What data looks like for this category
Refinery and chemical production document data comes from production execution systems, equipment management systems, and safety management systems on the enterprise intranet. It includes documents such as safety work permits, process operation cards, equipment inspection records, material balance reports, and emergency response plans. There are two update cycles: daily operation documents are generated in real time alongside production processes. Compliance documents such as process specifications and safety manuals are revised every quarter or half year. Document structures include standardized fields like equipment ID, operation period, and operation parameters. Most parameter units use industrial standards such as degrees Celsius, megapascals, and tons. Individual document lengths vary widely. Some are hundreds of characters for inspection records, while others reach thousands of characters for material balance reports.
What constraints these characteristics impose on deployment and upgrade
Refinery and chemical production documents contain fixed industrial fields and specialized units. During deployment, strictly align the platform’s variable mappings with the fields of the enterprise’s production systems to avoid parsing errors. High-frequency updates to real-time documents require database connections with sufficient stability and concurrent capacity. Configure reasonable connection pool parameters for this need. Periodic revisions for compliance documents require storage services to support version rollback functions. This ensures historical documents can be queried. Most refinery and chemical production documents involve internal production and safety information. During deployment, complete all data storage and transmission within the intranet environment to avoid external network exposure risks.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[intranet MongoDB username]:[intranet MongoDB password]@[enterprise intranet IP]:27017/fastgpt_refinery_and_chemical_document_library?authSource=admin | Refinery and chemical production document data is stored on the enterprise intranet MongoDB. Specify a dedicated database and authentication source to ensure data isolation |
STORAGE_S3_ENDPOINT | http://[enterprise intranet S3 storage address]:9000 | Most refinery and chemical documents are internal production and compliance documents. Store them on intranet object storage services to avoid external network access risks |
REDIS_URL | redis://:[intranet Redis password]@[enterprise intranet IP]:6379/1 | Cache document retrieval results and variable mapping rules to reduce database concurrent pressure and adapt to high-frequency updated operation documents |
FILE_TOKEN_KEY | 32-bit randomly generated hexadecimal string | Encrypt document download links to prevent unauthorized personnel from accessing internal production documents |
DB_MAX_LINK | 20–30 | Adapt to the concurrent read and write requirements of refinery and chemical documents. Avoid service interruptions caused by exhausted database connections |
ROOT_KEY | Enterprise internal unified administrator key | Restrict super administrator permissions in compliance with information security management specifications for state-owned enterprises and public sector entities in China |
Three common mistakes
- Phenomenon: Preset variable fields such as equipment ID and operation time are not returned in refinery and chemical documents generated via API calls, and splicing works normally during local debugging. Cause: Variable cache not synchronized due to incorrect
REDIS_URLconfiguration during deployment, or field names in API requests do not match the field mappings of the enterprise’s production systems. - Phenomenon: Docker containers remain in a starting loop after startup and cannot complete initialization. Cause: Database connection timeout caused by incorrect
MONGODB_URIfilling, or incorrect password configuration forREDIS_URLcausing the cache service to fail to start. - Phenomenon: Unable to query mongoose version information via npm list, dependency declarations can be viewed via package.json but there is no installation result. Cause: npm dependency environment not properly initialized during container deployment, or global npm configuration not correctly pointing to the enterprise intranet mirror source, causing dependency packages to fail to load properly.
How to confirm the configuration is complete
- Log in to the system settings page of the FastGPT v4.17.0 backend, run the database connection test, and confirm that the connection status of
MONGODB_URIis normal. - Upload a piece of refinery and chemical production equipment inspection record document, configure a text splicing template and add equipment ID and operation time variables, trigger local debugging, and check whether variables are filled normally.
- Call the official test API provided by FastGPT, pass preset variable parameters and document templates, and check whether the returned results contain complete document content and all variable fields.
- View the running logs of the Docker container, confirm that there are no error messages such as database connection timeout, key verification failure, or dependency loading error.
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.