# /n8n-chatbot - Chatbots e AI Agents n8n

Criar chatbots e workflows com AI usando LangChain.

---

## Uso

```
/n8n-chatbot create <descrição>    # Criar chatbot
/n8n-chatbot agent <tipo>          # Criar AI agent
/n8n-chatbot rag <knowledge_base>  # Criar sistema RAG
```

---

## Workflow Obrigatório

```
1. Health check       → mcp__n8n__n8n_health_check()
2. Pesquisar template → mcp__n8n__search_templates({ task: "ai_automation" })
3. Se template OK     → mcp__n8n__n8n_deploy_template()
4. Se criar do zero   → Seguir fluxo abaixo
```

---

## Nodes LangChain

### Core

| Node | Uso |
|------|-----|
| `@n8n/n8n-nodes-langchain.agent` | AI Agent principal |
| `@n8n/n8n-nodes-langchain.chainLlm` | Chain LLM simples |
| `@n8n/n8n-nodes-langchain.chainRetrievalQa` | RAG Q&A |

### Modelos

| Node | Provider |
|------|----------|
| `@n8n/n8n-nodes-langchain.lmChatOpenAi` | OpenAI GPT |
| `@n8n/n8n-nodes-langchain.lmChatAnthropic` | Claude |
| `@n8n/n8n-nodes-langchain.lmChatOllama` | Ollama local |

### Memória

| Node | Tipo |
|------|------|
| `@n8n/n8n-nodes-langchain.memoryBufferWindow` | Últimas N mensagens |
| `@n8n/n8n-nodes-langchain.memoryPostgresChat` | PostgreSQL |
| `@n8n/n8n-nodes-langchain.memoryRedisChat` | Redis |

### Tools

| Node | Função |
|------|--------|
| `@n8n/n8n-nodes-langchain.toolCalculator` | Cálculos |
| `@n8n/n8n-nodes-langchain.toolCode` | Executar código |
| `@n8n/n8n-nodes-langchain.toolHttpRequest` | Chamar APIs |
| `@n8n/n8n-nodes-langchain.toolWorkflow` | Chamar workflows |

### Vector Stores (RAG)

| Node | Sistema |
|------|---------|
| `@n8n/n8n-nodes-langchain.vectorStoreSupabase` | Supabase |
| `@n8n/n8n-nodes-langchain.vectorStorePinecone` | Pinecone |
| `@n8n/n8n-nodes-langchain.vectorStorePgVector` | PostgreSQL |

---

## Chatbot Básico

### Estrutura

```
Webhook/Trigger
  ↓
LLM Chat Model (OpenAI/Claude)
  ↓
Memory (Buffer)
  ↓
Agent
  ↓
Resposta
```

### Implementação

```javascript
// 1. Modelo LLM
mcp__n8n__get_node({
  nodeType: "@n8n/n8n-nodes-langchain.lmChatOpenAi",
  detail: "standard"
})

// 2. Memória
mcp__n8n__get_node({
  nodeType: "@n8n/n8n-nodes-langchain.memoryBufferWindow",
  detail: "standard"
})

// 3. Agent
mcp__n8n__get_node({
  nodeType: "@n8n/n8n-nodes-langchain.agent",
  detail: "standard"
})
```

---

## RAG (Retrieval Augmented Generation)

### Estrutura

```
Documentos
  ↓
Embeddings
  ↓
Vector Store
  ↓
Query (pergunta)
  ↓
Retriever
  ↓
LLM (resposta contextualizada)
```

### Configuração Vector Store

```javascript
mcp__n8n__validate_node({
  nodeType: "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
  config: {
    mode: "insert",  // ou "retrieve"
    tableName: "documents",
    queryName: "match_documents"
  },
  mode: "minimal"
})
```

---

## AI Agent com Tools

### Estrutura

```
Input
  ↓
Agent
  ├── Tool: Calculator
  ├── Tool: HTTP Request
  ├── Tool: Code
  └── Tool: Workflow
  ↓
Output
```

### Configuração Agent

```javascript
mcp__n8n__validate_node({
  nodeType: "@n8n/n8n-nodes-langchain.agent",
  config: {
    agentType: "conversationalAgent",
    systemMessage: "Tu és um assistente prestável...",
    options: {
      returnIntermediateSteps: true
    }
  },
  mode: "minimal"
})
```

---

## Exemplos Práticos

### Chatbot de Suporte

```
Webhook (mensagem cliente)
  ↓
Memory PostgreSQL (histórico)
  ↓
Vector Store (docs suporte)
  ↓
Chain Retrieval QA
  ↓
Webhook Response
```

### Agent CRM

```
Webhook (comando)
  ↓
Agent
  ├── Tool: Pesquisar clientes
  ├── Tool: Criar lead
  └── Tool: Actualizar tarefa
  ↓
Slack (resultado)
```

### Resumo de Documentos

```
Webhook (upload PDF)
  ↓
PDF Extract
  ↓
Text Splitter
  ↓
LLM Chain (resumo)
  ↓
Email (enviar resumo)
```

---

## Templates Recomendados

```javascript
// AI templates
mcp__n8n__search_templates({
  searchMode: "by_task",
  task: "ai_automation"
})

// Por keyword
mcp__n8n__search_templates({
  searchMode: "keyword",
  query: "chatbot langchain openai"
})
```

---

## Credenciais Necessárias

| Provider | Credencial | Node |
|----------|------------|------|
| OpenAI | API Key | lmChatOpenAi |
| Anthropic | API Key | lmChatAnthropic |
| Supabase | URL + Key | vectorStoreSupabase |
| Pinecone | API Key | vectorStorePinecone |

---

## Boas Práticas

| Prática | Razão |
|---------|-------|
| System prompt claro | Define comportamento |
| Temperatura baixa (0.1-0.3) | Respostas consistentes |
| Memória limitada | Performance |
| Retry on error | Resiliência |
| Logs de conversas | Debug e melhoria |

---

## Troubleshooting

| Problema | Solução |
|----------|---------|
| Respostas inconsistentes | Baixar temperatura |
| Contexto perdido | Verificar memória |
| RAG não encontra | Verificar embeddings |
| Timeout | Aumentar limite |
| Token limit | Resumir contexto |

---

*Skill v1.0 | Descomplicar®*