Database and Operations for Defense Equipment Investment

What the data for this category looks like

Data sources for defense equipment investment research include finalized test reports from military research institutes, equipment technology white papers, industry standard documents, procurement ledgers, and operation and maintenance logs. Update rhythms vary significantly: finalized documents and industry standards are updated at low frequency. Test reports are updated with equipment batch iterations. Operation logs are generated in real time during daily operations.

Document structures include structured parameter tables (such as equipment model, test conditions, performance metrics), semi-structured graphic-text test reports, and unstructured technical description text. Fields include model code, environmental parameters, performance values, and other items. Units cover multiple industrial standard units such as kilometers, hours, megapascals.

Constraints for database and operations workflows

Multi-source heterogeneous data structures require databases to support hybrid storage and retrieval of structured fields and unstructured text. Uneven update rhythms require flexible incremental synchronization scheduling rules to balance update needs for high-frequency operation logs and low-frequency finalized documents. Long-text test reports and large attachments raise higher requirements for database storage capacity and parsing timeout settings. Diverse units and field names require standardized mapping rules to prevent field mismatch issues during retrieval. The knowledge base must ensure data consistency, and operations workflows must regularly verify the integrity of data synchronization.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`vector_dim`	`1536`	Matches standard output dimensions of general embedding models, and supports hybrid embedding retrieval for defense equipment parameters and text
`chunk_size`	`800–1200 characters`	Balances semantic integrity of long test reports and vector recall accuracy, and adapts to average length of single documents
`incremental_sync_interval`	`2 times daily`	Adapts to mixed update rhythms of low-frequency finalized documents and high-frequency operation logs, and balances real-time performance and operation costs
`recall_top_k`	`Top 8–12 entries`	Balances information comprehensiveness required for investment research and retrieval efficiency, and avoids excessive redundant information interfering with decision-making
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Supports upload requirements for large attachments such as test videos and 3D models
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Reserves sufficient processing time for long document parsing, and prevents parsing processes from being interrupted by timeouts

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Phenomenon: Vector database query response delay is too high, with error showing 504 Gateway Timeout. Cause: The vector_search_batch_size parameter was not adjusted based on the volume of defense equipment data, and the single query load exceeds the database processing limit.
Phenomenon: Concurrent requests trigger OOM errors after vLLM deployment. Cause: The max_context_len and video memory allocation parameters were not adjusted based on the context length of defense equipment long texts, leading to resource exhaustion.
Phenomenon: Structured parameter fields in knowledge base documents are empty. Cause: The field_mapping rule was not configured to unify field names and units across different data sources, resulting in failed recognition of parsed fields.

How to confirm correct configuration

Upload a typical defense equipment test report, check if the parsed segment length falls within the preset chunk_size range, and adjust parameters until it matches.
Trigger an incremental synchronization task, verify that only newly updated data is added to the knowledge base, and no historical documents are synchronized repeatedly.
Retrieve parameters for a specified equipment model, confirm that both structured fields and text content can be retrieved, and the number of returned entries matches the set recall_top_k.
Upload the largest single attachment, verify that the upload and parsing processes do not trigger timeout errors, and confirm that the UPLOAD_FILE_MAX_SIZE and PARSE_FILE_TIMEOUT_SECONDS configurations are reasonable.

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.