Dialogue Logging and Auditing for Agrochemical Product

What the data for this category looks like

Data related to agrochemical products mainly comes from public registration information of the national pesticide inspection institute, quarterly supply and demand reports from industry associations, patent databases, public annual reports of agrochemical enterprises, and original field trial records. Data update cadence is divided into multiple tiers: registration certificate information updates in real time with approval progress, supply and demand reports are released quarterly, and field trial data is synchronized monthly. Document structures include product active ingredient content, formulation parameters, crop adaptation lists, residue limit standards, and circulating price data. Field units are mostly g/L, %, tons, ten thousand yuan. Some documents include multi-language compliance descriptions.

What constraints do these characteristics impose on dialogue logging and auditing

The multi-source, varied update cadence, and multiple field unit characteristics of agrochemical product data impose multiple constraints on dialogue logging and auditing. Multi-source data requires recording the data source identifier and call timestamp associated with each dialogue in the log to avoid confusion between data from different channels. Different update cadences require logging the version snapshot identifier when data is called, to ensure that compliance standards and supply and demand data at the corresponding point in time can be traced during audits. Multi-unit fields require recording both the original input parameters and standardized unit conversion results in the log to avoid unit ambiguity during audits. For queries of compliance-related data such as residue limit standards, the trigger result of compliance verification and the basis document number must be recorded in the log to meet regulatory traceability requirements.

How to configure the settings

Configuration Item	Recommended Setting	Rationale
`CHAT_LOG_ENABLE`	Enabled	Agrochemical investment research data must meet compliance traceability requirements. Enabling this option retains full-link interaction and data call records
`LOG_RETENTION_DAYS`	`180 days`	Matches the audit traceability period required by agrochemical industry regulatory requirements, ensuring core interaction data can be retrieved compliantly
`CHAT_ID_PERSIST`	Enabled	Agrochemical investment research dialogues are often associated with exclusive data for specific categories. Retaining chatId allows precise location of full-link logs for a single session
`LOG_DELETE_PROTECTION`	Enabled	Prevents accidental deletion of dialogue logs by non-logged-in users from breaking the audit chain, ensuring complete data retention
`AUDIT_LOG_FILTER_KEYWORDS`	`active ingredient content, registration certificate number, residue limit`	Quickly filter interaction records for core compliance fields in agrochemical investment research, improving audit efficiency
`LOG_ERROR_STACK_ENABLE`	Enabled	Agrochemical data documents have complex structures and numerous fields. Enabling error stack logs allows quick localization of specific links with parsing or call errors

The parameter values provided on this page are common recommended starting points for determining configurations. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.

Three common mistakes

Issue: After passing the chatId parameter when calling the API, the corresponding chatId field does not appear in the backend dialogue log. Cause: The CHAT_ID_PERSIST configuration item is not enabled, so the session identifier is not persistently written to the log.
Issue: After a non-logged-in link user deletes a dialogue, the backend retained dialogue log is also cleared. Cause: The LOG_DELETE_PROTECTION configuration item is not enabled, so front-end deletion operations trigger log deletion synchronously.
Issue: Calling the log query interface returns a 500 Internal Server Error with a prompt of abnormal data acquisition. Cause: The configured LOG_RETENTION_DAYS exceeds the system's default storage limit, or the Docker deployment did not mount the log volume, resulting in damaged log files.

How to confirm the configuration is complete

Log in to the FastGPT backend log management page, initiate a test dialogue containing core agrochemical fields, and check whether the chatId field and corresponding session identifier exist in the log.
Generate a non-logged-in test link, initiate a dialogue, manually delete the session, and check whether the backend log still retains the interaction records for this session.
Call the log query interface, pass the preset chatId parameter, and confirm that the full log content of the corresponding session can be accurately retrieved.
Trigger a data parsing error, and check whether the backend log contains complete error stack information and data source identifiers.

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.