What the data for this category looks like
Data related to defense electronics yield rates draws from three main sources: public market APIs of domestic stock exchanges, public datasets from defense industry associations, and compliant authorized third-party financial data APIs. There are two update schedules for this data:
- Real-time market data updates after daily market close
- Derived yield indicator data updates within three business days after quarterly earnings reports are released
Each data entry includes fields such as ticker code, ticker name, daily trading price range, daily trading volume, data collection timestamp, and data source identifier. Price fields use RMB yuan as their unit. Trading volume fields use ten thousand shares as their unit. Time fields use ISO format timestamps in the UTC+8 time zone. All core fields of the data document are required. Missing core fields will cause exceptions in subsequent broadcast workflows.
What constraints do these characteristics impose on database and operations work?
Multi-data source access requires databases to use fine-grained access permission controls to prevent sensitive financial data leaks. Data sources with different update schedules need split scheduling tasks. High-frequency market data requires short-cycle scheduled synchronization. Low-frequency derived data needs to adapt to quarterly-level scheduling cycles. Defense electronics data involves industry-sensitive information. Teams must enable database audit logs and sensitive field encryption to meet compliance requirements. Fast recall of historical market data requires creation of a joint index based on collection time and ticker code. Data must also be stored by date partitioning to control per-table data volume. Unstable data sources require retry mechanisms and data completion processes. Operations teams must monitor data source availability and database write success rates in real time.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGO_CONNECTION_STRING | mongodb://fastgpt_user:secure_pass@mongo-host:27017/fastgpt?authSource=admin&readPreference=secondaryPreferred | Supports identity authentication and read-write separation, adapts to high-concurrency write scenarios for multiple data sources, and ensures secure access to sensitive data |
VECTOR_DB_REPLICA_NUM | 3–5 replicas | Improves recall concurrency for defense electronics market data, and ensures high availability of query services |
DATA_SYNC_INTERVAL | 900 seconds | Matches the update schedule of daily market data, prevents data delay exceeding 15 minutes |
MONGO_MAX_POOL_SIZE | 50–80 | Adapts to bulk data write scenarios, prevents connection pool resource exhaustion |
BACKUP_RETENTION_DAYS | 30 days | Meets compliance audit log retention requirements, while controlling storage costs |
VECTOR_DB_SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-relevance search results, improves the accuracy of defense electronics yield rate broadcasts |
The parameter values provided on this page are general recommendations that serve as a starting point for configuration. Actual values are affected by material form, 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
- Symptom: Using a third-party MongoDB management tool to directly modify vector database documents results in empty FastGPT recall results. Cause: The metadata fields of the vector database documents do not follow FastGPT's preset format, causing the system to fail to recognize valid data.
- Symptom: The error
retry init root user mongo start connect init root user errorappears in container logs after startup. Cause: The MongoDB connection string is configured incorrectly, or the dedicated fastgpt database and user permissions have not been created. - Symptom: The scheduled defense electronics data synchronization task times out, returning status code 504. Cause: The
DATA_SYNC_TIMEOUTparameter has not been adjusted, and the default timeout period is insufficient to complete bulk data pulling and writing.
How to verify successful configuration
- Log in to the MongoDB client, run the
show dbsandshow collectionscommands. Confirm that the fastgpt database and corresponding market data collection exist, and check that the latest value of thecollection_timefield matches the current day's time. - Call the FastGPT vector recall API, pass in query terms related to defense electronics, and verify that the number of returned results matches the configured recall parameters.
- Check container runtime logs to confirm there are no errors of type
MONGO_CONNECT_ERRORorVECTOR_DB_CONNECT_FAILED. - Perform a manual backup operation, and verify that the exported file metadata matches the actual knowledge base data structure.
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.