5.14 Workflow Examples

This chapter provides multiple complete workflow examples to help you quickly understand real-world applications of Pop Workflows.
All examples can be directly reproduced or used as templates for your own business processes.

This chapter includes:

• Document analysis and summary generation
• Chart generation (data → visualization)
• Automated knowledge base construction
• Automated report generation (PDF/Excel)
• Web content extraction and processing
• AI content enhancement (translation, refinement, structuring)

---

📄 Example 1: Batch Document Summary Generation

Input multiple documents → Output summary list → Render with Layout components

🎯 Use Case

• Reading a batch of documents takes too much time
• Summaries need to follow a unified structure
• Results may need to be exported for further processing

🔧 Nodes Used

• File Read
• LLM (AI Text Generation)
• Loop
• Merge
• Output

🧩 Workflow Design

[Input: files]
      ↓
[Loop over files]
      ↓ For each file:
    [Read File → Extract Text]
      ↓
    [AI Summary]
      ↓
[Merge All Results]
      ↓
[Output: summary_list]

📝 Example Output Structure

[
  {
    "filename": "meeting-notes.pdf",
    "summary": "This meeting discussed…"
  },
  {
    "filename": "sales-data.docx",
    "summary": "This quarter’s sales performance…"
  }
]

🎨 Layout Display

• Left: File upload
• Center: Run button
• Right: Summary list (Table + Text)

---

📊 Example 2: Data → Chart Visualization

Input structured data → AI identifies chart type → Output chart data for front-end rendering

🎯 Use Case

• Need quick visualization of business data
• Prefer AI to auto-determine chart type

🔧 Nodes Used

• Text Input / File Upload
• LLM
• PSL (data transformation)
• Output

🧩 Workflow Design

[Input: data_json]
      ↓
[AI: Identify chart type & X/Y fields]
      ↓
[Script: Convert to chart.js structure]
      ↓
[Output: chart_data]

📝 Sample AI Output

{
  "chart": "line",
  "x": "date",
  "y": "value"
}

🎨 Layout Rendering

Bind to chart component:

{{workflow.outputs.chart_data}}

---

📚 Example 3: Automated Knowledge Base Construction (Documents → Chunks → Index)

Upload documents → Auto-split into chunks → Write to Solr / vector DB → Build KB

🎯 Use Case

• Automate KB ingestion
• Batch import PDF, DOCX, TXT
• Produce searchable chunks

🔧 Nodes Used

• File Read
• Text Split
• Embedding
• Solr / Vector DB Write
• Loop / Merge

🧩 Workflow Diagram

[Input: files]
      ↓
[Loop]
      ↓
[Read File → Extract Text]
      ↓
[Split into Chunks]
      ↓
[Embedding]
      ↓
[Index Write]
      ↓
[Merge Index Result]

📝 Output

{
  "imported": 128,
  "failed": 0
}

---

🧾 Example 4: Automated Report Generation (PDF/Excel)

Input data → AI generates content → Output PDF/Excel → User downloads file

🎯 Use Case

• Weekly/monthly reports
• Operational analysis documents
• Automatically generate shareable files

🔧 Nodes Used

• AI content generation
• Template formatting
• PDF Generator
• Excel Generator
• File output

🧩 Workflow Design

[Input: structured_data]
      ↓
[AI: Generate report content]
      ↓
[Script: Format data]
      ↓
[PDF Generator / Excel Generator]
      ↓
[Output: file_url]

📝 Output Example

/downloads/report_20240215.pdf

---

🌐 Example 5: Web Content Extraction & Processing

Input URL → Fetch webpage → Clean text → AI extracts key information

🔧 Nodes Used

• HTTP Request
• Regex Processing
• PSL for text cleaning
• AI Extraction
• Output

🧩 Workflow

[Input: url]
      ↓
[HTTP GET]
      ↓
[Clean HTML]
      ↓
[AI Extract]
      ↓
[Output: structured_info]

Sample Output

{
  "title": "OpenAI Releases New Model",
  "points": [
    "2x performance improvement",
    "Faster inference",
    "New voice input support"
  ]
}

---

✨ Example 6: AI Content Enhancement (Translation, Enhancement, Correction)

Workflow

[Input: text]
      ↓
[AI: Correct → Improve → Translate]
      ↓
[Output: enhanced_text]

Available styles include:

• Business American English
• Academic writing
• Friendly customer service tone
• Technical expert tone

---

🎯 Summary

This chapter covers:

• Documents
• Charts
• Knowledge bases
• Reports
• AI content enhancement
• Web data extraction

These examples demonstrate Pop Workflow’s versatility—from automation to hybrid AI scenarios—all built through low-code construction.