LocalFileTrigger Automate

For the LocalFileTrigger Automate, this workflow automatically monitors a designated folder for new documents, extracts their content, and generates structured notes such as study guides, timelines, and briefing documents. By leveraging AI agents, it efficiently summarizes and organizes information, enhancing learning and productivity. The generated documents are then exported alongside the original files, streamlining the documentation process for users.

7/8/2025
42 nodes
Complex
manualcomplexlocalfiletriggerlangchainsplitoutsplitinbatchesaggregatewaitconverttofilereadwritefileextractfromfilesticky noteadvancedfilesstoragelogicrouting
Categories:
Complex WorkflowManual Triggered
Integrations:
LocalFileTriggerLangChainSplitOutSplitInBatchesAggregateWaitConvertToFileReadWriteFileExtractFromFileSticky Note

Target Audience

This workflow is designed for:
- Students who need to create study materials from various sources efficiently.
- Educators looking to automate the generation of teaching materials such as study guides and timelines.
- Content Creators who require a systematic way to summarize and generate documents from raw content.
- Researchers needing to organize and synthesize large amounts of information quickly.
- Businesses that want to streamline documentation processes and improve knowledge management.

Problem Solved

This workflow addresses the challenge of manually creating study aids and summaries from various document types. It automates the process of:
- Monitoring a folder for new files.
- Extracting content from different formats such as PDF, DOCX, and TEXT.
- Summarizing the content and generating structured documents like study guides, briefing documents, and timelines.
- Storing the generated documents in a vector store for easy retrieval and further processing.

Workflow Steps

  • Local File Trigger: The workflow starts by monitoring a specified folder for new files. When a file is added, it triggers the workflow.
    2. File Import: The newly added file is imported based on its type (PDF, DOCX, TEXT).
    3. Content Extraction: Depending on the file type, the workflow extracts the content using the appropriate extraction node.
    4. Data Preparation: The extracted content is prepared for processing, including summarization and vectorization.
    5. Summarization: The workflow uses a summarization chain to condense the content into key points, making it easier to generate study materials.
    6. Vector Store Integration: The summarized content is stored in a vector database (Qdrant) for efficient retrieval.
    7. Template Generation: The workflow loops through predefined document templates (study guide, briefing document, timeline) and generates new documents based on the extracted content.
    8. AI Interaction: AI agents generate questions from the summarized content to enhance the study materials.
    9. Export: Finally, the generated documents are exported to the specified folder, ready for use.

    • Customization Guide

    Users can customize this workflow by:
    - Modifying the Folder Path: Change the path in the Local File Trigger to monitor a different directory.
    - Adding New Document Types: Extend the workflow to handle additional file formats by adding new extraction nodes.
    - Customizing Templates: Edit the document templates in the Get Doc Types node to create different types of notes or materials.
    - Adjusting Summarization Parameters: Modify the chunk size and summarization options in the Summarization Chain to fit specific content needs.
    - Integrating Additional AI Models: Users can integrate other AI models for different tasks, such as sentiment analysis or keyword extraction, enhancing the workflow's capabilities.