What the data for this category looks like
The data for ordnance equipment intelligent due diligence reports primarily comes from publicly disclosed equipment formalization documents, contractor qualification disclosure information, military industry standard documents, and public technical white papers issued by national defense science, technology and industry competent authorities. The update schedule follows equipment formalization milestones, qualification annual review cycles, and industry standard revision rhythms, with no fixed daily update frequency. Documents primarily use structured fields, including core fields such as equipment model, formalization time, and contracting entities. Core performance parameters are accompanied by standardized units, such as kilometers for range and rounds per minute for firing rate. Appendices include summaries of sub-item test data.
Constraints on model access and configuration
The multi-source, dispersed nature of ordnance equipment due diligence data requires configuring parsing rules for multiple formats, including PDF formalization reports and web-based qualification disclosures. This prevents errors in structured field parsing. Professional parameters with standardized units require preprocessing rules that bind parameter recognition to units. This ensures model output performance parameters are paired with correct units. The non-fixed update rhythm requires configuring incremental synchronization trigger mechanisms. This adapts to irregular data updates tied to formalization milestones and qualification annual reviews. Long documents and multiple parameters require adjusting context windows and recall parameters. This prevents model output deviations caused by fragment overload.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Public ordnance equipment formalization report single-page parameter fragments are approximately 1000 characters. This range covers the stitching needs of 3 to 4 core reports, and supports long document processing. |
SIMILARITY_THRESHOLD | 0.75–0.85 | Ordnance equipment professional terminology has high similarity. This range filters irrelevant documents while retaining parameter association information for the same type of equipment. |
RECALL_TOP_N | 8–12 entries | A single due diligence report covers multiple types of parameters including performance, qualification, and industry standards. This quantity covers multi-dimensional recall needs while avoiding excessive load. |
RERANK_TOP_N | 4–6 entries | The reranking model needs to screen the most relevant professional parameter fragments. This quantity balances response speed and information completeness. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large formalization report PDFs include multiple pages of test data. 300 seconds covers the full parsing duration for most non-classified documents. |
UPLOAD_FILE_MAX_SIZE | 500 MB | A single due diligence archive can include multiple supporting documents. This limit meets the needs of batch uploading core materials. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Model returned content is truncated. Background logs show context length limit errors. Cause: The context parameter set during model deployment does not match the
maxContextparameter configured in the FastGPT platform. The FastGPT platform configuration parameter takes priority during model runtime. - Phenomenon: No streaming output on the frontend. Results are returned all at once after generation completes. Cause: The streaming transmission switch is not enabled in the FastGPT model access configuration, or the model itself does not support streaming return formats.
- Phenomenon: Model call returns authentication failure. Logs show repeated key verification requests. Cause: Model keys and interface addresses are configured repeatedly in both the third-party transit service and the FastGPT platform, leading to verification conflicts.
How to confirm configurations are correct
- Upload a single ordnance equipment formalization report PDF. Check if parsed fields fully extract core information such as equipment model and formalization time. Confirm that the document parsing configuration is active.
- Initiate a query for specific equipment performance parameters. Verify the context coverage of returned content. Confirm that the
maxContextparameter configuration meets expectations. - View model call logs. Confirm that the streaming transmission identifier is returned normally. Check if the frontend displays generated content incrementally. Confirm that the streaming configuration is active.
- Test the model call authentication process. Confirm that keys and interface addresses are configured only once in the FastGPT platform, with no repeated verification issues.
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.