What the data for this category looks like
The data sources for automated equipment primarily include factory parameter manuals, regular maintenance records, calibration reports, troubleshooting logs, and industry-wide general equipment standard documents. These sources are used to generate intelligent due diligence reports for financial institutions conducting equipment financial leasing and factoring businesses. Static parameter documents such as equipment model, rated power, and operating speed have fixed content, and are uploaded once when the equipment is put into production. Dynamic data such as operating duration, failure frequency, and component replacement records are updated regularly as the equipment is used, with update cycles ranging from weekly to quarterly. Document formats are primarily PDF, CSV, and Excel. Structured fields include equipment number, unit (kW, r/min, hours), failure code, maintenance steps, and some documents include charts and parameter comparison tables.
What constraints do these characteristics impose on the knowledge base retrieval and recall process
The data characteristics of automated equipment impose multiple constraints on the retrieval and recall process. The fixed nature of static parameters requires precise matching of unique identifiers such as equipment number and model during retrieval, to avoid incorrect recall across models. The high-frequency updates of dynamic logs require the knowledge base to support incremental synchronization, to ensure retrieval results include the latest maintenance and failure data. The coexistence of multiple document formats requires the parsing process to adapt to different field extraction rules. For example, CSV logs must correctly identify timestamps and corresponding parameter units, while PDF manuals must retain structured data within tables. The fact that fields include clear units requires retrieval to match both parameter names and units, to avoid confusing similar parameters with different units.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Automated equipment documents often contain long sections of parameter descriptions and troubleshooting steps. Segments that are too long will lose contextual relevance, while segments that are too short cannot cover complete descriptions of parameter combinations |
Recall Count | Top 8–12 results | Equipment due diligence requires matching multiple fields such as model, power, and maintenance cycle. Too many recalled results will introduce irrelevant documents, while too few will fail to cover all matching items |
Similarity Threshold | 0.75–0.85 | Text descriptions of equipment parameters follow fixed formats. A threshold that is too low will introduce incorrect matches, while a threshold that is too high will fail to recall alternative documents for similar models |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Large equipment maintenance manual PDFs may include multiple pages of charts and tables, leading to long parsing times. Timeouts will cause document upload failures |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Bulk uploaded equipment operation log CSV files may have large individual file sizes. Too strict a size limit will prevent large log files from being uploaded |
Reranked Return Count | Top 3–5 results | Due diligence reports need to prioritize displaying the most matching core parameter documents, to avoid interference from redundant information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test against your own samples before finalizing settings.
Three common mistakes
- Issue: After uploading device operation logs in CSV format exported from WPS, garbled characters appear in parsed fields. Cause: CSV files exported from WPS may use non-UTF-8 encoding, and correct file encoding parsing parameters are not configured.
- Issue: When calling the API to query two knowledge bases under the same account simultaneously, the returned results are empty or do not include expected cross-knowledge base documents. Cause: The interface request does not correctly carry the IDs of multiple knowledge bases, or the permission settings for cross-knowledge base retrieval are not enabled.
- Issue: When retrieving documents related to equipment models, the returned results include a large number of irrelevant consumables and accessories documents. Cause: The similarity threshold is set too low, causing incorrect recall of non-target documents that include the keyword "equipment".
How to confirm correct configuration
- Upload a standard-format equipment parameter PDF and CSV log, and check that the parsed fields are complete and free of garbled characters.
- Call the retrieval interface, specify a single knowledge base, and verify that the number of returned results matches the configured recall count.
- Initiate retrieval by specifying two knowledge bases simultaneously, and confirm that the returned results include matching documents from both knowledge bases.
- Adjust the similarity threshold, observe changes in the matching accuracy of retrieval results, and confirm that the threshold meets business requirements.
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.