Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
The data sources for building construction general contracting mainly come from project approval documents, construction organization designs, site visa forms, work section acceptance records, cost settlement ledgers, and industry standard texts. Update rhythm adjusts dynamically with project progress. Site visa forms and change notices are updated in real time. Quarterly industry standards are updated every quarter. Documents include long texts (construction plans up to tens of thousands of characters per piece) and short texts (inspection records of hundreds of characters per piece). Fields include project section number, construction node, cost amount (unit: ten thousand yuan), acceptance date, material specification and model, and other related items.
What constraints do these characteristics impose on knowledge base retrieval and recall
The high proportion of long texts and multiple field dimensions in building construction general contracting data cause dispersed features after single-text embedding. This leads to irrelevant documents being included during recall. Dynamically updated visa forms and change notices require the retrieval link to support incremental synchronization, to avoid recalling expired construction plans. The low correlation between short-text inspection records and long documents requires distinguishing recall weights for different document types, to prevent retrieval results from being overwhelmed by high-frequency short texts. In addition, fields with units such as project section number and cost amount require structured field matching during retrieval. Relying solely on semantic matching is not enough to improve accuracy.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.78 | Semantic similarity range matching building construction documents, balances recall precision and coverage, adapts to mixed document structures of long and short texts |
limit | Top 8 entries | Large differences in single-document length for building construction documents, limiting recall entries avoids context overflow, while covering associated information across multiple sections and nodes |
searchMode | vector | Prioritizes matching semantic features, supplemented by structured field matching, adapts to multi-dimensional documents with section numbers and cost amounts |
usingReRank | Enabled | For document sets with high long-text proportion, re-ranking filters out semantically similar but business-irrelevant recall results, improves reply accuracy |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands query terms for user inquiries about engineering nodes, material models and other keywords, matches construction standards and site records |
collectionIds | Configured by project section | Distinguishes knowledge base sets for different sections, avoids recalling irrelevant documents across sections, adapts to business scenarios with parallel multiple projects |
Three common configuration mistakes
- Knowledge base question-answer pair extraction tasks get stuck during training, and no call records appear in invocation logs. The cause is failure to configure
collectionIdsby project section, which causes the system to attempt loading all building construction documents, with excessive resource occupation preventing task startup. - Knowledge base search times out, and response duration exceeds business expectations. The cause is failure to set a reasonable
limitfor recall entries, with full loading of multi-section documents overloading the retrieval link. - Knowledge base search results fail to fully match knowledge base content, and irrelevant replies appear. The cause is an excessively low
similarityvalue, which recalls a large number of semantically similar but business-irrelevant documents, without usingusingReRankfor secondary filtering.
How to confirm the configuration is correct
- Initiate an inquiry about a specific construction node, check whether the recall results include documents from the corresponding section, to confirm that the
collectionIdsconfiguration takes effect. - View task logs, confirm that there are call records after the knowledge base extraction task starts, to verify that the trigger logic of
datasetSearchUsingExtensionQueryandusingReRankworks normally. - Simulate a timeout scenario, adjust the values of
limitandsimilarity, observe whether the retrieval response duration meets business expectations, to confirm that the configuration balances retrieval efficiency and precision. - Export a single long-text document, confirm that the file size meets system requirements, to verify that the configuration adapts to the long-document characteristics of building construction projects.
The values provided here 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.