What This Category of Data Looks Like
This category of data comes primarily from customer-submitted warranty work orders, system operation logs, project contract warranty clauses, and software or hardware version records. Daily archived documents include warranty work orders, defect confirmation sheets, repair execution records, and customer confirmation receipts. Data updates occur daily or trigger per individual order. Each entry includes a work order number, defect description, associated project information, responsibility determination trail, and customer feedback fields. No additional statistical quantitative metrics are included.
Constraints on Reranking and Hybrid Retrieval Workflows
Data sources for this category are scattered, with large format differences across entries. Some data are fragmented log snippets, while others are long-text warranty contract clauses. Data also updates daily or per order, so the retrieval pipeline must support real-time response. Different document types have distinct core requirements. Warranty work orders focus on defect details, while contract clauses focus on responsibility boundaries. This means hybrid retrieval must balance short-text recall and long-text matching. Responsibility boundary judgment is the core time-consuming step of the workflow. The reranking stage must prioritize recalling content related to responsibility determination and repair solutions, to avoid irrelevant information interfering with core decision-making. It must also adapt to the wide fluctuation in single-entry data volume.
Configuration Settings
This configuration guide is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Similarity Threshold | 0.72–0.80 | Filter low-match content unrelated to warranty defects, focus on core business data |
| Maximum Single Recall Count | Top 10 entries | Adapt to the limited associated data volume per work order in this category, avoid redundant information entering the context |
| Retrieval Mode | Hybrid Retrieval | Meet the recall needs of multi-format data, balance short-text work orders and long-text contract clauses |
| Reranking Toggle | Enabled | Prioritize sorting content related to responsibility boundary judgment and repair solutions, improve core decision-making efficiency |
| Retrieval Timeout | 8–10 seconds | Match the reasonable response time for this category’s business, avoid excessive wait times that reduce customer support efficiency |
Three Common Misconfigurations
- Phenomenon: Hybrid retrieval tasks take longer than 10 seconds, and the interface displays a retrieval timeout prompt. Cause: The recall limit and reranking threshold have not been adjusted for this category’s data volume, and reasonable timeout parameters have not been configured.
- Phenomenon: After calling the reranking model, GPU video memory usage quickly rises to 6–7 GB, and usage appears on multiple cards. Cause: The concurrent call count for the reranking model has not been limited, or a reasonable model loading batch size per card has not been allocated.
- Phenomenon: When connecting to the reranking model offline, the interface returns a rerank error, with prompt information containing empty fields or a model not found message. Cause: The API address or local model path for the reranking model has not been correctly configured, preventing the model from loading and calling normally.
How to Confirm Proper Configuration
- View the FastGPT system’s retrieval pipeline logs to confirm that both the hybrid retrieval and reranking modules start normally, with no error messages.
- Submit a test warranty work order, and verify that recall results prioritize displaying document content related to responsibility determination and repair solutions.
- Test input text in different formats, confirm that both short-text work orders and long-text contract clauses are correctly recalled.
- Check GPU video memory usage to confirm that the reranking model’s memory usage falls within the expected range.
The values provided in this guide are common starting points. They should be evaluated against the reader’s own samples to ensure alignment with specific use cases.
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.