What Data for This Category Looks Like
Ordnance equipment investment research data mainly comes from publicly disclosed equipment finalization documents issued by the National Defense Science, Technology and Industry Administration, annual performance announcements of military industry groups, equipment performance white papers released by industry associations, open military academic journals, and public test reports from test ranges. Data updates are triggered by nodes such as equipment project initiation, finalization, and formal field service, and are supplemented with daily industry dynamics. Most documents include standardized performance parameter tables, chapter-by-chapter system composition descriptions, and test process records. Fields cover equipment models, finalization time, performance parameters and supporting information, and most parameters include standardized units.
Constraints Imposed on Model Access and Configuration
The dense professional terminology, mixed structured parameters and long texts of ordnance equipment investment research data create multiple constraints for model access and configuration. First, the data contains a large number of military professional parameters and equipment model terms. Select an embedding model adapted to domain semantics to ensure accurate recall results. Second, standardized performance parameter tables and long test reports coexist. Configure differentiated document parsing rules to balance structured field extraction and semantic integrity of long texts. Third, the fluctuation of data updates tied to equipment project initiation and finalization nodes requires a flexible synchronization interval configuration to adapt to sudden increases in new data volume.
How to Set Configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
embedding_model | Select a model that supports military professional terminology | Ordnance equipment investment research data contains a large number of professional parameters and model terms, requiring the model to accurately understand semantic connections |
chunk_size | 800–1200 characters | Balance the semantic integrity and recall accuracy of single-segment text, adapting to the segmentation needs of long documents such as test reports and system descriptions |
similarity_threshold | 0.75–0.85 | Meet the business requirement of accurately matching equipment models and parameters, raising the threshold to filter irrelevant results |
rerank_top_n | Top 8 entries | Supplement the accuracy of basic recall, and perform secondary ranking and screening for multi-dimensional equipment performance parameters |
PARSE_STRUCTURED_TABLE | Enable structured parsing | Adapt to standardized equipment performance parameter tables, fully extract field and unit information |
UPDATER_SYNC_INTERVAL | 3600 seconds | Synchronize newly released public data hourly, following the update rhythm of equipment formal service and finalization nodes |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Calls to the embedding model return a 503 status code, and requests fail under the default group. The volume of embedding requests during ordnance equipment industry dynamics nodes fluctuates greatly. The default group's concurrent quota cannot support peak traffic.
- Recall results contain mixed units for equipment parameters, such as both kilometers and miles appearing simultaneously. Structured table parsing is not enabled, and unit unified verification is not performed for parameter fields from different sources.
- Model comparison experiments cannot reproduce baseline results. Core configuration parameters such as
chunk_sizeandsimilarity_thresholdare not fixed, resulting in inconsistent recall datasets across runs.
How to Verify Proper Configuration
- Upload a public ordnance equipment performance parameter document, and check whether extracted fields and units fully match the original document content after parsing.
- Submit an equipment model retrieval request, and verify that the relevance of returned results meets business expectations.
- Adjust the value of
similarity_threshold, and confirm that the trend of recall result count changing with the threshold is reasonable. - Trigger a data synchronization task, and check whether newly released public equipment information is correctly imported into the knowledge base.
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.