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Code Issues 2.4k Packages Projects Releases 129 Wiki Activity

[GH-ISSUE #13969] issue: Duplicate text in code intepreter block if expanded when model is writing code #55756

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opened 2026-05-05 17:57:21 -05:00 by GiteaMirror · 3 comments
GiteaMirror commented 2026-05-05 17:57:21 -05:00
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Originally created by @i0ntempest on GitHub (May 16, 2025).
Original GitHub issue: https://github.com/open-webui/open-webui/issues/13969

Check Existing Issues

  • I have searched the existing issues and discussions.
  • I am using the latest version of Open WebUI.

Installation Method

Pip Install

Open WebUI Version

0.6.9

Ollama Version (if applicable)

No response

Operating System

macOS 14.5

Browser (if applicable)

Safari 18.4

Confirmation

  • I have read and followed all instructions in README.md.
  • I am using the latest version of both Open WebUI and Ollama.
  • I have included the browser console logs.
  • I have included the Docker container logs.
  • I have listed steps to reproduce the bug in detail.

Expected Behavior

There should not be duplicate text

Actual Behavior

Duplicate text in code intepreter block if expanded when model is writing code

Steps to Reproduce

  1. Give a model a task that involves calculation with code, and enable code intepreter
  2. When model is generating code and "Analyzing" is shown, expand the block
  3. You will see the code is being displayed incorrectly, with some duplicated text.
  4. After the task is completed, collapse and expand the block again. The code should be OK now.

Logs & Screenshots

Garbled code:
Image

Correct Code:

Image

Additional Information

[Warning] 2460 console messages are not shown.
[Log] Object (CxQ4wiE7.js, line 148)
[Log] Object (CxQ4wiE7.js, line 147)
[Log] Object (CxQ4wiE7.js, line 148)
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[Log] Object (CxQ4wiE7.js, line 148)
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[Log] import math (BlRZIbBZ.js, line 15)

# 单块硬盘 AFR
afr_single = 0.0073  # 即年失败率 0.73%

# 每块盘的成功率
success_single = 1 - afr_single

# 共有3块盘
n = 3

# RAID5 在三盘下，失效条件是同时有2块或更多盘失败
# 可容忍1块失败，即只要有2块或3块盘失败，则RAID5失败

# 使用二项分布概率计算方法
from math import comb

# P(all success) = 0失败
p_0_failed = success_single ** n

# P(1 failure) = 1块失败
p_1_failed = comb(n, 1) * (afr_single) * (success_single ** (n - 1))

# P(2 fail) + P(3 fail) = RAID5失败概率
p_2_failed = comb(n, 2) * (afr_single ** 2) * (success_single)
p_3_failed = comb(n, 3) * (afr_single ** 3)

# RAID5 整体 AFR，近似为P(2 failed) + P(3 failed)
afr_raid5 = p_2_failed + p_3_failed

# 将概率乘以100，转为百分比
afr_raid5_percent = afr_raid5 * 100

print(f"RAID5 (3块盘, 每盘AFR为0.73%) 的年故障率约为: {afr_raid5_percent:.5f}%")
[Log] codeblock – "python" – "import math↵↵# 单块硬盘 AFR↵afr_single = 0.0073  # 即年失败率 0.73%↵↵# 每块盘的成功率↵success_single = 1 - afr_single↵↵# 共有3块盘↵n = 3↵↵# RAID…" (FIeECii7.js, line 229)
"import math

# 单块硬盘 AFR
afr_single = 0.0073  # 即年失败率 0.73%

# 每块盘的成功率
success_single = 1 - afr_single

# 共有3块盘
n = 3

# RAID5 在三盘下，失效条件是同时有2块或更多盘失败
# 可容忍1块失败，即只要有2块或3块盘失败，则RAID5失败

# 使用二项分布概率计算方法
from math import comb

# P(all success) = 0失败
p_0_failed = success_single ** n

# P(1 failure) = 1块失败
p_1_failed = comb(n, 1) * (afr_single) * (success_single ** (n - 1))

# P(2 fail) + P(3 fail) = RAID5失败概率
p_2_failed = comb(n, 2) * (afr_single ** 2) * (success_single)
p_3_failed = comb(n, 3) * (afr_single ** 3)

# RAID5 整体 AFR，近似为P(2 failed) + P(3 failed)
afr_raid5 = p_2_failed + p_3_failed

# 将概率乘以100，转为百分比
afr_raid5_percent = afr_raid5 * 100

print(f\"RAID5 (3块盘, 每盘AFR为0.73%) 的年故障率约为: {afr_raid5_percent:.5f}%\")"
[Log] import math (BlRZIbBZ.js, line 15)

# 单块硬盘 AFR
afr_single = 0.0073  # 即年失败率 0.73%

# 每块盘的成功率
success_single = 1 - afr_single

# 共有3块盘
n = 3

# RAID5 在三盘下，失效条件是同时有2块或更多盘失败
# 可容忍1块失败，即只要有2块或3块盘失败，则RAID5失败

# 使用二项分布概率计算方法
from math import comb

# P(all success) = 0失败
p_0_failed = success_single ** n

# P(1 failure) = 1块失败
p_1_failed = comb(n, 1) * (afr_single) * (success_single ** (n - 1))

# P(2 fail) + P(3 fail) = RAID5失败概率
p_2_failed = comb(n, 2) * (afr_single ** 2) * (success_single)
p_3_failed = comb(n, 3) * (afr_single ** 3)

# RAID5 整体 AFR，近似为P(2 failed) + P(3 failed)
afr_raid5 = p_2_failed + p_3_failed

# 将概率乘以100，转为百分比
afr_raid5_percent = afr_raid5 * 100

print(f"RAID5 (3块盘, 每盘AFR为0.73%) 的年故障率约为: {afr_raid5_percent:.5f}%")
[Log] codeblock – "python" – "import math↵↵# 单块硬盘 AFR↵afr_single = 0.0073  # 即年失败��� 0.73%↵↵# 每块盘的成功率↵success_single = 1 - afr_single↵↵# 共有3块盘↵n = 3↵↵# RAID…" (FIeECii7.js, line 229)
"import math

# 单块硬盘 AFR
afr_single = 0.0073  # 即年失败率 0.73%

# 每块盘的成功率
success_single = 1 - afr_single

# 共有3块盘
n = 3

# RAID5 在三盘下，失效条件是同时有2块或更多盘失败
# 可容忍1块失败，即只要有2块或3块盘失败，则RAID5失败

# 使用二项分布概率计算方法
from math import comb

# P(all success) = 0失败
p_0_failed = success_single ** n

# P(1 failure) = 1块失败
p_1_failed = comb(n, 1) * (afr_single) * (success_single ** (n - 1))

# P(2 fail) + P(3 fail) = RAID5失败概率
p_2_failed = comb(n, 2) * (afr_single ** 2) * (success_single)
p_3_failed = comb(n, 3) * (afr_single ** 3)

# RAID5 整体 AFR，近似为P(2 failed) + P(3 failed)
afr_raid5 = p_2_failed + p_3_failed

# 将概率乘以100，转为百分比
afr_raid5_percent = afr_raid5 * 100

print(f\"RAID5 (3块盘, 每盘AFR为0.73%) 的年故障率约为: {afr_raid5_percent:.5f}%\")"
[Log] Object (2.DNUGnOXE.js, line 84)
[Log] false (2.DNUGnOXE.js, line 84)
[Log] destroy (CxQ4wiE7.js, line 15)
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[Log] Object (CxQ4wiE7.js, line 147)
[Log] saveSessionSelectedModels (2) (CxQ4wiE7.js, line 147)
Array (1)
"[\"OpenAI.chatgpt-4o-latest\"]"
[Log] saveSessionSelectedModels (2) (CxQ4wiE7.js, line 147)
Array (1)
"[\"OpenAI.chatgpt-4o-latest\"]"
[Log] saveSessionSelectedModels (2) (CxQ4wiE7.js, line 147)
Array (1)
"[\"OpenAI.chatgpt-4o-latest\"]"
[Log] size – 0 – 32 (CxQ4wiE7.js, line 141)
[Log] <div class="flex justify-start overflow-x-auto buttons text-gray-600 dark:text-gray-500 mt-0.5 svelte-1u5gq5j">…</div> (BEquo4v8.js, line 92)

[Log] content – "" (DD1pH8IM.js, line 204)
[Warning] [tiptap warn]: Duplicate extension names found: ['codeBlock']. This can lead to issues. (DD1pH8IM.js, line 113)
[Log] destroy (CxQ4wiE7.js, line 15)
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[Log] size – 0 – 32 (CxQ4wiE7.js, line 141)
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[Warning] [tiptap warn]: Duplicate extension names found: ['codeBlock']. This can lead to issues. (DD1pH8IM.js, line 113)
[Log] user-list – Object (0.CmToBqOU.js, line 1)
Object
[Log] usage – Object (0.CmToBqOU.js, line 1)
Object
Originally created by @i0ntempest on GitHub (May 16, 2025). Original GitHub issue: https://github.com/open-webui/open-webui/issues/13969 ### Check Existing Issues - [x] I have searched the existing issues and discussions. - [x] I am using the latest version of Open WebUI. ### Installation Method Pip Install ### Open WebUI Version 0.6.9 ### Ollama Version (if applicable) _No response_ ### Operating System macOS 14.5 ### Browser (if applicable) Safari 18.4 ### Confirmation - [x] I have read and followed all instructions in `README.md`. - [x] I am using the latest version of **both** Open WebUI and Ollama. - [x] I have included the browser console logs. - [x] I have included the Docker container logs. - [x] I have listed steps to reproduce the bug in detail. ### Expected Behavior There should not be duplicate text ### Actual Behavior Duplicate text in code intepreter block if expanded when model is writing code ### Steps to Reproduce 1. Give a model a task that involves calculation with code, and enable code intepreter 2. When model is generating code and "Analyzing" is shown, expand the block 3. You will see the code is being displayed incorrectly, with some duplicated text. 4. After the task is completed, collapse and expand the block again. The code should be OK now. ### Logs & Screenshots Garbled code: ![Image](https://github.com/user-attachments/assets/ce803323-c009-41f1-90e3-315cb0eac0a1) Correct Code: ![Image](https://github.com/user-attachments/assets/845de837-e4f3-4942-9361-41ad59a1bea7) ### Additional Information ``` [Warning] 2460 console messages are not shown. [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 148) [Log] Object (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 147) [Log] import math (BlRZIbBZ.js, line 15) # 单块硬盘 AFR afr_single = 0.0073 # 即年失败率 0.73% # 每块盘的成功率 success_single = 1 - afr_single # 共有3块盘 n = 3 # RAID5 在三盘下，失效条件是同时有2块或更多盘失败 # 可容忍1块失败，即只要有2块或3块盘失败，则RAID5失败 # 使用二项分布概率计算方法 from math import comb # P(all success) = 0失败 p_0_failed = success_single ** n # P(1 failure) = 1块失败 p_1_failed = comb(n, 1) * (afr_single) * (success_single ** (n - 1)) # P(2 fail) + P(3 fail) = RAID5失败概率 p_2_failed = comb(n, 2) * (afr_single ** 2) * (success_single) p_3_failed = comb(n, 3) * (afr_single ** 3) # RAID5 整体 AFR，近似为P(2 failed) + P(3 failed) afr_raid5 = p_2_failed + p_3_failed # 将概率乘以100，转为百分比 afr_raid5_percent = afr_raid5 * 100 print(f"RAID5 (3块盘, 每盘AFR为0.73%) 的年故障率约为: {afr_raid5_percent:.5f}%") [Log] codeblock – "python" – "import math↵↵# 单块硬盘 AFR↵afr_single = 0.0073 # 即年失败率 0.73%↵↵# 每块盘的成功率↵success_single = 1 - afr_single↵↵# 共有3块盘↵n = 3↵↵# RAID…" (FIeECii7.js, line 229) "import math # 单块硬盘 AFR afr_single = 0.0073 # 即年失败率 0.73% # 每块盘的成功率 success_single = 1 - afr_single # 共有3块盘 n = 3 # RAID5 在三盘下，失效条件是同时有2块或更多盘失败 # 可容忍1块失败，即只要有2块或3块盘失败，则RAID5失败 # 使用二项分布概率计算方法 from math import comb # P(all success) = 0失败 p_0_failed = success_single ** n # P(1 failure) = 1块失败 p_1_failed = comb(n, 1) * (afr_single) * (success_single ** (n - 1)) # P(2 fail) + P(3 fail) = RAID5失败概率 p_2_failed = comb(n, 2) * (afr_single ** 2) * (success_single) p_3_failed = comb(n, 3) * (afr_single ** 3) # RAID5 整体 AFR，近似为P(2 failed) + P(3 failed) afr_raid5 = p_2_failed + p_3_failed # 将概率乘以100，转为百分比 afr_raid5_percent = afr_raid5 * 100 print(f\"RAID5 (3块盘, 每盘AFR为0.73%) 的年故障率约为: {afr_raid5_percent:.5f}%\")" [Log] import math (BlRZIbBZ.js, line 15) # 单块硬盘 AFR afr_single = 0.0073 # 即年失败率 0.73% # 每块盘的成功率 success_single = 1 - afr_single # 共有3块盘 n = 3 # RAID5 在三盘下，失效条件是同时有2块或更多盘失败 # 可容忍1块失败，即只要有2块或3块盘失败，则RAID5失败 # 使用二项分布概率计算方法 from math import comb # P(all success) = 0失败 p_0_failed = success_single ** n # P(1 failure) = 1块失败 p_1_failed = comb(n, 1) * (afr_single) * (success_single ** (n - 1)) # P(2 fail) + P(3 fail) = RAID5失败概率 p_2_failed = comb(n, 2) * (afr_single ** 2) * (success_single) p_3_failed = comb(n, 3) * (afr_single ** 3) # RAID5 整体 AFR，近似为P(2 failed) + P(3 failed) afr_raid5 = p_2_failed + p_3_failed # 将概率乘以100，转为百分比 afr_raid5_percent = afr_raid5 * 100 print(f"RAID5 (3块盘, 每盘AFR为0.73%) 的年故障率约为: {afr_raid5_percent:.5f}%") [Log] codeblock – "python" – "import math↵↵# 单块硬盘 AFR↵afr_single = 0.0073 # 即年失败��� 0.73%↵↵# 每块盘的成功率↵success_single = 1 - afr_single↵↵# 共有3块盘↵n = 3↵↵# RAID…" (FIeECii7.js, line 229) "import math # 单块硬盘 AFR afr_single = 0.0073 # 即年失败率 0.73% # 每块盘的成功率 success_single = 1 - afr_single # 共有3块盘 n = 3 # RAID5 在三盘下，失效条件是同时有2块或更多盘失败 # 可容忍1块失败，即只要有2块或3块盘失败，则RAID5失败 # 使用二项分布概率计算方法 from math import comb # P(all success) = 0失败 p_0_failed = success_single ** n # P(1 failure) = 1块失败 p_1_failed = comb(n, 1) * (afr_single) * (success_single ** (n - 1)) # P(2 fail) + P(3 fail) = RAID5失败概率 p_2_failed = comb(n, 2) * (afr_single ** 2) * (success_single) p_3_failed = comb(n, 3) * (afr_single ** 3) # RAID5 整体 AFR，近似为P(2 failed) + P(3 failed) afr_raid5 = p_2_failed + p_3_failed # 将概率乘以100，转为百分比 afr_raid5_percent = afr_raid5 * 100 print(f\"RAID5 (3块盘, 每盘AFR为0.73%) 的年故障率约为: {afr_raid5_percent:.5f}%\")" [Log] Object (2.DNUGnOXE.js, line 84) [Log] false (2.DNUGnOXE.js, line 84) [Log] destroy (CxQ4wiE7.js, line 15) [Log] mounted (CxQ4wiE7.js, line 147) [Log] Object (CxQ4wiE7.js, line 147) [Log] saveSessionSelectedModels (2) (CxQ4wiE7.js, line 147) Array (1) "[\"OpenAI.chatgpt-4o-latest\"]" [Log] saveSessionSelectedModels (2) (CxQ4wiE7.js, line 147) Array (1) "[\"OpenAI.chatgpt-4o-latest\"]" [Log] saveSessionSelectedModels (2) (CxQ4wiE7.js, line 147) Array (1) "[\"OpenAI.chatgpt-4o-latest\"]" [Log] size – 0 – 32 (CxQ4wiE7.js, line 141) [Log] <div class="flex justify-start overflow-x-auto buttons text-gray-600 dark:text-gray-500 mt-0.5 svelte-1u5gq5j">…</div> (BEquo4v8.js, line 92) [Log] content – "" (DD1pH8IM.js, line 204) [Warning] [tiptap warn]: Duplicate extension names found: ['codeBlock']. This can lead to issues. (DD1pH8IM.js, line 113) [Log] destroy (CxQ4wiE7.js, line 15) [Log] mounted (CxQ4wiE7.js, line 147) [Log] size – 0 – 32 (CxQ4wiE7.js, line 141) [Log] content – "" (DD1pH8IM.js, line 204) [Warning] [tiptap warn]: Duplicate extension names found: ['codeBlock']. This can lead to issues. (DD1pH8IM.js, line 113) [Log] user-list – Object (0.CmToBqOU.js, line 1) Object [Log] usage – Object (0.CmToBqOU.js, line 1) Object ```
GiteaMirror added the bug label 2026-05-05 17:57:21 -05:00
GiteaMirror commented 2026-05-05 17:57:23 -05:00
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@tjbck commented on GitHub (May 16, 2025):

Please provide a concrete step-by-step way to reproduce!

<!-- gh-comment-id:2887145312 --> @tjbck commented on GitHub (May 16, 2025): Please provide a concrete step-by-step way to reproduce!
GiteaMirror commented 2026-05-05 17:57:24 -05:00
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@i0ntempest commented on GitHub (May 16, 2025):

Please provide a concrete step-by-step way to reproduce!

Uh I did though?
I'm using chatgpt 4o external model, If you want my prompt: Assume a single disk annual failure rate of 0.73% and ignore failure rate of the disk enclosure. Calculate at what number of disks the AFR of a RAID5 array overtakes the single disk AFR, and draw a plot. You may use code intepreter.

<!-- gh-comment-id:2887198012 --> @i0ntempest commented on GitHub (May 16, 2025): > Please provide a concrete step-by-step way to reproduce! Uh I did though? I'm using chatgpt 4o external model, If you want my prompt: `Assume a single disk annual failure rate of 0.73% and ignore failure rate of the disk enclosure. Calculate at what number of disks the AFR of a RAID5 array overtakes the single disk AFR, and draw a plot. You may use code intepreter.`
GiteaMirror commented 2026-05-05 17:57:25 -05:00
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@tjbck commented on GitHub (May 16, 2025):

Can't seem to reproduce on our end, keep us updated!

<!-- gh-comment-id:2887224417 --> @tjbck commented on GitHub (May 16, 2025): Can't seem to reproduce on our end, keep us updated!
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