What Data for This Category Looks Like
Aerospace equipment due diligence data sources include public military research institute project approval announcements, model formalization documents, annual disclosure information of supporting suppliers, and official records of space launch missions. Data updates follow no fixed cycle, and are released irregularly alongside milestones such as project approval, first flight, and formalization. Document structures fall into three categories: structured parameter reports, technical white papers, and compliance review documents. Core fields include subsystem thrust (unit: kilonewtons), effective payload (unit: kilograms), launch window cycle (unit: days), supporting manufacturer qualification level, and others. Some semi-public documents require access through compliant channels.
What Constraints These Characteristics Impose on Tool Calling and Plugins
Aerospace equipment due diligence data sources are scattered, covering both public and semi-public channels. Tool calling must support cross-source data pulling and permission adaptation. Document structures are diverse, ranging from structured parameter tables to long-form technical descriptions. Plugins must support multi-format parsing. Core fields use industry-specific units. Tools must support custom unit mapping to ensure data consistency. Data updates have no fixed cycle, and individual documents can be lengthy. Tools must support incremental synchronization and batch parsing to avoid ineffective full pulls and timeout interruptions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | 10–15 entries | Aerospace equipment due diligence needs to cover multiple dimensions including subsystems, supporting manufacturers, and launch records. Too few entries will result in loss of critical parameters |
Segment Length | 800–1200 characters | Aerospace equipment technical documents contain long, professional sentences. Excessive length will break semantic relevance, while insufficient length will split parameter groups |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large subsystem technical documents require extended parsing time. The default timeout is not enough to complete full parsing |
Custom Unit Mapping | kilonewton → kN, kilogram → kg, day → d | Aerospace equipment parameters use industry-specific units. Uniform mapping to standardized fields is required |
Incremental Sync Cycle | Triggered by model milestones | Aerospace equipment data updates have no fixed cycle. Triggering sync by milestones avoids ineffective data pulls |
batch_exec_limit | 20 tasks/batch | Aerospace equipment due diligence requires batch pulling data from multiple suppliers. Limiting tasks per batch prevents interface rate limiting |
The parameter values provided on this page are common recommendations used to determine a starting point 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 using sample datasets before finalizing values.
Three Common Errors
- Phenomenon: A workflow calling knowledge base search returns
400 Bad Requestwith a prompt about field format mismatch. Cause: The aerospace equipment-specific unit mapping parameter is not configured, causing parsed parameters to fail to match the due diligence report's verification rules. - Phenomenon: A plugin executes with specified parameters empty, without using preset default values. Cause: The configuration option that prioritizes variables over default values is not enabled, and fallback logic is not set for required parameters.
- Phenomenon: Batch execution tools only return partial task results, with subsequent tasks failing due to timeout. Cause: A reasonable
batch_exec_limitparameter is not set, and the number of tasks per batch exceeds the interface rate limiting threshold, causing some requests to be blocked.
How to Confirm Configuration Is Successful
- Upload a copy of an aerospace equipment subsystem parameter document, and check if the parsed fields include correct unit mappings. For example, the thrust field displays as
120 kNinstead of120 千牛. - Trigger a batch pull workflow for supplier data, and check if all tasks are executed in batches according to the configured
batch_exec_limitwith no timeout errors. - Configure plugin parameters as variable bindings, delete the variable value, then trigger execution, and check if preset default parameter values are automatically used.
- Enable streaming output testing, and check if the returned results include thought process content, displayed separately from the final conclusion.
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.