What data for this category looks like
Data for aerospace equipment intelligent due diligence reports comes primarily from public technical documents of aerospace equipment manufacturers, civil aviation or military airworthiness certification reports, supply chain supporting enterprise disclosure lists, original flight test logs and archived maintenance records. Update rhythms vary by data type: airworthiness certification documents update with approval progress, core aircraft parameters iterate annually, supply chain supporting data refreshes quarterly, and original flight test logs for a single test are archived immediately after task completion. Documents include structured parameter tables and unstructured text. Structured fields include engine thrust, wingspan, maximum takeoff weight, with corresponding units of kilonewtons, meters, kilograms. A single complete due diligence document can be dozens of pages long.
What constraints do these characteristics impose on the model access and configuration process
The mixed structure and differentiated update rhythm of aerospace equipment due diligence data impose multiple constraints on model access configuration. Structured parameters require precise matching of fields and units, so parameter mapping rules must be configured to avoid parsing deviations. Long documents and large-volume flight test logs require model context windows and file upload limits to be adapted, to prevent content truncation or upload failures. Data updates from multiple sources and with multiple frequencies require flexible synchronization trigger configurations, to avoid wasting resources from full pulls or missing critical updates in incremental syncs.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Aerospace equipment due diligence reports often include multiple long documents, and a single flight test log can reach thousands of characters. Complete context must be retained to ensure parsing accuracy |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single aircraft airworthiness certification document may exceed 1 GB. Large file upload support is required to cover all due diligence data |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Parsing large structured parameter tables requires long processing time, to avoid task failure from mid-process timeouts |
embedding_batch_size | 32 entries | Aerospace equipment has a large number of structured fields. Batch embedding improves parsing efficiency and reduces latency per embedding |
image_support_enabled | Enabled | Some due diligence reports include flight test photos and assembly drawings. Multimodal parsing support is required to cover all data types |
model_timeout | 1200 seconds | Generative parsing of long documents requires long computation time, to adapt to the processing needs of complex due diligence content |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Issue: Connection errors occur when deploying Qwen3-30B-A3B via VLLM. Cause: VLLM API endpoint address and port mapping are not configured correctly, preventing FastGPT from accessing the locally deployed model service.
- Issue: After enabling multimodal support and uploading an image, a prompt indicating inability to provide image content is returned. Cause: The model is not configured as a multimodal-compatible version, or image input permission is not enabled in the conversation node.
- Issue: Errors occur when testing the connection to Qwen3-Embedding-8B deployed via VLLM. Cause: The embedding model API path is not configured correctly, or network isolation exists between FastGPT and the VLLM service, preventing communication.
How to confirm successful configuration
- Initiate a connectivity test on the FastGPT model management page. Verify that the returned response status code is 200 to confirm correct network and endpoint configuration.
- Upload a small aerospace equipment parameter document to trigger a parsing task. Check that the parsed structured fields fully match the units and values of the original data.
- Enable multimodal testing, upload an aerospace equipment assembly drawing, and verify that the model returns analysis results based on the image content.
- Configure a scheduled synchronization task and manually trigger a full synchronization. Check that the task logs contain no timeout or parsing failure errors.
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.