Data Profile for This Category
Optical and optoelectronics investment research data mainly comes from public documents of industry associations, periodic financial reports of listed companies, patent databases, supply chain quotation platforms and professional research report repositories. The update rhythm varies significantly: supply chain quotation data is updated daily, patent data is updated in real time upon publication, and financial reports and research reports are released quarterly or irregularly. Document structures include mixed types: long text research reports with technical parameter descriptions, structured tables such as production capacity, gross margin and product unit price, patent structured fields including application number and IPC classification, and time-series quotation data. Fields contain specific physical units such as epitaxial wafer thickness, panel refresh rate and monthly production capacity, and some fields have dedicated model codes.
Constraints Imposed by These Characteristics on Model Access and Configuration
Mixed data structures require configuring multi-type parsing model access parameters to support structured table extraction and non-long text vectorization adaptation. Multiple update rhythm data sources require configuring incremental synchronization trigger thresholds and scheduled task parameters to match different data source update frequencies. Specific physical units and dedicated model fields require configuring custom dictionaries for entity recognition to avoid unit identification deviations and model matching errors. The length of long text research reports requires configuring reranker model input length limit parameters to adapt to the average content length of a single research report. Differences in the number of rows in structured tables require configuring the maximum number of rows parameter for table parsing to avoid extraction truncation or timeouts.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
reranker_max_length | 8192 characters | The average length of a single optical and optoelectronics research report exceeds 4000 characters, adapting to long text reranking requirements |
embedding_batch_size | 32 items/batch | Optical and optoelectronics supply chain data has large single-batch update volume, balancing computing resource usage and synchronization efficiency |
sync_interval | 3600 seconds | Supply chain quotation data is updated daily, hourly synchronization meets real-time requirements |
entity_extraction_dict | Load optical and optoelectronics special dictionary | Contains special units such as micrometer, Hz and product models, improving entity recognition accuracy |
parse_table_max_rows | 200 rows | Capacity tables in listed company financial reports for optical and optoelectronics industries usually do not exceed 150 rows, avoiding extraction truncation |
retrieve_top_k | Top 10 items | Balances the breadth of investment research recall and result relevance, avoiding redundant data interfering with analysis |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Configuration Errors
- Phenomenon: The reranker model deploys and tests normally, but the reranking mark returned by each retrieval is always
false. Cause: The reranker model is not bound to the retrieval link of the investment research knowledge base, or thereranker_thresholdparameter is set too high, resulting in zero number of reranking results that meet the threshold. - Phenomenon: After calling the MCP plugin, only XML or JSON code blocks are returned, and no visual charts of optical and optoelectronics production capacity or prices are rendered. Cause: The front-end rendering configuration item is not enabled, or the target container for chart rendering is not specified, causing the code block to fail to be parsed into visual content.
- Phenomenon: The log shows the error
Cannot read properties of null (reading 'q'), and format abnormalities occur during multi-model chained calls. Cause: Query parameter verification logic and format transfer rules for the model link are not configured, empty queries trigger null pointers, and input and output formats of different models do not match.
How to Verify Successful Configuration
- Execute a single optical and optoelectronics product parameter query, check that the vector recall result includes the target business field, confirming that the matching configuration between the embedding model and the knowledge base is correct.
- Trigger an incremental synchronization task, check that the updated data entries match the incremental update volume of the data source, confirming that the synchronization cycle matches the data source update rhythm.
- Submit a query with empty content, check that the system does not trigger a null pointer exception, confirming that the query verification configuration is effective.
- Call the reranker model test interface, check that the returned result sorting meets the investment research relevance requirements, confirming that the reranker model binding and parameter configuration are correct.
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.