What Data for This Category Looks Like
Livestock and poultry farming due diligence data primarily comes from batch breeding records of breeding enterprises, public records from official animal husbandry quarantine platforms, feed purchase ledgers, and epidemic prevention logs. Data update cycles cover daily inventory changes, weekly epidemic prevention records, and monthly slaughter plans. Documents take breeding batches as the core unit, including fields such as ear tag number, breed, breeding cycle (days), feed consumption (kilograms), number of vaccine administrations, and quarantine qualification status. Fields are bound to clear physical units, with no ambiguous numerical descriptions.
What Constraints These Characteristics Impose on Workflow Orchestration
The batch-split structure of breeding data requires the workflow to configure grouping nodes, ensuring unified integration of batch-specific records, quarantine certificates, and ledger data. The multi-frequency update cycle requires the workflow to support scheduled triggers and incremental pulls, avoiding repeated processing of full historical data. Clear field units require the workflow to embed validation nodes, verifying the unit and numerical legitimacy of fields such as feed consumption and breeding cycle. Mixed input of multiple document types requires the workflow to first perform file parsing and field extraction, before entering the knowledge base recall and due diligence content generation stage.
How to Set Configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
workflow_trigger_type | schedule + incremental_update | Matches the daily/weekly incremental update rhythm of livestock and poultry farming data, reducing redundant calculations |
batch_split_rule | Group by ear_tag_id | Corresponds to the document structure of breeding records split by batch (ear tag ID), ensuring unified processing of batch-specific data |
similarity_threshold | 0.75–0.85 | Adapts to the density of professional terminology in livestock and poultry farming, filtering low-correlation knowledge base recall results |
HTTP_REQUEST_TIMEOUT | 600 seconds | Adapts to long-duration scenarios such as breeding data parsing and quarantine certificate OCR, avoiding mid-run timeouts |
rag_top_k | Top 3 entries | Controls the number of knowledge base recall entries for compliance verification and expert interpretation, avoiding redundant information interfering with due diligence conclusions |
fallback_response_config | Trigger preset compliance prompt copy | Matches branch processing requirements when user queries are unrelated to the knowledge base, ensuring the compliance of due diligence reports |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: An HTTP node returns a
400 Bad Requesterror, and custom variables are not parsed correctly. Cause: The field names of breeding data are not bound in the workflow variable mapping panel, causing the interface to fail to recognize incoming parameters. - Phenomenon: The content of the due diligence report is not displayed in categories according to business systems, expert interpretations, and compliance verification, and recall results from different knowledge bases are mixed. Cause: Branch nodes for sorting by knowledge base tags are not set in the workflow, leading to uncategorized recall results.
- Phenomenon: The workflow runs beyond the preset duration and triggers a timeout error. Cause: The timeout threshold is not adjusted for long document parsing and multi-batch data processing in livestock and poultry farming, causing batch tasks to fail to complete.
How to Confirm Proper Configuration
- A test workflow is executed, and the variable mapping panel is reviewed to confirm that breeding data fields are correctly bound to the input parameters of the HTTP node.
- A test query unrelated to the knowledge base is simulated, and the workflow is confirmed to trigger the preset fallback response, with no knowledge base calls for content generation at this stage.
- Workflow run logs are reviewed to confirm that each batch of breeding data is correctly grouped and processed, with no timeout or missing field errors.
- A test version of the due diligence report is exported, and the content is confirmed to be correctly displayed in the categories of business systems, expert interpretations, and compliance verification, corresponding to recall results from different knowledge bases.
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.