What the data for this category looks like
Intelligent due diligence reports for financial institution construction machinery leasing and mortgage businesses use data sources including manufacturer factory parameter manuals, equipment maintenance records, condition monitoring logs, and industry compliance standard documents. Update frequencies vary: factory documents are delivered once, maintenance records are updated monthly, and condition monitoring data is updated in real-time streaming. Document structures include structured parameter tables, long-text fault analysis, and parts detail lists. Fields include rated power (unit: kW), operating weight (unit: t), cumulative operating hours (unit: h), equipment serial number, and others. Some documents contain cross-page parameter association content.
What constraints these characteristics impose on the "knowledge base retrieval and recall" link
Multi-source and heterogeneous data formats require retrieval systems to support mixed recall of structured tables, long-text passages, and real-time data streams. This meets the needs of multi-dimensional parameter verification in due diligence reports. Differences in update frequencies require distinguishing full and incremental recall logic. This avoids repeated retrieval of outdated data and ensures the timeliness of due diligence reports. The special nature of field units requires unified unit matching rules during retrieval. This prevents parameter matching errors caused by inconsistent units. Long documents and cross-page parameter associations require retaining context associations during segmented retrieval. This avoids truncating the complete expression of key parameters and affecting the accuracy of due diligence conclusions.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | top 10-15 results | Construction machinery documents mostly consist of long passages and structured tables, which sufficiently covers the retrieval needs of a single due diligence report |
Similarity Threshold | 0.72-0.85 | High precision is required for construction machinery parameters, to filter out irrelevant results with low matching degrees |
Segment Length | 800-1200 characters | Adapts to the passage length of long documents, avoiding truncation of cross-page parameter association content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large maintenance PDF and condition log documents take longer to parse, to prevent parsing interruptions |
Incremental Sync Interval | 1 hour | Balances the real-time performance of condition data and system load, adapting to the update rhythm of maintenance records |
Reorder Return Count | top 5 results | Reduces the cost of result filtering for engineers, focusing on high-matching content |
The parameter values provided on this page are common recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- When retrieving CSV documents for construction machinery spare parts lists, the number of returned entries is less than the actual number of stored entries. This occurs because table structured parsing is not enabled, and retrieval is only performed as plain text blocks, missing parameter association content across cells.
- A
MongoServerError: The dollar ($) perror occurs when deploying related plugins in the beta4 version. This is caused by using MongoDB 4.4.29, which has compatibility issues with the handling of the $ symbol in query statements. - After importing a FastGPT knowledge base backup across instances, the original multi-file classification is lost. This happens because importing is done directly via a single CSV file backup, without retaining the original file directory structure, and without following the official cross-instance migration process.
How to Confirm the Configuration Is Correct
- Upload a construction machinery parameter PDF that includes rated power and operating weight, perform targeted retrieval, and check whether the returned results contain the target parameter passages.
- Set up an incremental sync task, manually update a maintenance record, wait for the sync cycle to pass, and perform retrieval to confirm that the new data is normally recalled.
- Import a spare parts list in CSV format, perform full retrieval, and check whether the number of returned entries matches the actual number of table rows.
- View the system operation logs to confirm that there are no abnormal errors in document parsing and incremental sync tasks.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.