What this category of data looks like
Data for internal office and employee services in the pharmaceutical distribution sector comes primarily from inventory and sales management systems, GSP compliance archives, employee attendance and performance systems, and drug maintenance record databases. Data update cycles fall into three categories: real-time (such as inventory changes), daily (such as sales reports), and quarterly (such as compliance self-inspection documents). Individual documents typically include fields like drug code, batch number, inventory quantity, maintenance temperature, employee ID, and job title. Some documents include physical monitoring units such as temperature and humidity. Text length ranges from employee notices with hundreds of characters to annual compliance reports with tens of thousands of characters.
What constraints these characteristics impose on deployment and upgrade
Pharmaceutical distribution data is multi-source, heterogeneous, and has widely varying update cycles. During deployment, configure multiple data source synchronization links. During upgrade, ensure compatibility with incremental update tasks of different frequencies. Some documents include physical monitoring fields such as temperature and humidity. During deployment, confirm the vector model can recognize field content with units to avoid retrieval matching deviations. A wide range of document lengths exists, and many scenarios use offline intranet deployment. During deployment, preset chunking rules adapted for long texts. During upgrade, verify the synchronization logic for offline file uploads to prevent unresponsive uploads. Additionally, GSP-related documents have strict version management requirements. During deployment, retain document version identification fields. When upgrading the knowledge base, synchronously update version mapping rules.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
REDIS_URL | redis://:custom password@intranet IP:6379/0 | Adapts to the intranet deployment scenario of pharmaceutical distribution, used to store session and vector retrieval temporary data to ensure intranet access speed |
MONGODB_URI | mongodb://username:password@intranet IP:27017/fastgpt?authSource=admin | Stores unstructured content such as employee documents and drug data. Intranet deployment requires a dedicated database instance to ensure data isolation |
DB_MAX_LINK | 20–30 connections | Pharmaceutical distribution has a large data volume. Limit the number of database connections to avoid exhausting intranet resources, calibrated based on actual testing |
STORAGE_S3_ENDPOINT | http://intranet MinIO address:9000 | Stores sensitive data such as drug maintenance records and compliance documents in the intranet to avoid security risks from external network transmission |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Ensures the security of intranet file upload tokens and prevents unauthorized access to employee and business documents |
ROOT_KEY | Complex random string, no less than 16 characters in length | Configures the administrator permission key, only used for system initialization in intranet environments, adapts to the permission verification rules of FastGPT v4.17.0 |
Three common mistakes
- Phenomenon: After uploading drug maintenance documents in an intranet offline environment, the page displays a loading spinner with no progress feedback. Cause: FastGPT requires access to external APIs to pull dependent resources by default. In an offline environment, no local dependency mirror is configured or external access verification is skipped, causing the upload process to block.
- Phenomenon: Container startup fails after executing the docker-compose up command, and the console returns a database or cache connection timeout error. Cause: The
MONGODB_URIorREDIS_URLparameters are not configured correctly, causing FastGPT to fail to connect to the intranet database and cache instances, interrupting the startup process. - Phenomenon: When importing an annual compliance report with tens of thousands of characters, retrieval results show context overflow or matching deviations. Cause: Chunking rules are not adjusted based on the characteristics of long-text documents in pharmaceutical distribution. The default chunk length cannot cover complete compliance clause content, resulting in reduced vector retrieval accuracy.
How to confirm the configuration is complete
- Execute the docker ps command, confirm that all FastGPT-related containers are in a running state, and check that there are no database or cache connection failure errors in the container logs.
- Upload a pharmaceutical distribution employee notice document, wait for the upload to complete, and view the number of parsed chunks of the document in the knowledge base management interface to confirm that the chunking rules match the preset configuration.
- Access the address configured in
FE_DOMAINin the intranet environment, verify that the employee side interface loads normally, and that basic document retrieval operations can be completed without an external network connection. - Use the
ROOT_KEYto call the system management interface, verify that the administrator permission configuration takes effect, and that knowledge base data source mapping rules can be created and edited normally.
The values provided here 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.