Conversation Logging and Auditing for Advertising &

What Data for This Category Looks Like

Yield rate and market trend data for advertising and marketing scenarios comes from ad campaign management systems, third-party ad monitoring platforms, and industry market trend aggregation APIs.

Data update cadence: Full daily updates of campaign data from the previous day complete each day at midnight. Market trend data refreshes every hour.

Data is provided as structured tables, with fields including campaign unit identifier, ad slot type, delivery time slot, impression count, click count, conversion count, actual revenue, input cost, yield rate value, and more. Units are as follows:

Impression count and click count use "times"
Conversion count uses "units"
Input cost and actual revenue use "yuan"
Yield rate uses a decimal value

Constraints Imposed on Conversation Logging and Auditing Workflows

Daily T+1 updated campaign data requires conversation logs to be archived by natural day, to avoid mixing data across cycles. Hourly refreshed market trend data requires the auditing workflow to track data pull timestamps, to prevent using expired data and reduce audit accuracy risks. Structured fixed-field documents require the auditing workflow to verify field completeness, and identify missing critical information such as campaign unit identifiers and cost values. Multi-source data correlation verification needs require logs to record both the pull source and time of campaign data and market trend data, to ensure full data traceability during audits. Multi-turn conversation traceability needs require logs to bind unique identifiers for each question and AI response, to facilitate subsequent audit troubleshooting.

Configuration Settings

Configuration Item	Recommended Setting	Rationale
`maxContext`	`First 20 conversation contexts`	Advertising and marketing scenarios often involve multi-turn campaign data verification. Retaining the first 20 contexts covers complete query traceability logic, and aligns with the context processing specifications of FastGPT V4.9.13
`dataRefreshInterval`	`3600 seconds`	Market trend data updates every hour. This interval ensures that market trend data used in conversations stays synchronized with the data source, and avoids returning expired information
`logRetentionDays`	`180 days`	Advertising and marketing audit compliance requirements mandate retaining at least six months of conversation and data pull logs. This value meets general compliance standards
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Advertising and marketing daily reports typically include multi-dimensional campaign data, which takes longer to parse. 600 seconds covers the full document parsing process
`recallTopK`	`Top 8 matching records`	Advertising and marketing campaign data has many fields with close correlations. Recalling 8 records covers the core related data for user queries, and improves audit accuracy
`fieldCheckEnabled`	`Enabled`	Advertising and marketing data fields are fixed. Enabling field checks automatically identifies missing critical fields, and reduces manual audit costs

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Configuration Errors

Phenomenon: Results returned by the getConversationList API cannot be linked to their corresponding questions and AI responses. Session identifier binding is missing from logs. Cause: The unique session identifier transfer logic was not configured correctly, breaking the association between original questions and AI responses.
Phenomenon: AI responses use outdated advertising market trend data instead of the latest available results. Cause: The prompt trigger logic was not configured to match the real-time data update cadence, causing the system to reuse old context prompts and fail to pull the latest daily report data.
Phenomenon: A 408 Request Timeout error occurs when parsing advertising and marketing daily report documents. Cause: The PARSE_FILE_TIMEOUT_SECONDS configuration value is shorter than the actual document parsing time, and does not reserve sufficient parsing duration.

How to Confirm Successful Configuration

Call the getConversationList API, pass the session identifier parameter, and verify that the returned results can link corresponding questions and AI responses. Confirm that the ID binding configuration is active.
Manually trigger a data pull operation, compare the pull time with the data source update nodes, and confirm that the refresh interval configuration matches the data update cadence.
Upload a standard-format advertising and marketing daily report document, check the field completeness and execution time of the parsing results, and confirm that the timeout and parsing configuration match business requirements.
Initiate two or more consecutive conversations, check that logs fully record each turn of context and data pull records, and confirm that the context retention configuration meets conversation traceability requirements.

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.