PR Crisis Detector: Automate Brand Reputation Monitoring with n8n
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This n8n workflow acts as a PR Crisis Detector by leveraging AI to monitor incoming data, process it with LangChain components like Text Splitter, Embeddings, and Vector Store, and log potential crises to a Google Sheet. It utilizes Webhook, LangChain tools, and AI agents for real-time analysis and response.
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About This Workflow
Overview
This n8n workflow is designed to proactively detect potential public relations (PR) crises by continuously monitoring incoming information. It automates the process of receiving data via a Webhook, processing it using LangChain's text splitting and embedding capabilities, storing these embeddings in a Redis Vector Store for efficient searching, and then querying this store to identify anomalous or critical information. An AI agent, powered by a language model and memory, analyzes the query results to determine if a PR crisis is unfolding. Finally, critical findings or detected crises are logged to a Google Sheet for human review and action. This workflow solves the problem of manual, time-consuming, and often reactive PR monitoring by providing an automated, AI-driven solution for early crisis detection.
Key Features
- Real-time PR crisis detection via Webhook.
- AI-powered text processing with LangChain embeddings and vector store.
- Efficient data storage and retrieval with Redis Vector Store.
- Automated analysis and decision-making using an AI agent.
- Logging of detected crises to a Google Sheet for immediate action.
How To Use
- Configure Webhook: Set up the
Webhooknode to receive incoming data relevant to PR monitoring. This could be from social media alerts, news feeds, or customer feedback platforms. - Process and Embed Text: Use the
Splitternode to break down incoming text into manageable chunks, followed by theEmbeddingsnode to convert these chunks into vector representations using an OpenAI model. - Store Embeddings: Configure the
Insertnode (LangChain Vector Store Redis) to store these embeddings in a Redis instance, indexed bypr_crisis_detector. - Set up Crisis Detection Query: The
Querynode (LangChain Vector Store Redis) is designed to retrieve relevant information from the vector store based on incoming data or predefined queries. - Integrate AI Agent: Connect the
Tool(LangChain Vector Store) andMemory(LangChain Buffer Window Memory) nodes to theAgentnode. TheChatnode (LangChain HuggingFace) provides the language model for the agent. - Define Agent Logic: Configure the
Agentnode'spromptTypetodefineand use{{ $json }}to pass the query results and context to the AI for crisis assessment. - Log Findings: Connect the
Agentnode's output to theSheetnode (Google Sheets) to log any identified PR crises or significant findings to a specified Google Sheet for review.
Apps Used
Workflow JSON
{
"id": "d6906611-49ee-42c0-a488-2e47e31c63de",
"name": "PR Crisis Detector: Automate Brand Reputation Monitoring with n8n",
"nodes": 0,
"category": "Media & Public Relations",
"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: d6906611-49ee...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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