Code Automate

This workflow is designed for researchers, data scientists, and professionals in the field of ecological conservation and environmental science. It is particularly useful for those who need to analyze text data, fact-check statements, and derive insights from complex documents. Additionally, it can benefit educators and students in academia who are looking to automate their data processing and analysis tasks.

The workflow addresses the challenge of extracting meaningful insights from lengthy texts while ensuring factual accuracy. It automates the process of splitting text into sentences, analyzing claims, and identifying inaccuracies, thus saving time and reducing manual effort in fact-checking. This is especially crucial in fields where accurate information is vital for decision-making and research.

1. Manual Trigger: The workflow starts when the user manually initiates it.
2. Edit Fields: Users can input relevant text and facts that need to be analyzed.
3. Code Node: The input text is processed to split it into individual sentences, ensuring that dates and list items are preserved.
4. Merge Node: The sentences are merged for further processing.
5. Split Out: The sentences are split out for individual analysis.
6. Basic LLM Chain: Each claim is processed through a language model to assess its validity based on the provided facts.
7. Filter: The workflow filters out any irrelevant data based on predefined conditions.
8. Aggregate: The results from the analysis are aggregated for a comprehensive overview.
9. Final LLM Chain: A final language model evaluates the aggregated data and provides a summary of factual inaccuracies.
10. Output: The workflow outputs a structured summary, highlighting the number of incorrect statements and providing a final assessment of the text's accuracy.