LangChain Automate

用于LangChain,自动化深度研究工作流程,快速生成详细报告,节省数小时的人工研究时间。通过手动触发,用户输入研究主题和深度,系统自动生成相关查询,收集数据并提炼出关键学习,最终将结果保存到Notion数据库。此流程支持多层次的查询与分析,确保全面覆盖研究需求,提升研究效率。

7/8/2025
85 nodes
Complex
manualcomplexlangchainformtriggersplitoutnoopsplitinbatchesformexecuteworkflowtriggersticky noteexecuteworkflowexecutiondatanotionmarkdownfilteraggregatestopanderroradvancedlogicroutingconditionalapiintegration
Categories:
Complex WorkflowManual TriggeredBusiness Process Automation
Integrations:
LangChainFormTriggerSplitOutNoOpSplitInBatchesFormExecuteWorkflowTriggerSticky NoteExecuteWorkflowExecutionDataNotionMarkdownFilterAggregateStopAndError

Target Audience

This workflow is designed for researchers, analysts, and professionals who need to conduct in-depth research efficiently. It is particularly useful for:
- Market Researchers: Who require comprehensive data on market trends and competitors.
- Academics: Looking for detailed literature reviews and analysis.
- Business Analysts: Who need to gather insights on various topics quickly.
- Content Creators: Seeking to produce well-researched articles or reports.
- Students: Who want to streamline their research processes for assignments or projects.

Problem Solved

This workflow addresses the challenge of conducting extensive research in a time-efficient and cost-effective manner. Traditional research methods can take hours or even days, while this automated process can accomplish similar tasks in minutes. By utilizing AI and web scraping, it helps users gather relevant information, generate insights, and compile reports without manual intervention.

Workflow Steps

  • Form Submission: The process begins with the user submitting a form with their research query and specifying the depth (1-5) and breadth (1-5) for the research.
    2. Set Variables: The workflow captures the request ID, query, depth, and breadth from the form submission.
    3. Generate SERP Queries: It creates a list of search engine result page (SERP) queries based on the user's input, optimizing for unique and relevant searches.
    4. Web Scraping: The workflow employs the RAG Web Browser to scrape web content based on the generated queries, collecting relevant data.
    5. Learnings Extraction: AI models analyze the scraped content to extract concise learnings, which are then accumulated for further analysis.
    6. Clarifying Questions: If needed, the workflow generates follow-up questions to refine the research direction.
    7. Report Generation: Once all data is gathered, the workflow compiles a detailed research report in markdown format, which includes all learnings and insights.
    8. Notion Integration: Finally, the report is uploaded to a Notion page for easy access and sharing.
  • Customization Guide

    Users can customize this workflow by:
    - Adjusting Depth and Breadth: Modify the depth and breadth values in the form to control the extent of research.
    - Changing Data Sources: Integrate different web scraping services or APIs to gather data from preferred sources.
    - Modifying Output Format: Customize the report format by changing the markdown structure or integrating other output formats like PDF.
    - Adding Additional Nodes: Include additional processing nodes for further analysis or data manipulation as needed.
    - Customizing AI Models: Swap out the AI models used for generating queries or insights to better fit specific research needs.