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statistics "failed" status and more metrics #4716

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opened 2026-02-21 18:07:41 -05:00 by yindo · 1 comment
yindo commented 2026-02-21 18:07:41 -05:00
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Originally created by @highestpeak on GitHub (Jul 22, 2024).

Self Checks

  • I have searched for existing issues search for existing issues, including closed ones.
  • I confirm that I am using English to submit this report (我已阅读并同意 Language Policy).
  • 请务必使用英文提交 Issue，否则会被关闭。谢谢！:）
  • Please do not modify this template :) and fill in all the required fields.

1. Is this request related to a challenge you're experiencing? Tell me about your story.

Objective:

I am configuring a workflow to provide a service that evaluates customer messages for spam. The workflow will:

  1. Accept a list of customer messages, each separated by a newline (\n). eg:
  • "I wanna lookup my order\nHow can i check my orders?\n"
  1. Use an LLM to test whether the whole message is spam.
  2. Output a JSON result in the format: {"score": "", "reason": ""}.
  • "{\n "score": 2,\n "reason": "Messages inquire about order lookup and contain minimal content."\n}"

Current Status:

  • The workflow generally works well.
  • Occasionally, the LLM outputs multiple scores for each line of the user's message.
    • "{\n "messages": [\n {\n "score": 0,\n "reason": "Customer inquiring about order status."\n },\n {\n "score": 0,\n "reason": "Customer asking how to check orders."\n },\n {\n "score": 0,\n "reason": "Customer providing email and order number."\n },\n {\n "score": 7,\n "reason": "Message is meaningless and shows no interest in store."\n }\n ]\n}"

Challenges:

  • LLM outputs are inherently uncertain, and we should not rely solely on them.
  • While I can check results or errors using code blocks, a dashboard to show mistake frequency would be more efficient.
    The logs tab is difficult to use for this purpose.

Request:

I am looking for a solution to monitor and display the frequency of these mistakes in a user-friendly dashboard.

2. Additional context or comments

No response

3. Can you help us with this feature?

  • I am interested in contributing to this feature.
Originally created by @highestpeak on GitHub (Jul 22, 2024). ### Self Checks - [X] I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones. - [X] I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)). - [X] 请务必使用英文提交 Issue，否则会被关闭。谢谢！:） - [X] Please do not modify this template :) and fill in all the required fields. ### 1. Is this request related to a challenge you're experiencing? Tell me about your story. **Objective:** I am configuring a workflow to provide a service that evaluates customer messages for spam. The workflow will: 1. Accept a list of customer messages, each separated by a newline (\n). eg: - "I wanna lookup my order\nHow can i check my orders?\n" 3. Use an LLM to test whether the whole message is spam. 5. Output a JSON result in the format: {"score": "", "reason": ""}. - "{\n \"score\": 2,\n \"reason\": \"Messages inquire about order lookup and contain minimal content.\"\n}" **Current Status:** - The workflow generally works well. - Occasionally, the LLM outputs multiple scores for each line of the user's message. - "{\n \"messages\": [\n {\n \"score\": 0,\n \"reason\": \"Customer inquiring about order status.\"\n },\n {\n \"score\": 0,\n \"reason\": \"Customer asking how to check orders.\"\n },\n {\n \"score\": 0,\n \"reason\": \"Customer providing email and order number.\"\n },\n {\n \"score\": 7,\n \"reason\": \"Message is meaningless and shows no interest in store.\"\n }\n ]\n}" **Challenges:** - LLM outputs are inherently uncertain, and we should not rely solely on them. - While I can check results or errors using code blocks, a dashboard to show mistake frequency would be more efficient. The logs tab is difficult to use for this purpose. **Request:** I am looking for a solution to monitor and display the frequency of these mistakes in a user-friendly dashboard. ### 2. Additional context or comments _No response_ ### 3. Can you help us with this feature? - [ ] I am interested in contributing to this feature.
yindo added the 💪 enhancement🌊 feat:workflow labels 2026-02-21 18:07:42 -05:00
yindo closed this issue 2026-02-21 18:07:42 -05:00
yindo commented 2026-02-21 18:07:42 -05:00
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@alterxyz commented on GitHub (Aug 16, 2024):

To improve your spam evaluation workflow:

  1. Use an Iteration node to process each message separately:

  2. Within the Iteration node, implement a Question Classifier:

Hope this helps!

@alterxyz commented on GitHub (Aug 16, 2024): To improve your spam evaluation workflow: 1. Use an Iteration node to process each message separately: - Refer to: <https://docs.dify.ai/guides/workflow/node/iteration> - This ensures individual evaluation of each input. - The Iteration node handles input and output arrays. 2. Within the Iteration node, implement a Question Classifier: - Refer to: <https://docs.dify.ai/guides/workflow/node/question-classifier> - In simple terms, a Question Classifier is a pre-prompted, LLM-based single-choice question maker. Hope this helps!
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Reference: langgenius/dify#4716
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