Automated Document Processing and Knowledge Extraction
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Streamline your document analysis by automatically processing files, extracting key information, and organizing knowledge. This workflow leverages AI to create structured documents like study guides and timelines from your input.
About This Workflow
This powerful n8n workflow automates the process of ingesting and understanding your documents. It begins by monitoring a local directory for new files, automatically extracting relevant metadata like project, path, and filename. The content is then prepared for AI processing, where it's chunked and embedded using Mistral AI models. The workflow intelligently identifies different document types (study guides, timelines, briefing documents) and generates structured outputs for each. Finally, it utilizes a vector store retriever to enable efficient querying and retrieval of information from your processed documents, acting as a sophisticated knowledge management system.
Key Features
- Automated File Monitoring: Detects new files in a specified local directory.
- Intelligent Document Type Recognition: Identifies and categorizes documents like study guides, timelines, and briefing documents.
- AI-Powered Content Extraction: Leverages Mistral AI for embeddings and generating structured knowledge.
- Metadata Enrichment: Automatically captures and assigns project and filename metadata to your documents.
- Efficient Knowledge Retrieval: Integrates with vector stores for fast and accurate information lookup.
How To Use
- Configure Local File Trigger: Set the
pathparameter to the directory where your documents will be placed. EnsureusePollingis enabled for continuous monitoring. - Define Project Settings: In the
Settingsnode, customize theprojectname and ensure the path parsing correctly extracts your desired project identifier. - Prepare Incoming Documents: The
Prep Incoming Docnode is pre-configured to extract the text content of the incoming file. - Configure Document Type Definitions: The
Get Doc Typesnode contains predefined JSON structures forStudy Guide,Timeline, andBriefing Doc. You can modify these or add new types. - Process and Embed Documents: Connect the output of the document preparation to the
Default Data LoaderandEmbeddings Mistral Cloudnodes to prepare and embed your document content using Mistral AI. - Chunk Text: The
Recursive Character Text Splitternode divides large documents into manageable chunks for processing. - Generate Structured Documents: The workflow uses the
Mistral Cloud Chat Modelnodes to generate the specific document types based on the input and embeddings. - Utilize Vector Store Retriever: Connect the output to the
Vector Store Retrieverto enable efficient querying of your knowledge base. Ensure your vector store is configured appropriately.
Apps Used
Workflow JSON
{
"id": "ac3ff8cf-18f4-4732-bf7e-48a5f9a71b81",
"name": "Automated Document Processing and Knowledge Extraction",
"nodes": 12,
"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: ac3ff8cf-18f4...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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