This is a series of blogs that shows how one can use AI with Alteryx. This second article focuses on workflow XML analysis with LLMs, this time focused on comparing workflows to see differences!
Introduction
Alteryx helps automate data processes through workflows. These workflows are saved as XML files, which capture all configurations and connections, making it easier to analyze and document them.
Building on my previous blog post, AI Meets Alteryx: Exploring XML Automation and Insights, we will explore how to compare Alteryx workflows using AI. By leveraging Large Language Models (LLMs), we can gain valuable insights from XML files to enhance our data processes.
Let’s dive into these techniques and see how AI can make your Alteryx workflows more efficient!
The Role of LLMs in XML Comparison
Large Language Models (LLMs) have transformed the way we process and analyze textual data, and their application extends to understanding structured data formats like XML. Here’s how LLMs can assist in comparing Alteryx workflows:
- Natural Language Understanding: LLMs can interpret the semantics of XML tags and attributes, allowing them to grasp the meaning behind the data rather than just its structure. This capability is essential for understanding the context of changes in workflows.
- Contextual Awareness: When comparing two XML files, LLMs can leverage their contextual understanding to identify not just differences but also the implications of those differences. For example, changing a tool configuration might have downstream effects in the workflow.
- Prompt Engineering: By crafting specific prompts, users can guide LLMs to focus on particular aspects of the XML, such as tool configurations or connections. This targeted approach can yield more relevant insights during comparisons.
- Error Detection: LLMs can assist in identifying potential errors or inconsistencies in workflows by comparing expected configurations against actual XML representations.
Before going towards the fun stuff with AI, there are some key points to consider:
- XML Structure: Dive deep into the common elements in Alteryx XML files, such as tools, connections, and configuration settings. These elements will serve as the backbone of the comparison to work.
- LLM Training: Discuss how LLMs can be fine-tuned or prompted to understand specific XML structures. LLMs are still not perfect and will require fine-tuning to get the desired output.
- Comparison Techniques: Explore different strategies for comparing XML files, including visualization of differences and optimization reports.
- User Experience: Consider how this approach can enhance user experience, especially for users who may not be familiar with XML.
Methodology
To effectively compare Alteryx workflows using LLMs, follow these steps:
Step 1: Extract XML from Alteryx Workflows
- Have two workflows that you want to compare. In this example, I created two workflows called WF1 and WF1_v2. It is a random workflow with a few tools and random procedures (as an example).
Source: Alteryx Designer Sample Workflow - caltang’s WF1
Source: Alteryx Designer Sample Workflow - caltang’s WF1_v2
Once you have two workflows, you can open them via Notepad or Notepad++ and save them as XML files like below:
Source: caltang screenshot
Step 2: Load the XML Files
- It is time to load the XML files into your LLM of choice. Once again, I will be opting to use Poe!
- The prompt which kickstarts this process is:
Prompt: “Goal: To compare the differences between two Alteryx workflows by way of XML differences. Action: I will provide you with two workflow XMLs for comparison.”
Source: caltang’s Poe screenshot
- Then, load the XMLs into the LLM with a high-level prompt:
Prompt: “WF1_xml contains the original workflow. WF1_v2_xml is the changed version of WF1. Please study the files and provide a simple breakdown of the differences at a glance.”
Source: caltang’s Poe screenshot
- Then, review the output generated by the LLM. Focus on the insights it provides regarding changes, implications, and any potential errors.
Source: caltang’s Poe screenshot
Source: caltang’s Poe screenshot
Source: caltang’s Poe screenshot
- You can try with different prompts as well! Here are some examples of prompts you could use to guide the LLM:
- “What are the significant differences between these two workflow XMLs?”
- “Explain how changes in the tool configuration might affect the overall workflow.”
- “Identify any potential errors based on the following XML structures.”
Advanced Functionalities
Visualization of Differences
To go a step beyond what was shown, there needs to be more thought about the output from this exercise. In this example, I will go for a visualization and a report from the XMLs compared.
Source: caltang’s Poe screenshot
As you can see, LLMs are still not perfect as they can generate images but with gibberish words. This will not do. Trying again:
Source: caltang’s Poe screenshot
As you can see, although some words are readable, most of the image is still gibberish. Instead of going for an image, let us go for Python code!
Source: caltang’s Poe screenshot
Now, with Python code, it is easier to test and see the code firsthand! Code refactoring and optimization can be done later - but the beauty of getting this code is that it comes with some documentation as well:
Source: caltang’s Poe screenshot
You can then try running the Python code and see your output:
Source: caltang’s Poe screenshot - Python Output
As you can see, the Python code highlights the positions of WF1 and WF1_v2 and points out the expression change done in the filter tool. To go further, more experimentation is needed!
Explanation of Differences
Visualization of the differences is helpful to give a quick overview/glance of the situation. But the devil is always in the details! Imagine a scenario where you have over 100 tools in a workflow, and each of them has changed in terms of expressions, settings, and selections within the tool configurations. It will be a nightmare to go through each one!
That is where LLMs can help! Try doing it this way:
Prompt: “Goal: I want to have a report of the differences seen in expressions, tool configurations, tool locations, and the presence of tool containers or not. Please list them out and explain what has changed, and give your analysis on the change - whether it is optimized or not - and explain why you recommend those changes.”
This is the response by the LLM:
Here's a structured report of the differences observed in the Alteryx workflows, focusing on expressions, tool configurations, tool locations, and the presence of tool containers.
Report on Workflow Changes
1. Expressions
- Original Expression: Simple mode with date conditions in the Filter tool.
- Updated Expression: Changed to Custom mode with the expression [Name] IN (...).
Analysis:
- Change: The expression was adapted from a simple filtering mechanism to a more complex custom condition, allowing for greater flexibility in data selection.
- Optimization: This change is optimized as it can handle more complex filtering criteria, making the workflow more versatile.
- Recommendation: This is a positive change as it enhances the filtering logic, allowing users to include multiple conditions in one expression, which can lead to more efficient data processing.
2. Tool Configurations
- Original Configuration: The Filter tool was set to a simple mode.
- Updated Configuration: The Filter tool is now set to custom mode.
Analysis:
- Change: The configuration has shifted from a straightforward filter to a customizable one.
- Optimization: This is an optimized change as it allows for more tailored data manipulation.
- Recommendation: It is advisable to utilize custom configurations when the data requirements are complex, as this can improve the accuracy of data outputs.
3. Tool Locations
- Original Tool Locations:
- Input: (138, 126)
- Transpose: (246, 126)
- Filter: (378, 126)
- Histogram: (522, 114)
- Browse: (630, 114)
- Updated Tool Locations:
- Step 1: Input: (89, 77)
- Step 2: Transformation: (269, 65)
- Step 3: Output: (689, 65)
- Transpose: (294, 126)
- Filter: (426, 126)
- Histogram: (570, 114)
- Browse: (714, 114)
Analysis:
- Change: Nodes have been repositioned significantly, especially the Input and Output nodes.
- Optimization: The new layout provides a clearer flow of data from Input to Output, which can enhance readability and maintenance of the workflow.
- Recommendation: Maintaining a logical flow in node placement is crucial for clarity. The updated positions are better organized, facilitating easier debugging and understanding of the workflow.
4. Presence of Tool Containers
- Original Workflow: No explicit tool containers were mentioned.
- Updated Workflow: Presence of tool containers is implied as workflows are organized into steps.
Analysis:
- Change: The updated workflow appears to have a more modular design with clear step delineations.
- Optimization: Using tool containers can help in organizing workflows and improving manageability, especially in complex projects.
- Recommendation: Implementing tool containers is advisable for larger workflows to encapsulate related tools. This organization can improve performance and clarity, allowing users to focus on specific segments of the workflow without distraction.
Overall Recommendation
The changes made in the updated workflow are largely positive, enhancing flexibility, clarity, and manageability. The shift to custom filtering allows for more robust data processing, while reorganizing tool locations improves workflow readability. Adopting tool containers can further enhance organization, especially as workflows grow in complexity.
Conclusion
These adjustments indicate a thoughtful approach to optimizing workflow efficiency and clarity in Alteryx. It is recommended to continue evaluating workflow structures regularly to ensure they meet evolving data processing needs effectively.
Conclusion
Integrating AI into Alteryx workflow analysis enhances efficiency and clarity. By using Large Language Models (LLMs) to examine XML files, we can easily identify changes and gain insights into our data processes.
This approach simplifies workflow comparisons and streamlines documentation. Embrace these techniques to optimize your Alteryx projects and improve your data management practices!
This article was inspired by my work with Vicky (@Vicky_Lau) - her questions and curiosity led me to experiment with this solution that I now use every day. Now, everyone will be able to use this as well.
