What the data for this category looks like
Due diligence data for the optical and optoelectronics field comes primarily from public monitoring data released by the China Optical and Optoelectronics Industry Association, regular disclosure reports of listed entities, third-party supply chain price monitoring platforms, and public patent information from the National Intellectual Property Administration. Update frequencies vary by data source:
- Industry monitoring data is updated monthly
- Listed company financial reports are updated quarterly and annually
- Patent data is synchronized in real time
- Supply chain procurement data is updated weekly
Document formats include structured CSV/Excel tables, PDF industry analysis reports, and semi-structured enterprise business ledgers. Core fields and their corresponding units are:
- Product model: model identifier
- Yield rate: percentage
- Monthly shipment volume: ten thousand units
- Raw material procurement unit price: yuan per piece
- Number of patent authorizations: units
What constraints do these characteristics impose on workflow orchestration
The multi-data source setup, varied update frequencies, mixed document formats, and special field units in the optical and optoelectronics field impose multiple constraints on workflow orchestration. Parallel pulling from multiple data sources requires adapting to different interface permissions and return formats to avoid data pulling conflicts. Data sources with different update frequencies must be bound to independent scheduled trigger rules to ensure core business data is synchronized according to the corresponding cycle. Mixed structured and unstructured documents require separate configuration of structured parsing nodes and large model text extraction nodes to prevent format parsing failures. Special units such as percentage for yield rate and ten thousand units for shipment volume must have matching rules configured in the data verification stage to avoid confusion with cross-category data. The large volume of bulk shipment data requires configuring shard processing nodes to reduce resource usage per single workflow run.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | PDF industry reports in the optical and optoelectronics industry usually contain multi-page structured tables and technical terms. A longer timeout ensures complete parsing of all content |
Segment Length | 800–1200 characters | Technical documents and business data for this category contain long sentences and professional expressions. This range preserves semantic integrity and avoids context overflow |
Retrieval Count | Top 8 entries | Due diligence reports need to cover multi-dimensional information including supply chain, patents, and finance. This value balances information coverage and processing efficiency |
Similarity Threshold | 0.75–0.85 | Professional terms in the optical and optoelectronics industry have high similarity. This range filters irrelevant content and retains core matching results |
POSTGRESQL_CONN_TIMEOUT | 30 seconds | Database connections for supply chain data require fast response. A shorter timeout can quickly identify connection abnormalities and avoid workflow blocking |
Global Variable Dynamic Assignment Rule | Match by data source tag | Data sources for optical and optoelectronics are divided into three categories: industry reports, financial reports, and patent databases. Matching via tags automatically imports data into the corresponding knowledge base, simplifying subsequent calls |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, 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
- Symptom: When connecting to PostgreSQL using a database connection plugin, the interface prompts "Workflow verification failed, please check for missing or empty values, and whether connections are normal". Cause: The dedicated PostgreSQL connection parameters for optical and optoelectronics are not bound, or the mapping rules between data source tags and connection configurations are not configured.
- Symptom: When passing the
resultfield output by the code execution node to an AI model, thechat:ai_input_is_eerror is triggered. Cause: Theresultfield is not processed with string serialization, causing the passed parameter format to not meet the model's input requirements. - Symptom: Optical and optoelectronics professional fields (such as yield rate, shipment volume) returned by the text extraction node are empty or have incorrect units. Cause: Custom extraction rules are not configured for the special fields of this category, and general extraction rules cannot recognize professional terms and unit formats.
How to confirm the configuration is correct
- Trigger a test run, check whether the data pulling node pulls the corresponding optical and optoelectronics data according to the configured data source tags, and verify whether the returned fields and units meet expectations.
- Run the text extraction node, check whether the returned professional fields are complete, and adjust the corresponding configuration items until all target fields are covered.
- Trigger a database connection test, confirm that the connection status is normal, no verification failure prompts appear, and verify that the connection parameters match the dedicated optical and optoelectronics data sources.
- Pass the output result of the code execution node to the AI model, confirm that no format errors occur, and that the model can normally use the passed
resultdata to generate due diligence content.
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.