Deployment and Upgrade for Professional Services Yield Data

What the data for this category looks like

Data for professional services yield and daily market reports comes from licensed financial market APIs, publicly reported regulatory data, and in-house institutional accounting systems. Full updated data for the previous trading day is generated at fixed times each day. Documents use structured JSON or CSV format. Each record includes a unique product identifier, statistical cycle, revenue calculation value, associated underlying asset code, and data source identifier. Fields follow financial industry standard encoding rules. Statistical cycles use ISO 8601 date format. Revenue calculation values use standard financial units. Associated underlying asset codes match exchange standard encoding.

What constraints do these characteristics impose on deployment and upgrade?

This category has strict data compliance requirements, fixed update schedules, and clear structured characteristics. These impose three key constraints on deployment and upgrade. First, multi-source data access requires compliance verification rules to be configured. This ensures only licensed institutional legal data sources are accessed, and prevents non-compliant data from flowing into the knowledge base. Second, daily full update tasks at fixed times need precise scheduled trigger parameters. This avoids conflicts with market synchronization tasks from other systems. Third, fixed structured field requirements mean schema mapping rules must be preset during deployment. Compatibility logic for legacy fields must be retained during upgrades, to prevent parsing failures for historical data. Additionally, data volume grows gradually as product scale expands. Deployment must reserve storage expansion space for vector databases, to avoid insufficient storage after upgrades.

How to set configurations

Configuration Item	Recommended Value	Rationale
`CRON_EXPRESSION`	`0 18 * * *`	Matches the T+1 update schedule for professional service daily reports, avoids peak market trading hours
`PARSE_DATA_SCHEMA`	Preset standard schema for financial product yield data	Aligns with the fixed structured field requirements of this category
`ENABLE_DATA_VALIDATION`	`true`	Meets compliance verification needs for financial data, filters non-compliant data sources
`MAX_SYNC_WAIT_TIME`	`600 seconds`	Suitable reasonable wait duration for full historical data synchronization
`VECTOR_DB_INDEX_BATCH_SIZE`	`500–1000 records`	Balances synchronization efficiency and vector database load pressure
`LOG_RETENTION_DAYS`	`365 days`	Complies with regulatory data retention requirements for the financial industry

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

Three common configuration errors

Symptom: Unable to load configured market data source indexes after startup. The interface displays 500 Internal Server Error, and the log contains an index not found error. Cause: MONGODB_INDEX_AUTO_CREATE was not set to true in docker-compose environment variables, or the index initialization script was not run manually.
Symptom: Running npm list mongoose in the container returns no output. The dependency version cannot be confirmed, but the corresponding field can be found in package.json and cannot be queried via the npm command. Cause: The FastGPT docker image uses layered builds, and dependency packages are not mounted to the container's executable global directory, or a non-npm package manager was used for dependency installation.
Symptom: A 400 Bad Request error appears when viewing session details. The error message shows invalid log field. Cause: Log parsing schema rules were not updated synchronously after a version upgrade, and newly added yield data fields were not mapped in the configuration.

How to confirm successful configuration

Trigger a manual data synchronization, verify that there are no validation failed or parse error errors in the task log, and that synchronized data fields fully match the preset schema.
Run cat package.json | grep mongoose in the container, confirm that the dependency version field exists, or check the container's node_modules directory to confirm that mongoose files are present.
View the session log details page, confirm that all fields are displayed normally, with no 400 Bad Request or field missing errors.
Check scheduled task scheduling records, confirm that the synchronization task automatically triggers at the fixed daily time, with no delays or skips.

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.