feat(n8n): add interactive deploy and fix nodes
This commit is contained in:
Gabriel Ramos
parent
b751ee556f
commit
fbb23a1470
1 changed files with 523 additions and 189 deletions
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@ -1,5 +1,6 @@
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import * as fs from "fs";
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import * as path from "path";
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import * as readline from "readline";
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// Load .env variables
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const envPath = path.join(__dirname, "../.env");
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@ -19,7 +20,6 @@ if (fs.existsSync(envPath)) {
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const N8N_HOST = process.env.N8N_HOST || "https://n8n.gaboggamer.online";
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const N8N_API_KEY = process.env.N8N_API_KEY;
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const GEMINI_API_KEY = process.env.GEMINI_API_KEY;
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const SUPABASE_URL = process.env.NEXT_PUBLIC_SUPABASE_URL;
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const SUPABASE_SECRET_KEY = process.env.SUPABASE_SECRET_KEY;
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@ -28,6 +28,21 @@ if (!N8N_API_KEY) {
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process.exit(1);
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}
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// Interactive prompt helper
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function askQuestion(query: string): Promise<string> {
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const rl = readline.createInterface({
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input: process.stdin,
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output: process.stdout,
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});
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return new Promise((resolve) =>
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rl.question(query, (ans) => {
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rl.close();
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resolve(ans.trim());
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})
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);
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}
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async function n8nRequest(endpoint: string, method: string = "GET", body?: any) {
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const response = await fetch(`${N8N_HOST}${endpoint}`, {
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method,
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@ -68,206 +83,525 @@ async function getOrCreateCredential(name: string, type: string, data: any) {
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}
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}
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async function main() {
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console.log("Starting n8n Candidate Evaluation Flow deployment...");
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interface ProviderConfig {
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nodeType: string;
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credentialType: string;
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credentialData: any;
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nodeParameters: any;
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}
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// 1. Create/Retrieve Supabase credential
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console.log("Checking Supabase credentials...");
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const supabaseCredId = await getOrCreateCredential("Semillero2_Supabase_V2", "supabaseApi", {
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function getProviderConfig(provider: string, apiKey: string, modelName: string): ProviderConfig {
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switch (provider) {
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case "1": // Deepseek (Native)
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return {
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nodeType: "@n8n/n8n-nodes-langchain.lmChatDeepSeek",
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credentialType: "deepSeekApi",
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credentialData: {
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apiKey,
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allowedHttpRequestDomains: "none",
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},
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nodeParameters: {
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model: modelName || "deepseek-chat",
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options: {},
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},
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};
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case "2": // OpenAI
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return {
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nodeType: "@n8n/n8n-nodes-langchain.lmChatOpenAi",
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credentialType: "openAiApi",
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credentialData: {
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apiKey,
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header: false,
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allowedHttpRequestDomains: "none",
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},
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nodeParameters: {
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model: modelName || "gpt-4o-mini",
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options: {},
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},
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};
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case "3": // Google Gemini
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return {
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nodeType: "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
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credentialType: "googlePalmApi",
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credentialData: {
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apiKey,
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host: "https://generativelanguage.googleapis.com",
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allowedHttpRequestDomains: "none",
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},
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nodeParameters: {
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model: modelName || "gemini-1.5-flash",
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options: {},
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},
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};
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case "4": // Anthropic
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return {
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nodeType: "@n8n/n8n-nodes-langchain.lmChatAnthropic",
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credentialType: "anthropicApi",
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credentialData: {
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apiKey,
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allowedHttpRequestDomains: "none",
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},
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nodeParameters: {
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model: modelName || "claude-3-5-sonnet-latest",
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options: {},
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},
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};
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default:
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throw new Error("Invalid provider chosen");
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}
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}
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async function main() {
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console.log("\n==================================================");
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console.log("Welcome to interactive n8n workflow deployment");
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console.log("==================================================");
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// 1. Ask for Primary Provider
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console.log("\nSelect Primary LLM Provider:");
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console.log("1. Deepseek (Native Node)");
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console.log("2. OpenAI (Standard)");
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console.log("3. Google Gemini");
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console.log("4. Anthropic");
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const primaryProviderChoice = (await askQuestion("Enter choice (1-4) [default: 3]: ")) || "3";
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let defaultModel = "gemini-1.5-flash";
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if (primaryProviderChoice === "1") defaultModel = "deepseek-chat";
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else if (primaryProviderChoice === "2") defaultModel = "gpt-4o-mini";
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else if (primaryProviderChoice === "4") defaultModel = "claude-3-5-sonnet-latest";
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const primaryModelName = (await askQuestion(`Enter primary model name [default: ${defaultModel}]: `)) || defaultModel;
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let defaultKey = "";
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if (primaryProviderChoice === "1") defaultKey = process.env.DEEPSEEK_API_KEY || "";
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else if (primaryProviderChoice === "3") defaultKey = process.env.GEMINI_API_KEY || "";
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const primaryApiKey = (await askQuestion(`Enter API key [default: ${defaultKey ? "Loaded from .env" : "None"}]: `)) || defaultKey;
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if (!primaryApiKey) {
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console.error("Primary API Key is required.");
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process.exit(1);
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}
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// 2. Ask for Fallback Provider
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const configureFallback = ((await askQuestion("\nDo you want to configure a Fallback LLM Model? (y/n) [default: n]: ")) || "n").toLowerCase() === "y";
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let fallbackProviderChoice = "";
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let fallbackModelName = "";
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let fallbackApiKey = "";
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if (configureFallback) {
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console.log("\nSelect Fallback LLM Provider:");
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console.log("1. Deepseek (Native Node)");
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console.log("2. OpenAI (Standard)");
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console.log("3. Google Gemini");
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console.log("4. Anthropic");
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fallbackProviderChoice = (await askQuestion("Enter choice (1-4) [default: 1]: ")) || "1";
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let defaultFallbackModel = "deepseek-chat";
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if (fallbackProviderChoice === "2") defaultFallbackModel = "gpt-4o-mini";
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else if (fallbackProviderChoice === "3") defaultFallbackModel = "gemini-1.5-flash";
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else if (fallbackProviderChoice === "4") defaultFallbackModel = "claude-3-5-sonnet-latest";
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fallbackModelName = (await askQuestion(`Enter fallback model name [default: ${defaultFallbackModel}]: `)) || defaultFallbackModel;
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let defaultFallbackKey = "";
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if (fallbackProviderChoice === "1") defaultFallbackKey = process.env.DEEPSEEK_API_KEY || "";
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else if (fallbackProviderChoice === "3") defaultFallbackKey = process.env.GEMINI_API_KEY || "";
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fallbackApiKey = (await askQuestion(`Enter fallback API key [default: ${defaultFallbackKey ? "Loaded from .env" : "None"}]: `)) || defaultFallbackKey;
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if (!fallbackApiKey) {
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console.error("Fallback API Key is required.");
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process.exit(1);
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}
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}
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console.log("\nDeploying credentials to n8n...");
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const supabaseCredId = await getOrCreateCredential("Semillero2_Supabase_V3", "supabaseApi", {
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host: SUPABASE_URL,
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serviceRole: SUPABASE_SECRET_KEY,
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allowedHttpRequestDomains: "none",
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});
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// 2. Create/Retrieve Gemini credential
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console.log("Checking Gemini credentials...");
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const geminiCredId = await getOrCreateCredential("Semillero2_Gemini_V2", "googlePalmApi", {
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apiKey: GEMINI_API_KEY,
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host: "https://generativelanguage.googleapis.com",
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allowedHttpRequestDomains: "none",
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});
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const primaryConfig = getProviderConfig(primaryProviderChoice, primaryApiKey, primaryModelName);
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const primaryCredId = await getOrCreateCredential(
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`Semillero2_Primary_${primaryConfig.credentialType}`,
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primaryConfig.credentialType,
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primaryConfig.credentialData
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);
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// 3. Define the E2E Candidate Evaluation workflow
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const workflowDefinition = {
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name: "Semillero2: End-to-End Candidate Evaluation",
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settings: {},
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nodes: [
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{
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parameters: {
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httpMethod: "POST",
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path: "evaluate-candidate",
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responseMode: "responseNode",
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options: {},
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},
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id: "webhook-trigger",
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name: "Webhook Trigger",
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type: "n8n-nodes-base.webhook",
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typeVersion: 1.1,
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position: [100, 300],
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},
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{
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parameters: {
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promptType: "Define below",
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text: "={{ $json.body.text }}",
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systemMessage: "You are an AI recruitment assistant evaluating a candidate's CV for a job vacancy. Analyze the candidate's CV text. You MUST respond with a raw JSON object containing exactly these five keys:\n- summary: a brief candidate summary (max 3 sentences).\n- classification: 'Qualified', 'Unqualified', or 'Review'.\n- suggestions: an array of recommendations for next steps (e.g. ['Schedule interview', 'Reject', 'Verify references']).\n- riskLevel: 'Low', 'Medium', or 'High'.\n- ai_score: a number between 0 and 100 representing general suitability.\n\nDo not include markdown code blocks or any text outside the JSON.",
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},
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id: "llm-chain",
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name: "LLM Chain Evaluation",
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type: "@n8n/n8n-nodes-langchain.chainLlm",
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typeVersion: 1.4,
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position: [350, 300],
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},
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{
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parameters: {
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model: "gemini-1.5-flash",
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options: {},
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},
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id: "gemini-model",
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name: "Gemini Chat Model",
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type: "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
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typeVersion: 1,
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position: [300, 480],
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credentials: {
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googlePalmApi: {
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id: geminiCredId,
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name: "Semillero2_Gemini_V2",
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},
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},
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},
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{
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parameters: {
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jsonSchema: "{\n \"type\": \"object\",\n \"properties\": {\n \"summary\": {\n \"type\": \"string\"\n },\n \"classification\": {\n \"type\": \"string\",\n \"enum\": [\"Qualified\", \"Unqualified\", \"Review\"]\n },\n \"suggestions\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n }\n },\n \"riskLevel\": {\n \"type\": \"string\",\n \"enum\": [\"Low\", \"Medium\", \"High\"]\n },\n \"ai_score\": {\n \"type\": \"number\"\n }\n },\n \"required\": [\"summary\", \"classification\", \"suggestions\", \"riskLevel\", \"ai_score\"]\n}",
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},
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id: "json-parser",
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name: "Structured Output Parser",
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type: "@n8n/n8n-nodes-langchain.outputParserStructured",
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typeVersion: 1,
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position: [460, 480],
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},
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{
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parameters: {
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jsCode: `const input = $input.first().json;
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const webhookData = $('Webhook Trigger').first().json.body;
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return [{
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json: {
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candidate_id: webhookData.candidateId,
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interview_id: webhookData.interviewId,
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ai_score: input.ai_score,
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evaluation: {
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summary: input.summary,
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classification: input.classification,
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suggestions: input.suggestions,
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riskLevel: input.riskLevel
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}
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let fallbackConfig: ProviderConfig | null = null;
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let fallbackCredId = "";
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if (configureFallback) {
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fallbackConfig = getProviderConfig(fallbackProviderChoice, fallbackApiKey, fallbackModelName);
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fallbackCredId = await getOrCreateCredential(
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`Semillero2_Fallback_${fallbackConfig.credentialType}`,
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fallbackConfig.credentialType,
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fallbackConfig.credentialData
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);
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}
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}];`,
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},
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id: "format-data",
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name: "Format Evaluation Data",
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type: "n8n-nodes-base.code",
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typeVersion: 2,
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position: [600, 300],
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},
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{
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parameters: {
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operation: "insert",
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table: "scores",
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options: {},
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},
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id: "supabase-insert",
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name: "Insert Score to Supabase",
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type: "n8n-nodes-base.supabase",
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typeVersion: 1,
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position: [800, 300],
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credentials: {
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supabaseApi: {
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id: supabaseCredId,
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name: "Semillero2_Supabase_V2",
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},
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},
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},
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{
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parameters: {
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options: {},
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},
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id: "respond-webhook",
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name: "Respond to Webhook",
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type: "n8n-nodes-base.respondToWebhook",
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typeVersion: 1.1,
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position: [1000, 300],
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},
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],
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connections: {
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"Webhook Trigger": {
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main: [
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[
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{
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node: "LLM Chain Evaluation",
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type: "main",
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index: 0,
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},
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],
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],
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},
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"Gemini Chat Model": {
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ai_languageModel: [
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[
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{
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node: "LLM Chain Evaluation",
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type: "ai_languageModel",
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index: 0,
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},
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],
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],
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},
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"Structured Output Parser": {
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outputParser: [
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[
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{
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node: "LLM Chain Evaluation",
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type: "outputParser",
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index: 0,
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},
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],
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],
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},
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"LLM Chain Evaluation": {
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main: [
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[
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{
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node: "Format Evaluation Data",
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type: "main",
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index: 0,
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},
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],
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],
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},
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"Format Evaluation Data": {
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main: [
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[
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{
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node: "Insert Score to Supabase",
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type: "main",
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index: 0,
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},
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],
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],
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},
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"Insert Score to Supabase": {
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main: [
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[
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{
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node: "Respond to Webhook",
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type: "main",
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index: 0,
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},
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],
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],
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// 3. Define workflow nodes dynamically
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console.log("\nBuilding workflow nodes...");
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const webhookTriggerNode = {
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parameters: {
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httpMethod: "POST",
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path: "evaluate-candidate",
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responseMode: "responseNode",
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options: {},
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},
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id: "webhook-trigger",
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name: "Webhook Trigger",
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type: "n8n-nodes-base.webhook",
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typeVersion: 1.1,
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position: [100, 300],
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};
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const primaryChainNode = {
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parameters: {
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promptType: "defineBelow", // Fixed casing: camelCase!
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hasOutputParser: true, // Enforce "Require Specific Output Format"
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text: "={{ $('Webhook Trigger').item.json.body.text }}",
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systemMessage: "You are an AI recruitment assistant evaluating a candidate's CV for a job vacancy. Analyze the candidate's CV text. You MUST respond with a raw JSON object containing exactly these five keys:\n- summary: a brief candidate summary (max 3 sentences).\n- classification: 'Qualified', 'Unqualified', or 'Review'.\n- suggestions: an array of recommendations for next steps (e.g. ['Schedule interview', 'Reject', 'Verify references']).\n- riskLevel: 'Low', 'Medium', or 'High'.\n- ai_score: a number between 0 and 100 representing general suitability.\n\nDo not include markdown code blocks or any text outside the JSON.",
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},
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id: "llm-chain-primary",
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name: "LLM Chain Evaluation (Primary)",
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type: "@n8n/n8n-nodes-langchain.chainLlm",
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typeVersion: 1.4,
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position: [400, 300],
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continueOnFail: configureFallback, // continue on fail only if fallback exists
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};
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const primaryModelNode = {
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parameters: primaryConfig.nodeParameters,
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id: "primary-model",
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name: "Primary Chat Model",
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type: primaryConfig.nodeType,
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typeVersion: 1,
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position: [350, 480],
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credentials: {
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[primaryConfig.credentialType]: {
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id: primaryCredId,
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name: `Semillero2_Primary_${primaryConfig.credentialType}`,
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},
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},
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};
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console.log("Deploying workflow to n8n...");
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const jsonParserNode = {
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parameters: {
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jsonSchema: "{\n \"type\": \"object\",\n \"properties\": {\n \"summary\": {\n \"type\": \"string\"\n },\n \"classification\": {\n \"type\": \"string\",\n \"enum\": [\"Qualified\", \"Unqualified\", \"Review\"]\n },\n \"suggestions\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n }\n },\n \"riskLevel\": {\n \"type\": \"string\",\n \"enum\": [\"Low\", \"Medium\", \"High\"]\n },\n \"ai_score\": {\n \"type\": \"number\"\n }\n },\n \"required\": [\"summary\", \"classification\", \"suggestions\", \"riskLevel\", \"ai_score\"]\n}",
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},
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id: "json-parser",
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name: "Structured Output Parser",
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type: "@n8n/n8n-nodes-langchain.outputParserStructured",
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typeVersion: 1,
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position: [480, 480],
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};
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// Replace Code node with a native Edit Fields (Set) node to avoid code blocks
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const setNode = {
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parameters: {
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assignments: {
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assignments: [
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{
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name: "candidate_id",
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value: "={{ $('Webhook Trigger').item.json.body.candidateId }}",
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type: "string",
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},
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{
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name: "interview_id",
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value: "={{ $('Webhook Trigger').item.json.body.interviewId }}",
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type: "string",
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},
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{
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name: "ai_score",
|
||||
value: "={{ $json.ai_score }}",
|
||||
type: "number",
|
||||
},
|
||||
{
|
||||
name: "evaluation",
|
||||
value: "={{ { summary: $json.summary, classification: $json.classification, suggestions: $json.suggestions, riskLevel: $json.riskLevel } }}",
|
||||
type: "object",
|
||||
},
|
||||
],
|
||||
},
|
||||
options: {
|
||||
includeOtherFields: false, // Drop other fields to cleanly match schema
|
||||
},
|
||||
},
|
||||
id: "format-data",
|
||||
name: "Format Evaluation Data",
|
||||
type: "n8n-nodes-base.set",
|
||||
typeVersion: 3,
|
||||
position: [850, 300],
|
||||
};
|
||||
|
||||
const supabaseInsertNode = {
|
||||
parameters: {
|
||||
operation: "insert",
|
||||
table: "scores",
|
||||
options: {},
|
||||
},
|
||||
id: "supabase-insert",
|
||||
name: "Insert Score to Supabase",
|
||||
type: "n8n-nodes-base.supabase",
|
||||
typeVersion: 1,
|
||||
position: [1050, 300],
|
||||
credentials: {
|
||||
supabaseApi: {
|
||||
id: supabaseCredId,
|
||||
name: "Semillero2_Supabase_V3",
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
const respondWebhookNode = {
|
||||
parameters: {
|
||||
options: {},
|
||||
},
|
||||
id: "respond-webhook",
|
||||
name: "Respond to Webhook",
|
||||
type: "n8n-nodes-base.respondToWebhook",
|
||||
typeVersion: 1.1,
|
||||
position: [1250, 300],
|
||||
};
|
||||
|
||||
const wNodes: any[] = [
|
||||
webhookTriggerNode,
|
||||
primaryChainNode,
|
||||
primaryModelNode,
|
||||
jsonParserNode,
|
||||
setNode,
|
||||
supabaseInsertNode,
|
||||
respondWebhookNode,
|
||||
];
|
||||
|
||||
const wConnections: any = {
|
||||
"Webhook Trigger": {
|
||||
main: [
|
||||
[
|
||||
{
|
||||
node: "LLM Chain Evaluation (Primary)",
|
||||
type: "main",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
],
|
||||
},
|
||||
"Primary Chat Model": {
|
||||
ai_languageModel: [
|
||||
[
|
||||
{
|
||||
node: "LLM Chain Evaluation (Primary)",
|
||||
type: "ai_languageModel",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
],
|
||||
},
|
||||
"Structured Output Parser": {
|
||||
outputParser: [
|
||||
[
|
||||
{
|
||||
node: "LLM Chain Evaluation (Primary)",
|
||||
type: "outputParser",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
],
|
||||
},
|
||||
"Format Evaluation Data": {
|
||||
main: [
|
||||
[
|
||||
{
|
||||
node: "Insert Score to Supabase",
|
||||
type: "main",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
],
|
||||
},
|
||||
"Insert Score to Supabase": {
|
||||
main: [
|
||||
[
|
||||
{
|
||||
node: "Respond to Webhook",
|
||||
type: "main",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
],
|
||||
},
|
||||
};
|
||||
|
||||
if (configureFallback && fallbackConfig) {
|
||||
console.log("Configuring Fallback LLM Route...");
|
||||
|
||||
const checkErrorNode = {
|
||||
parameters: {
|
||||
conditions: {
|
||||
options: {
|
||||
caseSensitive: true,
|
||||
leftValue: "",
|
||||
typeValidation: "strict",
|
||||
},
|
||||
conditions: [
|
||||
{
|
||||
id: "err-cond",
|
||||
leftValue: "={{ $json.hasOwnProperty('error') }}",
|
||||
rightValue: "true",
|
||||
operator: {
|
||||
type: "boolean",
|
||||
operation: "equals",
|
||||
},
|
||||
},
|
||||
],
|
||||
combinator: "and",
|
||||
},
|
||||
},
|
||||
id: "check-error",
|
||||
name: "Check Primary Error",
|
||||
type: "n8n-nodes-base.if",
|
||||
typeVersion: 2.2,
|
||||
position: [600, 300],
|
||||
};
|
||||
|
||||
const fallbackChainNode = {
|
||||
parameters: {
|
||||
promptType: "defineBelow", // Fixed casing: camelCase!
|
||||
hasOutputParser: true, // Enforce "Require Specific Output Format"
|
||||
text: "={{ $('Webhook Trigger').item.json.body.text }}",
|
||||
systemMessage: "You are an AI recruitment assistant evaluating a candidate's CV for a job vacancy. Analyze the candidate's CV text. You MUST respond with a raw JSON object containing exactly these five keys:\n- summary: a brief candidate summary (max 3 sentences).\n- classification: 'Qualified', 'Unqualified', or 'Review'.\n- suggestions: an array of recommendations for next steps (e.g. ['Schedule interview', 'Reject', 'Verify references']).\n- riskLevel: 'Low', 'Medium', or 'High'.\n- ai_score: a number between 0 and 100 representing general suitability.\n\nDo not include markdown code blocks or any text outside the JSON.",
|
||||
},
|
||||
id: "llm-chain-fallback",
|
||||
name: "LLM Chain Evaluation (Fallback)",
|
||||
type: "@n8n/n8n-nodes-langchain.chainLlm",
|
||||
typeVersion: 1.4,
|
||||
position: [800, 450],
|
||||
};
|
||||
|
||||
const fallbackModelNode = {
|
||||
parameters: fallbackConfig.nodeParameters,
|
||||
id: "fallback-model",
|
||||
name: "Fallback Chat Model",
|
||||
type: fallbackConfig.nodeType,
|
||||
typeVersion: 1,
|
||||
position: [750, 630],
|
||||
credentials: {
|
||||
[fallbackConfig.credentialType]: {
|
||||
id: fallbackCredId,
|
||||
name: `Semillero2_Fallback_${fallbackConfig.credentialType}`,
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
const jsonParserFallbackNode = {
|
||||
parameters: {
|
||||
jsonSchema: "{\n \"type\": \"object\",\n \"properties\": {\n \"summary\": {\n \"type\": \"string\"\n },\n \"classification\": {\n \"type\": \"string\",\n \"enum\": [\"Qualified\", \"Unqualified\", \"Review\"]\n },\n \"suggestions\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n }\n },\n \"riskLevel\": {\n \"type\": \"string\",\n \"enum\": [\"Low\", \"Medium\", \"High\"]\n },\n \"ai_score\": {\n \"type\": \"number\"\n }\n },\n \"required\": [\"summary\", \"classification\", \"suggestions\", \"riskLevel\", \"ai_score\"]\n}",
|
||||
},
|
||||
id: "json-parser-fallback",
|
||||
name: "Structured Output Parser (Fallback)",
|
||||
type: "@n8n/n8n-nodes-langchain.outputParserStructured",
|
||||
typeVersion: 1,
|
||||
position: [880, 630],
|
||||
};
|
||||
|
||||
// Reposition Set node for fallback routing
|
||||
setNode.position = [1050, 450];
|
||||
supabaseInsertNode.position = [1250, 450];
|
||||
respondWebhookNode.position = [1450, 450];
|
||||
|
||||
wNodes.push(checkErrorNode, fallbackChainNode, fallbackModelNode, jsonParserFallbackNode);
|
||||
|
||||
// Primary chain routes to IF check
|
||||
wConnections["LLM Chain Evaluation (Primary)"] = {
|
||||
main: [
|
||||
[
|
||||
{
|
||||
node: "Check Primary Error",
|
||||
type: "main",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
],
|
||||
};
|
||||
|
||||
// IF Node routes
|
||||
wConnections["Check Primary Error"] = {
|
||||
main: [
|
||||
[
|
||||
{
|
||||
node: "LLM Chain Evaluation (Fallback)",
|
||||
type: "main",
|
||||
index: 0,
|
||||
},
|
||||
], // true branch (Output 0 -> Error happened)
|
||||
[
|
||||
{
|
||||
node: "Format Evaluation Data",
|
||||
type: "main",
|
||||
index: 0,
|
||||
},
|
||||
], // false branch (Output 1 -> Success)
|
||||
],
|
||||
};
|
||||
|
||||
// Connect fallback LLM components
|
||||
wConnections["Fallback Chat Model"] = {
|
||||
ai_languageModel: [
|
||||
[
|
||||
{
|
||||
node: "LLM Chain Evaluation (Fallback)",
|
||||
type: "ai_languageModel",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
],
|
||||
};
|
||||
|
||||
wConnections["Structured Output Parser (Fallback)"] = {
|
||||
outputParser: [
|
||||
[
|
||||
{
|
||||
node: "LLM Chain Evaluation (Fallback)",
|
||||
type: "outputParser",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
],
|
||||
};
|
||||
|
||||
wConnections["LLM Chain Evaluation (Fallback)"] = {
|
||||
main: [
|
||||
[
|
||||
{
|
||||
node: "Format Evaluation Data",
|
||||
type: "main",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
],
|
||||
};
|
||||
} else {
|
||||
// If no fallback, wire directly Primary Chain -> Set node
|
||||
wConnections["LLM Chain Evaluation (Primary)"].main = [
|
||||
[
|
||||
{
|
||||
node: "Format Evaluation Data",
|
||||
type: "main",
|
||||
index: 0,
|
||||
},
|
||||
],
|
||||
];
|
||||
}
|
||||
|
||||
// Define complete workflow object
|
||||
const workflowDefinition = {
|
||||
name: "Semillero2: End-to-End Candidate Evaluation",
|
||||
settings: {},
|
||||
nodes: wNodes,
|
||||
connections: wConnections,
|
||||
};
|
||||
|
||||
console.log("\nDeploying workflow to n8n...");
|
||||
// Check if it already exists
|
||||
const workflowsList = await n8nRequest("/api/v1/workflows");
|
||||
const existingWf = workflowsList.data.find(
|
||||
|
|
|
|||
Loading…
Reference in a new issue