What the Data for This Category Looks Like
Data sources for due diligence in the military electronics field include public qualification documents, model demonstration documents, supporting manufacturer capacity announcements, and industry procurement announcements. Updates follow a quarterly cadence. Core model data is adjusted every six months. Most documents are multi-page PDFs or scanned copies. They contain model parameters, capacity metrics, security classification, delivery cycles, supporting supply chain information, and other content. Fields and units cover model (no unit), capacity (units/set), R&D cycle (months), security classification (classified/public), and other professional dimensions.
What Constraints These Characteristics Impose on Deployment and Upgrade
Single due diligence reports have large data volumes, increasing the risk of file parsing timeouts. Adjust parsing-related parameters to support long document processing. There are many professional fields with high precision requirements. Adjust vector recall thresholds and segment lengths to align with the contextual association logic of professional terms. Some data has sensitive attributes. Configure local private models or vector stores to avoid public network calls that risk data leaks. Update frequency is low, but single data volume is high. Use incremental synchronization strategies during upgrades to avoid excessive resource usage from full re-runs.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Adapt to the long document parsing time of single military electronics due diligence reports |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Accommodate the volume of multi-page scanned due diligence documents |
maxContext | 8000-12000 characters | Retain professional term context and avoid segmenting core information |
Recall count | Top 8 entries | Cover the recall requirements for multi-dimensional professional fields in the military sector |
Similarity threshold | 0.75-0.85 | Improve the accuracy of professional term matching and reduce irrelevant recalls |
VECTOR_STORE_BATCH_SIZE | 50 | Control the load of batch vector inserts and avoid database timeouts |
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 specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Calling the Alibaba Cloud model version V4.9.7 for testing returns a 400 error. The cause is that the submitted due diligence report contains classified fields, which triggers model parameter verification interception.
- Using the V4.9.7 version API to upload due diligence files causes the agent backend to become unresponsive. The cause is that the file parsing timeout parameter was not adjusted, and incomplete long document parsing leads to process blocking.
- After deploying V4.9.7 via docker-compose, the workflow and knowledge base become blank after a period of time. The cause is that no persistent storage volume was configured, resulting in loss of vector store and configuration data after container restart.
How to Confirm Proper Configuration
- Upload a standard military electronics due diligence document, verify that the parsed segmented content retains core professional fields such as model and capacity.
- Call the model interface with non-sensitive test data, confirm that the return status code is 200 and there are no parameter exception errors.
- Restart the deployment container, verify that the knowledge base directory and workflow configuration are fully retained.
- Run an incremental synchronization task, confirm that newly added due diligence report data is properly written to the vector store.
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.