What Data for This Category Looks Like
Teams including data project managers, data engineers, and customer algorithm teams typically produce data for this category. Daily documents include dataset descriptions, version records, delivery checklists, and acceptance reports. Data sources cover object storage, data warehouses, annotation platforms, and customer acceptance feedback. The update frequency follows per batch or per version cycles. Data fields include version numbers, delivery package paths, sample difference descriptions, acceptance status, and more. The core focus is on traceability of delivery packages and versions, which differs from data cleaning and quality inspection categories that prioritize data quality verification.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Data sources for this category are scattered and mostly updated in batches. Retrieval systems must support multi-source mounting across object storage, data warehouses, and other sources. Data uses version as the core associated field, so retrieval processes must add a version number filter condition to avoid mixing delivery data from different batches. Daily documents include structured content such as version records and delivery checklists. Recall processes must prioritize matching exclusive fields such as version and delivery path, and must support attaching version information to returned results to meet traceability requirements. Data volume grows gradually with delivery batches, so operators must limit the number of single-recall results to avoid response timeouts that disrupt operations duty efficiency.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.88 | Matches structured delivery documents for this category, filters low-relevance non-version content |
limit | Top 6–10 entries | Controls the volume of single-recall results, adapts to the fast response scenario of operations duty |
searchMode | hybrid | Combines semantic and keyword retrieval, covers structured fields such as version numbers and delivery paths |
usingReRank | Enabled, re-rank top 3–5 entries | Optimizes recall result ranking, prioritizes displaying high-match version records |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands version-related query terms, improves recall accuracy of version records |
collectionIds | Specify exclusive delivery dataset collection ID | Isolates delivery data from different batches, avoids mixing data across projects |
Three Common Misconfigurations
- Symptom: When calling a chatbot via API, returned results do not include delivery dataset content. Cause: The
collectionIdsof the corresponding delivery dataset is not bound in the retrieval parameters, resulting in the retrieval scope not covering the target dataset. - Symptom: Retrieval response times out after knowledge base content increases with delivery batches. Cause: No reasonable
limitparameter is set, too many unnecessary delivery documents are recalled, exceeding system processing limits. - Symptom: Recall results include delivery documents with the same name but different versions, making batches indistinguishable. Cause:
datasetSearchUsingExtensionQueryis not enabled, and no version number filter is added to the retrieval condition, resulting in insufficient filename matching priority.
How to Confirm Configurations Are Set Correctly
- In the retrieval configuration panel of FastGPT v4.17.0, confirm that parameters such as
similarityandlimithave been set according to category requirements. - Initiate a test query containing a version number, verify whether the returned results include the corresponding version's delivery checklist and acceptance report.
- Call the API interface, check whether the returned results only include the exclusive delivery dataset content specified by
collectionIds. - Batch import delivery data from multiple batches, verify whether retrieval results filter out non-target batch documents.
The values listed are common starting points, and should be evaluated against relevant sample data.
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.