Conversation Logging and Auditing for Livestock and Poultry Farming Yield Rates

Data related to livestock and poultry farming yield rates comes primarily from farm daily feeding records, seedling purchase ledgers, slaughter

What Data for This Category Looks Like

Data related to livestock and poultry farming yield rates comes primarily from farm daily feeding records, seedling purchase ledgers, slaughter weighing ledgers, feed purchase invoices, and epidemic prevention cost records. Updates occur daily. Daily data for a single farm batch is entered before 24:00 on the same day. Document formats are mostly CSV or Excel. Each file contains multiple consecutive days of data for a single farm or breeding batch. Fields include batch number, statistical date, inventory count, feed consumption (unit: kg/head), seedling purchase unit price (unit: yuan/tail), slaughter weight (unit: kg), slaughter sales unit price (unit: yuan/kg), and single-time epidemic prevention cost (unit: yuan/head). Units for each field are fixed, with no mixed units.

Constraints for Conversation Logging and Auditing

Data updates daily and fields have clear defined units. This requires the conversation logging and auditing process to strictly match statistical dates and log timestamps. Otherwise, the basis for calculating single-batch yield rates cannot be traced. Multiple fields with fixed units require logs to fully record query fields specified during calls and parameter transfer logic. This prevents unit mismatch issues during audits. The structure of single files containing multiple days of data requires logs to associate batch numbers with file import records. This ensures each query can be mapped to a specific data source file. High-frequency daily updated data requires audit logs to retain call records for a sufficient duration. This meets internal verification or compliance requirements.

Configuration Settings

Configuration ItemRecommended ValueRationale
include_thinkingEnabledLivestock and poultry farming yield rate calculations require complete reasoning processes. Enabling this option records chain-of-thought steps in conversation logs to meet audit requirements
PARSE_FILE_TIMEOUT_SECONDS300 secondsMonthly data files for a single batch may contain more than 30 days of records. Parsing takes significant time. 300 seconds covers most bulk parsing scenarios
UPLOAD_FILE_MAX_SIZE200 MBMonthly data files for large farms can reach 150 MB. This setting reserves sufficient import space
recall_countTop 8 entriesSingle-batch yield rate calculations only require the last 3-5 days of historical data. A recall volume of 8 entries covers requirements and avoids introducing irrelevant data
LOG_LEVELDEBUGAudits of livestock and poultry farming data require detailed field verification and parameter transfer records. DEBUG level records full-link logs
API_RESPONSE_LOG_ENABLEEnabledFull records of all fields and calculation results returned by the API are required to meet audit traceability requirements

The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Analyze specific issues on a case-by-case basis. It is recommended to test on your own samples before finalizing settings.

Three Common Configuration Mistakes

  • Symptom: No chain-of-thought process appears in API call return results. The thinking field is empty. Cause: The include_thinking configuration item is not enabled, or the service was not restarted after configuration was updated.
  • Symptom: When uploading livestock and poultry farming data files, logs repeatedly report slow operation 12000ms errors. Cause: PARSE_FILE_TIMEOUT_SECONDS is set below 300 seconds, bulk parsing times out before completion, or UPLOAD_FILE_MAX_SIZE is not adapted for large file imports.
  • Symptom: After internal deployment, audit logs cannot record database operations, and a connection refused error appears. Cause: The internal MySQL address is not specified in the LOG_DB_CONN configuration, or the MySQL port is not open to the container network.

How to Verify Configurations Are Correct

  • Call the test interface with query parameters for a specified breeding batch. Check that the return result includes the thinking field. Confirm the include_thinking configuration is active.
  • Upload a 150 MB monthly livestock and poultry farming data file. Check that no slow operation errors appear in parsing logs. Confirm the PARSE_FILE_TIMEOUT_SECONDS and UPLOAD_FILE_MAX_SIZE configurations are correct.
  • View system logs. Confirm that MySQL connection logs are recorded normally, and no connection refused errors appear. Confirm the internal deployment database configuration is correct.
  • Export conversation logs from the last 7 days. Check that each record includes the queried batch number, statistical date range, and returned field list. Confirm the LOG_LEVEL and API_RESPONSE_LOG_ENABLE configurations are active.

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.