Dialogue Logging and Auditing for Livestock and Poultry

What this category of data looks like

Livestock and poultry farming investment research data mainly comes from publicly available monitoring data from industry associations, operational data submitted by designated farms, and real-time quotes from feed ingredient trading markets. Data update rhythms follow multiple dimensions: feed ingredient prices are updated daily, inventory count and slaughter volume statistics are updated weekly, and monthly industry analysis reports are updated monthly. Documents are divided into two categories: structured data tables and unstructured analysis reports. Structured data tables include fields such as category name, statistical cycle, inventory count (unit: head/bird), slaughter volume, feed cost (unit: yuan/ton), and market average price (unit: yuan/kg). Unstructured reports include content such as disease prevention and control analysis and industry trend forecasts.

What constraints do these characteristics impose on dialogue logging and auditing

The multi-update frequencies, multi-structure types, and differences in field units of livestock and poultry farming data impose clear constraints on dialogue logging and auditing. First, update data from different cycles must be bound to their corresponding versions via logs to prevent referencing expired market data during audits. Second, structured data accounts for a high proportion, so logs must accurately record the specific field keywords of user queries to facilitate tracing data call logic. Third, different documents have different units (such as inventory count using head/bird, average price using yuan/kg), so logs must retain the unit field of the called document to eliminate numerical ambiguity during audits. Finally, investment research conversations often involve cross-category and cross-cycle comparisons, so logs must fully record the associated document IDs of the context to ensure audit trail traceability.

How to Configure Settings

Configuration Item	Recommended Setting	Rationale
`log_retention_days`	`180 days`	Investment research audits require retaining at least six months of conversation and data call records to comply with industry compliance requirements
`audit_log_include_context`	Enabled	Full recording of the context content of user questions is required to support reference resolution and trail tracing
`document_version_tracking`	Mandatory Enabled	Livestock and poultry farming data has high update frequency, so the specific version of the called document must be bound to avoid referencing expired data
`query_field_extract`	Enable structured field extraction	Specific data fields queried by users (such as "white feather chicken inventory count") must be recorded to facilitate audit traceability
`error_log_threshold`	`500 milliseconds`	Livestock and poultry market data has strong timeliness; timed-out requests must be included in the audit scope to ensure abnormal calls can be traced
`api_request_log_sampling`	`100%`	Full log retention is required for investment research scenarios to avoid missing key data call trails

The parameter values provided on this page are common recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and testing on local samples is recommended before finalizing.

Three Common Configuration Mistakes

Symptom: After uploading a livestock and poultry farming market price image document, the log shows upload success and returns a 200 OK status code, but the knowledge base node cannot call the image, and the interface button is grayed out. Cause: The image_parse_enable configuration is not enabled, so the image cannot be parsed into retrievable content, and the log does not record the 500 Internal Server Error parsing failure.
Symptom: The audit log only displays the user's question and answer content, and does not associate the corresponding livestock farming data document ID and version number. Cause: The document_version_tracking configuration is not enabled, so the log does not bind the specific document version called, making it impossible to trace the accuracy of data references.
Symptom: When calling the knowledge base API, referential questions in historical conversations (such as "compare last month's pig price") cannot be parsed correctly, and the log does not record the associated field keywords of the context. Cause: The query_field_extract configuration is not enabled, so the log does not extract structured fields from the user's question, making it impossible to trace context optimization logic.

How to Verify the Configuration is Correct

Log in to the log management module of the FastGPT backend, randomly select one livestock and poultry farming investment research conversation log, and verify whether it includes the associated document ID, version number, and query field keywords.
Upload a structured livestock and poultry farming market price data table document, and check whether the system log records the complete chain of upload success, parsing completion, and version generation.
Initiate a conversation that includes a referential question, and verify whether the log records the associated context fields and historical conversation identifiers.
View the log retention policy configuration page, confirm that the log retention duration meets the compliance requirements for investment research audits, and that the full log collection switch is enabled.

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.