What data for this category looks like
Ordnance equipment yield rate data primarily comes from full life cycle management ledgers of equipment, internal financial systems of military industrial groups, and public defense equipment performance evaluation reports. Data is stored as structured tables, including fields such as equipment ID, procurement batch, deploying unit, daily operating duration, daily maintenance cost, daily task completion volume, cumulative investment cost, and cumulative task output value. Units include hours, ten thousand yuan, and sets/units. Data is updated by syncing real-time operating data daily, and aggregating full-cycle yield rate statistics weekly. The size of a single complete data entry is approximately 100KB to 500KB. No semi-structured or unstructured free text content is included.
What constraints these characteristics impose on deployment and upgrade
Daily incremental real-time operating data requires configuring high-frequency incremental sync tasks during deployment, to avoid excessive bandwidth and storage resource usage from full data pulls. Large structured data volume for cumulative input and output requires sufficient storage and recall resources to be reserved for the vector database. Compliance requirements for military industrial data require the deployment environment to be isolated from the public network. An uninterrupted blue-green deployment strategy must be used during upgrades to avoid interrupting classified data synchronization. Fields include sensitive content such as equipment ID and task information, so strict access permission verification must be configured. Permission control logic must not be modified during upgrades to prevent compliance vulnerabilities. Minor differences exist in fields across different equipment models, so dynamic schema adaptation must be configured to avoid index failures after upgrades.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SYNC_DATA_INTERVAL | 5 minutes | The incremental update frequency of ordnance equipment operating data must ensure data delay does not exceed 10 minutes, meeting the real-time requirements of daily yield rate reports |
VECTOR_STORAGE_MAX_SIZE | 2000 GB | Cumulative data for a single piece of equipment is approximately 100MB. 1,000 pieces of equipment require 200GB, with 10 times redundant space reserved to address long-term data accumulation |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Full life cycle ledger documents typically contain multi-page structured data, which takes longer to parse. This avoids timeout interruptions to the parsing process |
RECALL_TOP_K | Top 10 entries | The data volume of a single batch of equipment is large, so enough entries must be recalled to cover yield rate comparisons across different models, while avoiding excessive data consuming context space |
EMBEDDING_MODEL | ali-emb3 | Adapts to vector generation for structured military industrial data, and complies with index model standards supported by community open-source versions |
UPLOAD_FILE_MAX_SIZE | 5000 MB | The maximum single file size of full life cycle ledgers must meet upload requirements, avoiding parsing failures for large files |
The parameter values provided on this page are all conventional recommendations, used as 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 settings.
Three common mistakes
- Phenomenon: Disk space is rapidly exhausted after Docker deployment, with logs prompting "no space left on device". Cause: Temporary file mount path is not configured. Parsing and vector generation temporary files for FastGPT are stored in the container's internal directory by default, and are not mounted to the host machine's disk. This leads to residual temporary files or insufficient space after container restarts.
- Phenomenon: The frontend page cannot be accessed via HTTPS, with the browser prompting "SSL certificate invalid" or connection timeout. Cause: FastGPT's HTTPS certificate mount and port mapping are not correctly configured. The container's port 3000 is not mapped to the host's port 443, or the certificate file path is not specified.
- Phenomenon: Index task fails, with logs prompting "embedding model not found" or "invalid model name". Cause: An unsupported open-source model name is used incorrectly, or the deployment address of the
ali-emb3model is not correctly specified in the configuration, leading to vector generation failures.
How to confirm configuration is complete
- View sync task logs to confirm that incremental data pull intervals match the configured
SYNC_DATA_INTERVAL, with no consecutive failure records. - Upload a document that conforms to the ordnance equipment data format, confirm that the parsing process has no errors and field completeness meets expectations.
- Initiate a test report request, confirm that the returned results include the specified equipment type and data dimensions in the configuration.
- Check the vector database's resource usage, confirm that reserved storage and calculation thresholds are not exceeded. Thresholds must be calibrated based on actual data volume and deployment scale.
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.