What this category of data looks like
Engineering consulting supervision declaration material and file review data mainly comes from project supervision logs, engineering submission forms, completion acceptance files, qualification submission documents, and similar sources. The data update rhythm aligns with project progress milestones. Materials are added in real time during the submission stage, and locked for archiving after project completion. Most documents use a multi-chapter nested format, with fixed fields including project number, supervision unit, submission date, material type, review opinion, and others. Some materials include attachments such as bill of quantities and on-site photos. Common engineering measurement units include square meters, ten thousand yuan, working days, and other standard units used in engineering work.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Engineering consulting supervision documents are mostly staged, updated structured submission materials and unstructured files. The length of individual documents varies significantly. A large number of professional engineering terms and fixed business fields are included in these documents. These characteristics require the retrieval link to adapt to long-text recall, and avoid truncating key review basis. Real-time updated project data requires the knowledge base synchronization frequency to match submission milestones. Recalling old data will lead to review deviations if synchronization does not align. The multi-attachment nested document structure requires support for cross-attachment associated retrieval, to avoid missing supporting materials. Semantic differences in professional terms require retrieval to balance precise matching and semantic association, to avoid misjudging non-similar engineering materials.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Semantic similarity threshold for engineering professional terms. Avoid recalling irrelevant non-project materials, while balancing precision and recall coverage |
limit | 8–12 items | Declaration material and file review needs to associate multiple supporting materials from the same project. Too many items will exceed the model context window limit. Too few items will miss key review basis |
searchMode | Semantic + keyword hybrid retrieval | Engineering documents have both structured fields and professional descriptions. Hybrid mode balances precise field matching and semantic association of review key points |
usingReRank | Enabled | Filter the most relevant materials to review key points from multiple recall results, avoiding incorrect matching caused by professional term ambiguity |
datasetSearchUsingExtensionQuery | Enabled | Declaration materials often need to associate project background and previous submission records. Extended queries can supplement context-related information, improving recall relevance |
collectionIds | Bind exclusive knowledge base collections corresponding to supervision projects | Avoid mixing cross-project materials, ensuring recalled data only belongs to the current review project |
Three common mistakes
- Calling the knowledge base question answering interface returns a 422 status code. The total token count of recalled documents exceeds the model context window limit. The
limitparameter is not configured to control the number of recalled items, leading to an overly long incoming context. - The citations returned in conversations only include document titles, and do not show fields such as file number and submission date.
datasetSearchUsingExtensionQueryextended query is not enabled, and document metadata is not included in the recall matching scope. - Retrieval results mix non-current project supervision materials.
collectionIdsis not configured to bind the exclusive knowledge base collection, leading to recalled cross-project data.
How to confirm the configuration is correct
- Upload a single engineering submission form, test whether retrieval only recalls relevant materials from the same project. Verify that the
collectionIdsconfiguration takes effect. - Enter professional engineering terms, check whether the similarity scores of retrieval results fall within the
0.72–0.85interval. Verify that thesimilarityparameter is configured correctly. - Trigger retrieval, then check the returned citation fields. Confirm whether document metadata and extended query related content are included. Verify that
usingReRankanddatasetSearchUsingExtensionQueryare enabled. - Call the FastGPT v4.17.0 knowledge base question answering API interface. Check whether the returned results include both model answers and cited document details. Verify that the retrieval mode configuration is correct.
The values listed above are common starting points. Adjust them based on your own specific samples and use cases.
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.