Get real-time crypto token insights via Telegram with DexScreener and GPT-4o
Instantly access real-time decentralized exchange (DEX) insights directly in Telegram! This workflow integrates the DexScreener API with GPT-4o-powered AI and Telegram, allowing users to fetch the latest blockchain token analytics, liquidity pools, and trending tokens effortlessly. Ideal for crypto traders, DeFi analysts, and investors who need actionable market data at their fingertips. How It Works A Telegram bot listens for user queries about tokens or trading pairs. The workflow interacts with the DexScreener API (no API key required) to fetch real-time data, including: Token fundamentals (profiles, images, descriptions, and links) Trending and boosted tokens (hyped projects, potential market movers) Trading pair analytics (liquidity, price action, volumes, volatility) Order and payment activity (transaction insights, investor movements) Liquidity pool depth (market stability, capital flows) Multi-chain pair comparisons (performance tracking across networks) An AI-powered language model (GPT-4o-mini) enhances responses for better insights. The workflow logs session data to improve user interaction tracking. The requested DEX insights are sent back via Telegram in an easy-to-read format. What You Can Do with This Agent This AI-driven Telegram bot enables you to: ✅ Track trending and boosted tokens before they gain mainstream traction. ✅ Monitor real-time liquidity pools to assess token stability. ✅ Analyze active trading pairs across different blockchains. ✅ Identify transaction trends by checking paid orders for tokens. ✅ Compare market activity with detailed trading pair analysis. ✅ Receive instant insights with AI-enhanced responses for deeper understanding. Set Up Steps Create a Telegram Bot Use @BotFather on Telegram to create a bot and obtain an API token. Configure Telegram API Credentials in n8n Add your Telegram bot token under Telegram API credentials. Deploy and Test Send a query (e.g., "SOL/USDC") to your Telegram bot and receive real-time insights instantly! 🚀 Unlock powerful, real-time DEX insights directly in Telegram—no API key required! 📺 Setup Video Tutorial Watch the full setup guide on YouTube: [](https://www.youtube.com/watch?v=ZzlxBX6tDbk)
By Don Jayamaha JrInteractive knowledge base chat with Supabase RAG using AI 📚💬
Google Drive File Ingestion to Supabase for Knowledge Base 📂💾 Overview 🌟 This n8n workflow automates the process of ingesting files from Google Drive into a Supabase database, preparing them for a knowledge base system. It supports text-based files (PDF, DOCX, TXT, etc.) and tabular data (XLSX, CSV, Google Sheets), extracting content, generating embeddings, and storing data in structured tables. This is a foundational workflow for building a company knowledge base that can be queried via a chat interface (e.g., using a RAG workflow). 🚀 Problem Solved 🎯 Manually managing a knowledge base with files from Google Drive is time-consuming and error-prone. This workflow solves that by: Automatically ingesting files from Google Drive as they are created or updated. Extracting content from various file types (text and tabular). Generating embeddings for text-based files to enable vector search. Storing data in Supabase for efficient retrieval. Handling duplicates and errors to ensure data consistency. Target Audience: Knowledge Managers: Build a centralized knowledge base from company files. Data Teams: Automate the ingestion of spreadsheets and documents. Developers: Integrate with other workflows (e.g., RAG for querying the knowledge base). Workflow Description 🔍 This workflow listens for new or updated files in Google Drive, processes them based on their type, and stores the extracted data in Supabase tables for later retrieval. Here’s how it works: File Detection: Triggers when a file is created or updated in Google Drive. File Processing: Loops through each file, extracts metadata, and validates the file type. Duplicate Check: Ensures the file hasn’t been processed before. Content Extraction: Text-based Files: Downloads the file, extracts text, splits it into chunks, generates embeddings, and stores the chunks in Supabase. Tabular Files: Extracts data from spreadsheets and stores it as rows in Supabase. Metadata Storage: Stores file metadata and basic info in Supabase tables. Error Handling: Logs errors for unsupported formats or duplicates. Nodes Breakdown 🛠️ Detect New File 🔔 Type: Google Drive Trigger Purpose: Triggers the workflow when a new file is created in Google Drive. Configuration: Credential: Google Drive OAuth2 Event: File Created Customization: Specify a folder to monitor specific directories. Detect Updated File 🔔 Type: Google Drive Trigger Purpose: Triggers the workflow when a file is updated in Google Drive. Configuration: Credential: Google Drive OAuth2 Event: File Updated Customization: Currently disconnected; reconnect if updates need to be processed. Process Each File 🔄 Type: Loop Over Items Purpose: Processes each file individually from the Google Drive trigger. Configuration: Input: {{ $json.files }} Customization: Adjust the batch size if processing multiple files at once. Extract File Metadata 🆔 Type: Set Purpose: Extracts metadata like fileid, filename, mimetype, and webview_link. Configuration: Fields: file_id: {{ $json.id }} file_name: {{ $json.name }} mime_type: {{ $json.mimeType }} webviewlink: {{ $json.webViewLink }} Customization: Add more metadata fields if needed (e.g., size, createdTime). Check File Type ✅ Type: IF Purpose: Validates the file type by checking the MIME type. Configuration: Condition: mime_type contains supported types (e.g., application/pdf, application/vnd.openxmlformats-officedocument.spreadsheetml.sheet). Customization: Add more supported MIME types as needed. Find Duplicates 🔍 Type: Supabase Purpose: Checks if the file has already been processed by querying knowledge_base. Configuration: Operation: Select Table: knowledge_base Filter: fileid = {{ $node['Extract File Metadata'].json.fileid }} Customization: Add additional duplicate checks (e.g., by file name). Handle Duplicates 🔄 Type: IF Purpose: Routes the workflow based on whether a duplicate is found. Configuration: Condition: {{ $node['Find Duplicates'].json.length > 0 }} Customization: Add notifications for duplicates if desired. Remove Old Text Data 🗑️ Type: Supabase Purpose: Deletes old text data from documents if the file is a duplicate. Configuration: Operation: Delete Table: documents Filter: metadata->>'fileid' = {{ $node['Extract File Metadata'].json.fileid }} Customization: Add logging before deletion. Remove Old Data 🗑️ Type: Supabase Purpose: Deletes old tabular data from document_rows if the file is a duplicate. Configuration: Operation: Delete Table: document_rows Filter: datasetid = {{ $node['Extract File Metadata'].json.fileid }} Customization: Add logging before deletion. Route by File Type 🔀 Type: Switch Purpose: Routes the workflow based on the file’s MIME type (text-based or tabular). Configuration: Rules: Based on mime_type (e.g., application/pdf for text, application/vnd.openxmlformats-officedocument.spreadsheetml.sheet for tabular). Customization: Add more routes for additional file types. Download File Content 📥 Type: Google Drive Purpose: Downloads the file content for text-based files. Configuration: Credential: Google Drive OAuth2 File ID: {{ $node['Extract File Metadata'].json.file_id }} Customization: Add error handling for download failures. Extract PDF Text 📜 Type: Extract from File (PDF) Purpose: Extracts text from PDF files. Configuration: File Content: {{ $node['Download File Content'].binary.data }} Customization: Adjust extraction settings for better accuracy. Extract DOCX Text 📜 Type: Extract from File (DOCX) Purpose: Extracts text from DOCX files. Configuration: File Content: {{ $node['Download File Content'].binary.data }} Customization: Add support for other text formats (e.g., TXT, RTF). Extract XLSX Data 📊 Type: Extract from File (XLSX) Purpose: Extracts tabular data from XLSX files. Configuration: File ID: {{ $node['Extract File Metadata'].json.file_id }} Customization: Add support for CSV or Google Sheets. Split Text into Chunks ✂️ Type: Text Splitter Purpose: Splits extracted text into manageable chunks for embedding. Configuration: Chunk Size: 1000 Chunk Overlap: 200 Customization: Adjust chunk size and overlap based on document length. Generate Text Embeddings 🌐 Type: OpenAI Purpose: Generates embeddings for text chunks using OpenAI. Configuration: Credential: OpenAI API key Operation: Embedding Model: text-embedding-ada-002 Customization: Switch to a different embedding model if needed. Store Text in Supabase 💾 Type: Supabase Vector Store Purpose: Stores text chunks and embeddings in the documents table. Configuration: Credential: Supabase credentials Operation: Insert Documents Table Name: documents Customization: Add metadata fields to store additional context. Store Tabular Data 💾 Type: Supabase Purpose: Stores tabular data in the document_rows table. Configuration: Operation: Insert Table: document_rows Columns: datasetid, rowdata Customization: Add validation for tabular data structure. Store File Metadata 📋 Type: Supabase Purpose: Stores file metadata in the document_metadata table. Configuration: Operation: Insert Table: document_metadata Columns: fileid, filename, filetype, fileurl Customization: Add more metadata fields as needed. Record in Knowledge Base 📚 Type: Supabase Purpose: Stores basic file info in the knowledge_base table. Configuration: Operation: Insert Table: knowledge_base Columns: fileid, filename, filetype, fileurl, upload_date Customization: Add indexes for faster lookups. Log File Errors ⚠️ Type: Supabase Purpose: Logs errors for unsupported file types. Configuration: Operation: Insert Table: error_log Columns: errortype, errormessage Customization: Add notifications for errors. Log Duplicate Errors ⚠️ Type: Supabase Purpose: Logs errors for duplicate files. Configuration: Operation: Insert Table: error_log Columns: errortype, errormessage Customization: Add notifications for duplicates. Interactive Knowledge Base Chat with Supabase RAG using GPT-4o-mini 📚💬 Introduction 🌟 This n8n workflow creates an interactive chat interface that allows users to query a company knowledge base using Retrieval-Augmented Generation (RAG). It retrieves relevant information from text documents and tabular data stored in Supabase, then generates natural language responses using OpenAI’s GPT-4o-mini model. Designed for teams managing internal knowledge, this workflow enables users to ask questions like “What’s the remote work policy?” or “Show me the latest budget data” and receive accurate, context-aware responses in a conversational format. 🚀 Problem Statement 🎯 Managing a company knowledge base can be a daunting task—employees often struggle to find specific information buried in documents or spreadsheets, leading to wasted time and inefficiencies. Traditional search methods may not understand natural language queries or provide contextually relevant results. This workflow solves these issues by: Offering a chat-based interface for natural language queries, making it easy for users to ask questions in their own words. Leveraging RAG to retrieve relevant text and tabular data from Supabase, ensuring responses are accurate and context-aware. Supporting diverse file types, including text-based files (e.g., PDFs, DOCX) and tabular data (e.g., XLSX, CSV), for comprehensive knowledge access. Maintaining conversation history to provide context during interactions, improving the user experience. Target Audience 👥 This workflow is ideal for: HR Teams: Quickly access company policies, employee handbooks, or benefits documents. Finance Teams: Retrieve budget data, expense reports, or financial summaries from spreadsheets. Knowledge Managers: Build a centralized assistant for internal documentation, streamlining information access. Developers: Extend the workflow with additional tools or integrations for custom use cases. Workflow Description 🔍 This workflow consists of a chat interface powered by n8n’s Chat Trigger node, an AI Agent node for RAG, and several tools to retrieve data from Supabase. Here’s how it works step-by-step: User Initiates a Chat: The user interacts with a chat interface, sending queries like “Summarize our remote work policy” or “Show budget data for Q1 2025.” Query Processing with RAG: The AI Agent processes the query using RAG, retrieving relevant data from Supabase tables and generating a response with OpenAI’s GPT-4o-mini model. Data Retrieval and Response Generation: The workflow uses multiple tools to fetch data: Retrieves text chunks from the documents table using vector search. Fetches tabular data from the document_rows table based on file IDs. Extracts full document text or lists available files as needed. Generates a natural language response combining the retrieved data. Conversation History Management: Stores the conversation history in Supabase to maintain context for follow-up questions. Response Delivery: Formats and sends the response back to the chat interface for the user to view. Nodes Breakdown 🛠️ Start Chat Interface 💬 Type: Chat Trigger Purpose: Provides the interactive chat interface for users to input queries and receive responses. Configuration: Chat Title: Company Knowledge Base Assistant Chat Subtitle: Ask me anything about company documents! Welcome Message: Hello! I’m your Company Knowledge Base Assistant. How can I help you today? Suggestions: What is the company policy on remote work?, Show me the latest budget data., List all policy documents. Output Chat Session ID: true Output User Message: true Customization: Update the title and welcome message to align with your company branding (e.g., HR Knowledge Assistant). Add more suggestions relevant to your use case (e.g., What are the company benefits?). Process Query with RAG 🧠 Type: AI Agent Purpose: Orchestrates the RAG process by retrieving relevant data using tools and generating responses with OpenAI’s GPT-4o-mini. Configuration: Credential: OpenAI API key Model: gpt-4o-mini System Prompt: You are a helpful assistant for a company knowledge base. Use the provided tools to retrieve relevant information from documents and tabular data. If the query involves tabular data, format it clearly in your response. If no relevant data is found, respond with "I couldn’t find any relevant information. Can you provide more details?" Input Field: {{ $node['Start Chat Interface'].json.message }} Customization: Switch to a different model (e.g., gpt-3.5-turbo) to adjust cost or performance. Modify the system prompt to change the tone (e.g., more formal for HR use cases). Retrieve Text Chunks 📄 Type: Supabase Vector Store (Tool) Purpose: Retrieves relevant text chunks from the documents table using vector search. Configuration: Credential: Supabase credentials Operation Mode: Retrieve Documents (As Tool for AI Agent) Table Name: documents Embedding Field: embedding Content Field: content_text Metadata Field: metadata Embedding Model: OpenAI text-embedding-ada-002 Top K: 10 Customization: Adjust Top K to retrieve more or fewer results (e.g., 5 for faster responses). Ensure the match_documents function (see prerequisites) is defined in Supabase. Fetch Tabular Data 📊 Type: Supabase (Tool, Execute Query) Purpose: Retrieves tabular data from the document_rows table based on a file ID. Configuration: Credential: Supabase credentials Operation: Execute Query Query: SELECT rowdata FROM documentrows WHERE dataset_id = $1 LIMIT 10 Tool Description: Run a SQL query - use this to query from the documentrows table once you know the file ID you are querying. datasetid is the fileid and you are always using the rowdata for filtering, which is a jsonb field that has all the keys from the file schema given in the document_metadata table. Customization: Modify the query to filter specific columns or add conditions (e.g., WHERE datasetid = $1 AND rowdata->>'year' = '2025'). Increase the LIMIT for larger datasets. Extract Full Document Text 📜 Type: Supabase (Tool, Execute Query) Purpose: Fetches the full text of a document by concatenating all text chunks for a given file_id. Configuration: Credential: Supabase credentials Operation: Execute Query Query: SELECT stringagg(contenttext, ' ') as documenttext FROM documents WHERE metadata->>'fileid' = $1 GROUP BY metadata->>'file_id' Tool Description: Given file id fetch the text from the documents Customization: Add filters to the query if needed (e.g., limit to specific metadata fields). List Available Files 📋 Type: Supabase (Tool, Select) Purpose: Lists all files in the knowledge base from the document_metadata table. Configuration: Credential: Supabase credentials Operation: Select Schema: public Table: document_metadata Tool Description: Use this tool to fetch all documents including the table schema if the file is csv, excel or xlsx Customization: Add filters to list specific file types (e.g., WHERE file_type = 'application/pdf'). Modify the columns selected to include additional metadata (e.g., file_size). Manage Chat History 💾 Type: Postgres Chat Memory (Tool) Purpose: Stores and retrieves conversation history to maintain context. Configuration: Credential: Supabase credentials (Postgres-compatible) Table Name: n8nchathistory Session ID Field: session_id Session ID Value: {{ $node['Start Chat Interface'].json.sessionId }} Message Field: message Sender Field: sender Timestamp Field: timestamp Context Window Length: 5 Customization: Increase the context window length for longer conversations (e.g., 10 messages). Add indexes on session_id and timestamp in Supabase for better performance. Format and Send Response 📤 Type: Set Purpose: Formats the AI Agent’s response and sends it back to the chat interface. Configuration: Fields: response: {{ $node['Process Query with RAG'].json.output }} Customization: Add additional formatting to the response if needed (e.g., prepend with a timestamp or apply markdown formatting). Setup Instructions 🛠️ Prerequisites 📋 n8n Setup: Ensure you’re using n8n version 1.0 or higher. Enable the AI features in n8n settings. Supabase: Create a Supabase project and set up the following tables: documents: id (uuid), content_text (text), embedding (vector(1536)), metadata (jsonb) documentrows: id (uuid), datasetid (varchar), row_data (jsonb) documentmetadata: fileid (varchar), filename (varchar), filetype (varchar), file_url (text) knowledgebase: id (serial), fileid (varchar), filename (varchar), filetype (varchar), fileurl (text), uploaddate (timestamp) n8nchathistory: id (serial), session_id (varchar), message (text), sender (varchar), timestamp (timestamp) Add the match_documents function to Supabase to enable vector search: sql CREATE OR REPLACE FUNCTION match_documents ( query_embedding vector(1536), match_count int DEFAULT 5, filter jsonb DEFAULT '{}' ) RETURNS TABLE ( id uuid, content_text text, metadata jsonb, similarity float ) LANGUAGE plpgsql AS $$ BEGIN RETURN QUERY SELECT documents.id, documents.content_text, documents.metadata, 1 - (documents.embedding <=> query_embedding) as similarity FROM documents WHERE documents.metadata @> filter ORDER BY similarity DESC LIMIT match_count; END; $$;
By ImmanuelAutomate Glassdoor job search with Bright Data scraping & Google Sheets storage
🔍 Glassdoor Job Finder: Bright Data Scraping + Keyword-Based Automation A comprehensive n8n automation that scrapes Glassdoor job listings using Bright Data's web scraping service based on user-defined keywords, location, and country parameters, then automatically stores the results in Google Sheets. 📋 Overview This workflow provides an automated job search solution that extracts job listings from Glassdoor using form-based inputs and stores organized results in Google Sheets. Perfect for recruiters, job seekers, market research, and competitive analysis. Workflow Description: Automates Glassdoor job searches using Bright Data's web scraping capabilities. Users submit keywords, location, and country via form trigger. The workflow scrapes job listings, extracts company details, ratings, and locations, then automatically stores organized results in Google Sheets for easy analysis and tracking. ✨ Key Features 🎯 Form-Based Input: Simple web form for job type, location, and country 🔍 Glassdoor Integration: Uses Bright Data's Glassdoor dataset for accurate job data 📊 Smart Data Processing: Automatically extracts key job information 📈 Google Sheets Storage: Organized data storage with automatic updates 🔄 Status Monitoring: Built-in progress tracking and retry logic ⚡ Fast & Reliable: Professional scraping with error handling 🎯 Keyword Flexibility: Search any job type with location filters 📝 Structured Output: Clean, organized job listing data 🎯 What This Workflow Does Input Job Keywords: Job title or role (e.g., "Software Engineer", "Marketing Manager") Location: City or region for job search Country: Target country for job listings Processing Form Submission Data Scraping via Bright Data Status Monitoring Data Extraction Data Processing Sheet Update Output Data Points | Field | Description | Example | |-------|-------------|---------| | Job Title | Position title from listing | Senior Software Engineer | | Company Name | Employer name | Google Inc. | | Location | Job location | San Francisco, CA | | Rating | Company rating score | 4.5 | | Job Link | Direct URL to listing | https://glassdoor.com/job/... | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Google account with Sheets access Bright Data account with Glassdoor scraping dataset access 5–10 minutes for setup Step 1: Import the Workflow Copy the JSON workflow code from the provided file In n8n: Workflows → + Add workflow → Import from JSON Paste JSON and click Import Step 2: Configure Bright Data Set up Bright Data credentials in n8n Ensure access to dataset: gd_lpfbbndm1xnopbrcr0 Update API tokens in: "Scrape Job Data" node "Check Delivery Status of Snap ID" node "Getting Job Lists" node Step 3: Configure Google Sheets Integration Create a new Google Sheet (e.g., "Glassdoor Job Tracker") Set up Google Sheets OAuth2 credentials in n8n Prepare columns: Column A: Job Title Column B: Company Name Column C: Location Column D: Rating Column E: Job Link Step 4: Update Workflow Settings Update "Update Job List" node with your Sheet ID and credentials Test the form trigger and webhook URL Step 5: Test & Activate Submit test data (e.g., "Software Engineer" in "New York") Activate the workflow Verify Google Sheet updates and field extraction 📖 Usage Guide Submitting Job Searches Navigate to your workflow's webhook URL Fill in: Search Job Type Location Country Submit the form Reading the Results Real-time job listing data Company ratings and reviews Direct job posting links Location-specific results Processing timestamps 🔧 Customization Options More Data Points: Add job descriptions, salary, company size, etc. Search Parameters: Add filters for salary, experience, remote work Data Processing: Add validation, deduplication, formatting 🚨 Troubleshooting Bright Data connection failed: Check API credentials and dataset access No job data extracted: Validate search terms and location format Google Sheets permission denied: Re-authenticate and check sharing Form submission failed: Check webhook URL and form config Workflow execution failed: Check logs, add retry logic Advanced Troubleshooting Check execution logs in n8n Test individual nodes Verify data formats Monitor rate limits Add error handling 📊 Use Cases & Examples Recruitment Pipeline: Track job postings, build talent database Market Research: Analyze job trends, hiring patterns Career Development: Monitor opportunities, salary trends Competitive Intelligence: Track competitor hiring activity ⚙️ Advanced Configuration Batch Processing: Accept multiple keywords, loop logic, delays Search History: Track trends, compare results over time External Tools: Integrate with CRM, Slack, databases, BI tools 📈 Performance & Limits Single search: 2–5 minutes Data accuracy: 95%+ Success rate: 90%+ Concurrent searches: 1–3 (depends on plan) Daily capacity: 50–200 searches Memory: ~50MB per execution API calls: 3 Bright Data + 1 Google Sheets per search 🤝 Support & Community n8n Community Forum: community.n8n.io Documentation: docs.n8n.io Bright Data Support: Via your dashboard GitHub Issues: Report bugs and features Contributing: Share improvements, report issues, create variations, document best practices. Need Help? Check the full documentation or visit the n8n Community for support and workflow examples.
By Bright DataDetect holiday conflicts & suggest meeting reschedules with Google Calendar and Slack
Who’s it for Remote and distributed teams that schedule across time zones and want to avoid meetings landing on public holidays—PMs, CS/AM teams, and ops leads who own cross-regional calendars. What it does / How it works The workflow checks next week’s Google Calendar events, compares event dates against public holidays for selected country codes, and produces a single Slack digest with any conflicts plus suggested alternative dates. Core steps: Workflow Configuration (Set) → Fetch Public Holidays (via a public holiday API such as Calendarific/Nager.Date) → Get Next Week Calendar Events (Google Calendar) → Detect Holiday Conflicts (compare dates) → Generate Reschedule Suggestions (find nearest business day that isn’t a holiday/weekend) → Format Slack Digest → Post Slack Digest. How to set up Open Workflow Configuration (Set) and edit: countryCodes, calendarId, slackChannel, nextWeekStart, nextWeekEnd. Connect your own Google Calendar and Slack credentials in n8n (no hardcoded keys). (Optional) Adjust the Trigger to run daily or only on Mondays. Requirements n8n (Cloud or self-hosted) Google Calendar read access to the target calendar Slack app with permission to post to the chosen channel A public-holiday API (no secrets needed for Nager.Date; Calendarific requires an API key) How to customize the workflow Time window: Change nextWeekStart/End to scan a different period. Holiday sources: Add or swap APIs; merge multiple regions. Suggestion logic: Tweak the look-ahead window or rules (e.g., skip Fridays). Output: Post per-calendar messages, DM owners, or create tentative reschedule events automatically.
By Takuya Ojima