What the data for this category looks like
Steel trade investment research data sources include public industry news platforms, futures exchange market data APIs, publicly disclosed steel mill production capacities, logistics freight rate databases, and industry research reports. Update frequencies vary across sources: spot quotes update every 15 to 60 minutes, futures market data is pushed in real time, and monthly production capacity and quarterly inventory reports are updated on a fixed schedule.
Documents fall into three categories: structured quotation sheets, unstructured research reports, and contract templates. Structured fields include steel product name, specification, origin, pricing unit, and trading cycle. Most pricing units are yuan per ton. Specification fields often include parameters such as diameter and strength grade.
What constraints these characteristics impose on conversation logging and auditing
Multi-source, heterogeneous data structures require logs to mark data source types, and distinguish interactions related to spot, futures, or research report data. Frequently updated market data requires logs to record exact timestamps at the time of interaction. This ensures audit traceability of data timeliness, and prevents use of expired market data.
Fixed fields and units require logs to retain complete parameter verification records. This prevents mismatches between specifications and units. Business relevance for specific scenarios requires logs to associate corresponding knowledge base document IDs and trade process tags. This facilitates backtracking of the basis for specific investment research decisions.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
conversation_log_retention_days | 90 days | The standard audit cycle for steel trade investment research covers quarterly periods. Retaining logs for 90 days meets audit requirements while controlling storage costs |
api_log_include_human_input | Enabled | Steel trade investment research conversations often include precise parameters such as steel specifications and transaction quantities. Enabling this setting fully records user input, and avoids missing fields during audits |
user_conversation_scope | Visible only to the user | Steel trade practitioners’ conversations involve sensitive information such as customer quotes and inventory data. Restricting each user to view only their own conversations complies with data security requirements |
log_data_source_tag | Enabled | Steel trade data includes multiple source types such as spot, futures, and research reports. Enabling this setting marks sources in logs, and facilitates tracing data credibility during audits |
conversation_audit_export_format | CSV format | Auditors commonly use spreadsheet tools to analyze logs. CSV format supports batch processing and parameter verification |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material types, data volume, and business rules. Specific issues require specific analysis. It is recommended to test against relevant samples before finalizing configuration settings.
Three common misconfigurations
- Issue: After an API call generates a conversation, the
Humanfield is empty in the on-page preview of the conversation record. Cause: Theapi_log_include_human_inputconfiguration is not enabled, so user input passed via the API is not written to the log. - Issue: Different trade practitioners cannot view their own historical conversation records after logging in. Cause: The
user_conversation_scopeis not set toVisible only to the user, or permissions are incorrectly set to globally visible. - Issue: Output from
console.login workflows cannot be viewed in the console or audit interface. Cause: Log persistence configuration for the workflow is not enabled, or the log storage path does not point to an accessible audit directory.
How to confirm configurations are correctly applied
- Initiate an API call for the spot price of a specific specification steel product. Check that the returned log includes complete user input parameters, and confirm the
Humanfield is not empty. - Use two different test accounts to initiate conversations. Verify that each account can only view conversation records it initiated, and cannot access log content from other accounts.
- Export audit logs. Check that logs mark data source types, interaction timestamps, and corresponding knowledge base document IDs, to confirm configuration items are active.
- View the workflow log output directory. Confirm that
console.logcontent has been persistently stored and can be accessed via the audit interface.
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.