Babish Shrestha
I’m currently obsessed with building cool things using AI automation and AI agents — systems that save time, cut manual work, and actually do stuff on their own. If there’s a way to automate it, I’m probably already trying it out (or planning to). Having worked in IT industry for 15 years, I’m combining that background with AI to help businesses move smarter, not just faster.
Templates by Babish Shrestha
Build a RAG knowledge chatbot with OpenAI, Google Drive, and Supabase
🚀 Build Your Own Knowledge Chatbot Using Google Drive Create a smart chatbot that answers questions using your Google Drive PDFs—perfect for support, internal docs, education, or research. 🛠️ Quick Setup Guide Step 1: Prerequisites n8n instance (cloud or self-hosted) Google Drive account (with PDFs) Supabase account (vector database) OpenAI API key PostgreSQL database (for chat memory) else remove the node Step 2: Supabase Setup Create supabase account (its free) Create a project Copy the sql and paste it in supabase sql editor SQL -- Enable the pgvector extension to work with embedding vectors create extension vector; -- Create a table to store your documents create table documents ( id bigserial primary key, content text, -- corresponds to Document.pageContent metadata jsonb, -- corresponds to Document.metadata embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed ); -- Create a function to search for documents create function match_documents ( query_embedding vector(1536), match_count int default null, filter jsonb DEFAULT '{}' ) returns table ( id bigint, content text, metadata jsonb, similarity float ) language plpgsql as $$ variableconflict usecolumn begin return query select id, content, metadata, 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding limit match_count; end; $$; Step 3: Import & Configure n8n Workflow Import this template into n8n Add credentials: OpenAI API key Google Drive OAuth2 Supabase URL & service key PostgreSQL connection Set your Google Drive folder ID in triggers Step 4: Test & Use Add a PDF to your Drive folder → check Supabase for new entries Start the workflow and chat → ask questions about your documents. "What can you help me with?" Multi-turn chat → context is maintained per user ⚡ Features Auto-syncs new/updated PDFs from Google Drive Extracts, chunks, and vectorizes text Finds relevant info and answers questions Maintains chat history per user 📝 Troubleshooting Check folder permissions & IDs if no docs found Verify API keys & Supabase setup for errors Ensure PostgreSQL is connected for chat memory Tags: RAG, Chatbot, Google Drive, Supabase, OpenAI, n8n Setup Time: ~20 minutes
By Babish ShresthaQuery PostgreSQL database with natural language using GPT-4o-mini
This Database SQL Query Agent convert natural language into sql query to get results Turn your PostgreSQL database into a conversational AI agent! Ask questions in plain English and get instant data results without writing SQL. ✨ What It Does Natural Language Queries: "Show laptops under $500 in stock" → Automatic SQL generation Smart Column Mapping: Understands your terms and maps them to actual database columns Conversational Memory: Maintains context across multiple questions Universal Compatibility: Works with any PostgreSQL table structure 🎯 Perfect For Business analysts querying data without SQL knowledge Customer support finding information quickly Product managers analyzing inventory/sales data Anyone who needs database insights fast 🚀 Quick Setup Step 1: Prerequisites n8n instance (cloud/self-hosted) PostgreSQL database with read access OpenAI API key/You can use other LLM as well Step 2: Import & Configure Import this workflow template into n8n Add Credentials: OpenAI API: Add your API key PostgreSQL: Configure database connection Set Table Name: Edit "Set Table Name" node → Replace "table_name" with your actual table Test Connection: Ensure your database user has SELECT permissions Step 3: Deploy & Use Start the workflow Open the chat interface Ask questions like: "Show all active users" "Find orders from last month over $100" "List products with low inventory" 🔧 Configuration Details Required Settings Table Name: Update in "Set Table Name" node Database Schema: Default is 'public' (modify SQL if different) Result Limit: Default 50 rows (adjustable in system prompt) Optional Customizations Multi-table Support: Modify system prompt and add table selection logic Custom Filters: Add business rules to restrict data access Output Format: Customize response formatting in the agent prompt 💡 Example Queries E-commerce "Show me all electronics under $200 that are in stock" HR Database "List employees hired in 2024 with salary over 70k" Customer Data "Find VIP customers from California with recent orders" 🛡️ Security Features Read-only Operations: Only SELECT queries allowed SQL Injection Prevention: Parameterized queries and validation Result Limits: Prevents overwhelming queries Safe Schema Discovery: Uses information_schema tables 🔍 How It Works Schema Discovery: Agent fetches table structure and column info Query Planning: Maps natural language to database columns SQL Generation: Creates safe, optimized queries Result Formatting: Returns clean, user-friendly data ⚡ Quick Troubleshooting No Results: Check table name and ensure data exists Permission Error: Verify database user has SELECT access Connection Failed: Confirm PostgreSQL credentials and network access Unexpected Results: Try more specific queries with exact column names 🎨 Use Cases Inventory Management: "Show low-stock items by category" Sales Analysis: "Top 10 products by revenue this quarter" Customer Support: "Find customer orders with status 'pending'" Data Exploration: "What are the unique product categories?" 🔧 Advanced Tips Performance: Add database indexes on frequently queried columns Customization: Modify the system prompt for domain-specific terminology Scaling: Use read replicas for high-query volumes Integration: Connect to Slack/Teams for team-wide data access --- Tags: AI, PostgreSQL, Natural Language, SQL, Business Intelligence, LangChain, Database Query Difficulty: Beginner to Intermediate Setup Time: 10-15 minutes
By Babish Shrestha