What the data for this category looks like
Grain reserve document and material data mainly comes from daily operation ledgers of grain reserve warehouses, inbound and outbound vouchers, rotation plan documents, quality inspection reports, and annual inventory statements. Data update rhythms fall into two categories: real-time and scheduled. Inbound and outbound data updates in real time alongside business processes; monthly inventory and rotation plans update per calendar month. Most documents use fixed structured formats, including fields such as reserve warehouse name, grain variety, actual inventory quantity, inbound date, quality inspection grade, and others. Units uniformly use industry standard units such as tons, months, copies. Some templates include fixed headers and format validation rules.
What constraints these characteristics impose on the deployment and upgrade link
Fixed fields and unit requirements require configuring data parsing rules during deployment to ensure precise matching between business fields and knowledge base retrieval fields, avoiding field mapping errors or unit conversion anomalies. Phased update rhythms require configuring scheduled incremental synchronization tasks to reduce server resource usage from full synchronization and ensure business processes are not interrupted. Standardized document templates require retaining header and field associations during knowledge base chunking to avoid losing business context during retrieval. Some reserve warehouse servers have no public network access permissions, so the deployment process must support offline image pulling and local resource mounting to adapt to intranet deployment scenarios.
How to configure the settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
DB_MAX_LINK | 10–20 connections | Grain reserve document data volume is moderate. Too high per-database connection count will occupy server resources, while too low will cause concurrent request blocking |
MONGODB_URI | 192.168.1.100:27017/fastgpt_db | Adapts to FastGPT v4.17.0 database connection specifications, uses intranet database to avoid leakage of sensitive business data |
REDIS_URL | 127.0.0.1:6379/0 | Caches context and temporary data for document retrieval locally, reducing pressure from repeated database queries |
STORAGE_S3_ENDPOINT | http://192.168.1.200:9000 | Stores document files on intranet storage nodes, complies with data security management requirements for Chinese state-owned enterprises and public sector entities |
FE_DOMAIN | http://grain-storage.intranet | Limits front-end access scope, only allows internal business system calls, ensuring access permission security |
Three common errors
- Cannot view container runtime logs: Running the docker logs command prompts no log file or empty logs. The cause is failing to mount the container log directory to the host machine, resulting in permanent loss of log data after the container is destroyed.
- Deployment failure without public network environment: Docker image pulling times out or fails. The cause is not exporting the FastGPT offline image in advance, directly using public network pulling for intranet scenarios.
- Uploaded files cannot be read during conversation: Knowledge base file upload works normally, but files uploaded during conversation cannot be parsed. The cause is not configuring
FILE_TOKEN_KEYto generate correct file access tokens, resulting in failed file permission verification during conversation.
How to confirm the configuration is correct
- Run the docker logs fastgpt command to view container runtime logs, confirm there are no database connection errors, file permission errors, or port occupation errors.
- Upload a standard grain reserve inbound voucher to the knowledge base, retrieve the field information of the document, confirm that the parsing result completely matches the fields and units of the original document.
- Access the configured
FE_DOMAINin the intranet environment, confirm that the knowledge base page and document drafting function module load normally. - Trigger an incremental synchronization task, wait for the task to complete, then retrieve the updated business data, confirm that the retrieval result is updated synchronously.
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.