Automated Case Law Summarizer with AI and Vector Databases
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This workflow automates the summarization of legal case laws. It uses Langchain nodes like Splitter, Embeddings, and an Agent to process and query case documents stored in a Supabase vector store, outputting summaries to Google Sheets.
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About This Workflow
Overview
This n8n workflow is designed to automate the summarization of legal case law documents. It leverages the power of Langchain nodes within n8n to process and analyze legal texts. The workflow begins by receiving case law data via a Webhook. This data is then split into manageable chunks by the Splitter node, crucial for efficient processing by AI models. The Embeddings node (using Cohere) converts these text chunks into numerical representations that can be understood by machine learning models. These embeddings are then inserted into a Supabase vector database using the Insert node, creating a searchable index of the case law. For retrieval and summarization, the Query node interacts with the Supabase vector store. The Tool node acts as an interface for the vector store, and the Memory node helps maintain context for conversational AI. The Agent node, powered by OpenAI's chat model, orchestrates the summarization process by querying the case law and generating concise summaries. Finally, the Sheet node logs the generated summaries and relevant metadata to a Google Sheet for easy review and record-keeping.
Key Features
- Automated case law summarization using AI.
- Text chunking for efficient processing.
- Vectorization of legal documents for semantic search.
- Integration with Supabase for vector database storage.
- Orchestrated summarization via Langchain Agent.
- Logging of summaries to Google Sheets.
How To Use
- Set up Webhook: Configure the
Webhooknode to receive incoming case law data. - Configure Langchain Nodes: Set up the
Splitter(chunk size and overlap),Embeddings(choose your embedding model and provide credentials),InsertandQuery(specify your Supabase index name and provide credentials),Tool(vector store tool),Memory(buffer window size), andChat(OpenAI model and credentials) nodes. - Configure Agent: Ensure the
Agentnode is correctly configured to utilize the tools and memory for summarization. - Configure Google Sheets: Set up the
Sheetnode with your Google Sheet ID and desired sheet name, and provide the necessary Google Sheets API credentials. - Trigger the Workflow: Send case law data to the configured Webhook endpoint to initiate the summarization process.
Apps Used
Workflow JSON
{
"id": "9e899024-442f-4316-aece-3518ab97e40b",
"name": "Automated Case Law Summarizer with AI and Vector Databases",
"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: 9e899024-442f...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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