What this category’s data looks like
Data for aerospace equipment intelligent due diligence reports comes from public bulletins of defense and military industry competent units, official disclosure documents from model development organizations, government procurement tender announcements, and public statistical materials from industry associations. The update rhythm changes with project phases. Key nodes such as project initiation, test flight, and finalization trigger updates. Single report document lengths vary widely. Contents typically include development progress, core performance parameters, supplier information, and qualification certification documents. Many fields use professional units: thrust in kilonewtons, range in kilometers. Some fields include dates for finalization time and delivery milestones.
What constraints these characteristics impose on model access and configuration
The professional nature and document features of aerospace equipment due diligence reports create clear constraints for model access and configuration. Long document structures require adjustments to context window and chunking parameters to avoid losing context links for professional parameters. Professional units and terminology require the model to have precise semantic alignment capabilities. Custom prompts or dedicated embedding models must be configured. Multi-source heterogeneous data source formats require flexible document parsing rules to adapt to output formats from different channels. Some reports include multimodal materials. Corresponding visual recognition configurations must be enabled to parse drawings and photos.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Single aerospace equipment due diligence report has a long length, requiring space for complete model parameters, supplier information, and qualification documents |
chunkSize | 1000–1500 characters | Aerospace equipment documents contain professional formulas and units. Excessively long chunks break context links for parameters, while excessively short chunks split semantic integrity of professional terms |
embeddingModelUrl | Local VLLM deployment address (e.g., http://localhost:8000/v1/embeddings) | Compatible with local embedding models such as Qwen3-Embedding-8B, supporting accurate vector generation for aerospace professional terminology |
imageRecognitionEnabled | Enabled | Aerospace equipment due diligence reports include multimodal materials such as design drawings and test flight photos, requiring this configuration to parse visual information |
requestTimeout | 600 seconds | Long document parsing and multimodal inference take significant time, and the default timeout period is insufficient to complete the full processing workflow |
similarityThreshold | 0.75–0.85 | Aerospace professional terminology has high semantic similarity differentiation, adjusting the threshold can avoid mismatching or missing professional parameters |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: A connection timeout error is returned when calling the Qwen3-30B-A3B model deployed via VLLM. Cause: The port and path of
embeddingModelUrlare not configured correctly, or the local model service has not opened corresponding access permissions. - Phenomenon: After enabling image recognition, uploaded aerospace equipment design drawings cannot be parsed, and a prompt indicating that image content cannot be provided is returned. Cause: The
imageRecognitionEnabledswitch is not enabled separately in the AI conversation node, or the visual encoder parameters of the multimodal model are not configured. - Phenomenon: A
400 Bad Requesterror is returned during embedding model testing. Cause: The request body is not adapted to the input format of the local embedding model, or the parsing rules for aerospace professional terminology are not specified in the prompt.
How to confirm successful configuration
- Access the model management page in FastGPT, enter the test interface of the local VLLM deployment, initiate a connectivity test, and check if the returned model list includes the deployed Qwen3 series models.
- Upload a sample document of an aerospace equipment due diligence report, trigger chunked parsing, and check if the parsed text blocks retain complete professional parameters and unit information.
- Initiate a test conversation with a drawing attachment, and check if the model can correctly identify and describe key structural information in the drawing.
- Configure a scheduled synchronization task, and check if the knowledge base can automatically pull the latest aerospace equipment due diligence documents and complete vector updates after the data source is updated.
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.