What the data for this category looks like
Core data for engineering consulting supervision comes from on-site supervision logs, on-site supervision spot check records, subdivision and sub-subdivision engineering acceptance reports, supervision plans and implementation guidelines, and similar materials. Data updates follow project progress, with concentrated updates at key nodes such as hidden work acceptance and pre-completion acceptance, and routine synchronization of on-site patrol records weekly. Documents are a mix of structured and semi-structured formats, containing fields such as project number, supervision section, professional category, and rectification closure status. Units include engineering standard units such as construction area, project cost amount, and construction duration days.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
The data characteristics of engineering consulting supervision impose multiple constraints on retrieval and recall. Data sources updated concentratedly with project nodes require the retrieval link to prioritize matching the latest content within the current project cycle, avoiding calls to outdated older versions of supervision guidelines. The presence of multi-dimensional engineering fields requires precise matching of dimensions such as section and professional category during retrieval, otherwise invalid cross-project results are likely to be returned. Semi-structured documents have a high proportion of long text paragraphs, and after chunking, mixed cross-professional content is prone to appear, so targeted adjustments to chunking and retrieval matching logic are needed. The high-frequency updates of on-site patrol records also require the retrieval link to support near-real-time data synchronization to ensure the timeliness of results.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | There are many professional terms in engineering consulting supervision, so a sufficiently precise matching threshold must be retained to avoid mixing in low-relevance general engineering content |
limit | Top 8 entries | Reference content for supervision documents needs to cover multiple relevant reports; 8 entries balance retrieval breadth and context length limits |
searchMode | Hybrid retrieval | Balances the precision of keyword matching and the fuzzy matching capability of semantic retrieval, adapting to mixed scenarios of professional terms and colloquial questions |
usingReRank | Enabled | Long text chunks are prone to semantic drift; re-ranking corrects the sorting logic of initial retrieval results and improves the priority of core content |
datasetSearchUsingExtensionQuery | Enabled | Engineering questions often contain implicit professional background; query optimization can supplement associated terms to improve retrieval accuracy |
collectionIds | Exclusive supervision document collection ID bound to the current project | Avoid cross-project retrieval, only recall supervision data from the current section, reducing invalid results |
Three common mistakes
- Phenomenon: When the query is unrelated to the current supervision project, the system still returns knowledge base retrieval results without triggering the preset fallback response. Cause: No minimum relevance threshold is set, and no fallback trigger logic is configured, resulting in low-matching content being recalled.
- Phenomenon: Retrieval results include supervision documents from cross-sections or non-specified professional categories. Cause:
collectionIdsis not configured correctly, and multiple unrelated project document collections are bound, leading to cross-project data being recalled. - Phenomenon: Retrieval results from long text chunks have significant semantic deviation, with core rectification requirements or acceptance standards not ranked at the top. Cause:
usingReRankis not enabled; the initial retrieval sorting only relies on initial similarity, without correcting semantic drift caused by long text chunking.
How to confirm the configuration is correct
- Submit a general engineering query unrelated to the current supervision project, check whether the system returns the preset fallback response and does not invoke the knowledge base retrieval logic.
- Enter a query containing the specified section and professional category, verify that all recalled result documents come from the currently bound collection, with no cross-project content.
- Adjust the
similarityparameter between 0.7 and 0.9, compare the quantity and relevance of retrieval results, and confirm that the threshold matches business requirements. - Submit a long-text professional query, check whether the re-ranked results place core rectification requirements or acceptance standards at the top.
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 2026-09-28.