What this category’s data looks like
Power grid equipment investment research data comes from official technical manuals, type test reports, operation and maintenance logs, industry mandatory standard documents, and supplier product parameter packages. Update rhythms fall into three categories: industry standards are revised every 3 to 5 years, operation logs update in real time with equipment operation, and new product parameter documents update synchronously with manufacturer releases. Most document structures combine structured fields with unstructured text. Fields include equipment model, rated voltage, rated capacity, insulation resistance, fault codes, and operation cycle. Units mostly use International Electrotechnical Commission standards: kV, MW, Ω, hours, and similar units.
What constraints these characteristics impose on knowledge base retrieval and recall
The high proportion of structured fields and strict unit specifications require retrieval to support both precise field matching and semantic recall. This avoids incorrect matching caused by unit ambiguity. Multi-source data has significantly different update rhythms. Configure differentiated recall weights for different data types, and prioritize real-time data such as operation logs. Document lengths vary widely, from single-page parameter sheets to hundred-page test reports. Adapt recall logic to different segment lengths. Equipment models have high uniqueness requirements. Ensure recalled results fully match query requirements.
Configuration settings
| Configuration Item | Recommended Value Range | Basis for This Setting |
|---|---|---|
chunkSize | 800–1200 characters | Power grid equipment documents include both short parameter sheets and long test reports. This range balances semantic integrity and recall accuracy |
recallTopK | Top 10–15 entries | Power grid equipment investment research covers multi-dimensional parameters and background information. Too many entries increase context pressure, while too few miss key data |
similarityThreshold | 0.72–0.85 | Balance precise matching of models and parameter units. A threshold that is too low introduces irrelevant results, while a threshold that is too high misses valid recall items |
rerankTopN | Top 5–8 entries | Investment research scenarios focus on core parameters and authoritative documents. Reranking filters out low-correlation results |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large test reports take a long time to parse. This setting avoids parsing failure caused by timeout |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some full-life-cycle test reports of power grid equipment have large single-file volumes |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. Test on your own samples before finalizing the configuration.
Three common misconfigurations
- Phenomenon: After uploading Feishu multi-dimensional documents or Excel-format equipment parameter sheets, knowledge base retrieval fails to return corresponding field results. Reason: Structured parsing configuration for Excel/multi-dimensional documents is not enabled, so fields are not correctly indexed.
- Phenomenon: Unit errors appear in retrieval results, such as kV mistakenly identified as V. Reason: The similarity threshold is set too low, so the system fails to filter recall results with similar semantics but mismatched units.
- Phenomenon: After restoring the backed-up knowledge base, the retrieval function cannot be called normally. Reason: The backup file does not include retrieval index configuration. Only the original documents are restored, and the vector database is not rebuilt.
How to confirm the configuration is complete
- Upload a single equipment parameter Excel file, run a retrieval query for a specific model, and check whether returned results include correct units and field information.
- Compare the recall order of documents with different update frequencies, confirm that real-time operation documents have a higher recall weight than industry standard documents.
- Adjust the similarity threshold and the number of recalled entries, verify that retrieval result quantity and relevance meet investment research scenario needs.
- Upload a single hundred-page test report, check that parsing time does not exceed the configured timeout period, and segmented content can be normally recalled.
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.