What data for this category looks like
Data sources for military electronic intelligent due diligence reports include military electronic component quality and reliability data platforms, publicly available industry materials from the National Defense Science, Technology and Industry Administration, annual supporting lists of military industry groups, public bidding announcements for military equipment procurement, and finalization appraisal reports for various complete aircraft models.
Update rhythms vary by source: finalization appraisal reports are updated once after model finalization, procurement bidding announcements are updated monthly, and component quality data is updated quarterly.
Single report documents range from 30 to 80 pages in length. Their structure includes fields such as model parameters, production batches, reliability indicators, supporting equipment models, supplier qualifications, and more. Field units are mostly professional metrology identifiers such as ℃, hours, W, batch numbers, and similar markings.
What constraints these characteristics impose on workflow orchestration
The multi-source and decentralized nature of military electronic due diligence data requires workflows to configure multiple independent HTTP request nodes to pull data from different sources separately. This avoids data loss caused by single-node timeout or permission restrictions.
The special nature of professional fields and units requires workflows to add a field mapping step. This unifies parameter formats and units, avoiding incorrect results caused by unit mismatches during retrieval.
Differences in update rhythms across data sources require timed trigger nodes to configure trigger rules according to different cycles. This ensures the latest data is timely included in the due diligence process.
The long document structure requires the workflow's document parsing link to adapt to longer single-document processing times. This avoids parsing interruptions.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Military electronic due diligence reports have a large number of pages per document, and parsing time is significantly higher than general documents |
maxContext | 8000–12000 characters | Military professional terminology is dense, longer context must be retained to maintain professional association logic |
Knowledge base recall count | Top 8–12 results | Military electronic data fields are professional and scattered, sufficient recall volume is needed to cover key parameters |
Similarity threshold | 0.75–0.85 | Subtle differences in parameters of military components from the same model and different batches must be distinguished |
Global Variable Persistence Switch | Enabled | Core variables such as military model and batch number do not need frequent updates, and global storage is required to avoid loss |
HTTP Request Retry Count | 3 times | Most military data sources are official platforms. Occasional network fluctuations do not affect core data pulling, and this is compatible with workflow node configurations for version v4.8.10 and above |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: An error "variable not found" is prompted when calling the knowledge base node. Cause: The global variable persistence toggle is not enabled. Variables are only stored in single-turn conversation memory, and variables are lost after the workflow restarts.
- Phenomenon: The military batch number variable pulled by the HTTP request cannot be mapped to the knowledge base retrieval condition. Cause: The variable type is not set to "string". Hyphens in the batch number are automatically parsed into other formats.
- Phenomenon: After multiple variables are input, only the first variable triggers knowledge base retrieval. Cause: Independent knowledge base binding rules are not configured for each variable. Only the first output variable is used by default.
How to confirm the configuration is correct
- Run a single test workflow, check the variable storage log, and confirm that core model and batch number variables are not cleared after the workflow ends.
- Manually modify test military parameter variables, trigger knowledge base retrieval, and confirm that retrieval results match the parameters.
- Adjust the similarity threshold, check the number of retrieval results for changes, and confirm that the threshold adjustment takes effect.
- Simulate network fluctuations to interrupt HTTP requests, check whether the retry mechanism is triggered, and confirm that the request retry configuration takes effect.
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.