Doubt about the topk and threshold usage in rerank stage settings

[zfanswer]

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Dify version

0.5.10

Cloud or Self Hosted

Self Hosted (Docker)

Steps to reproduce

enable the rerank model, and set threshold value and topk value, debug the recall behavior.

✔️ Expected Behavior

topk and threshold value should only affect rerank stage, like the doc describes.
(sorry, I didn't find this explaination in English doc...)

the last line words says, the topk and threshold value will only affect the rerank stage.
(there is another doubt, somehow, I assume that dify has a mechanism that recall a limited item set from vectordb and send them to rerank stage)
so the RAG with rerank steps will be like:
sim search and get a bunch of doc chunks ==> somehow? to get n (n>k) chunks ==> rerank stage ==> filtered by the treshold
==> get top-k from reordered and filtered chunk list ==> got final reference

❌ Actual Behavior

from the code, seems the recall logic use topk and threshold both in the first retrival and rerank stage.
so current RAG with rerank steps are:
sim search ==> filtered by threshold and get top-k chunks ==> rerank stage to get reodered chunk list ==> again, filtered by threshold and get top-k chunks ==> got final reference
I think reranking chunks within the top-k chunks got from the first retrival stage is kind of meaningless, if user always tend to use very small top-k value like 2, 3... there is no meaning for reranking with in this kind of small chunk set.
what do you think?

[dosubot[bot]]

Based on the provided code snippets and the behavior you've observed, it appears that the topk and threshold values are indeed applied in both the initial retrieval and rerank stages in Dify's implementation. This is consistent with the code in the _retriever and _run methods, where these parameters are passed to the retrieval and reranking services respectively.

This two-tiered application of topk and threshold is intended to optimize the balance between efficiency and effectiveness in the retrieval process. In the initial retrieval stage, these parameters help to narrow down the dataset to a manageable subset of results that are most likely relevant to the query. This is crucial for efficiency, especially when dealing with large datasets, as it reduces the computational load for the subsequent processing stages.

The rerank stage then applies these parameters again, but this time to refine the results further, ensuring that the final output is not only relevant but also of high quality. The topk parameter in the reranking stage helps in selecting the top results according to the reranking model's criteria, which might include factors like relevance, freshness, or user preferences. The threshold parameter ensures that only results that meet a certain score threshold are considered, which helps in maintaining a high quality of the returned results.

As for your concern about the redundancy when using very small topk values, it's important to note that the topk value in the initial retrieval stage and the rerank stage serve different purposes. In the initial retrieval stage, topk is used to limit the number of documents retrieved for efficiency. In the rerank stage, topk is used to select the top results according to the reranking model's criteria. Therefore, even with small topk values, the two stages are not redundant but complementary, each contributing to the efficiency and effectiveness of the retrieval process in different ways.

Regarding the configuration of topk and threshold values, Dify allows these values to be set separately for the initial retrieval and rerank stages. This is evident from the ParamsConfig component, where both top_k and score_threshold values can be set and modified through the user interface. The handleParamChange function updates the tempDataSetConfigs state with new values for top_k and score_threshold based on the key provided to the function. This function is used as the onChange handler for both TopKItem and ScoreThresholdItem components, allowing users to input separate values for the initial retrieval (top_k) and reranking stages (score_threshold).

In conclusion, the observed behavior of applying topk and threshold in both the initial retrieval and rerank stages is intended and consistent with Dify's implementation. The application of these parameters in both stages allows for a two-tiered filtering process that optimizes both efficiency and result quality. The configuration of these values can be tailored for different stages of the retrieval process, allowing for flexibility and customization based on the specific requirements of the task at hand.

For more details, you can refer to the following code sources:

• api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py
• web/app/components/app/configuration/dataset-config/params-config/index.tsx

I hope this clarifies your question. If you have further questions or need more clarification, feel free to ask.

Dosu

Sources

• api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py
• api/core/model_runtime/model_providers/spark/llm/spark-2.yaml
• web/app/components/app/configuration/dataset-config/params-config/index.tsx

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[zfanswer]

@JohnJyong Hi, could you take a look and give some explaination, to see if it can resolve my doubt, thanks.

[zzzmc]

@JohnJyong 😀Hi,I have the same question as him. Should the top_k in the retrieval stage be greater than the top_n in the reranking stage? For example, top_k=10, top_n=3