What the data for this category looks like
Pharmaceutical distribution inquiry data falls into two categories. The first is structured pharmaceutical circulation ledgers, sourced from internal enterprise warehouse, transportation, and sales systems, with daily synchronization updates. The second is unstructured compliance documents, including GSP certification reports, drug package inserts, and cold chain transportation records. These documents come from public national drug regulatory platforms and internal enterprise archives, with updates adjusted alongside drug changes or compliance inspections. Document fields include drug approval numbers, manufacturers, circulation batch numbers, transport temperature thresholds, and more. Units include degrees Celsius, batch numbers, storage capacity, and others.
What constraints these characteristics impose on deployment and upgrade
Mixed structured and unstructured data requires deployments to support both relational database and object storage configurations. This prevents single storage types from failing to cover all data. Daily updated ledger data needs incremental sync configurations adapted to high-frequency write scenarios. This avoids data delays after deployment. Compliance document storage must meet pharmaceutical industry data retention standards. Deployments must confirm storage node permissions and encryption configurations to reduce data leakage risks. For FastGPT v4.17.0 version adaptation, pay attention to the mapping between database connection parameters and pharmaceutical data fields. This prevents field mismatch issues after deployment.
How to Define Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Adapts to concurrent retrieval and session requests for pharmaceutical distribution data, prevents connection exhaustion |
REDIS_URL | redis://127.0.0.1:6379/1 | Caches frequently consulted drug information and retrieval results, reduces database load |
STORAGE_S3_ENDPOINT | Configured per local compliant storage node | Stores pharmaceutical distribution documents and inquiry records, meets industry data retention requirements |
PG_URL | postgresql://user:pass@db-host:5432/fastgpt | Stores structured pharmaceutical circulation ledgers and user inquiry data |
ROOT_KEY | Custom 32-character random string | Validates highest platform management permissions, ensures deployment environment security |
FE_DOMAIN | Public domain name of the deployment server | Configures frontend access addresses, ensures users can access the inquiry interface normally |
Three Common Mistakes
- Phenomenon: Container startup fails after running
docker-compose up, returns exit code 1. Cause: Database permissions corresponding toPG_URLwere not adjusted based on pharmaceutical distribution data storage requirements, or JSON syntax errors exist when manually modifying configuration files. - Phenomenon: Knowledge base retrieval returns only a small number of entries, failing to cover all drug documents. Cause: The value of
DB_MAX_LINKwas not adjusted, resulting in insufficient database connections to pull all structured data. - Phenomenon: Connected large language models do not appear in the platform list. Cause: The cache configuration corresponding to
REDIS_URLwas not updated synchronously, causing model access information to not be loaded correctly.
How to Verify Successful Configuration
- Run the
docker pscommand, check that all container statuses are Up, with no abnormal exit records. - Access the public network address configured in
FE_DOMAIN, log in to the platform and enter the database connection test page, confirm that the connection tests forPG_URLandREDIS_URLpass. - Upload a GSP certification document for pharmaceutical distribution, confirm that the file uploads successfully and the corresponding content can be retrieved on the knowledge base management page.
- Submit an inquiry containing a pharmaceutical circulation batch number, confirm that the retrieval results include relevant information from the corresponding document.
The values provided are common starting points and should be evaluated against individual deployment 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.