What data for this category looks like
Data for this category primarily comes from product help centers, customer organizational structures, training recording videos, and ticket systems. Daily documents include training slides, user manuals, training attendance sheets, and question and answer lists, updated per order or per version. Document structures typically include operation steps, permission descriptions, and Q&A entries. Fields include document version number, associated customer ID, update time, and number of attendance records. There are no unified fixed units; adjustments are made based on the content of individual documents.
What constraints these characteristics impose on the database and operations link
Constraints introduced by multi-source data access: Must support importing data in different formats from product help centers, ticket systems, and other sources, to avoid data format conflicts. Non-fixed update frequencies require support for on-demand triggering of document synchronization and updates, using an on-demand triggering task mechanism. Large differences in document structures require support for flexible custom field mapping to adapt to the training document formats of different customers. Storage of long blocks of operation step text requires configuring sufficient single-item data storage space. Error consequences are linked to customer conversion and ticket volume, so supporting data verification and abnormal alert mechanisms is necessary to ensure data accuracy. This document adapts to the operation and maintenance configuration logic of FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://{user}:{pass}@{host}:27017/fastgpt | Adapts to native or domestically compatible MongoDB database instances, used to store unstructured documents such as training slides and user manuals |
PG_URL | postgresql://{user}:{pass}@{host}:5432/fastgpt | Used to store structured data such as customer training attendance sheets and question and answer lists |
DB_MAX_LINK | 20–50 | Adapts to scenarios where multiple customers access training manuals concurrently, to avoid connection exhaustion |
REDIS_URL | redis://{user}:{pass}@{host}:6379/0 | Accelerates high-frequency queries of training document metadata |
| Document storage shard size | 800–1200 characters | Split long blocks of operation step text to optimize read performance |
| Data backup trigger timing | After each version update is completed | Match the update frequency of this category to ensure data accuracy |
Three common mistakes
- Symptom: Database connection failure, returning the
ECONNREFUSEDstatus code. Cause: The host address and port forMONGODB_URIorPG_URLare not configured correctly, preventing connection to the deployed database instance. - Symptom: Fields are empty after parsing long training documents containing operation steps. Cause: A reasonable document storage shard size is not configured, causing long text to be truncated and unable to be stored completely.
- Symptom: Chinese characters cannot be displayed after connecting to MySQL. Cause: The character set is not specified as utf8mb4 in the database connection parameters, or the character encoding configuration of the database tables is not updated synchronously.
How to verify configurations are correct
- Execute a database connection test script to verify that the configurations for
MONGODB_URI,PG_URL, andREDIS_URLcan establish normal connections. - Upload a training document containing long blocks of operation steps, check that the stored text is complete and not truncated, to confirm that the document shard configuration is effective.
- Trigger a training document update operation, check that the data backup task automatically executes at the configured timing.
- Simulate a scenario where multiple customers access training manuals simultaneously, check that the number of database connections does not exceed the set value of
DB_MAX_LINK.
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.