Workflow Orchestration for Feed Intelligent Due Diligence

What the data for this category looks like

Data sources for feed intelligent due diligence include production management systems of feed production enterprises, test reports from third-party agricultural and animal husbandry testing institutions, feeding records from livestock farming operations, and supply chain filing data from industry associations.

Data update frequencies vary by source type: Production ledger data updates with each daily production batch. Quality inspection reports update after each inspection batch is completed. Industry statistical data releases monthly.

Document structure has two categories: Structured Excel/CSV procurement and production ledgers, with fields including batch number, raw material name, supplier information, and more. PDF-format quality inspection reports, containing raw material ratios, nutrient content, production process parameters, and more.

Field units are mostly grams per kilogram or milligrams per kilogram. Some process parameters are recorded as numerical values. Fields include production batch number, raw material procurement batch, supplier unified social credit code, crude protein concentration, crude fiber content, production time, inspection date, and more.

Constraints on workflow orchestration from these data characteristics

Feed category data characteristics create multi-dimensional constraints for workflow orchestration.

First, multi-source heterogeneous data formats require workflow configuration of field mapping rules. These rules unify fields such as supplier names and batch numbers returned by different systems, preventing data misalignment across sources.

Second, differences in data update frequencies across sources require layered trigger logic in workflows. For example, start the due diligence report generation process only after both production ledger and quality inspection report updates are complete. This avoids due diligence deviations caused by incomplete data.

Third, feed due diligence requires association of full supply chain data, including raw material procurement, production, and quality inspection links. This requires workflow configuration of associated query nodes. Use production batch number as the unique association key to connect information from different data sources.

Fourth, most quality inspection reports are long-form text. This requires workflow configuration of document segmentation and context splicing rules to adapt to large model context window limits, preventing professional detection fields from being truncated.

How to configure settings

Configuration Item	Recommended Value	Basis for This Value
`PARSE_DOC_SPLIT_SIZE`	800–1200 characters	Feed quality inspection reports are mostly long-form text. Single segment length adapts to large model context windows, avoiding truncation of professional detection fields
`trigger_type`	Triggered by structured data upload	Feed production data is mostly uploaded via ERP interfaces or bulk Excel uploads. Triggering by data upload accurately binds batch IDs and avoids invalid triggers
`field_mapping_strategy`	Match using batch ID + supplier name dual fields	Feed supply chain data has differences between abbreviated and full names. Dual-field matching reduces mapping errors across data sources
`parallel_tool_max`	2 times	Feed due diligence requires calling multiple supplier qualification query interfaces. Limiting concurrency avoids interface rate limiting and reduces repeated queries
`llm_system_prompt`	Feed due diligence exclusive prompt template	Professional indicators such as crude protein concentration and calcium-phosphorus ratio need verification. Exclusive templates improve the accuracy of professional field identification and compliance verification
`workflow_associate_key`	`production_batch_no`	Feed production uses batches as the core management unit. This field associates full-link data of raw materials, production, and quality inspection

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

Three common configuration mistakes

Phenomenon: An HTTP 400 error returns when configuring a tool call node, and workflow execution interrupts. Cause: The feed batch ID is not correctly passed as an interface request parameter, or the field format of detection data is not converted to the type required by the interface.
Phenomenon: The AI dialogue node returns empty results or an error, and the workflow terminates directly without exception logs. Cause: The error_handling_strategy parameter is not configured, no fallback logic for empty results is set, and no alternative data query tool is bound.
Phenomenon: Multiple tool nodes trigger simultaneously, leading to repeated queries of feed supplier data and duplicate entries in the due diligence report. Cause: No serial execution rules for tool calls are set, and the parallel_tool_max parameter is not configured to limit concurrency.

How to confirm correct configuration

Upload a test feed production log and quality inspection report, and check whether the workflow trigger status matches the configured trigger_type.
Enter the workflow log panel and check whether the field mapping of multi-source data complies with the preset field_mapping_strategy rules.
Simulate a scenario where the AI node returns empty results, and verify whether the preset fallback logic triggers and generates alternative content.
Configure a multi-tool call node and check whether the concurrent execution volume complies with the preset parallel_tool_max parameter limit.

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.