Automatic Job App Assistant | Multi-Assistant w/ subflows (Q&A Chain Tool)

Automatic Job Application Assistant Workflow Overview This workflow creates a comprehensive, AI-powered "Head Hunter" assistant designed to streamline the job application process. It operates in two primary capacities: 1. An Interactive Chat Assistant: A user can chat with the AI agent to find job openings, ask it to retrieve their professional experience, and instruct it to generate tailored resumes and cover letters by combining the job description with their personal data. 2. An Automated Knowledge Base Manager: The workflow automatically monitors a specific Google Drive folder. When a new resume is uploaded, it processes the document, converts it into a searchable format, and stores it in a Supabase vector database, ensuring the AI assistant always has access to the user's most current information. Technologies Used - AI & LLMs: OpenAI (GPT-3.5-Turbo, GPT-4o-mini, text-embedding-3-small) - AI Framework: LangChain (via n8n's integrated nodes for Agents, Tools, Memory, and Chains) - Cloud Storage: Google Drive - Vector Database: Supabase - Orchestration & Automation: n8n Automation & AI Value This workflow automates the most time-consuming aspects of job hunting. Instead of manually searching for jobs and copy-pasting experience to tailor each application, the user can simply command the AI assistant. The system intelligently fetches job data, retrieves the user's relevant skills from a database, and synthesizes customized application documents. How the AI is Integrated The AI is the core of this workflow, acting as both the brain and the engine: - AI Agent as Orchestrator: The "Head Hunter" AI Agent is the central coordinator. It uses an OpenAI LLM to understand natural language commands from the user. Based on the user's request, it intelligently decides which specialized "tool" to use—either the job_search tool to look for positions or the experience tool to consult the user's resume. - Retrieval-Augmented Generation (RAG): The workflow implements a sophisticated RAG pipeline. It transforms unstructured resume documents (PDFs) into structured, queryable data (vectors) stored in Supabase.