Automate MES Log Analysis with Webhook and RAG Automation
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This workflow analyzes Manufacturing Execution System (MES) logs via a Webhook, splitting and embedding the data with HuggingFace Embeddings, and storing it in Weaviate. It then queries the vector store using a LangChain Agent and saves the results to a Google Sheet, enabling efficient log analysis and problem-solving.
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About This Workflow
Overview
This n8n workflow automates the analysis of Manufacturing Execution System (MES) logs. It leverages a webhook to receive log data, then uses LangChain nodes to process, embed, and store the information in a Weaviate vector database. A LangChain Agent is employed to query this database and derive insights. Finally, the extracted information is appended to a Google Sheet for easy access and further analysis.
This workflow is ideal for manufacturing environments that generate large volumes of MES logs and require efficient methods for identifying issues, tracking production events, or performing historical analysis. It automates a traditionally manual and time-consuming process, providing a structured and searchable repository of log data.
Key Features
- Webhook Integration: Receives MES log data in real-time.
- Text Splitting and Embedding: Processes large log entries into manageable chunks and generates embeddings using HuggingFace.
- Vector Database Storage: Stores embeddings in Weaviate for efficient semantic search.
- LangChain Agent for Querying: Utilizes a powerful agent to intelligently query the vector database.
- Google Sheets Integration: Automatically appends analyzed data to a Google Sheet for reporting and further analysis.
How To Use
- Configure Webhook: Set up the
Webhooknode to receive incoming MES log data at your specified path (mes_log_analyzer). - Set up Text Splitter: Configure the
Splitternode to chunk the incoming log data appropriately. - Configure Embeddings: Connect the
Embeddingsnode and select your HuggingFace credentials to generate vector embeddings. - Set up Weaviate Insert: Configure the
Insertnode with your Weaviate credentials and the desired index name (mes_log_analyzer) to store the embeddings. - Set up Weaviate Query: Configure the
Querynode with your Weaviate credentials and the same index name. - Configure LangChain Tool: Set up the
Toolnode to use the Weaviate vector store. - Configure Memory: Set up the
Memorynode to maintain conversation history. - Configure Chat Model: Set up the
Chatnode with your OpenAI credentials. - Configure Agent: Configure the
Agentnode to define the behavior of your AI agent, linking the memory, chat model, and tool. - Configure Google Sheets: Set up the
Sheetnode with your Google Sheets credentials and the targetSHEET_IDandLogsheet name to save the analyzed results.
Apps Used
Workflow JSON
{
"id": "a4d50c06-fd90-4d4a-885c-5c254f49ac2b",
"name": "Automate MES Log Analysis with Webhook and RAG Automation",
"nodes": 0,
"category": "Manufacturing",
"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: a4d50c06-fd90...
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