Graph Workflows

import { 
  startNode,      // Graph entry point
  endNode,        // Graph exit point
  actionNode,     // Custom logic (async function)
  conditionNode,  // Conditional routing (returns edge name)
  llmNode,        // LLM generation with options
  retrieveNode,   // RAG search in knowledge base
  edge            // Connection between nodes
} from 'orkajs';
 
// startNode: Initialize context
startNode('start')
 
// endNode: Terminate execution
endNode('end')
 
// actionNode: Custom logic
actionNode('process', async (ctx) => {
  ctx.output = await someAsyncOperation(ctx.input);
  ctx.metadata.processed = true;
  return ctx;
})
 
// conditionNode: Dynamic routing
conditionNode('router', (ctx) => {
  if (ctx.metadata.score > 0.8) return 'high-quality';
  if (ctx.metadata.score > 0.5) return 'medium';
  return 'low';
})
 
// llmNode: LLM generation
llmNode('generate', {
  systemPrompt: 'You are an expert assistant.',
  temperature: 0.3,
  promptTemplate: 'Context: {{context}}\n\nQuestion: {{input}}'
})
 
// retrieveNode: RAG
retrieveNode('search', 'knowledge-base-name', { topK: 5 })

import { 
  createOrka, 
  OpenAIAdapter, 
  MemoryVectorAdapter,
  startNode, endNode, actionNode, conditionNode, llmNode, retrieveNode, edge
} from 'orkajs';
 
async function main() {
  const orka = createOrka({
    llm: new OpenAIAdapter({ apiKey: process.env.OPENAI_API_KEY! }),
    vectorDB: new MemoryVectorAdapter(),
  });
 
  // Create FAQ knowledge base
  await orka.knowledge.create({
    name: 'faq',
    source: [
      { text: 'Pour réinitialiser votre mot de passe, allez dans Paramètres > Sécurité.', metadata: { topic: 'password' } },
      { text: 'Les remboursements sont traités sous 5 jours ouvrés.', metadata: { topic: 'refund' } },
      { text: 'Contactez-nous à support@example.com pour toute question.', metadata: { topic: 'contact' } },
    ],
  });
 
  // Define the graph
  const graph = orka.graph({
    name: 'smart-support',
    nodes: [
      startNode('start'),
 
      // Classify question with LLM
      actionNode('classify', async (ctx) => {
        const result = await ctx.llm.generate(
          `Classify this question into one category: "technical", "billing", or "general".
Question: ${ctx.input}
Respond with ONLY the category.`,
          { temperature: 0, maxTokens: 10 }
        );
        ctx.output = result.content.trim().toLowerCase();
        ctx.metadata.category = ctx.output;
        return ctx;
      }),
 
      // Conditional router based on classification
      conditionNode('router', (ctx) => {
        const category = (ctx.metadata.category as string) ?? 'general';
        if (category.includes('technical')) return 'technical';
        if (category.includes('billing')) return 'billing';
        return 'general';
      }),
 
      // Technical branch: RAG + LLM
      retrieveNode('tech-retrieve', 'faq', { topK: 3 }),
      llmNode('tech-answer', {
        systemPrompt: 'You are a technical expert. Answer based on the context.',
        temperature: 0.3,
        promptTemplate: 'Context:\n{{context}}\n\nTechnical question: {{input}}',
      }),
 
      // Billing branch: Direct LLM
      llmNode('billing-answer', {
        systemPrompt: 'You are a billing expert. Be precise and professional.',
        temperature: 0.3,
      }),
 
      // General branch
      llmNode('general-answer', {
        systemPrompt: 'You are a general support assistant. Be helpful and concise.',
        temperature: 0.5,
      }),
 
      endNode('end'),
    ],
    edges: [
      edge('start', 'classify'),
      edge('classify', 'router'),
      // Edges conditionnels avec label
      edge('router', 'tech-retrieve', 'technical'),
      edge('router', 'billing-answer', 'billing'),
      edge('router', 'general-answer', 'general'),
      // Convergence vers end
      edge('tech-retrieve', 'tech-answer'),
      edge('tech-answer', 'end'),
      edge('billing-answer', 'end'),
      edge('general-answer', 'end'),
    ],
    onNodeComplete: (nodeId, ctx) => {
      console.log(`✅ Node "${nodeId}" completed`);
    },
  });
 
  // Run the graph
  const result = await graph.run('How do I reset my password?');
 
  console.log(`Output: ${result.output}`);
  console.log(`Path: ${result.path.join(' → ')}`);
  console.log(`Latency: ${result.totalLatencyMs}ms`);
  console.log(`Category: ${result.metadata.category}`);
}

// Generate the Mermaid diagram
const mermaidCode = graph.toMermaid();
console.log(mermaidCode);
 
// Output:
// graph TD
//   start([Start])
//   classify[classify]
//   router{router}
//   tech-retrieve[tech-retrieve]
//   tech-answer[tech-answer]
//   billing-answer[billing-answer]
//   general-answer[general-answer]
//   end([End])
//   start --> classify
//   classify --> router
//   router -->|technical| tech-retrieve
//   router -->|billing| billing-answer
//   router -->|general| general-answer
//   tech-retrieve --> tech-answer
//   tech-answer --> end
//   billing-answer --> end
//   general-answer --> end

interface GraphResult {
  output: string;           // Final output of the graph
  path: string[];           // Path taken: ['start', 'classify', 'router', 'tech-answer', 'end']
  metadata: Record<string, unknown>; // Accumulated metadata
  totalLatencyMs: number;   // Total execution time
  nodeResults: Map<string, NodeResult>; // Results by node
}
 
// Example usage
const result = await graph.run('Ma question...');
 
// Analyze the path taken
if (result.path.includes('tech-answer')) {
  console.log('Technical question detected');
}
 
// Access metadata
console.log('Category:', result.metadata.category);
 
// Time per node
for (const [nodeId, nodeResult] of result.nodeResults) {
  console.log(`${nodeId}: ${nodeResult.latencyMs}ms`);
}