What this type of data looks like
Optical module market and yield rate data is primarily sourced from public monitoring platforms for the optical communications industry, upstream chip manufacturer supply ledgers, and downstream data center procurement records. Data is updated daily, with full structured records for the previous day generated each early morning. Each record corresponds to a single optical module model, and includes fields such as product_sku (product SKU), manufacturer (manufacturer), nominal_speed_gbps (nominal speed, unit Gbps), operating_temperature_celsius (operating temperature, unit Celsius), factory_cost_yuan (factory cost, yuan per unit), retail_price_yuan (retail guide price, yuan per unit), daily_transaction_count (daily transaction volume, unit pieces). No percentage-based statistical indicators are included.
What constraints do these characteristics impose on the conversation logs and auditing link?
Optical module data has many fields tied to communication hardware parameters. Conversation logs must accurately match field names to avoid audit traceability issues caused by generalized records. The daily update feature requires audit logs to be archived by natural day to prevent cross-day data confusion. The independent parameter attributes of a single model require that conversation contexts be associated with historical query records for a specific product_sku, and data from other models must not be mixed. Fields include multiple types of physical parameters and transaction values, and logs must fully retain original values and their corresponding units, otherwise the accuracy of yield rate calculations cannot be verified.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
max_history_messages | 20–50 messages | Optical module single data records have many fields, and the context data carried in a single conversation is large. 20–50 balances memory effect and resource usage |
log_retention_days | 365 days | Optical module market data is updated daily, and audits require retaining at least one year of historical conversation and data call records for compliance checks |
audit_field_include_list | ["product_sku", "transmit_power_dbm", "daily_transaction_count"] | Optical module conversations center around specific model parameters and transaction data. Precise recording of key fields is required to avoid log redundancy |
api_request_timeout | 120 seconds | Optical module data source interfaces need to pull aggregated data from multiple manufacturers and models, with long response times. 120 seconds covers most normal calls |
context_token_threshold | 8000 characters | The character volume of a single optical module data record is large, and the total context character volume must be limited to avoid model inference timeouts |
The parameter values provided on this page are all common recommendations used as 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
- Only retaining fewer than 50 conversation context records makes it impossible to call previously mentioned optical module model parameters in subsequent conversations. This occurs because the
max_history_messagesconfiguration limit is not adjusted based on the number of fields in optical module data. The default value cannot cover the field transfer requirements of multi-round queries. - Calling the historical log interface returns a
401 Unauthorizederror, and normal record acquisition fails even after confirming credentials are correct. This occurs because a dedicated audit log call credential is not configured, and mixing general credentials leads to permission verification failure. - The optical module model parameters mentioned in the conversation are not associated with the context, making it impossible for the model to answer based on historical query results. This occurs because
product_skuis not added toaudit_field_include_list, and the core associated field is not recorded in the log, leading to the context being unable to match the target model of historical queries.
How to confirm the configuration is complete
- Call the
/api/chat/historyinterface, check that the number of returned records falls within the range configured formax_history_messages, to verify that the context call logic operates normally. - View the audit log panel, confirm that the recorded fields include the contents of the configured
audit_field_include_list, to verify that the field whitelist is effective. - Initiate two query conversations associated with the same optical module model, check that the second conversation can call the parameter information from the first query, to verify that the context association logic operates normally.
- Simulate a slow interface call scenario, confirm that no timeout error is triggered, to verify that the
api_request_timeoutconfiguration threshold adapts to the data source response speed.
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.