What the data for this category looks like
Electronic component investment research data primarily comes from official manufacturer datasheets, industry association specification documents, real-time supply chain quotation platforms, and third-party test reports. Update cadence aligns with product iterations. Core parameters are updated synchronously when new mass-produced models launch. Standard product categories receive quarterly updates for supply and quotation information. Most individual documents use structured tables paired with parameter descriptions, including fields such as model number, package type, electrical parameters, operating temperature range, and pin definitions. Units follow industry standard measurements including ohms, farads, volts, degrees Celsius, and other common industry units.
Constraints on conversation logs and auditing from these data characteristics
The multi-source heterogeneous and high-frequency update nature of electronic component investment research data requires conversation logs to fully record the data source version, parameter filtering dimensions, and unit matching status for each invocation. The high volume of structured parameters requires logs to retain field filtering rules specified during invocation, to prevent cross-category parameter confusion. The rapid iteration of real-time supply data requires the auditing link to associate invocation times with data source update timestamps, to ensure result timeliness can be verified during backtracking. The unique identification requirement for electronic component models also requires logs to bind the target model ID specified during invocation, to avoid cross-mixing of research results across different models.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_KEEP_DAYS | 90 days | Electronic component investment research data must be retained for at least a quarterly cycle per industry compliance requirements, to meet audit traceability needs |
LOG_INCLUDE_SOURCE | Enabled | Electronic component data has multiple sources and updates frequently. It is necessary to record the data source ID and update time associated with each invocation to ensure audit traceability |
MAX_CONTEXT_HISTORY | First 20 conversation entries | Electronic component investment research conversations often involve multi-round parameter comparisons. Retaining sufficient context ensures complete invocation links during audits |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Electronic component documents often contain long tables and multi-field content. Sufficient parsing time must be reserved to avoid timeout errors |
AUDIT_ALERT_THRESHOLD | Calibrated via actual testing | Response latency varies across different intranet environments. A reasonable timeout alert threshold must be set based on actual invocation performance |
API_LOG_RESPONSE_FIELDS | Model number, parameter value, unit, data source | Audits for electronic component investment research require clear core result information and traceability basis, so only key fields specified here are logged |
The parameter values provided on this page are common recommended starting points 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 settings.
Three Common Misconfigurations
- Issue: After deploying version 4.8.21 via Docker, invoking the knowledge base to parse electronic component documents repeatedly triggers
slow operation xxxxmslog errors. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted for long or multi-field electronic component documents, leading to unhandled parsing timeouts. - Issue: API calls do not return chain-of-thought processes, and no corresponding execution records appear in logs. Cause: The
LOG_INCLUDE_SOURCEconfiguration was not enabled, so detailed chain-of-thought execution logs were not retained. - Issue: Intranet deployment logs prompt MySQL connection refused, while MySQL service is confirmed to be running normally. Cause: Correct intranet IP and port were not specified in the
DB_CONNECTION_STRINGconfiguration, preventing the auditing module from connecting to the database for log storage.
How to Verify Proper Configuration
- View the system log panel to confirm the
LOG_INCLUDE_SOURCEconfiguration is enabled, and that the data source ID and update time for each invocation have been recorded. - Initiate an electronic component parameter query invocation, check that returned results match log retention content, and confirm context and field filtering rules are fully recorded.
- Simulate a timeout invocation scenario, confirm the auditing module generates corresponding alert logs after the
AUDIT_ALERT_THRESHOLDconfiguration is triggered. - View database connection logs to confirm the auditing module has normal connection status with MySQL and MongoDB, with no connection refused errors.
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.