What the data for this category looks like
The data for this category mainly comes from creator backends, enterprise credit information query platforms, account history systems, and contract management systems. Daily processed documents include onboarding application forms, identity qualification scan copies, audit verification records, and cooperation agreement files. Data updates follow a rhythm of daily batch synchronization or on-demand updates triggered by individual tickets. Each individual data entry includes fields such as account identifier, qualification type, audit status, and cooperation period. There is no unified fixed unit; fields and units vary based on the document type.
What constraints these characteristics impose on the reranking and hybrid retrieval link
Data for this category is scattered across multiple systems, requiring hybrid recall of cross-source qualification materials, audit records, and contract documents. The retrieval link must support hybrid matching of multi-source data. Individual data entry structures vary significantly, with a wide range of text lengths. This spans from audit statuses of tens of characters to qualification scan copies of thousands of characters. The reranking model must adapt to input content of different lengths. Data updates trigger with application tickets. Retrieval must prioritize recalling the latest audit status and qualification documents, so the weight for the update time dimension must be adjusted. At the same time, retrieval results directly link to the credibility of account access. The reranking link must prioritize keywords related to qualification authenticity and compliance.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Enable reranking | Enabled | This category requires filtering low-relevance cross-source data and raising the priority of audit-related results |
| Similarity threshold | 0.75–0.85 | Balance the recall coverage of qualification materials and result relevance, and avoid mismatching unrelated content |
| Single recall limit | Top 10 entries | Adapt to the regular data volume of single batch applications and reduce computational pressure on the reranking link |
| Retrieval mode | Hybrid retrieval | Must recall multi-source data such as creator backends and enterprise credit information at the same time |
| Reranking model call timeout | 30–60 seconds | Adapt to the text length of qualification materials and avoid timeouts caused by long document processing |
Three common configuration mistakes
- Phenomenon: When FastGPT is deployed offline, clicking model test after configuring the reranking model triggers a request error. Cause: The local path of the reranking model is not mapped to the container network, or the service port of the reranking model is not open.
- Phenomenon: After upgrading to FastGPT v4.17.0, the reranking model configuration fails to take effect. Cause: The reranking function switch is not enabled in the system global configuration, or configuration parameters are not synchronized to the retrieval service node.
- Phenomenon: Internal ticket Q&A response slows down after enabling reranking. Cause: The single recall limit is set too high, or the selected reranking model has a large computational load and does not adapt to the text length characteristics of this category.
How to confirm the configuration is complete
- Navigate to the configuration page for internal ticket Q&A, confirm that the enable reranking switch is turned on, and the retrieval mode is set to hybrid retrieval.
- Submit a test question containing onboarding application keywords, and check whether the sorted retrieval results prioritize displaying the latest audit records and qualification materials.
- Call the test interface of the reranking model, verify that the response time is within the preset threshold of 30–60 seconds, and there are no timeout or parameter error prompts.
- View the system operation logs, confirm that the reranking model call request has been initiated normally, and there are no error messages indicating missing configuration parameters.
The values provided are common starting points and should be validated against corresponding local samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.