What the data for this category looks like
This category of data primarily originates from customers' historical business systems, CSV/Excel templates, and database export files. Update frequency follows per-order or irregular schedules. Daily supporting documents include data templates, migration plans, field mapping tables, and migration verification reports. Data fields cover all types of attributes of customer business entities, with no unified fixed units. Matching must be completed based on the field definitions of the customer's original system. Execution parties are mostly implementation consultants, data implementation engineers, and customer IT liaisons. Core processing steps include field mapping confirmation, dirty data cleaning, and post-import sampling verification.
What constraints these characteristics impose on the database and operations link
The diversity of data sources requires the database module to support connections to multiple types of data sources. Parameters such as MONGODB_URI and PG_URL must be configured to adapt to different database types. No fixed update cycle means migration must support on-demand triggering, and transaction consistency during the import process must be guaranteed to avoid data chaos caused by partial import failures. The core requirement of field mapping means the database module must support custom field matching rules to prevent field misalignment issues. The dirty data cleaning step must reserve database-side data verification and repair interfaces to reduce the risk of historical data loss. Migration verification reports must be linked to database import logs to facilitate tracing import results and troubleshooting issues.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Fill in according to the actual connection string of the data source, must include authentication information | Used to connect to MongoDB-type customer historical data sources, must match the access address, port, and authentication parameters of the customer's database |
PG_URL | Fill in according to the actual connection string of the PostgreSQL data source, must include the database name | Used for connecting to PostgreSQL or database scenarios with compatible protocols |
DB_MAX_LINK | 10–20 connections | Limits the maximum number of connections in the database connection pool to avoid excessive occupation of database resources during concurrent imports |
REDIS_URL | Fill in according to the connection string of the Redis instance in the deployment environment | Used to cache temporary data and status of import tasks, supporting resumable uploads |
| Import timeout threshold | 600 seconds | Adapts to the time limit for a single batch of data imports, preventing task unresponsiveness due to network fluctuations or excessive data volume |
Three common mistakes
- Symptom: The interface prompts a connection failure, the log shows
connection refused, and recovery fails even when using official documentation methods. Cause: Authentication parameters forMONGODB_URIorPG_URLare not configured correctly, or network policies restrict access permissions between the FastGPT service and the target database. - Symptom: After upgrading FastGPT v4.17.0, knowledge base import tasks report an error
text index required for $text query. Cause: A full-text index is not created on the corresponding field of the target PostgreSQL database, causing text queries to fail to execute normally. - Symptom: After upgrading the FastGPT version, previously imported historical data cannot be accessed normally. Cause: The connection configuration and write permissions of the target database were not properly retained during the upgrade, or database import logs were not backed up in advance.
How to confirm the configuration is correct
- Perform a database connection test to check whether the table structure and field information of the target data source can be normally retrieved, and verify that the connection parameters match the configured items.
- Import a small batch of test data to check whether the import log completely records the data writing results with no error messages.
- Compare the field mapping relationship before and after import to confirm that the fields in the target database match the mapping table definitions with no misalignment.
- Check the import task status cached in Redis to confirm that temporary data has been properly cleaned and no residual abnormal task information remains.
The values provided are common starting points and should be measured against the reader's own samples.
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.