Conversation Logging and Auditing for Snack Food Investment

What this category’s data looks like

Snack food investment research data sources include offline supermarket POS sales data, top e-commerce platform product reviews and sales volume data, monthly production and sales reports from industry associations, raw material futures market data such as upstream grain and oil, brand public financial reports and new product announcements. Document structures cover structured sales volume tables, unstructured consumer review text, PDF-format industry research reports, and tabular raw material price curves. Fields include SKU number, number of sales stores, monthly sales revenue, number of consumer review likes, raw material purchase price, and food additive usage standard number. Sales data is updated daily, industry research reports are updated weekly or monthly, raw material market data is updated in real time, and review data is crawled in real time.

What constraints these characteristics impose on conversation logging and auditing

The multi-source, multi-format data characteristics of snack food investment research require conversation logs to record call contexts for both structured parameters and unstructured text, to avoid mixing up data types during traceability. Data sources with different update frequencies require the audit system to bind call timestamps, ensuring that real-time updated raw material data versions can be traced. Subdivided fields such as SKU and sales region require logs to associate corresponding identifiers, making it easy to split audit scope by product or region. Fields related to food additives require audits to cover compliance-related conversations, meeting food safety regulatory retention requirements.

Configuration Settings

Configuration Item	Recommended Setting	Rationale
`chatId`	Required, and bound to SKU/region identifiers	Used to associate conversations with specific investment research scenario products or regions, resolving the issue where chatId does not appear in logs
`LOG_RETENTION_DAYS`	180 days	Covers the monthly update cycle of industry research reports, while meeting the audit retention requirements of food safety regulations
`LOG_EXPORT_FIELD_WHITELIST`	`["chatId", "skuCode", "salesRegion", "timestamp", "query", "answer"]`	Only retains core fields related to investment research, avoiding redundant data occupying storage, and complying with data governance requirements
`AUDIT_TRIGGER_CONDITION`	Trigger when `query` contains keywords "additive", "compliance", "SKU"	Targets food safety compliance scenarios for snack foods, automatically triggering audit verification
`MAX_CHAT_LOG_SIZE`	2000 characters per entry	Adapts to the length of unstructured review text, preventing single log entry overflow
`LOG_PRESERVE_WHEN_USER_DELETE`	Enabled	Prevents background logs from being cleared synchronously after logged-out users delete conversations, complying with audit retention requirements

The parameter values given on this page are common starting points for configuration. Actual values will depend on data formats, data volume and business rules. Each scenario should be analyzed individually, and testing against local samples is recommended before finalizing settings.

Three Common Misconfigurations

Symptom: Background logs for a conversation are deleted synchronously after a logged-out user deletes the conversation. Cause: The LOG_PRESERVE_WHEN_USER_DELETE configuration item is not enabled, causing user-side deletion operations to clear background logs synchronously.
Symptom: The chatId parameter is passed in API calls, but the field does not appear in logs. Cause: The API request parameter name is misspelled, or the CHAT_ID_RECORD_ENABLE switch is not enabled in the FastGPT open-source edition V4.9.7 backend.
Symptom: Calling the log interface returns 500 Internal Server Error with a prompt of data acquisition exception. Cause: The LOG_DB_QUERY_TIMEOUT parameter is not set, or the value is too short, causing log query timeout.

How to Verify Configuration is Correct

Generate a test conversation carrying chatId and SKU identifiers, check whether the backend log correctly records the chatId and skuCode fields.
Trigger a query containing the keyword "additive", confirm whether the audit system automatically generates a corresponding verification record.
Simulate a logged-out user deleting a test conversation, check whether the background log still retains the record.
Call the /api/chat/log interface, confirm that the return status code is 200 and there is no data timeout error.

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.