🤖 Documentação para LLMs & AI Agents

# N8N Templates (13,269 workflows) https://www.automationscookbook.com/automacoes_db.csv # Zapier Templates (162 workflows) https://www.automationscookbook.com/automacoes_zapier_db.csv # Download both: wget https://www.automationscookbook.com/automacoes_db.csv wget https://www.automationscookbook.com/automacoes_zapier_db.csv

software_a,software_b,tipo_evento,caso_uso_resumido,titulo_pagina,slug_url,descricao_curta,json_n8n_url,passos_resumo,tags Exemplo: "Gmail","Google Sheets","Email","Lead capture","Como enviar emails do Gmail para Google Sheets","gmail-para-google-sheets-n8n","Monitora novos emails e salva em planilha","https://n8n.io/workflows/...","Trigger Gmail|Mapear campos|Adicionar linha no Sheets","email,automation"

# Human + Machine readable endpoint https://www.automationscookbook.com/llm Features: - Structured data com Schema.org DataCatalog - Quick access cards - Estatísticas em tempo real - Links diretos para CSVs

# Sitemap Index https://www.automationscookbook.com/sitemap-index.xml # Specific Categories https://www.automationscookbook.com/sitemap-integracoes-n8n.xml https://www.automationscookbook.com/sitemap-integracoes-zapier.xml https://www.automationscookbook.com/sitemap-blog.xml

import pandas as pd from openai import OpenAI client = OpenAI(api_key="YOUR_API_KEY") # Load dataset df = pd.read_csv("https://www.automationscookbook.com/automacoes_db.csv") # Create combined text for embeddings df['combined'] = ( df['titulo_pagina'] + " | " + df['software_a'] + " → " + df['software_b'] + " | " + df['descricao_curta'] + " | " + df['caso_uso_resumido'] ) # Generate embeddings def get_embedding(text): response = client.embeddings.create( model="text-embedding-3-small", input=text ) return response.data[0].embedding df['embedding'] = df['combined'].apply(get_embedding) # Save to vector database df.to_pickle('automations_embeddings.pkl')

from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings from langchain.chains import RetrievalQA from langchain.llms import OpenAI import pandas as pd # Load data df = pd.read_csv("https://www.automationscookbook.com/automacoes_db.csv") texts = df['combined'].tolist() metadatas = df[['titulo_pagina', 'software_a', 'software_b', 'slug_url']].to_dict('records') # Create vector store embeddings = OpenAIEmbeddings() vectorstore = FAISS.from_texts(texts, embeddings, metadatas=metadatas) # Create RAG chain llm = OpenAI(temperature=0) qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever(search_kwargs={"k": 5}) ) # Query query = "Como sincronizar leads do Salesforce para o HubSpot?" result = qa_chain.run(query) print(result)