AI-Powered Data Insight Engine with RAG
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Automate intelligent data processing using a powerful Retrieval-Augmented Generation (RAG) architecture. This workflow receives data via webhook, leverages a vector database and large language models to generate context-aware insights, and logs the outcomes to Google Sheets while providing Slack alerts for critical errors.
About This Workflow
This n8n workflow establishes a sophisticated AI-driven pipeline for processing incoming data intelligently. Utilizing a Retrieval-Augmented Generation (RAG) architecture, it ingests data via a webhook, then leverages Cohere for embedding and Weaviate as a vector store to retrieve relevant external context. An OpenAI Chat Model, combined with a Window Memory for conversational history and a Vector Tool, powers a sophisticated RAG Agent. This agent processes the data, generating informed responses or insights which are then logged in Google Sheets, ensuring a transparent record of all AI-driven decisions. Critical errors are promptly communicated via Slack, ensuring reliability and observability.
Key Features
- Intelligent Data Ingestion: Automatically trigger AI processing for new data received through a configurable webhook.
- Advanced AI Processing (RAG): Leverage Cohere embeddings, Weaviate vector store, and OpenAI's Chat Model for context-aware data analysis and insightful generation.
- Conversational Memory: Maintain context across interactions with a built-in Window Memory, enabling the AI agent to remember past exchanges.
- Automated Logging & Monitoring: Record all AI-generated outputs and processing statuses in Google Sheets and receive instant Slack alerts for any processing errors.
- Scalable Knowledge Base: Dynamically update and query your knowledge base in Weaviate for rich, up-to-date context, enhancing AI accuracy.
How To Use
- Set up Webhook Trigger: Configure the "Webhook Trigger" node to receive data from your desired source, such as a CRM, e-commerce platform, or custom application. Copy the generated webhook URL.
- Configure AI Credentials: Provide your API keys for Cohere (for embeddings) and OpenAI (for the chat model) in their respective credential settings within n8n.
- Initialize Weaviate Vector Store: Ensure your Weaviate instance is running and accessible. The "Weaviate Insert" node can be used to initially populate your
woo_order_mailchimpindex with relevant domain-specific data for the RAG agent to retrieve. - Customize RAG Agent: Adjust the "RAG Agent" node's prompt and system message (e.g.,
You are an assistant for Woo Order Mailchimp) to tailor the AI's persona and processing instructions to your specific data and operational goals. - Connect Google Sheets & Slack: Set up credentials for Google Sheets to log the AI's output and for Slack to receive error notifications, customizing the sheet ID, sheet name, and Slack channel as needed for your organization.
Apps Used
Workflow JSON
{
"id": "b48f53c7-0696-4ea0-96e4-2f28c526c0e0",
"name": "AI-Powered Data Insight Engine with RAG",
"nodes": 11,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: b48f53c7-0696...
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