Code Automate

Code Automate streamlines the process of fact-checking by automatically splitting input text into sentences, analyzing each for accuracy, and generating a concise summary of errors. This efficient workflow integrates advanced language models to enhance the accuracy of content, ensuring high-quality outputs while saving time and effort in manual verification.

7/8/2025
18 nodes
Complex
manualcomplexsplitoutlangchainfilterexecuteworkflowtriggeraggregatesticky noteadvanced
Categories:
Complex WorkflowManual TriggeredBusiness Process Automation
Integrations:
SplitOutLangChainFilterExecuteWorkflowTriggerAggregateSticky Note

Target Audience

This workflow is designed for:
- Researchers: Individuals conducting studies that require precise data extraction from text, especially in ecological and conservation fields.
- Data Scientists: Professionals needing to analyze and summarize large volumes of text data for insights and reporting.
- Content Creators: Writers and editors who need to fact-check and ensure the accuracy of their articles or reports.
- Environmental Analysts: Experts focused on ecological data who require automation in processing textual information related to biodiversity and conservation.

Problem Solved

This workflow addresses the challenge of efficiently processing and analyzing large text documents to extract meaningful insights and verify factual accuracy. It automates the splitting of text into sentences, allows for detailed fact-checking, and aggregates findings into a concise summary, significantly reducing manual effort and increasing productivity in data analysis.

Workflow Steps

  • Manual Trigger: The workflow begins with a manual trigger, allowing users to initiate the process at their convenience.
    2. Edit Fields: Users input relevant data, including facts and text that need analysis.
    3. Code Node: The input text is processed to split it into individual sentences while preserving important elements like dates and lists.
    4. Sentence Splitting: The workflow uses a specialized function to ensure accurate sentence separation.
    5. Split Out: The sentences are split out for further processing.
    6. LLM Chain Integration: Each sentence is analyzed using a language model to derive insights and validate claims.
    7. Merge and Filter: The results are merged and filtered based on specific conditions, focusing on factual accuracy.
    8. Aggregation: All processed data is aggregated to provide a comprehensive overview of findings.
    9. Final Output: The workflow generates a structured summary that highlights factual inaccuracies and provides an assessment of overall accuracy.

    • Customization Guide

    Users can customize this workflow by:
    - Modifying Input Fields: Adjust the 'facts' and 'text' values in the 'Edit Fields' node to suit different topics or data sets.
    - Changing the Language Model: Users can select different models in the 'Ollama Chat Model' and 'Ollama Model' nodes to tailor the analysis based on specific requirements.
    - Adjusting Filter Conditions: Modify conditions in the 'Filter' node to focus on different criteria for fact-checking.
    - Adding Additional Nodes: Users can integrate more nodes for further processing, such as additional data sources or output formats.
    - Customizing Output Format: Change the structure of the summary in the 'Basic LLM Chain' to fit specific reporting needs.