This workflow streamlines the creation and storage of customized privacy policies using AI technologies integrated with n8n. It starts with a webhook trigger that allows users to submit privacy policy prompts via HTTP POST requests. The input text is split into manageable chunks to facilitate detailed processing. These chunks are then embedded into vector representations using Cohere’s API, enabling semantic search and retrieval. The workflow includes a Redis vector store for de-duplication, storage, and quick querying of privacy policy content.
The system uses an AI language model (via Anthropic) to generate, clarify, or improve privacy policy drafts based on user input. It maintains context with Memory nodes, ensuring that the AI can handle ongoing conversations or revisions effectively. The final generated privacy policies are logged directly into Google Sheets for record-keeping and further review.
This setup is ideal for websites or organizations that want to automate the process of generating and updating privacy policies, ensuring compliance and customization based on user or legal requirements. Users can trigger the process easily through a simple web interface, and all generated content is stored systematically for future reference and editing.
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