What this category’s data looks like
Data for aerospace equipment financing daily reports comes from publicly disclosed financing announcements for supporting projects from the National Defense Science, Technology and Industry Administration, official disclosure notices from aerospace research institutes, and third-party military industry databases. The data is updated once daily. Each individual data entry includes the following fields: project name, contractor unit, financing amount, financing method, disclosure date, supporting model. The financing amount unit is ten thousand yuan. Disclosure dates use the YYYY-MM-DD format. The supporting model field marks the category of the launch vehicle, satellite, or missile model it belongs to. Word count-related parameters for individual documents vary widely. It is recommended to confirm values based on independent sample statistics or actual testing.
Constraints on workflow orchestration from these characteristics
The workflow must be configured with a scheduled trigger node to pull the latest data at a fixed daily time, to match the daily updated data sources. The workflow must connect to a knowledge base for precise recall of the supporting model classification field, to match historical financing cases for the corresponding model. A numerical verification node must be added to the workflow to handle the ten thousand yuan unit for financing amounts, avoiding cross-unit conversion errors. Built-in deduplication logic must be included in the workflow to filter duplicate entries based on project name and disclosure date, to address duplicate disclosures across multiple data sources. The workflow’s text processing node must adapt to the length range of individual documents, to avoid truncation or redundant processing.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
schedule_cron | 0 9 * * * | Adapts to the morning disclosure rhythm of aerospace equipment financing announcements, ensuring the latest data is pulled as soon as each day starts |
Recall count | Top 8 entries | Aerospace equipment financing data has high professional requirements, requiring sufficient associated cases to support analysis conclusions |
text_splitter_chunk_size | 600–900 characters | Matches the document length range of individual aerospace equipment financing daily reports, avoiding excessive truncation or redundant processing |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Aerospace equipment data includes multi-dimensional professional fields, requiring longer verification time for the parsing process |
Deduplication Fields | project name, disclosure date | The same project may be disclosed repeatedly through multiple channels, and duplicate entries can be accurately identified using these two fields |
ai_node_streaming | Calibrated via actual testing | Needs to combine the streaming support characteristics of the called model to adapt to the interaction logic of workflow nodes |
The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to confirm values through testing on independent samples.
Three common errors
- Phenomenon: After the workflow runs, replies from all
<ai对话>nodes appear in the final chat conversation. Cause: No output filtering rules are configured for the AI nodes, and no specification is made to retain only the output content of designated nodes. - Phenomenon: An AI node calling the Qwen3 model returns a
400 Bad Requesterror, prompting that non-streaming calls are not supported. Cause: The streaming call configuration of the AI node does not match the calling method supported by the model, and a non-streaming request is forced to trigger the model. - Phenomenon: The knowledge base search node fails to correctly read the variables passed in by the workflow, returning empty results. Cause: The variable reference function is not enabled in the knowledge base retrieval configuration, and variable placeholders are not correctly bound to workflow input parameters.
How to confirm the configuration is correctly set
- Manually trigger the workflow, check the execution log, and confirm that the data pull time matches the configured scheduled rule.
- View the number of knowledge base recall results, confirm that it matches the configured recall quantity, and adjust the threshold to adapt to business needs.
- Check the workflow output results, confirm that only the expected node replies are retained, and adjust the output filtering configuration.
- Verify the knowledge base variable reference function, confirm that the retrieval results are associated with the incoming workflow parameters, and adjust the variable binding configuration.
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.