What this category of data looks like
Data sources for this category include defect management systems, test reports, code commit records, and user feedback. The update frequency is daily or per version release cycle. Daily processed documents include standardized defect tickets, version regression checklists, repair verification records, and defect statistical reports. Fields include defect ID, priority, reproduction steps, repair version, verification results, and more. Some documents are semi-structured tables or text records, with data structures centered around the full lifecycle of defects.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
Decentralized data sources and daily or per-version update frequencies require the retrieval link to support quick synchronization of the latest defect and verification records, to avoid using expired data. Document structures are designed around the defect lifecycle, with clear fields such as priority and reproduction steps. This requires retrieval to accurately match key fields, to avoid recalling low-relevance content. Missing high-priority defects can cause version delays or customer complaints, so recalled results must have sufficient confidence. At the same time, single-document length and the number of recalled entries must be adapted to the context window to prevent token limits from affecting response efficiency.
How to set configurations
These configurations apply to FastGPT v4.17.0
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Match the precise fields of defect tickets, filter low-relevance historical records, and avoid interfering with priority determination |
limit | Top 10 entries | Control the number of recalled entries, adapt to the single-document length of defect management documents, and prevent total tokens from exceeding the context window |
searchMode | semantic | Perform semantic matching for reproduction steps and repair records, quickly locate solutions for similar defects |
usingReRank | Enabled | Reorder recalled results, prioritize displaying verification records for high-priority defects, and improve decision-making efficiency |
datasetSearchUsingExtensionQuery | Disabled | Defect management documents have a fixed structure, no need to expand query terms, and avoid introducing irrelevant retrieval results |
collectionIds | Bind the specified defect management knowledge base ID | Limit the retrieval scope, exclude documents from other categories, and ensure recalled results only relate to defect management data |
Three common mistakes
- The symptom is that a single response exceeds 3000 tokens, even if the input question is short. The cause is setting
limitto 2000, where the total length of recalled documents exceeds the context window. - The symptom is that knowledge base search results are displayed directly in the interface, and subsequent AI responses repeat the same content. The cause is not disabling the direct output switch for search results, leading to two outputs of retrieval results in the workflow.
- The symptom is that recalled results cannot accurately match the reproduction steps of the target defect. The cause is not organizing documents according to the standardized structure of defect tickets, causing the embedding model to fail to recognize key field information.
How to confirm the configuration is set correctly
- Initiate a test query containing high-priority defect keywords, check whether the matching score of recalled results falls within the preset range.
- View workflow output logs, confirm that retrieval results are only output in the specified link, with no duplicate retrieval content displayed.
- Import a standard defect ticket document, initiate a matching query, and confirm that the recalled results include the core field information of this document.
- Adjust the
limitparameter, verify that the number of recalled entries meets expectations, and the total tokens do not exceed the context window limit.
The values provided here 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.