Automate Legal Document Processing with AI-Powered Embeddings
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Streamline the ingestion and analysis of legal documents by leveraging AI-powered embeddings. This workflow automatically processes ZIP archives containing PDF legal texts, extracts their content, and generates vector representations for efficient search and retrieval.
About This Workflow
This n8n workflow automates the complex task of processing legal documents, making them readily searchable and analyzable. It begins by downloading a ZIP file containing PDF legal statutes. The workflow then extracts the contents of these PDFs, parses them into structured sections (title, content, chapter, section), and applies recursive character text splitting to manage document length. Crucially, it utilizes Mistral AI's cloud embeddings to generate vector representations of the document chunks. These embeddings are essential for powering advanced semantic search and retrieval functionalities, allowing for quick and accurate access to relevant legal information within large document sets. The entire process is designed for robust data handling and intelligent content segmentation.
Key Features
- Automated ZIP file download and extraction of PDF documents.
- Intelligent parsing of PDF content into structured sections with chapter and section metadata.
- Advanced text splitting for efficient chunking of large documents.
- Integration with Mistral AI for high-quality text embeddings.
- Preparation for semantic search and AI-driven document analysis.
How To Use
- Trigger Workflow: Manually trigger the workflow by clicking 'Test workflow'.
- Download & Extract: The 'Get Tax Code Zip File' node downloads the specified ZIP archive, and 'Extract Zip Files' unpacks its contents.
- Process PDFs: The 'Files as Items' node separates the extracted files, and 'Extract PDF Contents' reads the text from each PDF.
- Structure Content: 'Extract From Chapter' and 'Map To Sections' nodes parse the PDF text into structured data including chapter, section, title, and content.
- Chunk Content: 'Content Chunking @ 50k Chars' divides large content into manageable chunks.
- Generate Embeddings: The 'Embeddings Mistral Cloud' node uses Mistral AI to create vector embeddings for each content chunk, associating relevant metadata like chapter and section.
- Load Data: 'Default Data Loader' prepares the chunked and embedded data for further processing or storage.
Apps Used
Workflow JSON
{
"id": "467bf436-cedc-4b0b-a735-59fb43e9770e",
"name": "Automate Legal Document Processing with AI-Powered Embeddings",
"nodes": 9,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 467bf436-cedc...
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