What data looks like for this category
Data for elderly care service institution supervision document and material drafting typically comes from elderly care service information platforms, civil affairs statistics systems, fire safety inspection records, and institution operation ledgers. Daily generated documents include filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. The data update frequency is monthly. Most document structures include fields such as institution identification, number of beds, inspection items, and rectification status. Some documents include images or attachments, and there are no unified fixed unit format requirements.
What constraints do these characteristics impose during deployment and upgrade
Because supervision data comes from multiple heterogeneous systems, connection parameters for multiple data sources must be configured during deployment to ensure that ledgers and inspection records from different systems can be synchronized and accessed. The monthly update frequency requires that the cycle configuration of data synchronization tasks matches the rhythm of supervision business, to avoid excessive occupation of system resources. The storage requirements for multiple types of documents require configuring a file storage service compatible with multiple formats, and supporting permission configuration to ensure that sensitive elderly admission files are not accessed without authorization. Errors in supervision documents can cause serious consequences, so strict data verification rules must be configured during deployment to ensure that the fields of generated materials are complete and accurate.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 | Connect to multi-source operation ledgers and inspection records, avoid exhausting database connection pools |
MONGODB_URI | mongodb://<username>:<password>@<host>:<port>/admin?authSource=admin | Store unstructured documents such as safety hazard rectification ledgers and elderly admission files |
PG_URL | postgresql://<user>:<pass>@<host>:5432/pension_regulation | Store structured data such as institution filing materials and operation subsidy applications |
REDIS_URL | redis://:<password>@<host>:6379/0 | Cache frequently queried data such as institution bed counts and filing status, improve query efficiency |
STORAGE_S3_ENDPOINT | Fill in according to the actual address of the local object storage | Store supervised documents with attachments such as service quality inspection forms and rectification photos |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Protect uploaded sensitive supervision documents from unauthorized access |
Three common mistakes
- Symptom: A database connection error occurs after running the local Docker upgrade script, with a 500 status code returned. Cause: The original FastGPT service was not stopped in advance, causing a port conflict, or the
DB_MAX_LINKvalue is too small, causing the connection pool to be exhausted. - Symptom: After upgrading to FastGPT v4.17.0, third-party model connection fails, and supervision documents cannot be generated. Cause: The environment variables for model docking were not configured correctly, or the service was not restarted to load the new configuration.
- Symptom: Uploaded elderly care institution filing materials cannot be parsed normally. Cause: The
STORAGE_S3_ENDPOINTconfiguration is incorrect, causing the file storage path to be abnormal and unable to read document content.
How to confirm the configuration is complete
- Run the built-in database connection test tool to verify that the data sources configured via
PG_URLandMONGODB_URIcan be accessed normally. - Upload a test elderly care institution filing material to confirm that the file can be stored normally and recognized and parsed by the system.
- Call the model generation interface to generate a simulated service quality inspection form, confirm that the model can be called normally and generate documents that meet the required format.
- View the Redis cache monitoring panel to confirm that the cache service configured via
REDIS_URLcan store and read cached data normally.
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 2026-09-28.