Automated NDA Risk Detection with n8n and LangChain
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This n8n workflow leverages LangChain to automate the detection of potential risks within Non-Disclosure Agreements (NDAs). It processes incoming NDA documents, analyzes them using AI, and logs identified risks to a Google Sheet.
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About This Workflow
Overview
This n8n workflow provides an automated solution for identifying potential risks within Non-Disclosure Agreements (NDAs). It utilizes the power of LangChain's AI capabilities within n8n to analyze the content of NDAs. The workflow starts by receiving NDA data via a Webhook, then splits the document into manageable chunks using the Splitter node. These chunks are then embedded into vector representations using Embeddings and stored in a Redis vector store via the Insert node. For analysis, the Query node retrieves relevant information from the vector store, and the Tool node prepares it for an Agent. The Agent node, orchestrated with Chat and Memory nodes, performs the risk assessment based on predefined prompts and the provided NDA content. Finally, any identified risks are logged to a Google Sheet using the Sheet node for review and action.
This workflow is crucial for legal professionals and businesses to quickly and efficiently screen NDAs, reducing manual review time and improving compliance.
Key Features
- Automated NDA Analysis: Automatically scans NDAs for potential risks.
- AI-Powered Risk Detection: Leverages LangChain's AI for intelligent analysis.
- Scalable Data Handling: Uses a
Splitterto manage large document sizes. - Vector Store Integration: Stores and retrieves document embeddings using Redis for efficient querying.
- Centralized Logging: Logs identified risks to a Google Sheet for easy tracking and review.
How To Use
- Set up Webhook: Configure the
Webhooknode to receive incoming NDA data. Ensure the path matches your desired endpoint (e.g.,/nda_risk_detector). - Configure LangChain Nodes:
- Splitter: Adjust
chunkSizeandchunkOverlapbased on your document complexity and desired analysis granularity. - Embeddings: Ensure your HuggingFace API credentials are set up correctly.
- Vector Store (Insert/Query/Tool): Configure your Redis connection details and ensure the
indexNamematches acrossInsertandQuerynodes. - Agent/Chat/Memory: Set up your OpenAI API credentials and adjust the
promptTypein theAgentnode if necessary. Thetextparameter in theAgentnode is set to={{ $json }}which assumes the input to the agent is the entire JSON payload from the webhook.
- Splitter: Adjust
- Configure Google Sheets: Set up your Google Sheets OAuth2 credentials and specify the
documentIdandsheetNamein theSheetnode for logging. - Connect Nodes: Ensure all nodes are correctly connected as per the workflow structure.
- Trigger Workflow: Send NDA data to the configured Webhook endpoint to initiate the risk detection process.
Apps Used
Workflow JSON
{
"id": "9231d0fc-93ad-4c82-8e45-53500cea2064",
"name": "Automated NDA Risk Detection with n8n and LangChain",
"nodes": 0,
"category": "Legal Tech",
"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: 9231d0fc-93ad...
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Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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