What this category of data looks like
Data for party building learning Q&A in infrastructure construction comes mainly from project party branch meeting minutes, construction team party building learning records, guidance documents issued by superior party committees, and clean government archives. Data is stored classified by project section. Each document includes fields such as title, issuing unit, release date, affiliated section number, learning theme, and main text content. The main text is mostly long text of 1000 to 5000 characters. The update synchronization frequency is once every half month.
What constraints do these characteristics impose on knowledge base retrieval and recall
The section-based storage structure requires retrieval to bind the dataset of the corresponding section. This prevents irrelevant cross-project data from being mixed into results. Long main text requires that long paragraph splitting adapts to the paragraph logic of party building documents. This avoids destroying complete learning key points. The half-month update rhythm requires retrieval to prioritize recalling documents updated within the last 30 days. This ensures the timeliness of learning content. The section number field can be used to accurately filter the retrieval scope. This narrows the recall pool and improves retrieval accuracy.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
collectionIds | Fill in the dataset ID of the target section, up to 10 per batch | Matches the section-classified storage structure to limit the precise retrieval scope |
similarity | 0.75–0.85 | Adapts to the semantic similarity requirements of party building documents to filter low-relevance irrelevant results |
limit | Top 8 entries | Controls the number of recall results to adapt to the short context display requirements of party building learning Q&A |
searchMode | embedding | Prioritizes semantic retrieval to improve matching accuracy for party building documents with few structured fields |
usingReRank | Enabled | Performs secondary sorting on recall results to improve the display priority of highly relevant documents |
datasetSearchUsingExtensionQuery | Enabled | Performs semantic expansion on user questions to adapt to multiple expression forms of party building-specific terminology |
Three common configuration errors
- Phenomenon: After calling the API to start a conversation, the generated reply does not reference documents in the knowledge base, and the retrieval log shows no matching results. Cause: The
collectionIdsparameter is not configured correctly. The party building dataset of the target section is not bound, so the retrieval scope does not cover valid data. - Phenomenon: Retrieval results include party building documents from other construction sections, which do not match the current project's learning needs. Cause: The
collectionIdsparameter is filled with dataset IDs of non-target sections. The retrieval scope is not accurately limited. - Phenomenon: Recalled party building document body fragments are incomplete, and key learning points are missing. Cause: The long text chunking logic is not adapted, or the parameter value exceeds the context carrying limit. Valid content is filtered out.
How to confirm the configuration is correct
- Check the
collectionIdsconfiguration of the dataset. Confirm that only the dataset IDs of the target construction sections are included, with no redundant or incorrect ID values. - Initiate a test query. Verify that the number of recall results in the retrieval log matches the value of the
limitparameter. - Check whether the reply content includes matching party building document fragments. Confirm that the semantic matching degree meets the expectations of the current scenario.
- Call the API to obtain retrieval results. Verify that the returned document fields include relevant information of the target section to ensure the filtering logic takes effect.
The values provided are common starting points and should be evaluated 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 2026-09-28.