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The knowledge base paging query is confused #5724

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opened 2026-02-21 18:12:19 -05:00 by yindo · 3 comments
yindo commented 2026-02-21 18:12:19 -05:00
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Originally created by @lvxinliang on GitHub (Sep 21, 2024).

Self Checks

  • This is only for bug report, if you would like to ask a question, please head to Discussions.
  • 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.

Dify version

0.6.16

Cloud or Self Hosted

Self Hosted (Docker)

Steps to reproduce

When multiple documents are upload at the same time, confusion occurs when the Document is queried

✔️ Expected Behavior

There are no duplicate records for different pages

❌ Actual Behavior

Different pages have the same result

Originally created by @lvxinliang on GitHub (Sep 21, 2024). ### Self Checks - [X] This is only for bug report, if you would like to ask a question, please head to [Discussions](https://github.com/langgenius/dify/discussions/categories/general). - [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. ### Dify version 0.6.16 ### Cloud or Self Hosted Self Hosted (Docker) ### Steps to reproduce When multiple documents are upload at the same time, confusion occurs when the Document is queried ### ✔️ Expected Behavior There are no duplicate records for different pages ### ❌ Actual Behavior Different pages have the same result
yindo added the 🐞 bug label 2026-02-21 18:12:19 -05:00
yindo closed this issue 2026-02-21 18:12:19 -05:00
yindo commented 2026-02-21 18:12:19 -05:00
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@dosubot[bot] commented on GitHub (Sep 21, 2024):

I found a similar open issue and discussion that might be relevant to your problem:

  1. Can dify support using knowledge base via Annual Report or Annual Document of many different year - This discussion is still open and might provide some insights.
  2. Error in using knowledge base recall test - This issue is closed but was referenced by another issue related to validation errors.

To resolve the issue of different pages having the same result when querying multiple documents in the knowledge base using Dify version 0.6.16, you should review and configure the retrieval strategy appropriately. Specifically, you need to ensure that the correct recall strategy is being used.

Dify offers two recall strategies:

  1. N-to-1 Recall: This mode executes knowledge base queries through function calling and requires the selection of a system reasoning model.
  2. Multi-way Recall: This mode requires configuring a Rerank model for result re-ranking.

Steps to Configure Retrieval Strategy

  1. Create Knowledge Base & Upload Documents:

    • Segment and clean the document, and preview the effect.
    • Choose and configure Index Mode and Retrieval Settings.
    • Wait for the chunks to be embedded.

  2. Text Preprocessing and Cleaning:

    • Use either Automatic or Custom mode for chunking and cleaning the text.
    • Ensure the text is properly segmented and cleaned to improve recall accuracy.

  3. Indexing Mode:

    • Choose the appropriate indexing method (High-Quality, Economical, or Q&A Mode).
    • Configure the retrieval settings based on the indexing method.

  4. Retrieval Settings:

    • In High-Quality indexing mode, choose between Vector Search, Full-Text Search, or Hybrid Search.
    • Configure the Rerank Model, TopK, and Score Threshold settings to optimize retrieval results.

Connecting Knowledge and Setting Retrieval Mode

In applications that utilize multiple knowledge bases, it is essential to configure the retrieval mode to enhance the precision of retrieved content. To set the retrieval mode for the knowledge bases, navigate to Context -- Retrieval Settings -- Rerank Setting.

The retriever scans all knowledge bases linked to the application for text content relevant to the user's question. The results are then consolidated. Below is the technical flowchart for the Multi-path Retrieval mode:

This method simultaneously queries all knowledge bases connected in "Context", seeking relevant text chunks across multiple knowledge bases, collecting all content that aligns with the user's question, and ultimately applying the Rerank strategy to identify the most appropriate content to respond to the user. This retrieval approach offers more comprehensive and accurate results by leveraging multiple knowledge bases simultaneously.

For instance, in application A, with three knowledge bases K1, K2, and K3. When a user sends a question, multiple relevant pieces of content will be retrieved and combined from these knowledge bases. To ensure the most pertinent content is identified, the Rerank strategy is employed to find the content that best relates to the user's query, enhancing the precision and reliability of the results.

In practical Q&A scenarios, the sources of content and retrieval methods for each knowledge base may differ. To manage the mixed content returned from retrieval, the Rerank strategy acts as a refined sorting mechanism. It ensures that the candidate content aligns well with the user's question, optimizing the ranking of results across multiple knowledge bases to identify the most suitable content, thereby improving answer quality and overall user experience.

Considering the costs associated with using Rerank and the needs of the business, the multi-path retrieval mode provides two Rerank settings:

Weighted Score

This setting uses internal scoring mechanisms and does not require an external Rerank model, thus avoiding any additional processing costs. You can select the most appropriate content matching strategy by adjusting the weight ratio sliders for semantics or keywords.

  • Semantic Value of 1

    This mode activates semantic retrieval only. By utilizing the Embedding model, the search depth can be enhanced even if the exact words from the query do not appear in the knowledge base, as it calculates vector distances to return the relevant content. Furthermore, when dealing with multilingual content, semantic retrieval can capture meanings across different languages, yielding more accurate cross-language search results.

  • Keyword Value of 1

    This mode activates keyword retrieval only. It matches the user's input text against the full text of the knowledge base, making it ideal for scenarios where the user knows the exact information or terminology. This method is resource-efficient, making it suitable for quickly retrieving information from large document repositories.

  • Custom Keyword and Semantic Weights

    In addition to enabling only semantic or keyword retrieval modes, we offer flexible custom Weight Score. You can determine the best weight ratio for your business scenario by continuously adjusting the weights of both.

Rerank Model

The Rerank model is an external scoring system that calculates the relevance score between the user's question and each candidate document provided, improving the results of semantic ranking and returning a list of documents sorted by relevance from high to low.

While this method incurs some additional costs, it is more adept at handling complex knowledge base content, such as content that combines semantic queries and keyword matches, or cases involving multilingual returned content.

Click here to learn more about the Re-ranking.

Dify currently supports multiple Rerank models. To use external Rerank models, you'll need to provide an API Key. Enter the API Key for the Rerank model (such as Cohere, Jina, etc.) on the "Model Provider" page.

Configuring the Rerank model in the Model Provider

Adjustable Parameters

  • TopK

    This parameter filters the text segments that are most similar to the user's question. The system dynamically adjusts the number of segments based on the context window size of the selected model. A higher value results in more text segments being recalled.

  • Score Threshold

    This parameter establishes the similarity threshold for filtering text segments. Only those segments with a vector retrieval similarity score exceeding the set threshold will be recalled. A higher threshold value results in fewer texts being recalled, but those recalled are likely to be more relevant. Adjust this parameter based on your specific needs for precision versus recall.

The multi-recall mode can achieve higher quality recall results when retrieving from multiple knowledge bases; therefore, it is recommended to set the recall mode to multi-recall.

By ensuring the correct configuration of these settings, you should be able to resolve the issue of different pages having the same result when querying multiple documents [1][2][3][4].

To continue talking to Dosu, mention @dosu.

@dosubot[bot] commented on GitHub (Sep 21, 2024): <!-- Answer --> I found a similar open issue and discussion that might be relevant to your problem: 1. [Can dify support using knowledge base via Annual Report or Annual Document of many different year](https://github.com/langgenius/dify/discussions/6226) - This discussion is still open and might provide some insights. 2. [Error in using knowledge base recall test](https://github.com/langgenius/dify/issues/5350) - This issue is closed but was referenced by another issue related to validation errors. To resolve the issue of different pages having the same result when querying multiple documents in the knowledge base using Dify version 0.6.16, you should review and configure the retrieval strategy appropriately. Specifically, you need to ensure that the correct recall strategy is being used. Dify offers two recall strategies: 1. **N-to-1 Recall**: This mode executes knowledge base queries through function calling and requires the selection of a system reasoning model. 2. **Multi-way Recall**: This mode requires configuring a Rerank model for result re-ranking. ### Steps to Configure Retrieval Strategy 1. **Create Knowledge Base & Upload Documents**: - Segment and clean the document, and preview the effect. - Choose and configure Index Mode and Retrieval Settings. - Wait for the chunks to be embedded. 2. **Text Preprocessing and Cleaning**: - Use either Automatic or Custom mode for chunking and cleaning the text. - Ensure the text is properly segmented and cleaned to improve recall accuracy. 3. **Indexing Mode**: - Choose the appropriate indexing method (High-Quality, Economical, or Q&A Mode). - Configure the retrieval settings based on the indexing method. 4. **Retrieval Settings**: - In High-Quality indexing mode, choose between Vector Search, Full-Text Search, or Hybrid Search. - Configure the Rerank Model, TopK, and Score Threshold settings to optimize retrieval results. ### Connecting Knowledge and Setting Retrieval Mode In applications that utilize multiple knowledge bases, it is essential to configure the retrieval mode to enhance the precision of retrieved content. To set the retrieval mode for the knowledge bases, navigate to **Context -- Retrieval Settings -- Rerank Setting**. The retriever scans all knowledge bases linked to the application for text content relevant to the user's question. The results are then consolidated. Below is the technical flowchart for the Multi-path Retrieval mode: <figure><img src="../../../img/rerank-flow-chart.png" alt=""><figcaption></figcaption></figure> This method simultaneously queries all knowledge bases connected in **"Context"**, seeking relevant text chunks across multiple knowledge bases, collecting all content that aligns with the user's question, and ultimately applying the Rerank strategy to identify the most appropriate content to respond to the user. This retrieval approach offers more comprehensive and accurate results by leveraging multiple knowledge bases simultaneously. <figure><img src="../../../img/en-rag-multiple.png" alt=""><figcaption></figcaption></figure> For instance, in application A, with three knowledge bases K1, K2, and K3. When a user sends a question, multiple relevant pieces of content will be retrieved and combined from these knowledge bases. To ensure the most pertinent content is identified, the Rerank strategy is employed to find the content that best relates to the user's query, enhancing the precision and reliability of the results. In practical Q&A scenarios, the sources of content and retrieval methods for each knowledge base may differ. To manage the mixed content returned from retrieval, the [Rerank strategy](https://docs.dify.ai/learn-more/extended-reading/retrieval-augment/rerank) acts as a refined sorting mechanism. It ensures that the candidate content aligns well with the user's question, optimizing the ranking of results across multiple knowledge bases to identify the most suitable content, thereby improving answer quality and overall user experience. Considering the costs associated with using Rerank and the needs of the business, the multi-path retrieval mode provides two Rerank settings: #### Weighted Score This setting uses internal scoring mechanisms and does not require an external Rerank model, thus **avoiding any additional processing costs**. You can select the most appropriate content matching strategy by adjusting the weight ratio sliders for semantics or keywords. - **Semantic Value of 1** This mode activates semantic retrieval only. By utilizing the Embedding model, the search depth can be enhanced even if the exact words from the query do not appear in the knowledge base, as it calculates vector distances to return the relevant content. Furthermore, when dealing with multilingual content, semantic retrieval can capture meanings across different languages, yielding more accurate cross-language search results. - **Keyword Value of 1** This mode activates keyword retrieval only. It matches the user's input text against the full text of the knowledge base, making it ideal for scenarios where the user knows the exact information or terminology. This method is resource-efficient, making it suitable for quickly retrieving information from large document repositories. - **Custom Keyword and Semantic Weights** In addition to enabling only semantic or keyword retrieval modes, we offer flexible custom Weight Score. You can determine the best weight ratio for your business scenario by continuously adjusting the weights of both. #### Rerank Model The Rerank model is an external scoring system that calculates the relevance score between the user's question and each candidate document provided, improving the results of semantic ranking and returning a list of documents sorted by relevance from high to low. While this method incurs some additional costs, it is more adept at handling complex knowledge base content, such as content that combines semantic queries and keyword matches, or cases involving multilingual returned content. > Click here to learn more about the [Re-ranking](https://docs.dify.ai/learn-more/extended-reading/retrieval-augment/rerank). Dify currently supports multiple Rerank models. To use external Rerank models, you'll need to provide an API Key. Enter the API Key for the Rerank model (such as Cohere, Jina, etc.) on the "Model Provider" page. <figure><img src="../../../img/en-rerank-model-api.png" alt=""><figcaption><p>Configuring the Rerank model in the Model Provider</p></figcaption></figure> ##### Adjustable Parameters - **TopK** This parameter filters the text segments that are most similar to the user's question. The system dynamically adjusts the number of segments based on the context window size of the selected model. A higher value results in more text segments being recalled. - **Score Threshold** This parameter establishes the similarity threshold for filtering text segments. Only those segments with a vector retrieval similarity score exceeding the set threshold will be recalled. A higher threshold value results in fewer texts being recalled, but those recalled are likely to be more relevant. Adjust this parameter based on your specific needs for precision versus recall. The multi-recall mode can achieve higher quality recall results when retrieving from multiple knowledge bases; therefore, it is **recommended to set the recall mode to multi-recall**. By ensuring the correct configuration of these settings, you should be able to resolve the issue of different pages having the same result when querying multiple documents <sup>[[1]](https://github.com/langgenius/dify-docs/blob/main/en/guides/workflow/node/knowledge-retrieval.md)</sup><sup>[[2]](https://github.com/langgenius/dify-docs/blob/main/en/guides/knowledge-base/create-knowledge-and-upload-documents.md)</sup><sup>[[3]](https://github.com/langgenius/dify-docs/blob/main/en/guides/knowledge-base/integrate-knowledge-within-application.md)</sup><sup>[[4]](https://github.com/langgenius/dify-docs/blob/main/zh_CN/guides/knowledge-base/create-knowledge-and-upload-documents.md)</sup>. <!-- Dosu Comment Footer --> *To continue talking to [Dosu](https://dosu.dev), mention @dosu.*
yindo commented 2026-02-21 18:12:19 -05:00
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@lvxinliang commented on GitHub (Sep 21, 2024):

The new version has been resolved： https://github.com/langgenius/dify/commit/2fe2e350ce55c85cf63d94fd3f5596bd781f7f17

@lvxinliang commented on GitHub (Sep 21, 2024): The new version has been resolved： https://github.com/langgenius/dify/commit/2fe2e350ce55c85cf63d94fd3f5596bd781f7f17
yindo commented 2026-02-21 18:12:19 -05:00
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@crazywoola commented on GitHub (Sep 21, 2024):

Please close the issue if this is resolved.

@crazywoola commented on GitHub (Sep 21, 2024): Please close the issue if this is resolved.
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