What this category of data looks like
Ready-mixed concrete production scenario data comes from engineering orders, sand, gravel, cement admixture inspection records, aggregate moisture content measurement data, mixing station control system logs, and laboratory test reports. Daily documents include mix proportion notices, opening identification forms, production and transport orders, raw material inspection reports, test block strength records, and more. Each individual document contains fields such as material proportion parameters, moisture content values, mixing time, transport mileage, and pouring location. Units include kilograms, cubic meters, percentage, hours, and others. The data update frequency follows each vehicle production batch, each batch of raw material delivery, and daily laboratory testing cycles.
What constraints do these characteristics impose on the "knowledge base retrieval and recall" link
The high update frequency and fine granularity of the data require that knowledge base retrieval support near-real-time recall of the latest moisture content and proportion data, to avoid using expired parameters. Multi-source heterogeneous data from different systems such as control systems and laboratories require unified field mapping rules, to ensure that relevant information related to equipment inspection and maintenance can be matched during retrieval. Each individual document contains professional engineering parameters, so retrieval must accurately match document fragments corresponding to equipment models and maintenance procedures, to avoid interference from irrelevant content. Lagging data such as test block strength reports need to be assigned separate recall weights, to ensure that historical strength anomaly records can be associated during maintenance or inspection.
How to set the configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Meets the precision requirement for matching professional engineering parameters, and avoids recalling irrelevant general maintenance documents |
limit | Top 8 entries | Covers the multi-dimensional parameters of a single vehicle production, while controlling the context length |
searchMode | mixedRecall | Combines keyword and semantic retrieval, adapting to matching professional terminology and structured parameters |
usingReRank | Enabled | Sorts recall results by relevance, filtering out content directly related to equipment inspection and maintenance |
datasetSearchUsingExtensionQuery | Enabled | Expands question semantics, supplements professional terminology related to concrete production, and improves recall coverage |
collectionIds | Bound to mixing station equipment library and test document library | Limits the retrieval scope, avoiding mixing in maintenance data from other categories |
Three common mistakes
- Phenomenon: The answer returned in AI conversation does not reference knowledge base content, and the retrieval log shows that the recall result is empty. Cause: The corresponding category's
collectionIdsare not correctly bound, and the retrieval scope covers irrelevant datasets. - Phenomenon: The retrieval result returns a large number of general machinery maintenance documents, and cannot match the professional parameters of concrete equipment. Cause: The
similaritythreshold is set too low, recalling low-relevance content. - Phenomenon: The answer delay exceeds 12 seconds after submitting a test query, and the interface shows loading timeout. Cause: The
limitvalue is set too high, resulting in too many recalled document fragments exceeding the model's context carrying limit.
How to confirm the configuration is correctly set
- Access the FastGPT knowledge base management page, verify whether the bound
collectionIdsinclude the mixing station equipment library and test document library. - Submit the test query "Maintenance plan for abnormal noise of mixing station host bearing", check whether the retrieval result preferentially returns maintenance documents for the corresponding equipment.
- Review the retrieval log, confirm that the number of recall results within the
similaritythreshold meets expectations, with no excessive irrelevant content. - Upload the latest aggregate moisture content test data, simulate a real-time update scenario, and verify whether the latest data can be recalled during retrieval.
The values provided here are common starting points, and should be evaluated against the samples used in actual deployment.