What data looks like for this category
Investment research data for the optical and optoelectronics industry mainly comes from public research reports, patent documents, device parameter manuals, supply chain quotation databases, and laboratory test reports. The update frequency varies widely: device parameter manuals are updated every six months alongside new product iterations, patents are published in real time, industry research reports are updated weekly or monthly, and supply chain quotation data is updated daily. Document structures include structured parameter tables (with fields such as model number, wavelength, refractive index, power consumption, etc., with units mostly nanometers, milliamps, and percentages), unstructured technical analysis paragraphs, and test reports with charts. Individual document lengths range from hundreds of characters to tens of thousands of characters.
What constraints these characteristics impose on vector models and indexing
The fine-grained matching requirements of structured parameter tables require vector models to have precise encoding capabilities for professional terms and units, to avoid semantic loss caused by splitting parameter rows. High-frequency updated data sources need to support incremental indexing to avoid increased time consumption from full reconstruction. Long document segmentation must adapt to the typesetting logic of parameter tables, to avoid breaking parameter associations caused by cross-row splitting. Reasonable deduplication rules must be configured for similar data from different sources, to prevent duplicate indexing from occupying storage resources.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the average length of parameter tables and technical paragraphs in optical and optoelectronics documents, avoiding splitting across parameter rows |
similarity_threshold | 0.72–0.85 | Distinguishes subtle parameter differences between device models of the same category, reducing false recall probability |
recall_top_k | Top 10 entries | Covers device test data and research report analysis from multiple sources, avoiding missing key parameters |
incremental_index_enable | Enabled | Adapts to the high-frequency update rhythm of supply chain data and industry research reports, reducing time spent on full indexing |
parse_file_timeout_seconds | 600 seconds | Prevents task failure due to parsing timeout when processing large patent documents or batch test reports |
duplicate_detection_enable | Enabled | Identifies duplicate uploaded documents or identical content fragments, reducing indexing redundancy |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: When creating a knowledge base, no added vector model options appear in the text understanding model dropdown menu. Cause: The vector model has not been bound to the text understanding model pool, or the model type does not match the vector encoding requirements for document parsing.
- Phenomenon: The number of knowledge base segments does not match the initial parsing results, with duplicate indexed fragments. For example, the initial display shows 8 segments, which later becomes 13. Cause: Incremental indexing has not been configured with correct deduplication rules, or temporary cache copies are included when uploading documents, leading to repeated parsing.
- Phenomenon: When adjusting vector model parameters in bulk, only single-file operations are supported, and unified adjustment cannot be performed for batch documents in a directory. Cause: The bulk indexing configuration item has not been enabled, or the corresponding data source directory for batch processing has not been selected.
How to confirm correct configuration
- Upload a standard optical and optoelectronics device parameter document (such as an LED chip test report), and check whether the segmentation results retain complete parameter rows without cross-row splitting.
- Initiate a recall test, input a specific device model and parameter, and confirm that the recall results include the corresponding document with no duplicate fragments.
- View the indexing logs, and confirm that incremental updates only process new or modified documents, with no records of full repeated indexing.
- Check the model management page, and confirm that the vector model has been bound to the current knowledge base's indexing configuration, with no unauthorized or incompatible model entries.
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.