Multi-language Telegram RAG chatbot with supervisor AI & automated Google Drive pipeline
N8N Hybrid RAG Chatbot with Multiple AI Agents
One of the most powerful system in the market, this template creates a sophisticated, multi-agent hybrid RAG (Retrieval-Augmented Generation) chatbot that can handle diverse user queries by routing them to a “Supervisor AI agent”. The Supervisor agent will then send the request to “Expert AI agents”, agents specializing in specific domains. In addition, this system automates data ingestion from various sources (including websites and Google Drive), processes and stores the information in a vector database, and interacts with users through Telegram in multiple languages.
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Who’s it for?
This template is ideal for:
- Developers and Businesses looking to build a powerful, knowledge-based chatbot for customer support, internal knowledge management, or lead generation.
- AI Enthusiasts who want to explore advanced concepts like multi-agent systems, RAG, and automated data pipelines.
- n8n Users who want to build a scalable and customizable AI solution that integrates multiple services.
Key Features
- Multi-Agent Architecture: Utilizes a supervisor agent to route queries to specialized agents for different domains (e.g., Products, News, Academy).
- Automated Data Ingestion: Automatically scrapes data from websites and syncs new or updated files from Google Drive.
- Retrieval-Augmented Generation (RAG): Enriches the chatbot's knowledge by retrieving relevant information from a Supabase vector store and a Postgres database.
- Telegram Integration: Provides a seamless, multi-language chat interface for users to interact with the bot.
- Dynamic Data Handling: Automatically processes and embeds data from various sources like Google Docs, PDFs, and Word documents.
- Data Management: Keeps the knowledge base up-to-date by automatically handling document creation, updates, and deletions.
How it works
The workflow is divided into three main parts: data ingestion, data management, and the chat interface.
- Data Ingestion & Processing:
- Web Scraping: The workflow fetches URLs from a Google Sheet, scrapes the content using Crawl4ai, cleans it with an AI agent, and saves it to a Google Doc.
- Google Drive Sync: It monitors specific Google Drive folders for new or updated files (Google Docs, PDFs, Word documents).
- Embedding & Storage: The content from these sources is then chunked, converted into vector embeddings using OpenAI, and stored in a Supabase vector database for efficient retrieval.
- Data Deletion:
- A scheduled trigger periodically checks a Google Sheet for records marked as "deleted."
- It then removes the corresponding data from the Supabase vector store and deletes the file from Google Drive to ensure the chatbot's knowledge remains current.
- Chat Interface & Logic (Telegram):
- User Input: The chatbot receives user messages via a Telegram trigger.
- Language Detection: It first detects the language of the query and translates it to English if necessary.
- Supervisor Agent: A central "Supervisor" AI agent analyzes the user's query.
- Agent Routing: Based on the query, the Supervisor delegates the task to the most appropriate specialized agent:
- News AI Agent: Handles questions about current events.
- Product AI Agent: Answers queries about product details from a Postgres database.
- Academy AI Agent: Responds to questions about courses and educational content.
- Response Generation: The selected agent processes the query, retrieves the necessary information using RAG, generates a response, and translates it back to the user's original language before sending it via Telegram.
Requirements
To use this template, you will need accounts and credentials for the following services:
- n8n
- OpenAI
- Supabase (for vector storage)
- Google Workspace (Google Drive, Google Sheets, Google Docs)
- Telegram Bot
- Postgres Database
- Crawl4AI
Step-by-step Setup
- Configure Credentials: Add your API keys and credentials for all the required services (OpenAI, Supabase, Google, Telegram, Postgres) in the n8n Credentials section.
- Set up Google Drive: Create two folders in your Google Drive: one for documents scraped from websites and another for manual document uploads. Note the folder IDs.
- Set up Google Sheets:
- Clone the Google Sheet template, or create a Google Sheet with two tabs: Website Links and Manual Documents.
- In the Website Links tab, add columns for Link, Category Code, Is Scraped, and Is Deleted.
- In the Manual Documents tab, add columns for Document ID, Title, Category Code, and Is Deleted.
- Set up Supabase:
- Create a new project in Supabase.
- Run the provided SQL script to create the documents table for vector storage.
- Set up Postgres:
- Set up a Postgres database (in Supabase).
- Run the provided SQL script to create the products table to store product details.
- Configure the Main Workflow:
- Open the AIAutomationPro Ultimate RAG Chatbot main workflow.
- Update the Google Drive, Google Sheets, Supabase, and Postgres nodes with your specific Folder IDs, Sheet Names, and table names.
- Link the three sub-workflows (News AI Agent, Product AI Agent, Academy AI Agent) in the corresponding Workflow Tool nodes.
- Activate Workflows: Activate the main workflow and all three sub-flow workflows.
- Start Chatting: Send a message to your Telegram bot to start interacting with your new RAG chatbot.
How to Customize the Workflow
- Add More Agents: You can create new sub-workflows with specialized agents for different topics (e.g., a "Finance AI Agent"). Simply add a new Workflow Tool node in the main flow and update the Supervisor Agent's system prompt to include the new agent's capabilities.
- Change Data Sources: Modify the data ingestion part of the workflow to pull data from other sources like Notion, HubSpot, or a CRM by adding the relevant n8n nodes.
- Adjust the AI Model: You can switch to a different LLM by replacing the OpenAI Chat Model nodes.
- Modify Prompts: Fine-tune the system prompts in the Agent nodes to alter the personality, instructions, or output format of the chatbot and its specialized agents.
n8n Google Drive to Supabase RAG Chatbot Pipeline
This n8n workflow automates the process of extracting information from Google Drive documents, processing it for a Retrieval-Augmented Generation (RAG) chatbot, and storing it in a Supabase vector store. It also includes a Telegram interface for interacting with the RAG chatbot.
What it does
This workflow simplifies and automates the following steps:
- Monitors Google Drive for new files: It acts as a trigger, initiating the workflow whenever new files are added to a specified Google Drive folder.
- Extracts content from various file types: It can process Google Docs, Google Sheets, and other files by extracting their text content.
- Splits text into manageable chunks: The extracted text is broken down into smaller, overlapping segments suitable for embedding.
- Generates embeddings using OpenAI: Each text chunk is converted into a numerical vector (embedding) using OpenAI's embedding models.
- Stores embeddings in Supabase: The generated embeddings are then stored in a Supabase vector database for efficient retrieval.
- Provides a Telegram chatbot interface: A separate part of the workflow listens for messages on Telegram.
- Processes Telegram queries with an AI Agent: When a message is received, an AI Agent uses the Supabase vector store to retrieve relevant information.
- Generates responses using OpenAI Chat Model: The AI Agent then uses an OpenAI Chat Model to formulate a coherent and informative response based on the retrieved information and the user's query.
- Sends responses back to Telegram: The generated response is sent back to the user via Telegram.
- Manages chat history with Postgres Memory: The chatbot maintains conversational context using a Postgres Chat Memory, allowing for more natural and continuous interactions.
- Includes a supervisor AI for complex tasks: The AI Agent is configured with a "Plan and Execute" strategy, suggesting a supervisor AI that can break down complex queries into smaller steps and utilize various tools.
- Allows calling other n8n workflows as tools: The AI Agent can execute other n8n workflows as tools, enabling it to perform a wide range of actions beyond just retrieving information.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n instance: A running n8n instance.
- Google Drive Account: With credentials configured in n8n for accessing files.
- OpenAI API Key: For generating embeddings and chat model responses.
- Supabase Account: With a configured vector store and credentials in n8n.
- Telegram Bot Token: For setting up the Telegram trigger and sending messages.
- PostgreSQL Database: For the Postgres Chat Memory.
Setup/Usage
- Import the workflow: Download the JSON provided and import it into your n8n instance.
- Configure Credentials:
- Set up your Google Drive credentials.
- Set up your OpenAI credentials for both the Embeddings OpenAI and OpenAI Chat Model nodes.
- Set up your Supabase credentials for the Supabase Vector Store node.
- Set up your Telegram Bot credentials for the Telegram Trigger and Telegram nodes.
- Configure your Postgres credentials for the Postgres Chat Memory node.
- Google Drive Trigger (Node 531): Configure this node to monitor the specific Google Drive folder where your documents are stored.
- Google Docs (Node 495), Google Sheets (Node 18), Google Drive (Node 58): Ensure these nodes are configured to read the relevant file types and content.
- Supabase Vector Store (Node 1231): Configure the table and column names in your Supabase database where embeddings will be stored.
- AI Agent (Node 1119): Review the agent's prompt and tools to ensure it aligns with your desired chatbot behavior.
- Call n8n Workflow Tool (Node 1205): If you intend for the AI Agent to call other n8n workflows, ensure these sub-workflows are properly configured and their IDs are referenced here.
- Activate the workflow: Once all credentials and configurations are set, activate the workflow.
The workflow will then automatically ingest new documents from Google Drive into your Supabase vector store, making them available for your Telegram RAG chatbot.
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