Customer Sentiment Analysis Using RAG and Webhook Trigger
detail.loadingPreview
Automate customer sentiment analysis by integrating a webhook trigger with a RAG agent. This workflow processes incoming data, leverages Pinecone for vector storage, and logs results to a Google Sheet.
🚀Ready to Deploy This Workflow?
About This Workflow
Overview
This n8n workflow automates the process of analyzing customer sentiment. It utilizes a webhook to receive incoming data, which is then split into manageable chunks by the Text Splitter node. These chunks are embedded using Embeddings and stored in Pinecone via Pinecone Insert for efficient retrieval.
A Pinecone Query node retrieves relevant information, which is then processed by a RAG Agent. This agent, powered by Chat Model and Window Memory, is configured for customer sentiment analysis. The results are logged to a Google Sheet using Append Sheet, and errors are reported via Slack Alert.
Key Features
- Trigger analysis via webhook.
- Process and embed text data using Langchain nodes.
- Store and retrieve vectorized data with Pinecone.
- Perform sentiment analysis with a RAG agent.
- Log results to Google Sheets.
- Alert on errors via Slack.
How To Use
- Configure Webhook Trigger: Set up your webhook endpoint to receive customer feedback data.
- Set up Pinecone: Ensure your Pinecone index (
customer_sentiment_analysis) is created and accessible. - Configure Credentials: Provide API keys for Cohere, Pinecone, Anthropic, and Google Sheets.
- Map Google Sheet: Specify the
SHEET_IDandLogsheet name in theAppend Sheetnode. - Configure Slack: Set up your Slack API credentials and channel for alerts.
- Deploy and Test: Activate the webhook and send test data to observe the sentiment analysis in action and check the Google Sheet for logged results.
Apps Used
Workflow JSON
{
"id": "f5e1028d-d648-402d-bd2d-2e20caf1e510",
"name": "Customer Sentiment Analysis Using RAG and Webhook Trigger",
"nodes": 0,
"category": "AI/ML",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: f5e1028d-d648...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
Statistics
Verification Info
Related Workflows
Discover more workflows you might like
AI Image Captioning with RAG Agent in n8n
Automate image captioning using a RAG Agent in n8n. This workflow leverages Langchain nodes to process image data, generate captions, and store results.
RAG AI Agent with Milvus and Cohere
Automates the creation of a Retrieval-Augmented Generation (RAG) AI agent. It ingests documents from Google Drive, processes them, embeds them using Cohere, stores them in Milvus, and enables chat-based interaction for context-aware responses.
OpenAI Assistant for File Retrieval with Citation Formatting
Automates generating structured metadata from OpenAI assistant responses, ensuring citations and file sources are correctly identified and formatted.