What the Data Looks Like
Defense equipment investment research data primarily comes from defense white papers, official announcements from defense industry groups, publicly available industry research reports, equipment qualification test records, and professional exhibition materials. Updates follow no fixed cycle, and are triggered by new equipment fielding, formal qualification news, or industry policy releases. Documents include long-form technical analysis paragraphs, structured parameter tables with units (such as range, protection level), equipment orthographic views and on-site test images, plus some unstructured industry interpretation text.
Constraints Imposed on Vector Models and Indexing
Multimodal data combining text, structured parameters, and images requires vector models to support multimodal embedding. Without this support, technical details and visual information of equipment cannot be fully encoded. Unpredictable updates with no fixed cycle require indexing systems to support incremental refresh. This avoids wasted computing resources from full index rebuilding. Structured parameters with units must retain semantic integrity, so chunking strategies must avoid breaking the connection between parameters and their context. Information from a single research report may be spread across multiple paragraphs, so indexing must support associative retrieval. This ensures retrieval results cover complete equipment technical information.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Select a multimodal-capable model such as Embedding-3 or an equivalent vector model | Defense equipment investment research materials include text analysis, structured parameters, and supporting images. Multimodal models can fully encode all types of data |
chunk_size | 800-1200 characters | Balances long text chunking and retention of parameter context, avoids separating equipment technical parameters from their corresponding analysis content |
index_refresh_strategy | Incremental trigger mode, triggered by new data volume thresholds | Adapts to the irregular update rhythm of investment research data, reduces computing costs of full index rebuilding |
retrieval_top_k | Top 8-12 results | Information related to a single piece of equipment may be spread across multiple paragraphs. A sufficient number of associated results must be retrieved to cover complete technical details |
similarity_threshold | 0.72-0.85 | Distinguishes technical parameters of similar equipment models, avoids retrieving irrelevant entries, and balances retrieval accuracy and coverage |
image_embedding_enabled | Enabled | Supports joint vectorization and retrieval of equipment images and their corresponding text, matching the text-image combination feature of investment research materials |
The parameter values provided on this page are common 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 Mistakes
- Symptom: After calling the
create_train_orderAPI, newly added data does not appear in knowledge base search results, and the system interface displays "Index not completed" status. Cause: The training order was mistakenly used for full index rebuilding, and no incremental trigger rules were configured. As a result, newly added data was not included in the real-time index. - Symptom: After uploading investment research materials containing images, search results only return text fragments with no associated image information. Cause: The
image_embedding_enabledconfiguration was not enabled, or images were not uploaded bound to their corresponding text fragments. As a result, images were not vectorized. - Symptom: An
embedding model connection failederror pops up during local deployment, even though the oneAPI connectivity test passes. Cause:EMBEDDING_API_KEYand the vector model endpoint address were not correctly configured in environment variables, or the selected vector model version does not support multimodal input.
How to Verify Successful Configuration
- Upload a single defense equipment research report containing technical parameter text and supporting images. Check the vector task queue to confirm that embedding tasks for both text and images are generated simultaneously.
- Enter equipment model keywords to initiate a search request, and verify that returned results include associated text paragraphs and corresponding image links.
- Add new non-public equipment qualification announcement data, and check whether the index automatically refreshes within the set threshold without waiting for full rebuilding.
- Check system operation logs to confirm that vector model call requests include multimodal input identifiers with no format errors.
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.