What the data for this category looks like
Aerospace equipment research reports draw data from public technical white papers from domestic state-owned aerospace technology groups, industry research reports released by national defense and military industry associations, and specialized aerospace equipment research reports from securities research institutions. Update frequency shifts with major model milestones, including before and after launch missions and when annual industry summaries are published. Most documents include model parameter tables, subsystem design details, test data, and industrial chain analysis. Fields cover takeoff mass, low Earth orbit payload capacity, and other metrics, with units such as tons, kilonewtons, and others. Single document lengths vary significantly.
What constraints these characteristics impose on workflow orchestration
Research reports from different sources have significant format differences, including encrypted PDFs and Word documents with complex tables. The workflow must support multi-format parsing. Many parameter fields have detailed unit subdivisions, so standardized processing must be applied during extraction to avoid unit confusion. Single documents are lengthy, so parsing and segmentation must adapt to long-text processing logic to prevent context truncation. Update frequency shifts with major model milestones, so the workflow must support scheduled batch updates of the knowledge base configuration.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single aerospace equipment research reports have many pages and complex parameter tables, resulting in significantly longer parsing time than general documents |
Chunk size | 1000–1500 characters | Parameter sections of single research reports contain multiple sets of related data, so segmentation must retain the integrity of parameter context |
Recall count | Top 6–10 entries | Aerospace equipment research reports have many detailed parameter dimensions, so retrieval results covering multiple related parameter types are required |
Similarity threshold | 0.78–0.85 | High precision is required for parameter fields, so low-relevance non-professional research report content must be filtered out |
Global Variable Dynamic Assignment | Match corresponding knowledge base ID by document tag | Different aerospace equipment models correspond to exclusive knowledge bases, so retrieval scope must be dynamically bound |
Code Run Node Timeout | 300 seconds | Parameter extraction code needs to traverse multiple fields and verify unit consistency, resulting in longer processing time |
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 situations require individual analysis, and testing on available samples prior to finalization is recommended.
Three common configuration mistakes
- Phenomenon: The workflow throws a
chat:ai_input_is_emptyerror after running, with no valid input content for the AI model. Cause: Theresultoutput field of the code running node is not correctly bound to the user question parameter of the AI model, and variable mapping configuration is not completed. - Phenomenon: The global variable "Select Knowledge Base" cannot switch dynamically with retrieval requirements, and a fixed knowledge base is always used. Cause: The
Global Variable Dynamic Assignmentrule is not configured, and the mapping relationship between document tags and corresponding knowledge base IDs is not established. - Phenomenon: The parameter units returned by the text extraction node are inconsistent, with mixed use of kilonewtons and newtons. Cause: Unit standardization processing logic is not configured in the text extraction node, and unified conversion of extracted parameter fields is not completed.
How to confirm successful configuration
- Trigger a single workflow test, check the output logs of the code running node, and confirm that the
resultfield contains correct parameter extraction results. - Upload test documents with different model tags, verify whether the global variable "Select Knowledge Base" automatically matches the corresponding knowledge base.
- Submit a query containing multiple sets of aerospace equipment parameters, confirm that the number of retrieved results matches the configured
Recall countsetting. - Parse a single long document, confirm that no timeout error occurs in the parsing node, and that segmented content retains complete parameter-related information.
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.