Conversation Logging and Auditing for Power Industry

What the data for this category looks like

Power is a segmented public utility category. Its investment research data is core material for financial, insurance, and wealth management institutions conducting industry research. The data covers multiple sources: power enterprise annual operational reports, monthly grid dispatch logs, generating unit technical parameter documents, national energy policy documents, and industry standards.

Update cadences vary significantly: annual reports update per fiscal year, monthly dispatch logs update monthly, and real-time grid operation data is pushed near-real-time. Document structures include structured tables (such as unit ID, generating power, grid load, etc.), unstructured technical white papers, and policy interpretation texts. Most fields have clear units: generating power uses MW as the unit, and electricity price uses yuan per kilowatt-hour as the unit.

What constraints do these characteristics impose on conversation logging and auditing?

The multi-type, varied update cadence characteristics of power investment research data create clear constraints for conversation logging and auditing. First, log both structured parameter calls and unstructured document parsing results to ensure audits cover all types of data interactions. Second, bind precise timestamps and source identifiers to real-time data in logs to meet regulatory traceability requirements. Third, fields have fixed units. Audits must verify that units of parameters extracted or generated during conversations match specified norms to prevent data misuse. Fourth, multi-round investment research conversations often involve cross-document parameter comparisons. Logs must retain complete context chains to support issue backtracking and responsibility identification.

How to set the configurations

Configuration Item	Recommended Setting	Rationale
`logRetentionDays`	180 days	Power industry investment research data must meet standard regulatory audit cycles; 180 days covers most compliance requirements
`maxContext`	Previous 12 conversations	Multi-round power investment research conversations often involve cross-unit, cross-cycle parameter comparisons; 12 entries covers complete scenario context
`auditLogIncludeSource`	Enabled	Power data sources must be traceable; audit logs must record file IDs and upload times of knowledge base fragments
`PARSE_FILE_TIMEOUT_SECONDS`	300 seconds	Large generating unit parameter documents are common in the power industry; longer parsing time is needed to complete full content extraction
`auditFieldCheckEnabled`	Enabled	Power data has clear unit and field specifications; must verify consistency of field units extracted during conversations
`maxChatHistoryLength`	20 entries	Balances log storage load and audit requirements; retains sufficient multi-round conversation chain information

The parameter values provided on this page are common recommendations for establishing configuration baselines. Actual values are affected by material formats, data volumes, 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

Issue: Local Docker deployment results in inability to access port 3000, with database connection failure shown in logs. Cause: For FastGPT v4.8.10, the audit log database storage path was not configured correctly, or logRetentionDays was set beyond the container disk quota.
Issue: Using a text extraction node in a workflow fails to trigger multi-round conversations, with no error logs output. Cause: auditLogIncludeSource was not enabled, leading to lost context association, or maxChatHistoryLength was set to 0, so no conversation history was retained.
Issue: The text extraction node returns inconsistent units for power parameters, such as both MW and kilowatts appearing. Cause: auditFieldCheckEnabled was not enabled, so field unit matching was not verified, allowing non-standard data to enter the conversation flow.

How to confirm configurations are correctly applied

Access the system audit log page, verify that conversation records from the last 180 days are included, and that each record is attached with source file identifiers for knowledge base fragments.
Launch a conversation with multi-round power parameter queries, confirm that the conversation history retains context information from the first 12 rounds for subsequent backtracking.
Upload a large power generating unit parameter document, confirm that the parsing process does not trigger timeout errors, verifying that the PARSE_FILE_TIMEOUT_SECONDS configuration takes effect.
Launch a query that includes unit verification, such as "What is the power generation corresponding to a unit output of 100MW", confirm that the system returns results with unit verification prompts, verifying that the auditFieldCheckEnabled configuration takes effect.

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.