Mantaka Mahir
Al Automation Expert || Al Agents || n8n || Python || LangChain || Helping businesses scale revenue and reduce costs with Al driven automation .
Templates by Mantaka Mahir
AI study assistant with RAG - Google Gemini with Drive & Supabase vector search
How it works A complete AI-powered study assistant system that lets you chat naturally with your documents stored in Google Drive: The system has two connected workflows: Document Indexing Pipeline (Sub-workflow): • Accepts Google Drive folder URLs • Automatically fetches all files from the folder • Converts documents to plain text • Generates 768-dimensional embeddings using Google Gemini • Stores everything in Supabase vector database for semantic search Study Chat Agent (Main workflow): • Provides a conversational chat interface • Automatically detects and processes Google Drive links shared in chat • Searches your indexed documents using semantic similarity • Maintains conversation history across sessions • Includes calculator for math problems • Responds naturally using Google Gemini 2.5 Pro Use Cases: Students studying for exams, researchers managing papers, professionals building knowledge bases, anyone needing to query large document collections conversationally. Set up steps Prerequisites: • Google Drive OAuth2 credentials • Google Gemini API key (free tier available) • Supabase account with Postgres connection • ~15 minutes setup time Complete Setup: Part 1: Document Indexing Workflow Add Google Drive OAuth2 credentials to the Drive nodes Configure Supabase Postgres credentials in the SQL node Add Supabase API credentials to the Vector Store node Add Google Gemini API key to the Embeddings node Part 2: Study Agent Workflow Import the Study Agent workflow Verify the "Folder all file to vector" tool links to the indexing workflow Add Google Gemini API credentials to both Gemini nodes Configure Supabase API credentials in the Vector Store node Add Postgres credentials for Chat Memory Deploy and access the chat via webhook URL How to Use: Open the chat interface (webhook URL) Paste a Google Drive folder link in the chat Wait for indexing to complete (~1-2 minutes) Start asking questions about your documents The AI will search and answer from your materials Note: The indexing workflow runs automatically when you share Drive links in chat, or you can run it manually to pre-load documents. System Components: Main Agent: Gemini 2.5 Pro with conversational AI Vector Search: Supabase with pgvector (768-dim embeddings) Memory: Postgres chat history (10-message context window) Tools: Document retrieval, Drive indexing, calculator Embedding Model: Google Gemini text-embedding-004
By Mantaka MahirAutomate Google Classroom with Gemini AI: Topics, assignments & student tracking
Automate Google Classroom: Topics, Assignments & Student Tracking Automate Google Classroom via the Google Classroom API to efficiently manage courses, topics, teachers, students, announcements, and coursework. Use Cases Educational Institution Management Sync rosters, post weekly announcements, and generate submission reports automatically. Remote Learning Coordination Batch-create assignments, track engagement, and auto-notify teachers on new submissions. Training Program Automation Automate training modules, manage enrollments, and generate completion/compliance reports. Prerequisites n8n (cloud or self-hosted) Google Cloud Console access for OAuth setup Google Classroom API enabled Google Gemini API key (free) for the agent brain — or swap in any other LLM if preferred Setup Instructions Step 1: Google Cloud Project Create a new project in Google Cloud Console. Enable Google Classroom API. Create OAuth 2.0 Client ID credentials. Add your n8n OAuth callback URL as a redirect URI. Note down the Client ID and Client Secret. Step 2: OAuth Setup in n8n In n8n, open HTTP Request Node → Authentication → Predefined Credential Type. Select Google OAuth2 API. Enter your Client ID and Client Secret. Click Connect my account to complete authorization. Test the connection. Step 3: Import & Configure Workflow Import this workflow template into n8n. Link all Google Classroom nodes to your OAuth credential. Configure the webhook if using external triggers. Test each agent for API connectivity. Step 4: Customization You can customize each agent’s prompt to your liking for optimal results, or copy and modify node code to expand functionality. All operations use HTTP Request nodes, so you can integrate more tools via the Google Classroom API documentation. This workflow provides a strong starting point for deeper automation and integration. Features Course Topics List, create, update, or delete topics within a course. Teacher & Student Management List, retrieve, and manage teachers and students programmatically. Course Posts List posts, retrieve details and attachments, and access submission data. Announcements List, create, update, or delete announcements across courses. Courses List all courses, get detailed information, and view grading periods. Coursework List, retrieve, or analyze coursework within any course. Notes Once OAuth and the LLM connection are configured, this workflow automates all Google Classroom operations. Its modular structure lets you activate only what you need—saving API quota and improving performance.
By Mantaka MahirCreate RAG vector database from Google Drive documents using Gemini & Supabase
How it works This workflow automates the process of converting Google Drive documents into searchable vector embeddings for AI-powered applications: • Takes a Google Drive folder URL as input • Initializes a Supabase vector database with pgvector extension • Fetches all files from the specified Drive folder • Downloads and converts each file to plain text • Generates 768-dimensional embeddings using Google Gemini • Stores documents with embeddings in Supabase for semantic search Built for the Study Agent workflow to power document-based Q&A, but also works perfectly for any RAG system, AI chatbot, knowledge base, or semantic search application that needs to query document collections. Set up steps Prerequisites: • Google Drive OAuth2 credentials • Supabase account with Postgres connection details • Google Gemini API key (free tier available) Setup time: ~10 minutes Steps: Add your Google Drive OAuth2 credentials to the Google Drive nodes Configure Supabase Postgres credentials in the SQL node Add Supabase API credentials to the Vector Store node Add Google Gemini API key to the Embeddings node Update the input with your Drive folder URL Execute the workflow Note: The SQL query will drop any existing "documents" table, so backup data if needed. Detailed node-by-node instructions are in the sticky notes within the workflow. Works with: Study Agent (main use case), custom AI agents, chatbots, documentation search, customer support bots, or any RAG application.
By Mantaka Mahir