What data for this category looks like
Data for this category primarily comes from order design systems, sheet material and wood batch ledgers, CNC cutting and drilling equipment, ERP/MES systems, and moisture content testing equipment. Daily documents include order breakdown drawings, cutting lists, edge banding and drilling programs, coating process documents, hardware material lists, trial assembly and packaging records. Data updates occur per order, daily, or per material batch. Core fields include precise panel dimensions, sheet batch numbers, moisture content parameters, hardware models, and installation point information.
Constraints during deployment and upgrade
The requirement to access multi-source heterogeneous production data means the deployment phase must adapt to interface formats of different systems, to avoid data parsing errors. Frequent order and material data updates require stable database and cache connection parameter configurations, to prevent concurrent write timeouts. Large-volume documents such as order breakdown drawings and process files require parameters configured for large-file storage, to avoid upload or loading failures. Customized panel field requirements mean the upgrade phase must retain existing data field mapping rules, to prevent loss of historical tender data. Additionally, private deployment scenarios must be compatible with enterprise intranet access restrictions, to avoid conflicts with existing network policies.
How to set configurations
The following configurations are based on FastGPT v4.17.0, and are adapted for the tender document and quotation preparation use case in furniture and wood products manufacturing:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapts to concurrent read/write demands of multi-source production data, prevents database connection exhaustion |
MONGODB_URI | Points to a locally or private cloud-deployed MongoDB instance, add dedicated authentication for the business database | Stores structured documents such as tender materials and order breakdown data, ensures data isolation and access security |
REDIS_URL | Configured as a local cache instance, set a key expiration time of 15–30 minutes | Caches frequently queried sheet batch and hardware model data, reduces database load |
STORAGE_S3_ENDPOINT | Points to the enterprise’s self-built object storage service address | Stores large-volume documents such as order breakdown drawings and process files, adapts to batch file upload requirements |
FILE_TOKEN_KEY | Generate a 32-bit random string in accordance with enterprise internal key specifications | Verifies the legitimacy of document upload and download requests, prevents unauthorized access |
FE_DOMAIN | Configured as the enterprise internal access domain name or IP:port | Restricts front-end access sources, adapts to enterprise intranet deployment scenarios |
Three common configuration mistakes
- Symptom: A voice recognition interface call returns a 404 error or empty result. Cause:
REDIS_URLis not correctly configured to cache temporary session data for voice recognition, causing the interface to fail to start normally. - Symptom: Existing tender data is lost after a private deployment upgrade, or core configuration items fail to load. Cause: The business database corresponding to
MONGODB_URIwas not backed up beforehand, and existing data mapping rules were not retained during the upgrade process. - Symptom: Conversation logs cannot be queried or are accidentally lost, and the interface shows empty logs. Cause: A reasonable value for
DB_MAX_LINKwas not configured, leading to database connection exhaustion and failed log writes; or the docker configuration did not mount the log storage volume.
How to verify configuration is correct
- Access the configured
FE_DOMAIN, attempt to upload a document in order breakdown drawing format, verify that the file upload and storage process works normally. - Call the `
/v1/audio/transcriptions` interface, upload a test audio file, verify that the voice recognition function can return results normally. - Connect to the database instance corresponding to
MONGODB_URI, confirm that test tender document records exist, verify that data writes work normally. - Check the docker container logs, confirm that configuration items such as
DB_MAX_LINKandREDIS_URLload without errors, and there are no connection timeout exceptions. - Access the model channel configuration interface, confirm that external large model services can be connected via custom interfaces, adapting to GPT-5 access requirements for non-upgraded versions.
The values provided are common starting points and should be measured against the reader's own samples.