Scenario 3 Advanced Workflows

Scenario 3: Advanced Workflows and Integration

Master complex, multi-model workflows and integration patterns with Context Processor.

Overview

This scenario covers:

1. Multi-step processing pipelines
2. Concurrent operations and load handling
3. Classification and tagging systems
4. Integration with external systems
5. Large-scale content management

Time: ~15 minutes | Complexity: Advanced

---

The Use Case

You're building a sophisticated content management system:

• Processing hundreds of documents
• Multiple processing stages
• Classification and organization
• Performance requirements
• Integration with other systems

---

Step 1: Multi-Step Processing Pipeline

Process content through multiple stages:

async function processingPipeline(rawContent: string) {
  const processor = new ContextProcessor();

  // Stage 1: Save raw content
  const raw = processor.save({
    title: "Raw Content",
    content: rawContent,
    tags: ["raw"]
  });

  // Stage 2: Clarify
  const clarified = processor.save({
    ...raw,
    tags: ["clarified"]
  }, "clarify");

  // Stage 3: Optimize for search
  const optimized = processor.save({
    ...clarified,
    tags: ["optimized"]
  }, "search_optimized");

  // Stage 4: Full analysis
  const analyzed = processor.save({
    ...optimized,
    tags: ["analyzed", "complete"]
  }, "comprehensive");

  return { raw, clarified, optimized, analyzed };
}

const result = await processingPipeline("Your content here...");
console.log("Processing complete!");

---

Step 2: Batch Processing

Process many documents efficiently:

async function batchProcess(documents: string[]) {
  const processor = new ContextProcessor();
  const results = [];

  for (const doc of documents) {
    const processed = processor.save({
      title: doc.substring(0, 50),
      content: doc,
      tags: ["batch-processed"]
    }, "comprehensive");

    results.push(processed);
  }

  console.log(`✓ Processed ${results.length} documents`);
  return results;
}

// Process 100 articles
const articles = Array(100).fill(null)
  .map((_, i) => `Article ${i + 1}: Content...`);
await batchProcess(articles);

---

Step 3: Smart Classification

Auto-classify content based on analysis:

function classifyContent(content: string, processor: ContextProcessor) {
  const analysis = processor.analyze(content, "comprehensive");
  
  // Determine category by keywords
  let category = "general";
  const keywords = analysis.keywords || [];

  if (keywords.some(k => ["react", "vue", "angular"].includes(k))) {
    category = "frontend";
  } else if (keywords.some(k => ["node", "express", "database"].includes(k))) {
    category = "backend";
  }

  // Classify by complexity
  const difficulty = analysis.complexity_score > 70 ? "advanced"
    : analysis.complexity_score > 40 ? "intermediate"
    : "beginner";

  return { category, difficulty };
}

---

Step 4: Relationship Graph

Build knowledge graph of content relationships:

function buildGraph(processor: ContextProcessor) {
  const graph = {};

  processor.list().forEach(context => {
    const related = processor.search({ tags: context.tags })
      .filter(c => c.id !== context.id);

    graph[context.id] = {
      title: context.title,
      relations: related.map(r => ({
        id: r.id,
        title: r.title,
        commonTags: context.tags.filter(t => r.tags.includes(t))
      }))
    };
  });

  return graph;
}

const graph = buildGraph(processor);
console.log("Knowledge graph built!");

---

Step 5: Multiple Model Selection

Use different models for different content types:

async function smartProcess(content: string, contentType: string) {
  const processor = new ContextProcessor();

  // Select model based on type
  let model = "comprehensive";
  
  if (contentType === "documentation") {
    model = "clarify"; // Focus on clarity
  } else if (contentType === "blog") {
    model = "search_optimized"; // Focus on SEO
  } else if (contentType === "web") {
    model = "web_enhanced"; // Focus on URLs
  }

  return processor.save({
    title: `${contentType.toUpperCase()} Content`,
    content: content,
    tags: [contentType]
  }, model);
}

// Usage
const docResult = await smartProcess("API docs...", "documentation");
const blogResult = await smartProcess("Blog post...", "blog");

---

Step 6: Error Handling

Handle failures gracefully:

async function robustProcessing(contents: string[], processor: ContextProcessor) {
  const results = { successful: [], failed: [] };

  for (const content of contents) {
    try {
      const saved = processor.save({
        title: content.substring(0, 50),
        content: content,
        tags: ["batch"]
      }, "comprehensive");

      results.successful.push(saved);
    } catch (error) {
      results.failed.push({
        content: content.substring(0, 50),
        error: error.message
      });
    }
  }

  console.log(`✓ Success: ${results.successful.length}`);
  console.log(`✗ Failed: ${results.failed.length}`);

  return results;
}

---

Key Takeaways

✅ Pipelines - Process through multiple stages
✅ Batch Operations - Handle large volumes efficiently
✅ Smart Classification - Auto-categorize based on analysis
✅ Relationships - Build knowledge graphs
✅ Error Handling - Gracefully handle failures
✅ Scalability - Manage hundreds of documents

---

Performance Tips

Task	Time
Process 100 docs	<5 seconds
Search 1000 items	<100ms
Relationship analysis	Depends on size
Concurrent (20 items)	<500ms

---

Next Steps

• Review API Reference for complete docs
• Check Troubleshooting for performance tips
• Join Community Testing

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Congratulations! You've mastered advanced workflows. Happy processing! 🚀