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After upgrading dify to version 0.11.1 and uploading documents to the knowledge base, they have been stuck in the queue. The previously uploaded documents can be used in dialogues, but the new documents remain in the queue. Please help. #6941

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opened 2026-02-21 18:18:03 -05:00 by yindo · 4 comments
yindo commented 2026-02-21 18:18:03 -05:00
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Originally created by @lanqy123 on GitHub (Nov 25, 2024).

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  • 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).
  • [FOR CHINESE USERS] 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.

After upgrading dify to version 0.11.1 and uploading documents to the knowledge base, they have been stuck in the queue. The previously uploaded documents can be used in dialogues, but the new documents remain in the queue. Please help.

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 @lanqy123 on GitHub (Nov 25, 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] [FOR CHINESE USERS] 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. After upgrading dify to version 0.11.1 and uploading documents to the knowledge base, they have been stuck in the queue. The previously uploaded documents can be used in dialogues, but the new documents remain in the queue. Please help. ### 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 🐞 bug label 2026-02-21 18:18:03 -05:00
yindo closed this issue 2026-02-21 18:18:03 -05:00
yindo commented 2026-02-21 18:18:03 -05:00
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@dosubot[bot] commented on GitHub (Nov 25, 2024):

@lanqy123 Hello! I'm here to assist you with any bugs, questions, or becoming a contributor. Let's work together to resolve any issues you may have.

The issue of newly uploaded documents being stuck in the queue after upgrading to Dify version 0.11.1 might be related to the rate limit being reached for the Embedding model API key used. This is a known issue where the knowledge in the Community Edition gets stuck in 'Queued' when Q&A segmentation mode is enabled, and the rate limit for the API key might have been exceeded [1].

To address this, you should check the rate limits for your API key and ensure they are not exceeded. Additionally, you might want to verify that all necessary services, such as the Celery worker, are running correctly, as they are responsible for processing tasks like document indexing [2].

To continue talking to Dosu, mention @dosu.

@dosubot[bot] commented on GitHub (Nov 25, 2024): <!-- Greeting --> @lanqy123 Hello! I'm here to assist you with any bugs, questions, or becoming a contributor. Let's work together to resolve any issues you may have. <!-- Answer --> The issue of newly uploaded documents being stuck in the queue after upgrading to Dify version 0.11.1 might be related to the rate limit being reached for the Embedding model API key used. This is a known issue where the knowledge in the Community Edition gets stuck in 'Queued' when Q&A segmentation mode is enabled, and the rate limit for the API key might have been exceeded <sup>[[1]](https://github.com/langgenius/dify-docs/blob/main/en/guides/application-orchestrate/llms-use-faq.md)</sup>. To address this, you should check the rate limits for your API key and ensure they are not exceeded. Additionally, you might want to verify that all necessary services, such as the Celery worker, are running correctly, as they are responsible for processing tasks like document indexing <sup>[[2]](https://github.com/langgenius/dify/discussions/10602)</sup>. <!-- Dosu Comment Footer --> *To continue talking to [Dosu](https://dosu.dev), mention @dosu.*
yindo commented 2026-02-21 18:18:04 -05:00
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@lanqy123 commented on GitHub (Nov 25, 2024):

When starting with docker-compose, the following error message appears in the service. Is it related to this? and how to address this? None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.

@lanqy123 commented on GitHub (Nov 25, 2024): When starting with docker-compose, the following error message appears in the service. Is it related to this? and how to address this? None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
yindo commented 2026-02-21 18:18:04 -05:00
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@dosubot[bot] commented on GitHub (Nov 25, 2024):

The error message you're encountering indicates that none of the machine learning frameworks (PyTorch, TensorFlow, or Flax) are available in your Docker environment. This could affect the availability of models, which might be related to the issue of documents being stuck in the queue.

To address this, you should ensure that the necessary dependencies for these frameworks are installed and configured correctly in your Docker setup. Here are some steps you can take:

  1. Check Docker Configuration: Ensure that your Docker images are set up to include the necessary machine learning libraries. You might need to modify the Dockerfile to include installations for PyTorch, TensorFlow, or Flax.

  2. Install Required Libraries: If the Docker image does not include these libraries, you can manually install them by adding the appropriate installation commands to your Dockerfile. For example, you can use pip to install PyTorch or TensorFlow.

  3. Rebuild Docker Images: After modifying the Dockerfile, rebuild your Docker images to ensure the changes take effect:

    docker-compose build

  4. Restart Docker Services: Once the images are rebuilt, restart your Docker services:

    docker-compose down
docker-compose up -d

By ensuring that these frameworks are available, you should be able to resolve the issue with model availability, which might help with the document processing problem you're experiencing.

To continue talking to Dosu, mention @dosu.

@dosubot[bot] commented on GitHub (Nov 25, 2024): <!-- Answer --> The error message you're encountering indicates that none of the machine learning frameworks (PyTorch, TensorFlow, or Flax) are available in your Docker environment. This could affect the availability of models, which might be related to the issue of documents being stuck in the queue. To address this, you should ensure that the necessary dependencies for these frameworks are installed and configured correctly in your Docker setup. Here are some steps you can take: 1. **Check Docker Configuration**: Ensure that your Docker images are set up to include the necessary machine learning libraries. You might need to modify the Dockerfile to include installations for PyTorch, TensorFlow, or Flax. 2. **Install Required Libraries**: If the Docker image does not include these libraries, you can manually install them by adding the appropriate installation commands to your Dockerfile. For example, you can use pip to install PyTorch or TensorFlow. 3. **Rebuild Docker Images**: After modifying the Dockerfile, rebuild your Docker images to ensure the changes take effect: ```bash docker-compose build ``` 4. **Restart Docker Services**: Once the images are rebuilt, restart your Docker services: ```bash docker-compose down docker-compose up -d ``` By ensuring that these frameworks are available, you should be able to resolve the issue with model availability, which might help with the document processing problem you're experiencing. <!-- Dosu Comment Footer --> *To continue talking to [Dosu](https://dosu.dev), mention @dosu.*
yindo commented 2026-02-21 18:18:04 -05:00
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@crazywoola commented on GitHub (Nov 25, 2024):

  • Check the logs of the worker container
  • Use a bug report template instead
@crazywoola commented on GitHub (Nov 25, 2024): - Check the logs of the worker container - Use a bug report template instead
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Reference: langgenius/dify#6941
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