What the data for this category looks like
Data for automated equipment intelligent due diligence reports comes primarily from device ledger systems, operation log databases, third-party compliance test documents, and factory quality inspection reports. Update rhythms vary across data sources: ledger information for newly launched devices is updated in real time, daily operation logs are archived on a daily basis, and quarterly compliance reports are synced quarterly.
Document structure consists mostly of structured fields, supplemented by unstructured attachments. Structured fields include device ID, rated power (unit: kW), rated voltage (unit: V), total operating hours (unit: h), maintenance cycle (unit: month), and others. Unstructured attachments are mostly PDF maintenance records and test reports, with individual attachments reaching thousands of characters in length.
Constraints imposed on vector models and indexing by these data characteristics
The above data characteristics create multiple constraints for the vector models and indexing link. Structured fields have clear units, requiring embedding models to support semantic binding of numerical values and units to avoid confusion over the physical meaning of different parameters. Unstructured attachments vary widely in length, requiring an adapted long-text segmentation strategy to avoid losing critical technical context after splitting. Data update frequencies are uneven, requiring support for differentiated index refresh rules to balance retrieval timeliness for real-time device data and archived historical data. Device ID serves as a unique identifier, requiring a deduplication index to avoid redundant recall of duplicate data for the same device.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Maintenance records and compliance documents for automated equipment due diligence reports are mostly long paragraphs. This segmentation length preserves complete technical parameter context and avoids splitting that breaks parameter associations |
similarity_top_k | 10–15 entries | Device parameter comparison requires multi-dimensional recall samples. This value balances retrieval efficiency and recall coverage |
refresh_interval | 1 minute (real-time devices) / 24 hours (archived documents) | Matches the update rhythms of different data types. Real-time device data requires fast index synchronization, while archived documents do not need frequent refreshes |
vector_store_index_type | HNSW | Adapted to scenarios where automated equipment data volume is typically large. This index type balances retrieval speed and recall accuracy |
embedding_model_batch_size | 8–16 | Adapted to the video memory limit of RTX2070. Single-batch embedding computation does not exceed the hardware's carrying capacity |
similarity_threshold | 0.72–0.85 | Semantic matching of device parameters requires a high threshold to avoid redundant recall results with mismatched units or model numbers |
The parameter values provided on this page are all common recommended 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
- Symptom: The interface returns the
No available channelerror, and logs show a failed embedding model call. Cause: No adapted embedding model was selected for the structured data with units for automated equipment. A purely text-only embedding model was used, which cannot correctly encode the combined semantics of numerical values and units. - Symptom: Knowledge base search takes longer than 30 seconds, resulting in a timeout error. Cause:
chunk_sizeis set to more than 1500 characters, andsimilarity_top_kis configured to 20 or more entries. Single-batch vector computation exceeds the video memory carrying capacity of RTX2070, leading to computation timeout. - Symptom: Recall results include entries with a rated power of 220V, which does not match the kW unit parameter in the query. Cause:
similarity_thresholdis set below 0.7, and no separate index for structured fields is enabled, resulting in semantic confusion between voltage and power unit parameters.
How to confirm the configuration is correct
- Upload a CSV of automated equipment ledger data containing rated power and rated voltage fields. Check if the parsed segments retain complete parameter combinations, and verify the actual effective length of
chunk_size. - Initiate a retrieval request including "rated power 100kW". Check if the number of returned recall entries is 10–15, and if the results only include device data with matching power parameters.
- View the embedding model's operation logs to confirm that the actual call value of
embedding_model_batch_sizematches the configured value, and that no video memory overflow error messages appear. - Upload a new piece of device ledger data. Wait for the configured
refresh_intervalduration, then initiate a retrieval to confirm that the newly uploaded device data can be recalled normally.
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.