What Data Looks Like for This Category
Data for this category primarily comes from public technical parameter manuals of automated equipment manufacturers, supply chain and capacity reports released by industry associations, investment research documents for segmented tracks released by brokerage firms, and historical logs of equipment operation and maintenance. Document structures include structured parameter tables, long-form technical descriptions, and fragmented industry news snippets. Common fields include equipment model, rated power, operating speed, unit energy consumption, with corresponding units such as kW, r/min, kWh. Update frequency varies significantly by data source type: manufacturer technical documents are updated alongside product iterations, industry research reports are released on fixed cycles, and operation and maintenance logs accumulate over equipment operating cycles.
Constraints Imposed on Model Access and Configuration
The mixed structure of structured parameter tables, long-form technical descriptions and fragmented industry news requires the model access link to support configurations for both structured field extraction and unstructured text encoding. Dedicated units attached to equipment parameters require vector models to support semantic recognition of numerical values with units, to prevent field matching deviations. Differentiated update frequencies across multiple data sources require configuring incremental synchronization trigger rules to distinguish synchronization cycles for different data sources. Time-series operation and maintenance log data requires additional configuration of time-series vector encoding parameters to adapt to performance trend analysis needs in investment research.
How to Set Configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
chunkSize | 800–1200 characters | Automated equipment technical documents mostly contain long-form parameter descriptions and technical details. This segment length preserves complete parameter context |
chunkOverlap | 100–200 characters | Prevents cutting parameter fields during segmentation, preserves associated information across segments |
vectorModel | Match based on data source type | Structured parameter tables are compatible with general vector models. Time-series operation and maintenance logs are compatible with time-series vector models |
similarityThreshold | 0.75–0.85 | Semantic matching of equipment parameters has high precision requirements. This range filters low-relevance non-target documents |
topK | Top 6–8 entries | Investment research scenarios require balancing recall coverage and result conciseness, avoiding excessive redundant data that interferes with analysis |
embeddingBatchSize | 32–64 entries | Automated equipment documents are mostly single long-form texts. This batch size balances embedding efficiency and memory usage |
The parameter values provided on this page are all conventional recommendations used to determine a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to conduct tests on your own samples before finalizing values.
Three Common Mistakes
- When calling a third-party model interface, the error
tls: failed to verify certificate: x509: certificate signed by unknown authorityis returned. The cause is that no proxy or certificate trust rules are configured, making it impossible to verify the SSL certificate of overseas models. - After uploading mixed data sources, only a small number of matching results are returned. The platform only supports selecting one vector model, leading to mismatched encoding for structured parameters and time-series logs. The cause is that no grouping mapping rules for multiple vector models are configured.
- Documents split locally cannot retrieve expected content after being uploaded to the server, and queries return empty values. The cause is that the vector model encoding dimensions used locally and on the server are inconsistent, and vector embeddings corresponding to the server model have not been regenerated.
How to Confirm Configuration Is Complete
- Upload a single automated equipment technical document, check the parsed segment results to confirm that the segment length and overlap parameters meet the configuration requirements.
- Initiate a query targeting equipment parameters, verify that the fields and units of the recalled results are consistent with the source data.
- Trigger an incremental synchronization task, check whether the update cycles of different data sources in the synchronization log comply with preset rules.
- Call the model test interface to verify that there are no certificate verification or vector dimension mismatch errors in the returned error logs.
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.