[Bug]: Search Error When Querying Knowledge Base In Knowledge_graph Mode

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Introduction

In this article, we will discuss a bug that occurs when querying a Knowledge Base (KB) in knowledge_graph mode. The bug results in a search error, which can be frustrating for users who rely on the KB for information retrieval. We will provide a step-by-step guide on how to reproduce the issue and offer potential solutions to resolve the problem.

Self Checks

Before submitting this bug report, we have performed the following self checks to ensure that the issue is not already reported:

  • We have searched for existing issues, including closed ones, on the GitHub repository for RAGFlow.
  • We confirm that we are using English to submit this report, as per the language policy.
  • We have not modified the template and have filled in all the required fields.

RAGFlow Workspace Code Commit ID

Unfortunately, we do not have the RAGFlow workspace code commit ID available at this time.

RAGFlow Image Version

We are using the nightly version of RAGFlow, with the commit ID f71b484d221c from the infiniflow/ragflow repository.

Other Environment Information

# Environment Information

*   Operating System: [Insert OS]
*   RAGFlow Version: [Insert version]
*   Browser: [Insert browser]
*   Browser Version: [Insert version]

Actual Behavior

When performing search operations on a Knowledge Base (KB) configured with knowledge_graph parsing mode, the system throws an error. The error details are as follows:

ERROR: KGSearch.retrieval() got an unexpected keyword argument 'aggs'

The error is accompanied by an image, which is shown below:

Image

Expected Behavior

The expected behavior is that the search operation should complete successfully, without any errors.

Steps to Reproduce

To reproduce the issue, follow these steps:

  1. Create a Knowledge Base (KB) with knowledge_graph chunk method.
  2. Navigate to the Search Page and perform a query.

Additional Information

No additional information is available at this time.

Potential Solutions

Based on the error message, it appears that the aggs keyword is not recognized by the KGSearch.retrieval() function. To resolve this issue, we can try the following potential solutions:

  • Check the documentation for the KGSearch.retrieval() function to ensure that the aggs keyword is not supported.
  • Update the RAGFlow code to remove the aggs keyword from the KGSearch.retrieval() function.
  • Provide additional information about the search query and the Knowledge Base configuration to help identify the root cause of the issue.

Conclusion

In conclusion, we have reported a bug that occurs when querying a Knowledge Base (KB) in knowledge_graph mode. The bug results in a search error, which can be frustrating for users who rely on the KB for information retrieval. We have provided a step-by-step guide on how to reproduce the issue and offered potential solutions to resolve the problem. We hope that this bug report will help the RAGFlow development team to identify and fix the issue.

Recommendations

Based on our analysis, we recommend the following:

  • Update the RAGFlow code to remove the aggs keyword from the KGSearch.retrieval() function.
  • Provide additional information about the search query and the Knowledge Base configuration to help identify the root cause of the issue.
  • Test the updated code to ensure that the issue is resolved.

Future Work

In the future, we plan to:

  • Continue to monitor the RAGFlow repository for updates and fixes.
  • Provide additional feedback and suggestions to improve the RAGFlow code.
  • Collaborate with the RAGFlow development team to resolve any issues that may arise.
    Q&A: Bug Report - Search Error When Querying Knowledge Base in Knowledge Graph Mode ====================================================================================

Introduction

In our previous article, we reported a bug that occurs when querying a Knowledge Base (KB) in knowledge_graph mode. The bug results in a search error, which can be frustrating for users who rely on the KB for information retrieval. In this article, we will provide a Q&A section to address some of the common questions and concerns related to this bug report.

Q: What is the knowledge_graph mode in RAGFlow?

A: The knowledge_graph mode in RAGFlow is a parsing method that allows users to create a Knowledge Base (KB) with a graph-based structure. This mode is useful for applications that require complex relationships between entities and concepts.

Q: What is the error message "ERROR: KGSearch.retrieval() got an unexpected keyword argument 'aggs'"?

A: The error message indicates that the KGSearch.retrieval() function is receiving an unexpected keyword argument called aggs. This argument is not recognized by the function, which is causing the error.

Q: How can I reproduce the issue?

A: To reproduce the issue, follow these steps:

  1. Create a Knowledge Base (KB) with knowledge_graph chunk method.
  2. Navigate to the Search Page and perform a query.

Q: What are the potential solutions to resolve the issue?

A: Based on the error message, we can try the following potential solutions:

  • Check the documentation for the KGSearch.retrieval() function to ensure that the aggs keyword is not supported.
  • Update the RAGFlow code to remove the aggs keyword from the KGSearch.retrieval() function.
  • Provide additional information about the search query and the Knowledge Base configuration to help identify the root cause of the issue.

Q: How can I provide additional information about the search query and the Knowledge Base configuration?

A: To provide additional information, you can:

  • Share the search query and the Knowledge Base configuration files.
  • Describe the specific steps you took to reproduce the issue.
  • Provide any relevant logs or error messages.

Q: How can I stay updated on the status of this bug report?

A: To stay updated on the status of this bug report, you can:

  • Monitor the RAGFlow repository for updates and fixes.
  • Follow the RAGFlow development team on social media or forums.
  • Subscribe to the RAGFlow newsletter or mailing list.

Q: Can I contribute to the resolution of this issue?

A: Yes, you can contribute to the resolution of this issue by:

  • Reporting any additional information or insights you may have.
  • Providing feedback on the potential solutions.
  • Collaborating with the RAGFlow development team to resolve the issue.

Conclusion

In conclusion, we hope that this Q&A section has provided valuable information and insights related to the bug report. We encourage users to continue to provide feedback and suggestions to help resolve the issue.