What data for this category looks like
Optoelectronics industry investment research data mainly comes from industry association public reports, upstream wafer fab and device manufacturer specification documents, patent databases, production line monitoring logs, and academic papers. Industry reports are updated monthly. Patent documents are added to storage in real time as applications are filed. Device specification sheets are updated simultaneously with new product releases.
Document structures include long text analysis sections, structured parameter tables, and time-series monitoring data snippets. Fields cover device model, operating wavelength (unit: nanometers), response speed, yield rate, patent application date, and more. Some individual documents can be hundreds of pages long.
What constraints these characteristics impose on conversation logs and auditing
Optoelectronics investment research conversations often involve long document recall, precise parameter matching, and cross-verification of multi-source data. Conversation logs must fully record interaction context, data source versions, and parameter call details.
Frequently updated data sources require the auditing link to associate conversation records with knowledge base version changes. This prevents decision traceability failure caused by data iterations.
Structured parameters and time-series data require logs to support both natural language interactions and retention of structured fields. This meets multi-dimensional verification requirements for compliance auditing.
The multi-person collaboration feature of investment research scenarios requires audit logs to have user permission isolation capabilities. This prevents sensitive investment research data leaks.
How to set configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
log_retention_days | 180 days | Optoelectronics investment research involves long-term supply chain and patent tracking. At least six months of audit records are required to meet industry compliance requirements, while also controlling storage costs |
audit_log_include_context | Enabled | Investment research conversations often involve long document recall and parameter matching. Fully recording context allows traceability of decision-making basis and avoids information gaps |
api_conversation_log_switch | Enabled | Logs for API calls and page-side conversations must be recorded simultaneously, to cover auditing needs for full-channel interactions |
max_log_entry_size | 2048 KB | Addresses storage requirements for long-text interactions and structured parameter logs, to avoid truncation of critical investment research data |
log_export_format | JSON + CSV dual format | Meets structured export requirements for compliance auditing and tabular requirements for manual verification |
user_conversation_visible_only | Enabled | Restricts users to viewing only their own conversations, in line with privacy protection requirements for investment research data |
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 against your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Console.log output from workflow modules cannot be displayed in conversation logs, and can only be viewed in the in-container console. Cause: Log write-back configuration for workflow nodes is not enabled, and in-container console logs are not synchronized to the FastGPT audit log system.
- Phenomenon: After batch cleaning conversation logs, some conversation records created via API are not deleted. Cause: The batch cleaning scope is not configured to cover API-side conversations, and only conversation entries generated on the page side are cleaned.
- Phenomenon: Different users can view each other's historical conversation content after logging in. Cause: The
user_conversation_visible_onlyconfiguration is not enabled, and user access permissions for conversations are not restricted.
How to confirm the configuration is correct
- Initiate an API-created conversation, check whether the audit log list includes the complete interaction record of this conversation, including context, parameter calls, and data source association information.
- Initiate a conversation as a test user, switch to another user account, and verify that the test user's historical conversation content cannot be viewed.
- Trigger a workflow node that includes console.log, check whether the corresponding output entry exists in the FastGPT audit logs.
- Attempt to perform a log export operation, verify that the export format includes both JSON and CSV types, and that the content is complete and not truncated.
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.