Workflow Orchestration for Aquaculture Research Report

What this type of data looks like

Aquaculture research report data primarily comes from aquaculture monitoring institutions directly under the Ministry of Agriculture and Rural Affairs, local aquatic scientific research institutes, public reports from industry associations, and special surveys from third-party research institutions. Updates follow weekly, monthly, and annual cycles. Weekly reports focus on seedling prices and disease trends for the current day or week. Monthly reports include regional breeding scale and yield per unit data. Annual reports cover full industrial chain trends.

Core monitoring fields typically include breeding density, feed coefficient, yield per unit, disease incidence rate, and feed purchase cost. Their respective units are tail/square meter, unitless ratio, kg/mu, percentage, and yuan/kg. Most documents are in PDF format, some contain structured data embedded in tables, and a small number are plain text meeting minutes.

What constraints do these characteristics impose on workflow orchestration

The high-frequency weekly update feature requires configuring timed trigger nodes in the workflow to avoid delays from manual scheduling. Multi-dimensional fields with fixed units require enabling structured table extraction during the data parsing phase, and configuring field validation rules to ensure unit matching. Differences in dimensions across reports of different cycles require setting branch judgment nodes in the workflow to route to the retrieval phase of the corresponding knowledge base based on the report cycle. PDF documents with embedded tables require enabling the table recognition switch in the parsing module to avoid losing structured data. Additionally, aquaculture has strong regional attributes, with some reports covering only specific areas. Regional filtering parameters must be configured during the retrieval phase to narrow the recall scope.

How to Set Configurations

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	Aquaculture research reports often contain multiple sets of monitoring tables per document. Excessively long context will trigger model truncation. This range covers the core content of most single research reports
`parse_table_enable`	`Enabled`	Structured tables are embedded in a high proportion of aquaculture research reports. Disabling parsing will lose core fields such as breeding density and yield per unit
`recall_top_k`	`Top 8–12 results`	Aquaculture data has rich dimensions. A small number of recalls will miss key indicators such as disease incidence rate and feed cost
`similarity_threshold`	`0.72–0.78`	Industry terminology is highly specialized. A threshold that is too low will introduce irrelevant general agricultural reports, while a threshold that is too high will fail to recall professional content for targeted segments
`schedule_interval`	`1 day` or `7 days`	Weekly price reports require daily checks for updates, while monthly scale reports can be scheduled at 7-day intervals
`workflow_timeout`	`600 seconds`	The total time for multi-node retrieval, parsing, and model calls must be controlled within a reasonable range to avoid task interruptions

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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 mistakes

Symptom: The workflow runs normally during local testing, but returns "undefined is not valid json" when called via a public channel link. Cause: The channel link has not correctly configured the response_format parameter, or there are unserialized temporary variables in the workflow, which cannot generate compliant JSON responses in a public network environment.
Symptom: The workflow cannot initiate retrieval to the specified knowledge base, and the recall results do not cover aquaculture-related content. Cause: The knowledge_base_id parameter of the specified knowledge base is not bound to the retrieval node, or the corresponding knowledge base has not uploaded valid research report data.
Symptom: Context overflow occurs in a workflow with multiple models connected in series, or the token consumption of a single model call exceeds the preset limit. Cause: The maxContext parameter is not configured to limit the context length, and the independent token counting rule for multiple models is not enabled, resulting in cross-node token sharing exceeding the model's supported range.

How to confirm the configuration is complete

Trigger a manual run of the workflow, check the execution logs of each node, and confirm that the table parsing node successfully extracted core fields such as breeding density and yield per unit.
Verify the recall results of the retrieval node, confirm that the number of recalled results matches the configured recall_top_k value, and that all results come from aquaculture-related knowledge bases.
Check the logs of the scheduled task, confirm that the task automatically triggers according to the set schedule_interval cycle.
Call the public channel link, verify that the returned content is in compliant JSON format, and confirm that the response_format parameter is configured correctly.

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.