What the Data for This Category Looks Like
The data for defense electronics intelligent due diligence reports comes primarily from public annual reports of defense industrial groups, production capacity and supply chain data released by industry associations, specialized defense electronics bidding announcements and supporting technical parameter documents. Data updates follow annual, quarterly, and monthly cycles. Documents include two categories: structured financial fields and unstructured technical text. Structured fields include equipment model, core component model, delivery cycle, unit price, and other items. Units include ten thousand yuan, units, hours, and others. Unstructured text includes technical principle explanations, performance indicator descriptions, and similar content.
Constraints Imposed on Vector Models and Indexing
The mixed structure of defense electronics due diligence data requires vector models to adapt to both precise encoding of structured fields and semantic capture of unstructured text, while avoiding confusion of professional terminology. Multi-cycle updated data requires indexes to support differentiated refresh strategies, avoiding excessive resource usage from full refreshes or missed real-time updated bidding information. Documents of varying lengths, from short bidding announcements to long annual reports, require a chunking strategy that balances semantic integrity and splitting accuracy, while retaining field unit information to avoid semantic deviation in vector dimensions.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Select a vector model that supports semantic encoding for industrial domains | Defense electronics documents contain a large number of specialized component and equipment terms, requiring the model to accurately capture semantic associations within the domain |
chunk_size | 800–1200 characters | Balance semantic integrity for long annual report text and accurate splitting for short bidding announcement text, and avoid breaking critical information |
chunk_overlap | 50–80 characters | Compensate for information gaps after long text chunking, and adapt to the long paragraph structural characteristics of defense electronics documents |
recall_top_k | Top 8–12 results | Cover multi-dimensional information required for defense electronics due diligence, including supply chain, production capacity, technical parameters, and avoid recalling excessive irrelevant content |
similarity_threshold | 0.72–0.80 | Distinguish similar models of defense electronics components and technical parameters, and avoid including low-similarity irrelevant content in recall results |
index_refresh_interval | Daily full refresh + real-time incremental refresh | Adapt to the mixed update rhythm of annual reports, quarterly bidding data, and monthly production capacity data, and ensure the timeliness of index data |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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
- Scenario: A
400 Bad Requesterror is returned when configuring a multimodal vector model. The cause is using the call protocol of a generic text model by mistake, and failing to adapt to the model-specific call logic. - Scenario: A vector dimension mismatch error occurs after mixing index models and text models in configuration. The cause is failing to confirm that the output vector dimensions of the two models are consistent, leading to index construction failure.
- Scenario: GraphRAG associated queries return empty results. The cause is failing to include structured entity fields for defense electronics, such as core component model and equipment model, in the entity extraction scope, resulting in no valid association relationships to build.
How to Verify Correct Configuration
- Upload a sample of a defense electronics annual report and bidding announcement, and check that the length of chunked text blocks matches the preset
chunk_sizeconfiguration. - Initiate a due diligence query, and verify that the similarity scores of recall results fall within the preset
similarity_thresholdrange. - View the index refresh log, and confirm that full refresh and real-time incremental refresh tasks execute according to the preset
index_refresh_interval. - Test uploading multimodal technical documents, and confirm that the vector model can normally generate cross-modal vector embeddings.
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.