallback = LLMNode(
    id="fallback",
    model="claude-haiku-4-5-20251001",
    prompt="Quick analysis: {{input.text}}"
)

# Deterministic for factual tasks
factual = LLMNode(
    id="factual",
    model="claude-sonnet-4-5-20250929",
    prompt="Extract facts: {{input.text}}",
    temperature=0.0
)

# Creative for writing
creative = LLMNode(
    id="creative",
    model="claude-sonnet-4-5-20250929",
    prompt="Write a story about: {{input.topic}}",
    temperature=1.2
)

node = LLMNode(
    id="tweet",
    model="claude-haiku-4-5-20251001",
    prompt="Write a tweet about: {{input.topic}}",
    max_tokens=100
)

from fibonacci import Workflow, LLMNode, RetryConfig

workflow = Workflow(name="content-pipeline")

# Outline generator
outliner = LLMNode(
    id="outliner",
    model="claude-sonnet-4-5-20250929",
    system_prompt="You are a content strategist.",
    prompt="Create an outline for a blog post about: {{input.topic}}",
    temperature=0.7,
    output_format="json",
    output_schema={
        "type": "object",
        "properties": {
            "title": {"type": "string"},
            "sections": {
                "type": "array",
                "items": {"type": "string"}
            }
        }
    }
)

# Content writer
writer = LLMNode(
    id="writer",
    model="claude-sonnet-4-5-20250929",
    system_prompt="You are a skilled blog writer.",
    prompt="""
    Write a blog post following this outline:
    Title: {{outliner.title}}
    Sections: {{outliner.sections}}
    
    Style: {{input.style}}
    """,
    dependencies=["outliner"],
    max_tokens=2000,
    retry=RetryConfig(max_attempts=2)
)

# Editor
editor = LLMNode(
    id="editor",
    model="claude-opus-4-5-20251101",
    system_prompt="You are an expert editor.",
    prompt="Edit for clarity and engagement: {{writer}}",
    dependencies=["writer"],
    temperature=0.3
)

workflow.add_node(outliner)
workflow.add_node(writer)
workflow.add_node(editor)

# Execute
result = workflow.execute(inputs={
    "topic": "AI in Healthcare",
    "style": "informative yet engaging"
})

print(result["editor"])