What data for this category looks like
Heat treatment scenario data comes from material grade databases, customer technical specification documents, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic equipment, and manufacturing execution systems (MES). Data updates once per heat or per batch. Daily documents include heat treatment process cards, charging charts, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling sheets. Fields include heating temperature (unit ℃), holding time (unit hours), cooling rate (unit ℃/minute), hardness value (unit HV), and metallographic structure rating, among others. All data is generated and synced in real time during the production process.
What constraints these characteristics impose on deployment and upgrade
Heat treatment data is generated in batches per heat and originates from dispersed sources. Deployments must adapt storage and connection configurations to support multi-source data access. Daily documents include non-text attachments such as charging charts and furnace temperature curves. Upgrades must add support for new file parsing formats and large file storage capabilities. Data updates frequently and includes quantified fields. Configure unified field mapping rules in advance to avoid format conflicts during data import. Data volume increases with production scale. Reserve expansion space for database connection pools and vector storage.
How to set configurations
This chapter applies to FastGPT v4.17.0. Specific parameters are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 connections | Matches the high-frequency connection demand of batch data writes per heat, avoids write failures caused by exhausted connection pools |
STORAGE_S3_ENDPOINT | http://192.168.1.100:9000 | Adapts to storage needs for large file attachments such as charging charts and furnace temperature curves, compatible with local object storage deployment |
PG_URL | postgresql://heat_user:SecurePass123@db-host:5432/fastgpt_heat?sslmode=disable | Stores hundreds of thousands of structured heat treatment documents and indexes; PostgreSQL supports precise queries for quantified fields |
REDIS_URL | redis://:RedisPass456@redis-host:6379/0 | Caches real-time furnace temperature data and user sessions, reduces real-time query pressure on the primary database |
ROOT_KEY | 32-bit randomly generated secure string | Ensures access security for background management interfaces, prevents unauthorized operations |
FILE_TOKEN_KEY | Signature key matching the object storage bucket | Verifies the legitimacy of file upload requests, blocks malicious file submissions |
Three common configuration errors
- Phenomenon: After starting the service, only one GPU is utilized. Logs show full utilization of the default GPU 0, while remaining GPUs have completely idle video memory. Cause: Multi-GPU scheduling parameters were not configured during deployment. The model is bound only to the default GPU, so multi-GPU computing power cannot be used to accelerate document parsing and vector generation.
- Phenomenon: After the knowledge base completes indexing hundreds of thousands of data entries, search tests return no matching results. The interface shows 0 search results. Cause: The
PG_URLconnection parameter was not configured correctly. Index data fails to write to the target database, or field mapping does not match the quantified field format of heat treatment documents. - Phenomenon: The container cannot connect to the third-party interface service after restarting. The interface prompts a connection timeout. Cause: A fixed container network or domain name was not used to configure the third-party service address. Dynamic container IP addresses are relied on, which become invalid after restart, preventing normal service calls.
How to confirm the configuration is correct
- Run the database connection test command, enter the connection string configured in
PG_URL, and verify that the database can normally read and write structured data for heat treatment documents. - Upload a standard heat treatment process card document through the upload interface verified by
FILE_TOKEN_KEY, and confirm that the file can be stored normally and returns an accessible link. - After starting the service, check the running logs to confirm that the connection pool configured in
DB_MAX_LINKdoes not show connection exhaustion errors, and that multi-GPU scheduling loads normally. - Access the frontend access address configured in
FE_DOMAIN, and verify that the interface loads normally and calls backend interfaces without permission or connection error prompts.
The values provided are common starting points and should be measured against the reader's own samples.