What the data for this category looks like
Core data for data collection task design comes from customer requirements, public data sources, collection tools, and sample libraries. Daily documents include collection plans, sample specifications, authorization statements, and task schedules. Data update frequency is adjusted based on project cycles or weekly cycles. Each individual document contains multiple fields such as sample scope, field specifications, collection legality, and schedule milestones. Data formats and semantic focuses vary significantly across different sources. Some documents involve compliance content, which has high requirements for retrieval accuracy.
What constraints these characteristics impose on the reranking and hybrid retrieval link
Multi-source heterogeneous data sources require hybrid retrieval that combines vector recall and keyword recall to cover different types of document content. Frequently updated datasets require retrieval results to distinguish between new and old versions, to avoid using expired collection plans or authorization statements. The multi-field document structure requires the reranking stage to prioritize matching core retrieval targets such as sample scope and field specifications, and filter out irrelevant compliance documents or schedule content. The real-time requirements of operations on-call and work orders require balancing retrieval accuracy and response time, to avoid too many low-correlation results slowing down the process.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
usingReRank | Enabled, specify BGE-Reranker model | Documents for data collection task design include multi-dimensional fields. Reranking can filter out low-correlation results and improve retrieval accuracy for operations work order and on-call scenarios |
similarity | 0.72–0.80 | Semantic similarity threshold for matching core documents such as sample specifications and authorization statements, to avoid recalling irrelevant collection schedule content |
limit | Top 10 results | Meets the real-time response requirements of operations scenarios, limits the number of returned results to avoid increased processing time caused by overly long context |
searchMode | Hybrid retrieval | Covers multiple types of data sources such as customer requirements and public data sources, combines vector recall and keyword recall to improve recall coverage for different types of documents |
Three common mistakes
- Troubleshooting item: If GPU memory usage quickly rises to 6-7 GB after calling the reranker model, and multiple GPUs are occupied at the same time, check whether the concurrent call volume of the reranker model is not limited, which causes each retrieval request to load a separate model instance and exceed the memory quota.
- Troubleshooting item: If the hybrid retrieval response time exceeds 10 seconds, which is much higher than the time taken for a single vector retrieval, check whether the number of recalled entries for the
limitparameter is not set reasonably, which causes too many documents to enter the reranking stage and increases overall processing time. - Troubleshooting item: If the interface returns
rerank errorand thedetailfield is empty, check whetherAIPROXY_API_ENDPOINT(root address of the AI Proxy service) andAIPROXY_API_TOKEN(AI Proxy administrator token, consistent with the AI Proxy server configuration and cannot be empty) are correctly configured on the App/Admin server, or whether the AI Proxy root address is confused with the API address of the upstream model.
How to confirm the configuration is complete
- Check the App/Admin server environment variables for FastGPT v4.17.0, confirm that
AIPROXY_API_ENDPOINT(root address of the AI Proxy service) andAIPROXY_API_TOKEN(AI Proxy administrator token, consistent with the AI Proxy server configuration and cannot be empty) are correctly configured and not empty. - Initiate a test retrieval targeting sample scope and field specifications, check whether the returned results are sorted to prioritize matching core retrieval targets, and exclude irrelevant schedule or compliance documents.
- Verify that hybrid retrieval mode returns both vector recall and keyword recall results, and that the result priority order is adjusted via reranking.
- Check the AI Proxy service logs to confirm there are no permission errors or timeout exceptions for reranker model calls.
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.