What this category of data looks like
This category of data primarily comes from ad platform backends, the National Enterprise Credit Information Publicity System, customer-submitted opening applications, business licenses, industry qualification materials, and industry access rules. Update frequency is daily or triggered per individual application. Daily processed documents include opening application forms, main qualification documents, audit records, and similar items. Most fields relate to entity names, qualification numbers, industry categories, and audit statuses. There are no unified fixed units; corresponding information must be matched based on specific materials.
Constraints imposed on reranking and hybrid retrieval
Dispersed data sources create inconsistent recall result formats. Use hybrid retrieval to integrate multi-source heterogeneous data. A single opening application has associated documents with many fields and strong relevance. Use reranking to filter irrelevant recall results and focus on core qualification information. Updates trigger per individual application. Retrieval models must adapt to the latest industry access rules in real time. The audit process has strict accuracy requirements. Strictly control thresholds for retrieval and reranking to avoid missing key qualification information. Differences from ad material audits mean retrieval must prioritize matching entity qualification documents. Exclude material-related content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Similarity Threshold | 0.75–0.85 | Filters low-relevance non-qualification recall results, meets the requirement for strong association between entities and industries |
| Single Search Limit | Top 10 results | The number of associated documents for a single opening application is limited. Excessive recall increases reranking computation load |
| Retrieval Mode | Hybrid Retrieval | Must simultaneously retrieve three types of heterogeneous data sources: customer-submitted materials, industry rules, and platform backend data |
| Reranking Toggle | Enabled | Re-sorts hybrid recall results by qualification relevance. Prioritizes content related to entity verification |
| Reranking Recall Count | Top 5 results | Focuses on core qualification information. Avoids redundant content interfering with audit judgments |
Three Common Misconfigurations
- Symptom: An error occurs when testing the reranking model, while other configurations run normally. Cause: The access key and deployment address of the private reranking model were not configured in FastGPT v4.17.0, leading to model call failure.
- Symptom: When the reranking function is disabled, the relevance field of retrieval results has no practical reference value and invalid results cannot be filtered. Cause: Hybrid retrieval recall results include multi-source heterogeneous data. The original similarity is only based on vector matching, without sorting combined with business rules, so it cannot be directly used for qualification audit judgment.
- Symptom: The number of returned results after hybrid retrieval configuration does not match the set reference limit. Cause: The recall count limit in the reranking link was not adjusted synchronously, leading to truncated or over-limit reranked results.
How to Verify Correct Configuration
- Upload a single opening application document and industry access rule document. Run a retrieval, then check if returned results prioritize content related to entity qualifications.
- Disable the reranking function. View the relevance field of retrieval results, confirm it can be used for preliminary screening of high-matching documents.
- Check if configuration item values match the current business's document count and data source types. Adjustments must be combined with the time consumption of the actual audit process.
- Test the private reranking model's call link. Confirm there are no access errors before officially enabling it.
The values provided are common starting points and should be measured against the reader's own 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.