What this category of data looks like
This category serves data service project managers, collection teams, and customer algorithm teams. Its data mainly comes from customer requirements, public data sources, collection tools, and internal sample libraries. Daily outputs include four types of documents: collection plans, sample specifications, authorization statements, and task schedules. Data update rhythm follows single project cycles or weekly intervals. Core fields include sample scope, collection fields, compliance check items, and task schedule nodes. Common units are sample count, character count, and workdays. Data generation goes through three steps: sample scope definition, field specification confirmation, and collection legality verification. Errors during this process may lead to unavailable samples or compliance risks.
What constraints these characteristics impose on the reranking and hybrid retrieval link
Data sources are scattered and multi-source heterogeneous. Hybrid retrieval must integrate different types of information, including customer requirement documents, public data source metadata, and collection tool logs. The weight of each data source must be clarified to ensure retrieval accuracy. Data updates fluctuate with project cycles. The system must adapt to dynamically updated sample libraries and task schedule data, to avoid retrieving expired collection plans or changed sample specifications. Core fields include compliance check items. The reranking link must prioritize associating authorization statements and legality verification results, to prevent returning non-compliant sample data. Single-project data volume changes with task scale. The return range of retrieval results must be limited, to avoid redundant results interfering with key information retrieval related to task schedules.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
usingReRank | Enabled | The documents for data collection task design include compliance check items. Enabling reranking can prioritize returning results that meet authorization requirements |
similarity | 0.75–0.85 | For matching structured documents such as collection plans and sample specifications, this range balances recall precision and recall scope |
limit | Top 8–12 entries | Single-project data volume fluctuates with task scale. Limiting the number of returned entries avoids redundant results interfering with task schedule retrieval |
searchMode | Hybrid retrieval mode | Multi-source data including customer requirements, public data sources, and collection tool logs need to be integrated. Hybrid retrieval can cover information from different sources |
| Reranking model invocation method | Calibrated based on actual testing | Adapts to compliance and precision requirements of different projects, supports API invocation or local deployment of open-source models |
Three common misconfigurations
- Phenomenon: After configuring the reranking model, the order of knowledge base retrieval results does not change, and no effective feedback is provided. Cause: The
usingReRankswitch is not enabled correctly, or thesimilaritythreshold is set outside the reasonable range, causing the reranking logic to not trigger. - Phenomenon: In the result reranking module of the application configuration page, the reranking model configured in config.js cannot be selected. Cause: The invocation permission of the reranking model is not correctly declared in the system configuration, or the configured model format does not meet the platform compatibility requirements.
- Phenomenon: After deploying an open-source reranking model on a virtual machine, the model cannot be invoked during retrieval. Cause: The port permission of the model service is not opened, or the correct model access address and key are not filled in the platform configuration.
How to confirm the configuration is complete
- Enter the application configuration page, confirm that the
usingReRankswitch is enabled, and the corresponding reranking model has been selected. - Submit a test retrieval request, compare the retrieval results with and without reranking enabled, to verify whether the sorting logic changes as expected.
- Check the system operation logs, confirm that the reranking model invocation request has been sent normally, and no error is returned.
- Adjust the
similaritythreshold, verify whether the matching precision of the retrieval results changes correspondingly with the threshold adjustment.
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.