Workflow Examples

from noxus_sdk.client import Client
from noxus_sdk.workflows import WorkflowDefinition

client = Client(api_key="your_api_key_here")

# Create blog post generation workflow
blog_workflow = WorkflowDefinition(name="Blog Post Generator")

# Input nodes
topic_input = blog_workflow.node("InputNode").config(
    label="Blog Topic",
    type="str"
)

audience_input = blog_workflow.node("InputNode").config(
    label="Target Audience",
    type="str",
    fixed_value=True,
    value="general audience"
)

# Research phase
research_node = blog_workflow.node("TextGenerationNode").config(
    template="""Research the topic "((Blog Topic))" and provide:
1. Key facts and statistics
2. Current trends and developments
3. Common questions people have
4. Expert opinions or quotes

Topic: ((Blog Topic))
Target Audience: ((Target Audience))""",
    model=["gpt-4o-mini"],
    temperature=0.3,
    max_tokens=800
)

# Outline creation
outline_node = blog_workflow.node("TextGenerationNode").config(
    template="""Based on this research, create a detailed blog post outline:

Research: ((Research))

Create an outline with:
- Compelling headline
- Introduction hook
- 3-5 main sections with subpoints
- Conclusion with call-to-action

Target audience: ((Target Audience))""",
    model=["gpt-4o-mini"],
    temperature=0.5,
    max_tokens=400
)

# Content generation
content_node = blog_workflow.node("TextGenerationNode").config(
    template="""Write a complete blog post based on this outline and research:

Outline: ((Outline))
Research: ((Research))

Requirements:
- Engaging and informative tone
- Include relevant examples
- Use subheadings for readability
- 800-1200 words
- Target audience: ((Target Audience))""",
    model=["gpt-4o"],
    temperature=0.7,
    max_tokens=1500
)

# SEO optimization
seo_node = blog_workflow.node("TextGenerationNode").config(
    template="""Optimize this blog post for SEO:

Blog Post: ((Blog Post))

Add:
- Meta description (150-160 characters)
- 5-7 relevant keywords
- Suggested internal/external links
- Social media snippet""",
    model=["gpt-4o-mini"],
    temperature=0.3,
    max_tokens=300
)

# Final composition
final_post = blog_workflow.node("ComposeTextNode").config(
    template="""# Complete Blog Post Package

## Blog Post
((Blog Post))

## SEO Optimization
((SEO Details))

---
Generated on: {{current_date}}
""")

output_node = blog_workflow.node("OutputNode")

# Connect the workflow
blog_workflow.link(topic_input.output(), research_node.input("variables", "Blog Topic"))
blog_workflow.link(audience_input.output(), research_node.input("variables", "Target Audience"))
blog_workflow.link(audience_input.output(), outline_node.input("variables", "Target Audience"))
blog_workflow.link(audience_input.output(), content_node.input("variables", "Target Audience"))

blog_workflow.link(research_node.output(), outline_node.input("variables", "Research"))
blog_workflow.link(research_node.output(), content_node.input("variables", "Research"))
blog_workflow.link(outline_node.output(), content_node.input("variables", "Outline"))

blog_workflow.link(content_node.output(), seo_node.input("variables", "Blog Post"))
blog_workflow.link(content_node.output(), final_post.input("variables", "Blog Post"))
blog_workflow.link(seo_node.output(), final_post.input("variables", "SEO Details"))
blog_workflow.link(final_post.output(), output_node.input())

# Save and test
blog_generator = client.workflows.save(blog_workflow)

# Run with sample input
result = blog_generator.run(body={
    "Blog Topic": "The Future of Remote Work in 2024"
}).wait()

print(result.output)

social_workflow = WorkflowDefinition(name="Social Media Content Creator")

# Inputs
topic_input = social_workflow.node("InputNode").config(
    label="Content Topic",
    type="str"
)

brand_voice = social_workflow.node("InputNode").config(
    label="Brand Voice",
    type="str",
    fixed_value=True,
    value="friendly, professional, engaging"
)

# Core message creation
core_message = social_workflow.node("TextGenerationNode").config(
    template="""Create a core message about "((Content Topic))" that:
- Captures the main value proposition
- Is engaging and shareable
- Reflects this brand voice: ((Brand Voice))
- Can be adapted for different platforms

Keep it concise but impactful.""",
    model=["gpt-4o-mini"],
    temperature=0.7,
    max_tokens=200
)

# Platform-specific adaptations
twitter_post = social_workflow.node("TextGenerationNode").config(
    template="""Adapt this core message for Twitter:

Core Message: ((Core Message))

Requirements:
- Under 280 characters
- Include 2-3 relevant hashtags
- Engaging and conversation-starting
- Brand voice: ((Brand Voice))""",
    model=["gpt-4o-mini"],
    temperature=0.6,
    max_tokens=100
)

linkedin_post = social_workflow.node("TextGenerationNode").config(
    template="""Adapt this core message for LinkedIn:

Core Message: ((Core Message))

Requirements:
- Professional tone
- 1-3 paragraphs
- Include a thought-provoking question
- Suitable for business audience
- Brand voice: ((Brand Voice))""",
    model=["gpt-4o-mini"],
    temperature=0.5,
    max_tokens=300
)

instagram_post = social_workflow.node("TextGenerationNode").config(
    template="""Adapt this core message for Instagram:

Core Message: ((Core Message))

Requirements:
- Visual and engaging
- Include emoji where appropriate
- 5-10 relevant hashtags
- Call-to-action in comments
- Brand voice: ((Brand Voice))""",
    model=["gpt-4o-mini"],
    temperature=0.7,
    max_tokens=200
)

# Combine all content
social_package = social_workflow.node("ComposeTextNode").config(
    template="""# Social Media Content Package

## Core Message
((Core Message))

## Twitter
((Twitter Post))

## LinkedIn
((LinkedIn Post))

## Instagram
((Instagram Post))

---
Created: {{current_date}}
Topic: ((Content Topic))
""")

output_node = social_workflow.node("OutputNode")

# Connect workflow
social_workflow.link(topic_input.output(), core_message.input("variables", "Content Topic"))
social_workflow.link(brand_voice.output(), core_message.input("variables", "Brand Voice"))

social_workflow.link(core_message.output(), twitter_post.input("variables", "Core Message"))
social_workflow.link(core_message.output(), linkedin_post.input("variables", "Core Message"))
social_workflow.link(core_message.output(), instagram_post.input("variables", "Core Message"))

social_workflow.link(brand_voice.output(), twitter_post.input("variables", "Brand Voice"))
social_workflow.link(brand_voice.output(), linkedin_post.input("variables", "Brand Voice"))
social_workflow.link(brand_voice.output(), instagram_post.input("variables", "Brand Voice"))

social_workflow.link(topic_input.output(), social_package.input("variables", "Content Topic"))
social_workflow.link(core_message.output(), social_package.input("variables", "Core Message"))
social_workflow.link(twitter_post.output(), social_package.input("variables", "Twitter Post"))
social_workflow.link(linkedin_post.output(), social_package.input("variables", "LinkedIn Post"))
social_workflow.link(instagram_post.output(), social_package.input("variables", "Instagram Post"))

social_workflow.link(social_package.output(), output_node.input())

# Save workflow
social_creator = client.workflows.save(social_workflow)

contract_workflow = WorkflowDefinition(name="Contract Analyzer")

# File input
contract_input = contract_workflow.node("FileInputNode").config(
    label="Contract Document",
    accepted_types=["pdf", "docx", "txt"],
    max_size_mb=20
)

# Extract text
text_extractor = contract_workflow.node("ExtractTextNode").config(
    preserve_formatting=True,
    extract_tables=True
)

# Key terms extraction
key_terms = contract_workflow.node("TextGenerationNode").config(
    template="""Analyze this contract and extract key terms:

Contract Text: ((Contract Text))

Extract and format:
1. Parties involved
2. Contract duration/dates
3. Payment terms
4. Key obligations for each party
5. Termination clauses
6. Liability limitations
7. Governing law

Present in a structured format.""",
    model=["gpt-4o"],
    temperature=0.1,
    max_tokens=800
)

# Risk assessment
risk_analysis = contract_workflow.node("TextGenerationNode").config(
    template="""Perform a risk assessment of this contract:

Contract Text: ((Contract Text))
Key Terms: ((Key Terms))

Identify:
1. High-risk clauses
2. Missing standard protections
3. Unusual or concerning terms
4. Recommendations for negotiation
5. Overall risk level (Low/Medium/High)

Provide specific examples and explanations.""",
    model=["gpt-4o"],
    temperature=0.2,
    max_tokens=600
)

# Summary generation
contract_summary = contract_workflow.node("SummaryNode").config(
    summary_format="Key Points",
    summary_topic="Contract overview and main provisions",
    max_length=300
)

# Compliance check
compliance_check = contract_workflow.node("TextGenerationNode").config(
    template="""Check this contract for common compliance issues:

Contract Text: ((Contract Text))

Review for:
1. Required legal disclosures
2. Industry-specific regulations
3. Data protection compliance (GDPR, etc.)
4. Employment law compliance (if applicable)
5. Consumer protection requirements

Flag any potential compliance issues.""",
    model=["gpt-4o"],
    temperature=0.1,
    max_tokens=400
)

# Final report
final_report = contract_workflow.node("ComposeTextNode").config(
    template="""# Contract Analysis Report

## Executive Summary
((Contract Summary))

## Key Terms & Provisions
((Key Terms))

## Risk Assessment
((Risk Analysis))

## Compliance Review
((Compliance Check))

---
Analysis completed: {{current_date}}
Report ID: {{uuid}}

**Disclaimer**: This analysis is for informational purposes only and does not constitute legal advice.
""")

output_node = contract_workflow.node("OutputNode")

# Connect workflow
contract_workflow.link(contract_input.output(), text_extractor.input())
contract_workflow.link(text_extractor.output(), key_terms.input("variables", "Contract Text"))
contract_workflow.link(text_extractor.output(), risk_analysis.input("variables", "Contract Text"))
contract_workflow.link(text_extractor.output(), contract_summary.input())
contract_workflow.link(text_extractor.output(), compliance_check.input("variables", "Contract Text"))

contract_workflow.link(key_terms.output(), risk_analysis.input("variables", "Key Terms"))

contract_workflow.link(contract_summary.output(), final_report.input("variables", "Contract Summary"))
contract_workflow.link(key_terms.output(), final_report.input("variables", "Key Terms"))
contract_workflow.link(risk_analysis.output(), final_report.input("variables", "Risk Analysis"))
contract_workflow.link(compliance_check.output(), final_report.input("variables", "Compliance Check"))

contract_workflow.link(final_report.output(), output_node.input())

# Save workflow
contract_analyzer = client.workflows.save(contract_workflow)

research_workflow = WorkflowDefinition(name="Research Paper Processor")

# Inputs
paper_input = research_workflow.node("FileInputNode").config(
    label="Research Paper",
    accepted_types=["pdf"],
    max_size_mb=50
)

research_focus = research_workflow.node("InputNode").config(
    label="Research Focus",
    type="str",
    fixed_value=True,
    value="methodology, findings, and implications"
)

# Extract text
text_extractor = research_workflow.node("ExtractTextNode").config(
    preserve_formatting=True,
    extract_tables=True,
    extract_metadata=True
)

# Structure identification
structure_analyzer = research_workflow.node("TextGenerationNode").config(
    template="""Analyze the structure of this research paper:

Paper Text: ((Paper Text))

Identify and extract:
1. Title and authors
2. Abstract
3. Introduction/background
4. Methodology
5. Results/findings
6. Discussion
7. Conclusion
8. References (key ones)

Format each section clearly.""",
    model=["gpt-4o"],
    temperature=0.1,
    max_tokens=1000
)

# Methodology analysis
methodology_analysis = research_workflow.node("TextGenerationNode").config(
    template="""Analyze the methodology of this research:

Paper Structure: ((Paper Structure))
Full Text: ((Paper Text))

Focus on:
1. Research design and approach
2. Data collection methods
3. Sample size and characteristics
4. Analysis techniques used
5. Limitations acknowledged
6. Validity and reliability considerations

Provide a critical assessment.""",
    model=["gpt-4o"],
    temperature=0.2,
    max_tokens=600
)

# Key findings extraction
findings_extractor = research_workflow.node("TextGenerationNode").config(
    template="""Extract and summarize the key findings:

Paper Structure: ((Paper Structure))
Full Text: ((Paper Text))

Identify:
1. Main research questions answered
2. Primary findings and results
3. Statistical significance (if applicable)
4. Unexpected or surprising results
5. Practical implications
6. Theoretical contributions

Present findings clearly and objectively.""",
    model=["gpt-4o"],
    temperature=0.1,
    max_tokens=700
)

# Critical evaluation
critical_evaluation = research_workflow.node("TextGenerationNode").config(
    template="""Provide a critical evaluation of this research:

Methodology: ((Methodology Analysis))
Findings: ((Key Findings))
Full Paper: ((Paper Text))

Evaluate:
1. Strengths of the research
2. Potential weaknesses or limitations
3. Quality of evidence presented
4. Generalizability of findings
5. Contribution to the field
6. Suggestions for future research

Be balanced and constructive.""",
    model=["gpt-4o"],
    temperature=0.3,
    max_tokens=800
)

# Executive summary
executive_summary = research_workflow.node("SummaryNode").config(
    summary_format="Key Points",
    summary_topic="Research paper overview for non-experts",
    max_length=400
)

# Final report
research_report = research_workflow.node("ComposeTextNode").config(
    template="""# Research Paper Analysis Report

## Executive Summary
((Executive Summary))

## Paper Structure & Content
((Paper Structure))

## Methodology Assessment
((Methodology Analysis))

## Key Findings
((Key Findings))

## Critical Evaluation
((Critical Evaluation))

---
Analysis completed: {{current_date}}
Focus area: ((Research Focus))
""")

output_node = research_workflow.node("OutputNode")

# Connect workflow
research_workflow.link(paper_input.output(), text_extractor.input())
research_workflow.link(text_extractor.output(), structure_analyzer.input("variables", "Paper Text"))
research_workflow.link(text_extractor.output(), executive_summary.input())

research_workflow.link(structure_analyzer.output(), methodology_analysis.input("variables", "Paper Structure"))
research_workflow.link(text_extractor.output(), methodology_analysis.input("variables", "Paper Text"))

research_workflow.link(structure_analyzer.output(), findings_extractor.input("variables", "Paper Structure"))
research_workflow.link(text_extractor.output(), findings_extractor.input("variables", "Paper Text"))

research_workflow.link(methodology_analysis.output(), critical_evaluation.input("variables", "Methodology Analysis"))
research_workflow.link(findings_extractor.output(), critical_evaluation.input("variables", "Key Findings"))
research_workflow.link(text_extractor.output(), critical_evaluation.input("variables", "Paper Text"))

research_workflow.link(research_focus.output(), research_report.input("variables", "Research Focus"))
research_workflow.link(executive_summary.output(), research_report.input("variables", "Executive Summary"))
research_workflow.link(structure_analyzer.output(), research_report.input("variables", "Paper Structure"))
research_workflow.link(methodology_analysis.output(), research_report.input("variables", "Methodology Analysis"))
research_workflow.link(findings_extractor.output(), research_report.input("variables", "Key Findings"))
research_workflow.link(critical_evaluation.output(), research_report.input("variables", "Critical Evaluation"))

research_workflow.link(research_report.output(), output_node.input())

# Save workflow
research_processor = client.workflows.save(research_workflow)

support_workflow = WorkflowDefinition(name="Support Ticket Processor")

# Inputs
ticket_input = support_workflow.node("InputNode").config(
    label="Support Ticket",
    type="str"
)

customer_tier = support_workflow.node("InputNode").config(
    label="Customer Tier",
    type="str",
    fixed_value=True,
    value="standard"
)

# Ticket classification
classifier = support_workflow.node("TextGenerationNode").config(
    template="""Classify this support ticket:

Ticket: ((Support Ticket))

Classify by:
1. Category (Technical, Billing, Account, Feature Request, Bug Report, Other)
2. Priority (Low, Medium, High, Critical)
3. Urgency (Low, Medium, High)
4. Complexity (Simple, Moderate, Complex)
5. Department (Support, Engineering, Sales, Billing)

Provide reasoning for each classification.""",
    model=["gpt-4o-mini"],
    temperature=0.1,
    max_tokens=300
)

# Sentiment analysis
sentiment_analyzer = support_workflow.node("TextGenerationNode").config(
    template="""Analyze the sentiment and tone of this support ticket:

Ticket: ((Support Ticket))

Determine:
1. Overall sentiment (Positive, Neutral, Negative, Very Negative)
2. Emotional indicators (frustrated, confused, angry, satisfied, etc.)
3. Urgency level from customer perspective
4. Communication style needed in response

Provide specific examples from the text.""",
    model=["gpt-4o-mini"],
    temperature=0.2,
    max_tokens=200
)

# Knowledge base search (simulated)
kb_search = support_workflow.node("TextGenerationNode").config(
    template="""Based on this ticket classification, suggest relevant knowledge base articles:

Ticket: ((Support Ticket))
Classification: ((Classification))

Suggest:
1. Most relevant help articles (by title)
2. Common solutions for this type of issue
3. Troubleshooting steps
4. Related documentation

Format as a helpful resource list.""",
    model=["gpt-4o-mini"],
    temperature=0.3,
    max_tokens=400
)

# Response generation
response_generator = support_workflow.node("TextGenerationNode").config(
    template="""Generate a professional support response:

Ticket: ((Support Ticket))
Classification: ((Classification))
Sentiment Analysis: ((Sentiment Analysis))
Suggested Resources: ((KB Search))
Customer Tier: ((Customer Tier))

Create a response that:
1. Acknowledges the customer's issue
2. Shows empathy if needed
3. Provides initial troubleshooting steps
4. References helpful resources
5. Sets expectations for follow-up
6. Matches the appropriate tone

Keep it professional but personalized.""",
    model=["gpt-4o"],
    temperature=0.4,
    max_tokens=500
)

# Escalation check
escalation_check = support_workflow.node("ConditionalNode").config(
    condition="priority == 'Critical' or priority == 'High'",
    condition_type="custom"
)

# Escalation notice
escalation_notice = support_workflow.node("TextGenerationNode").config(
    template="""Generate escalation notice:

Ticket: ((Support Ticket))
Classification: ((Classification))
Sentiment: ((Sentiment Analysis))

Create internal escalation notice including:
1. Reason for escalation
2. Customer impact
3. Recommended next steps
4. Timeline requirements""",
    model=["gpt-4o-mini"],
    temperature=0.1,
    max_tokens=200
)

# Final package
support_package = support_workflow.node("ComposeTextNode").config(
    template="""# Support Ticket Analysis

## Ticket Classification
((Classification))

## Sentiment Analysis
((Sentiment Analysis))

## Suggested Response
((Response))

## Recommended Resources
((KB Search))

## Escalation Status
((Escalation Notice))

---
Processed: {{current_date}}
Customer Tier: ((Customer Tier))
""")

output_node = support_workflow.node("OutputNode")

# Connect workflow
support_workflow.link(ticket_input.output(), classifier.input("variables", "Support Ticket"))
support_workflow.link(ticket_input.output(), sentiment_analyzer.input("variables", "Support Ticket"))

support_workflow.link(ticket_input.output(), kb_search.input("variables", "Support Ticket"))
support_workflow.link(classifier.output(), kb_search.input("variables", "Classification"))

support_workflow.link(ticket_input.output(), response_generator.input("variables", "Support Ticket"))
support_workflow.link(classifier.output(), response_generator.input("variables", "Classification"))
support_workflow.link(sentiment_analyzer.output(), response_generator.input("variables", "Sentiment Analysis"))
support_workflow.link(kb_search.output(), response_generator.input("variables", "KB Search"))
support_workflow.link(customer_tier.output(), response_generator.input("variables", "Customer Tier"))

# Escalation logic
support_workflow.link(classifier.output(), escalation_check.input())
support_workflow.link(escalation_check.output("true"), escalation_notice.input("variables", "Classification"))
support_workflow.link(ticket_input.output(), escalation_notice.input("variables", "Support Ticket"))
support_workflow.link(sentiment_analyzer.output(), escalation_notice.input("variables", "Sentiment Analysis"))

# Final assembly
support_workflow.link(classifier.output(), support_package.input("variables", "Classification"))
support_workflow.link(sentiment_analyzer.output(), support_package.input("variables", "Sentiment Analysis"))
support_workflow.link(response_generator.output(), support_package.input("variables", "Response"))
support_workflow.link(kb_search.output(), support_package.input("variables", "KB Search"))
support_workflow.link(escalation_notice.output(), support_package.input("variables", "Escalation Notice"))
support_workflow.link(customer_tier.output(), support_package.input("variables", "Customer Tier"))

support_workflow.link(support_package.output(), output_node.input())

# Save workflow
support_processor = client.workflows.save(support_workflow)

survey_workflow = WorkflowDefinition(name="Survey Response Analyzer")

# Input
survey_data = survey_workflow.node("InputNode").config(
    label="Survey Responses",
    type="str",
    description="Paste survey responses (CSV format or structured text)"
)

survey_topic = survey_workflow.node("InputNode").config(
    label="Survey Topic",
    type="str"
)

# Data preprocessing
data_processor = survey_workflow.node("TextGenerationNode").config(
    template="""Process and structure this survey data:

Raw Data: ((Survey Responses))
Topic: ((Survey Topic))

Tasks:
1. Identify response patterns
2. Count total responses
3. Categorize response types
4. Flag incomplete or invalid responses
5. Prepare data for analysis

Present in a structured format.""",
    model=["gpt-4o-mini"],
    temperature=0.1,
    max_tokens=600
)

# Sentiment analysis
sentiment_analysis = survey_workflow.node("TextGenerationNode").config(
    template="""Analyze sentiment in these survey responses:

Processed Data: ((Processed Data))

Provide:
1. Overall sentiment distribution (% positive, neutral, negative)
2. Key positive themes
3. Key negative themes
4. Sentiment by question/topic (if applicable)
5. Notable emotional indicators""",
    model=["gpt-4o-mini"],
    temperature=0.2,
    max_tokens=500
)

# Theme extraction
theme_extractor = survey_workflow.node("TextGenerationNode").config(
    template="""Extract key themes from survey responses:

Processed Data: ((Processed Data))
Survey Topic: ((Survey Topic))

Identify:
1. Most frequently mentioned topics
2. Emerging themes or patterns
3. Unexpected insights
4. Common suggestions or requests
5. Areas of consensus vs. disagreement

Group similar responses and quantify when possible.""",
    model=["gpt-4o"],
    temperature=0.3,
    max_tokens=700
)

# Statistical summary
stats_generator = survey_workflow.node("TextGenerationNode").config(
    template="""Generate statistical summary:

Processed Data: ((Processed Data))
Themes: ((Themes))
Sentiment: ((Sentiment Analysis))

Create summary including:
1. Response rate and demographics (if available)
2. Key metrics and percentages
3. Statistical significance of findings
4. Confidence levels
5. Data quality assessment""",
    model=["gpt-4o-mini"],
    temperature=0.1,
    max_tokens=400
)

# Recommendations
recommendations = survey_workflow.node("TextGenerationNode").config(
    template="""Based on the survey analysis, provide actionable recommendations:

Themes: ((Themes))
Sentiment: ((Sentiment Analysis))
Statistics: ((Statistics))
Survey Topic: ((Survey Topic))

Recommend:
1. Priority actions based on feedback
2. Areas requiring immediate attention
3. Long-term strategic considerations
4. Follow-up survey questions
5. Implementation timeline suggestions

Focus on practical, data-driven recommendations.""",
    model=["gpt-4o"],
    temperature=0.4,
    max_tokens=600
)

# Executive summary
exec_summary = survey_workflow.node("SummaryNode").config(
    summary_format="Key Points",
    summary_topic="Survey results and main insights",
    max_length=300
)

# Final report
survey_report = survey_workflow.node("ComposeTextNode").config(
    template="""# Survey Analysis Report: ((Survey Topic))

## Executive Summary
((Executive Summary))

## Response Overview
((Statistics))

## Sentiment Analysis
((Sentiment Analysis))

## Key Themes & Insights
((Themes))

## Recommendations
((Recommendations))

---
Analysis Date: {{current_date}}
Report ID: {{uuid}}
""")

output_node = survey_workflow.node("OutputNode")

# Connect workflow
survey_workflow.link(survey_data.output(), data_processor.input("variables", "Survey Responses"))
survey_workflow.link(survey_topic.output(), data_processor.input("variables", "Survey Topic"))

survey_workflow.link(data_processor.output(), sentiment_analysis.input("variables", "Processed Data"))
survey_workflow.link(data_processor.output(), theme_extractor.input("variables", "Processed Data"))
survey_workflow.link(survey_topic.output(), theme_extractor.input("variables", "Survey Topic"))

survey_workflow.link(data_processor.output(), stats_generator.input("variables", "Processed Data"))
survey_workflow.link(theme_extractor.output(), stats_generator.input("variables", "Themes"))
survey_workflow.link(sentiment_analysis.output(), stats_generator.input("variables", "Sentiment Analysis"))

survey_workflow.link(theme_extractor.output(), recommendations.input("variables", "Themes"))
survey_workflow.link(sentiment_analysis.output(), recommendations.input("variables", "Sentiment Analysis"))
survey_workflow.link(stats_generator.output(), recommendations.input("variables", "Statistics"))
survey_workflow.link(survey_topic.output(), recommendations.input("variables", "Survey Topic"))

survey_workflow.link(data_processor.output(), exec_summary.input())

survey_workflow.link(survey_topic.output(), survey_report.input("variables", "Survey Topic"))
survey_workflow.link(exec_summary.output(), survey_report.input("variables", "Executive Summary"))
survey_workflow.link(stats_generator.output(), survey_report.input("variables", "Statistics"))
survey_workflow.link(sentiment_analysis.output(), survey_report.input("variables", "Sentiment Analysis"))
survey_workflow.link(theme_extractor.output(), survey_report.input("variables", "Themes"))
survey_workflow.link(recommendations.output(), survey_report.input("variables", "Recommendations"))

survey_workflow.link(survey_report.output(), output_node.input())

# Save workflow
survey_analyzer = client.workflows.save(survey_workflow)

# Run the blog post generator
blog_result = blog_generator.run(body={
    "Blog Topic": "The Impact of AI on Small Businesses"
}).wait()

print("Generated Blog Post:")
print(blog_result.output)

# Test with a sample contract (you'd upload a real file)
contract_result = contract_analyzer.run(body={
    # File would be uploaded through the UI or API
}).wait()

print("Contract Analysis:")
print(contract_result.output)

# Test support ticket processing
support_result = support_processor.run(body={
    "Support Ticket": """
    Hi, I'm really frustrated. I've been trying to log into my account for 3 days
    and keep getting an error message saying 'invalid credentials' even though I'm
    sure my password is correct. I've tried resetting it twice but still can't get in.
    This is blocking me from accessing important files for a client presentation tomorrow.
    Please help ASAP!
    """,
    "Customer Tier": "premium"
}).wait()

print("Support Analysis:")
print(support_result.output)