IA a n8n

6.1. AI Nodes a N8N

N8N té suport natiu per a IA amb nodes especialitzats.

Nodes d'IA disponibles:

- OpenAI (GPT-4, GPT-3.5, DALL-E, Whisper)
- Anthropic Claude
- Google PaLM/Gemini
- Hugging Face
- Cohere
- AI Agent (LangChain)
- Text Classifier
- Sentiment Analysis

Exemple: OpenAI GPT-4

[Trigger: New Support Ticket]
  ↓
[OpenAI Chat Model]
  Model: gpt-4
  System Message: "You are a customer support specialist..."
  User Message: {{$json.ticket_description}}
  Temperature: 0.3
  Max Tokens: 500
  ↓
[Parse AI Response]
  ↓
[Update Ticket with AI Suggestion]

6.2. Casos d'ús amb IA

1. Anàlisi de Sentiment

Pipeline de sentiment analysis:

[Get Customer Reviews]
  ↓
[Split In Batches: 50]
  ↓
[OpenAI Chat Model]
  Prompt: "Analyze sentiment of this review and return JSON:
          {sentiment: 'positive'|'negative'|'neutral', 
           confidence: 0-1,
           key_topics: []}"
  Review: {{$json.review_text}}
  ↓
[Parse JSON Response]
  ↓
[IF: Negative sentiment]
  ├─ true → [Alert Customer Success Team]
  └─ false → [Store in Analytics DB]

2. Extracció d'Informació

[Receive Invoice PDF]
  ↓
[Extract Text from PDF]
  ↓
[OpenAI Chat Model]
  Prompt: "Extract invoice data as JSON:
          {
            invoice_number: '',
            date: '',
            vendor: '',
            total_amount: 0,
            line_items: []
          }"
  Text: {{$json.pdf_text}}
  ↓
[Validate Extracted Data]
  ↓
[Save to Accounting System]

3. Classificació de Textos

// Code Node: Batch classification
const texts = $input.all();
const batches = [];

// Agrupa en lots de 10
for (let i = 0; i < texts.length; i += 10) {
  batches.push(texts.slice(i, i + 10));
}

// Classifica cada lot
const results = [];
for (const batch of batches) {
  const prompt = `Classify these texts into categories: Tech, Finance, Health, Other
  ${batch.map((t, i) => `${i+1}. ${t.json.text}`).join('\n')}
  Return JSON array: [{text_id: 1, category: 'Tech'}, ...]`;

  const response = await openai.complete(prompt);
  results.push(...response);
}

return results.map(r => ({json: r}));

4. Generació de Resums

[Daily News Articles] (100 articles)
  ↓
[Filter: Technology category]
  ↓
[OpenAI Chat Model]
  System: "Summarize tech news in 2-3 sentences"
  Articles: {{$json.articles}}
  ↓
[Combine Summaries]
  ↓
[Generate Email Newsletter]
  ↓
[Send to Subscribers]

6.3. AI en pipelines de dades

Enriquiment intel·ligent:

[Raw Customer Data]
  ↓
[OpenAI: Standardize Company Names]
  Prompt: "Standardize company name: {{$json.company_raw}}"
  Examples: "MSFT → Microsoft, GOOGL → Google"
  ↓
[OpenAI: Infer Industry]
  Prompt: "Based on company name and description, 
          classify industry"
  ↓
[OpenAI: Generate Company Summary]
  ↓
[Enriched Data]

Detecció d'anomalies:

// Code Node: AI-powered anomaly detection
const metrics = $input.all();
const historicalData = metrics.slice(0, -1);
const currentData = metrics[metrics.length - 1];

const prompt = `
Historical metrics (mean±std):
${JSON.stringify(calculateStats(historicalData))}

Current metrics:
${JSON.stringify(currentData)}

Analyze if current metrics show anomalies. Return JSON:
{
  is_anomaly: boolean,
  anomalous_fields: [],
  severity: 'low'|'medium'|'high',
  explanation: ''
}
`;

const aiAnalysis = await openai.complete(prompt);

if (aiAnalysis.is_anomaly) {
  // Trigger alert
}

Neteja intel·ligent:

[Messy Data]
  ↓
[AI Agent: Data Cleaning]
  Tools:
    - Detect and fix typos
    - Standardize formats
    - Infer missing values
    - Remove duplicates (fuzzy matching)
  ↓
[Validate Cleaned Data]
  ↓
[Load to Clean Database]