What This Type of Data Looks Like
Typical data sources for customer data import migration include export files from legacy customer systems, CSV or Excel templates, export results from third-party databases, and built-in import logs from the FastGPT product. Daily supporting documents include standardized data templates, migration execution plans, field mapping comparison tables, and migration verification reports. Data update cycles are per order or irregular. Single-batch imported data volume varies based on customer business scale. Core processing steps include field mapping confirmation, dirty data cleaning, and post-import sampling verification. Data processing errors can cause lost historical data, misplaced fields, and customer rejection.
Constraints on Database and Operations Workflows
The diversity of data sources requires database operations workflows to adapt to multiple connection protocols and formats, and support import verification for both structured and unstructured data. Irregular data update cycles require import configurations to support flexible batch adjustments, to avoid resource waste or import failures caused by fixed thresholds. The high time consumption of field mapping and dirty data cleaning requires database connection configurations to balance stability and processing efficiency, to prevent single-batch task timeouts. The severe consequences of data errors require operations workflows to retain complete import logs, to support fault retrospective and quick rollback operations.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Full connection string containing authentication information, port number, and target database path | Adapts to MongoDB database deployments from customer legacy systems, ensures cross-environment connection permissions |
PG_URL | Connection string formatted as postgresql://user:pass@host:port/dbname | Meets structured data storage requirements from customer exports, supports field mapping verification |
DB_MAX_LINK | 10–20 concurrent connections | Matches the upper limit of single-batch imported data volume, prevents database connection exhaustion |
| Single import chunk size | 5000–10000 data entries | Adapts to the processing rhythm of field mapping verification, reduces single-transaction timeout risk |
| Import log retention period | 30 days | Meets log tracing requirements for fault retrospective, complies with operations archiving standards |
Three Common Misconfigurations
- Phenomenon: Database connection nodes do not allow input of port numbers. Cause: FastGPT v4.17.0 and later versions integrate port numbers into the
MONGODB_URIorPG_URLconnection strings, and no separate input box is provided. - Phenomenon: No query results are available after using mongorestore to import a knowledge base. Cause: Data index reconstruction was not performed, or vector embedding tasks were not completed during the import process.
- Phenomenon: No mongo/data folder exists in the same-level directory after startup. Cause: FastGPT v4.17.0 adjusted the MongoDB data volume mount path, and no local folder is generated in the startup directory by default.
How to Confirm Proper Configuration
- Perform a small-batch sample import, review the import log for connection timeout or permission error messages.
- Verify the database connection string configuration, confirm that
MONGODB_URIorPG_URLcontains correct authentication and path information. - Check the import log retention period configuration, confirm it aligns with the required cycle for retrospective work.
- Conduct sampling verification to confirm that imported data fields match the mapping table, with no misplaced or missing fields.
The values provided are common starting points and should be measured against relevant sample datasets.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.