Autonomous Vehicle Log Summarizer and Analyzer
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Automate the summarization and analysis of autonomous vehicle logs using n8n and Langchain. This workflow processes raw log data, chunks it, generates embeddings, stores them in a vector database, and uses an AI agent for analysis and querying, ultimately storing insights in Google Sheets.
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About This Workflow
Overview
This n8n workflow is designed to ingest, process, and analyze logs generated by autonomous vehicles. It leverages the power of Langchain nodes within n8n to handle complex natural language processing tasks. The workflow begins by receiving log data via a Webhook, then splits the text into manageable chunks using the Splitter node. These chunks are then converted into embeddings using the Embeddings node (HuggingFace). These embeddings are stored and indexed in a Weaviate vector database using the Insert node. For querying and analysis, a Query node retrieves relevant information from Weaviate, which is then processed by an AI Agent that utilizes a Tool (vector store integration) and Memory for context. The AI model (OpenAI Chat) provides intelligent summarization and analysis capabilities. Finally, the insights and summarized data are appended to a Google Sheet for long-term storage and review.
This workflow is ideal for engineers, researchers, and developers working with autonomous vehicle data who need an automated way to make sense of large volumes of log files, identify patterns, and extract key information without manual review.
Key Features
- Ingest log data via Webhook.
- Automatically chunk and process large log files.
- Utilize HuggingFace embeddings for semantic understanding.
- Store and index data in Weaviate vector database for efficient retrieval.
- Employ an AI agent with memory and tool integration for intelligent analysis and summarization.
- Output summarized insights to Google Sheets.
How To Use
- Configure Webhook: Set up the
Webhooknode to receive incoming log data. Ensure the pathautonomous_vehicle_log_summarizeris correctly configured. - Set up Text Splitter: Configure the
Splitternode with appropriatechunkSizeandchunkOverlapvalues to segment your log data effectively. - Configure Embeddings: Connect the
Embeddingsnode (e.g., HuggingFace) and provide your API credentials. Ensure it's set to generate embeddings from the split text. - Configure Weaviate: Set up both the
InsertandQuerynodes for Weaviate. Provide your Weaviate API credentials and ensure theindexName(autonomous_vehicle_log_summarizer) matches across both nodes. - Set up AI Agent: Configure the
Toolnode to use the Weaviate vector store. Configure theMemorynode (e.g.,MemoryBufferWindow) and theChatnode (e.g., OpenAI) with your respective API credentials. - Define Agent Prompt: In the
Agentnode, use thetextparameter to define your prompt, referencing the incoming data and setting the desiredpromptType(e.g., 'define'). - Configure Google Sheets: Set up the
Sheetnode to append data to your desired Google Sheet. Provide your Google Sheets API credentials and specify thedocumentIdandsheetName('Log'). - Establish Connections: Ensure all nodes are connected in the correct order as per the workflow diagram, linking data flow from input to output.
Apps Used
Workflow JSON
{
"id": "0232dec5-91a8-4b6a-a8ac-9187b278bc5f",
"name": "Autonomous Vehicle Log Summarizer and Analyzer",
"nodes": 0,
"category": "Automotive",
"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: 0232dec5-91a8...
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