What the data for this category looks like
Data sources for aerospace equipment intelligent due diligence reports include public annual reports of aircraft manufacturers, certification documents released by airworthiness regulatory authorities, qualification documents of aviation material suppliers, and supply chain fulfillment records. Update cycles vary by type. Airworthiness certification documents are updated annually. Aircraft performance parameters are updated irregularly alongside R&D iterations. Supply chain data is updated in real time as orders are fulfilled. Most document structures combine structured parameter tables and unstructured explanatory text. They include dedicated fields such as engine thrust, endurance duration, serial number, airworthiness certificate number, delivery lead time, and others. Some classified due diligence data must be accessed through encrypted channels.
What constraints these characteristics impose on workflow orchestration
The multi-source, dispersed nature of aerospace equipment due diligence data requires workflows to be configured with multiple data source pull nodes. These nodes cover different channels such as public documents and qualification documents. Differences in update cycles require support for both scheduled triggering and manual triggering modes. This avoids missing temporarily updated performance parameters. Professional fields and dedicated units require built-in parameter verification nodes in workflows. These nodes prevent incorrect units or values from being extracted. Multi-dimensional information coverage requirements mean workflows must support calling multiple vertical tools. They must accurately extract variables related to professional terminology, and ensure global variables remain valid during cross-module calls.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
variable_extract_threshold | 0.85–0.9 | Aerospace equipment has a high proportion of professional terminology. A threshold that is too low will extract irrelevant content, while a threshold that is too high will miss key parameters such as engine thrust and airworthiness certificate number |
workflow_trigger_mode | Scheduled trigger + manual trigger | Airworthiness documents are updated annually, and supply chain data needs to be pulled temporarily. Dual modes cover all due diligence scenarios |
MCP_tool_auto_select | Enabled | Due diligence requires calling multiple tools such as airworthiness databases and supply chain queries. Autonomous model selection reduces redundant execution steps |
global_variable_pass_timeout | 300 seconds | Pulling due diligence data across multiple data sources takes a certain amount of time. A timeout will cause global variable tokens to expire and affect tool calls |
file_parse_chunk_size | 1000–1200 characters | Aerospace equipment documents contain long parameter tables. Too long a segment will lose context, while too short a segment will split professional terminology combinations |
rag_recall_top_k | Top 8 entries | Due diligence reports need to cover multiple dimensions including aircraft parameters, supply chain, and airworthiness certification. Too few recalls will miss key information |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Professional fields such as engine thrust and delivery batch are empty or matched incorrectly. Cause: The
variable_extract_thresholdparameter is not adjusted for aerospace equipment professional terminology, resulting in low-weight professional nouns not being recognized. - Phenomenon: The MCP tool call returns a
400error code, prompting that the global variable token was not found. Cause: Theglobal_variable_pass_timeoutparameter is not configured, or the value is too short, causing the token to expire during cross-module calls. - Phenomenon: The workflow repeatedly calls irrelevant tools, and the execution duration exceeds the preset threshold. Cause: The range of callable tools is not limited, or the pre-check for
MCP_tool_auto_selectis not enabled, causing the model to select unnecessary tools.
How to Verify Proper Configuration
- Run a single-node test workflow, upload an aerospace equipment due diligence document, verify that the extracted fields such as airworthiness certificate number and endurance duration match the source document, and adjust
variable_extract_thresholdto the required range. - Trigger a cross-module call process, check whether the MCP tool can normally obtain the global variable token, and confirm that the value of
global_variable_pass_timeoutadapts to the current data source pull duration. - Enable the model's autonomous tool selection function, run a simulated due diligence process, confirm that the model only calls tools related to aerospace equipment due diligence, and adjust the tool call range configuration.
- Upload a due diligence document containing a long parameter table, check whether the segmented parsed content retains complete professional parameter units, and adjust
file_parse_chunk_sizeto an appropriate length.
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.