What the data for this category looks like
Data for pharmaceutical distribution declaration materials and file review comes primarily from two sources. Enterprises submit documents such as pharmaceutical business license applications and cold chain transport filings to regulatory authorities. Internal teams retain GSP inspection records and batch distribution files.
Submission and inspection workflows trigger data updates. Each single declaration material includes fixed fields: declaring entity name, generic drug name, batch number, distribution scope, and approval status. It also includes attachments such as scanned documents and transport record forms.
Fields include strings and numerical units. Batch numbers have no unit identifier. Transport distance uses kilometers as the unit. Storage temperature uses degrees Celsius as the unit.
Document structure splits into two parts: structured metadata and unstructured attachments. Attachment sizes vary widely across single declaration materials.
What constraints these characteristics impose on deployment and upgrade
The multi-format and sudden update characteristics of pharmaceutical distribution declaration materials and files impose clear constraints on deployment and upgrade workflows.
Deployments must configure a separate large-file storage path to store structured metadata and unstructured attachments together. This prevents local disk overload.
Deployments must adapt elastic configuration for database connection pools to handle sudden write peaks from submission batches. This stops service interruptions caused by exhausted connections.
Upgrades must retain field parsing logic to maintain compatibility with older file formats. This prevents metadata loss when importing historical data.
Text extraction must be configured to not strip unit information to meet the requirement to retain specific unit fields. This maintains field accuracy for review workflows.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Adapts to sudden write peaks from pharmaceutical distribution submissions, prevents database connection exhaustion |
STORAGE_S3_ENDPOINT | Internal enterprise object storage access address | Stores large files such as declaration material scans and file attachments, separates load from local disk |
MONGODB_URI | Independently deployed replica set connection address | Stores unstructured file metadata and embedding vectors, ensures high availability and read/write performance |
REDIS_URL | Password-authenticated standalone or cluster address | Caches frequently queried approval status and submission batch information, reduces load on core databases |
FE_DOMAIN | Internal enterprise access domain | Generates permissioned file access links, ensures access security for sensitive pharmaceutical materials |
Three common configuration mistakes
- Phenomenon: Docker image pull times out or fails, with a network connection error prompt. Cause: Enterprise internal image acceleration address not configured, or external network access is restricted, making it impossible to pull official FastGPT images.
- Phenomenon: Plugin import prompts
internal server errorwith no detailed error logs. Cause:REDIS_URLconfiguration error or connection timeout, causing cache verification failure during plugin loading.FILE_TOKEN_KEYnot correctly configured, causing permission verification exceptions. - Phenomenon: Disk usage continues to grow after deployment, with no way to locate the source of usage. Cause:
STORAGE_S3_ENDPOINTnot configured to store large files in external object storage, causing original files, split chunks, and embedding vectors to all be stored on local disk. Expired declaration files are not cleaned up regularly.
How to confirm configuration is successful
- Run the
docker pscommand, confirm that FastGPT containers and associated database, Redis containers are all in a running state. - Upload a pharmaceutical distribution declaration material scan, test file upload and access functions via the interface, verify that the
STORAGE_S3_ENDPOINTconfiguration is effective. - Submit a simulated declaration data entry, check database connection logs, confirm that the
DB_MAX_LINKconfiguration does not trigger connection exhaustion alerts. - Access the address bound to
FE_DOMAIN, verify that the frontend page loads normally and displays the declaration file list.
Reference only; verify against the target FastGPT release and deployment documentation. The values provided are common starting points and should be evaluated 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.