What the data for this category looks like
Ordnance equipment research report data comes from securities firm military industry research reports, publicly disclosed documents from national defense science, technology and industry competent authorities, and public materials from ordnance equipment industry associations. Update rhythms include both real-time dynamic and regular in-depth types. Real-time industry content is updated alongside equipment commissioning and policy adjustments. In-depth research reports are released on a fixed cycle. Document structures typically include sections for equipment model parameters, procurement plan summaries, supporting industrial chain information, and industry policy interpretations. Covered fields include equipment models, finalization milestones, supporting system types, unit production capacity, and more. Common units include sets, units, person-times, and ten thousand yuan.
Constraints on Vector Models and Indexes
Ordnance equipment research reports contain large numbers of professional model terms and industry-specific units. General vector models cannot accurately capture term associations, so fine-tuned vector models trained on military sub-field corpus are required. The mixed update rhythm of research reports causes excessive resource usage during full index reconstruction. Systems must support incremental index updates. Single documents include both long professional interpretation paragraphs and short entry-style parameter lists. When splitting content into chunks, avoid truncating professional term combinations. Retain field-level metadata associations to prevent loss of key information such as units and models during retrieval.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the dual structure of ordnance equipment research reports, which have both long paragraph interpretations and short parameter entries, and avoids truncating core professional terms |
vector_model | Open-source vector model fine-tuned on military sub-field corpus | General models cannot accurately match exclusive terms such as ordnance equipment models and policies, and fine-tuning improves retrieval relevance |
recall_top_k | Top 10–15 results | Research report content has high professionality, so a sufficient number of candidate results must be recalled to avoid missing relevant parameters or policy information |
similarity_threshold | 0.72–0.80 | Filters low-relevance non-professional documents and retains research report content strongly related to ordnance equipment topics |
enable_incremental_index | Enabled | Adapts to the mixed update rhythm of real-time research report updates and regular in-depth reports, reducing resource consumption from full index reconstruction |
parse_chunk_overlap | 50–80 characters | Retains term associations between chunks, prevents professional terms from being broken by chunk truncation, and improves vector retrieval accuracy |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: When creating a knowledge base in a local deployment of version v4.9.0, the
image_index_modeloption is not displayed. Cause: In the open-source package of this version, the image index model is only integrated by default in the commercial edition compiled package. Open-source deployments require manual configuration of the corresponding model files. - Phenomenon: Index parameters cannot be adjusted directly through the retrieval page during debugging, and must be operated in the knowledge base content management page. Cause: The index debugging entry was migrated to the knowledge base content editing interface in the new version, and the retrieval page only retains the result display function.
- Phenomenon: The vector retrieval interface call returns a
403 Forbiddenstatus code. Cause: Commercial edition index enhancement permissions are not configured. The open-source version only supports basic vector indexes and cannot use advanced parsing and index optimization functions.
How to Confirm the Configuration Is Complete
- Upload an ordnance equipment model parameter research report, check the vector chunking results, and confirm that professional terms are not truncated.
- Initiate a retrieval request containing professional model names, and verify that the relevance and number of recalled results match the preset configuration.
- Upload a new research report file, check the index update log, and confirm that the incremental index function is triggered normally.
- Check the vector model configuration item, confirm that the military-domain fine-tuned model has been selected, and ensure the accuracy of professional term matching for retrieval.
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.