What Data for This Category Looks Like
Data sources include equipment operating parameters automatically collected from optical optoelectronics production lines via partnerships with financial institutions, material ledgers from supply chain management systems, marketing exposure and click data from financial e-commerce platforms, and customer lead information collected offline. Update cadences vary by data source type: real-time production line data updates every second, material ledger data updates daily, marketing exposure data updates hourly, and lead information syncs in real time.
Most documents use structured JSON or semi-structured CSV formats. Equipment parameter documents include fields such as equipment ID, operating temperature (unit: degrees Celsius), operating current (unit: amperes), and production beat (unit: units per minute). Material ledger documents include fields such as material code, purchase unit price (unit: yuan), and inventory balance. Marketing data documents include fields such as exposure volume, click volume, and lead count.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
Heterogeneous data source structures require tool calling to adapt to multi-format parsing logic. Configure field mapping rules for JSON-formatted production line data, and specify header matching logic for CSV-formatted material data.
Different update cadences require plugin trigger frequencies to match scenario requirements. Plugins that call real-time production line data must be set to second-level polling. Material data plugins only require daily synchronization.
Fields carry clear physical units. Tool calling must validate unit consistency to avoid directly substituting values with different units into the marketing content generation process. Differences in the real-time nature of marketing data require plugins to be configured with reasonable cache expiration times to prevent the use of outdated data when generating marketing content.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PLUGIN_REQUEST_TIMEOUT | 30–60 seconds | Response delays for optical optoelectronics production line data collection are typically under 20 seconds. Setting 30-60 seconds covers most normal requests and avoids timeout interruptions |
REQUIRE_FIELD_UNIT_VALIDATE | Enabled | Most data fields for this category carry physical units. Enabling validation prevents marketing content generation errors caused by unit mismatches |
DATA_SOURCE_PARSE_RULE | Preset JSON/CSV parsing templates by data source type | Data source structures for this category are fixed as JSON and CSV. Preset templates reduce format parsing errors |
CACHE_EXPIRE_SECONDS | 3600 seconds (marketing data), 86400 seconds (material data) | Marketing data requires hourly freshness. Material data only needs daily updates to meet requirements |
PLUGIN_TRIGGER_FREQUENCY | Real-time (production line/lead data), hourly (marketing data), daily (material data) | Matches the update cadence of different data sources to avoid excessive calls or outdated data |
MAX_PARSE_FIELD_COUNT | 20 | The number of fields per data source for this category typically does not exceed 20. Setting this limit improves parsing efficiency |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When calling an image generation plugin, passing correct Lora weights, scale parameters, and Lora file names still returns a 400 status code. Cause: Plugin context parameters are not configured. Optical optoelectronics marketing content requires association with product compliance parameters. The plugin cannot obtain the associated context, causing the generation logic to fail.
- Phenomenon: After enabling the knowledge base plugin's question optimization function, the final answer's plugin call results are truncated. Cause: The maximum length of plugin return results is not set. The knowledge base optimization logic compresses overly long content, preventing complete parameters from optical optoelectronics production line data from being included in marketing content.
- Phenomenon: When using a PDF to Markdown tool to process product manuals, extracted table fields lose unit information. Cause: The tool's unit retention rule is not configured. Units for parameters such as current and temperature in optical optoelectronics product manuals are not correctly extracted, resulting in missing key parameter information in generated marketing content.
How to Verify Proper Configuration
- View plugin call logs to confirm that trigger frequencies for different data source plugins match preset configurations.
- Manually pass a test data set containing units to verify that the field unit validation function correctly blocks parameters with mismatched units.
- Call the PDF to Markdown tool to process a single product manual, and check that extracted fields include complete unit information.
- Enable the knowledge base plugin's question optimization function, and verify that complete plugin return data is correctly included in the final answer.
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.