This n8n workflow automates the process of summarizing legal case documents using AI, storing embeddings for quick retrieval, and maintaining logs in Google Sheets for tracking. It integrates webhooks, AI models, and cloud services to streamline legal research or case management.
The workflow begins with a webhook trigger that receives case law content via a POST request. The content is then split into manageable chunks using a text splitter node, which helps in processing lengthy legal documents. These chunks are converted into embeddings through Cohere’s AI service, enabling semantic search capabilities. The embeddings are stored in a Supabase vector database for efficient retrieval.
When a specific case law query is made, the system retrieves relevant chunks from the database using vector similarity search. An AI-powered chat or agent then processes these chunks to generate a comprehensive summary or response. The workflow also features a memory buffer to maintain context during interactions.
Finally, the summarized or processed data is logged into a Google Sheet for record-keeping. This setup can be used by legal professionals, researchers, or law firms to automate case law analysis, enable quick retrieval of relevant legal arguments, and keep detailed logs of all interactions and summaries.
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