Knowledge Base Retrieval and Recall for Livestock and

What the Data for This Category Looks Like

Core data for livestock and poultry farming comes from farm daily ledgers, livestock station monitoring reports, feed supplier delivery records, and animal quarantine reports. Data update cadence covers daily inventory changes, weekly feed consumption statistics, monthly slaughter volumes and disease screening results. Document formats include structured Excel ledgers, semi-structured livestock farming log PDFs, and structured quarantine report tables. Fields include livestock breed, age in days, pen number, feed formula proportion, slaughter weight (unit: kilogram), antibody titer (unit: log2), and others. Some fields have fixed unit requirements.

Constraints on Knowledge Base Retrieval and Recall

The multi-dimensional fields and fixed unit requirements of livestock and poultry farming data require retrieval to match both field names and units, to avoid recalling irrelevant data. Data sources with different update frequencies require the knowledge base to configure incremental updates by daily, weekly, and monthly tiers. Failure to do so will result in data lag or redundancy. Mixed input of multiple document formats requires the parsing link to support merged cells and multi-Sheet tables. Otherwise, some field data will be lost. Strong correlations between data (such as feed consumption and slaughter weight) require the recall link to support multi-field joint matching, to avoid the one-sidedness of single-keyword recall.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`800–1200 characters`	The length of a single core livestock farming record is about 500 characters. An overly long context will introduce irrelevant data, while an overly short one will truncate valid content
`Recall Count`	`Top 8–12 entries`	Livestock and poultry farming due diligence requires covering multi-dimensional data including inventory, feed, and disease. A value that is too small will fail to cover all scenarios, while a value that is too large will increase downstream processing load
`Similarity Threshold`	`0.72–0.80`	Livestock farming data has high requirements for field accuracy. A threshold that is too low will recall irrelevant disease or feed records, while a threshold that is too high will miss valid matching results
`PARSE_EXCEL_MERGED_CELL`	`Enabled`	Livestock farming ledger Excel files often use merged cells to mark pen areas. Enabling this option can correctly identify field ownership and avoid data loss
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Packaged monthly farming report files usually do not exceed this size, adapting to conventional bulk upload scenarios
`Incremental Update Trigger Cycle`	`Configured by data type tiers`	Inventory data is updated daily, quarantine reports are updated monthly. Tiered configuration balances data timeliness and indexing overhead

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 specific analysis. It is recommended to test on your own samples before finalizing.

Three Common Misconfigurations

Symptom: After uploading a livestock farming Excel ledger, some pen inventory data fields are empty. Cause: The PARSE_EXCEL_MERGED_CELL configuration is not enabled. The pen ownership marked by merged cells is not correctly identified, so the corresponding field data is not extracted.
Symptom: When searching for "30-day-old white feather chicken feed consumption", only a small number of relevant records are recalled, with insufficient recall accuracy and breadth. Cause: The Similarity Threshold is set too high, or multi-field joint matching rules are not configured. Only keywords are matched without associating limiting conditions such as breed and age.
Symptom: After calling the knowledge base module in a workflow, the returned results cannot be referenced by downstream tool nodes. Cause: The maxContext is set too small, so the recalled document content is truncated and cannot be recognized by downstream nodes, or the vector database index has not completed incremental update, so data is not synchronized to the retrieval library.

How to Confirm Proper Configuration

A single livestock farming ledger Excel file may be uploaded, and the parsed fields checked to confirm they include core fields such as pen number, age in days, and inventory volume, verifying that the PARSE_EXCEL_MERGED_CELL configuration is effective.
A query term for a specific livestock farming scenario may be entered, and the field matching degree of the returned results checked, adjusting the Similarity Threshold to the range that meets business requirements.
An incremental update task may be triggered, and after the configured cycle ends, checked to confirm the latest livestock farming data in the knowledge base has been synchronized, verifying that the update configuration is correct.
A workflow may be configured to call the knowledge base module, and the returned results verified to be normally readable by downstream nodes, confirming that the configurations of maxContext and Recall Count are adapted to the business scenario.

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.