This document applies to FastGPT v4.17.0.
What the data for this category looks like
Data sources involved in supplier and process quality audit for heat treatment include material grades, customer technical specifications, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic equipment, and manufacturing execution systems. Data update frequency is per heat and per batch. Daily documents include heat treatment process cards, furnace loading diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling forms. Data fields include furnace temperature parameters (unit: ℃), holding time (unit: hours), hardness values (unit: HV), metallographic structure ratings, furnace loading position numbers, and more. Supported formats cover structured tables, time-series curve files, and graphic and text reports.
What constraints these characteristics impose on deployment and upgrade
The high-frequency, small-batch update rhythm requires queue scheduling configured for short-cycle tasks during deployment. This prevents data backlogs. Multiple heterogeneous data sources and diverse document formats require the system to support multi-format preprocessing and cross-source data synchronization. Interfaces for equipment and systems such as temperature control instruments and manufacturing execution systems must be connected in advance. Core data includes high-risk performance parameters. Data verification rules must be configured during deployment. This prevents unqualified parameters from entering the audit process. During upgrades, the stability of the parsing service must be maintained. This prevents parsing exceptions for key documents such as furnace temperature curves and metallographic reports caused by version updates. The upgrade must also be compatible with historical batch document formats.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Adapts to high-frequency database queries per heat, prevents connection pool resource exhaustion |
REDIS_URL | redis://:{custom password}@{deployment host}:6379/1 | Stores temporary parsing results and session cache, supports high-frequency, small-batch heat treatment data processing |
STORAGE_S3_ENDPOINT | Fill in according to the access address of the local object storage service | Stores large-volume documents in multiple formats such as furnace temperature curves and metallographic reports, matches business data storage requirements |
| Single search limit | Top 10 entries | Matches the limited range of associated documents for single-batch audits, prevents context overflow |
| File parsing timeout | 600 seconds | Adapts to the parsing duration of large furnace temperature curve reports, prevents parsing timeout errors |
ROOT_KEY | Generate a random key based on the deployment environment | Ensures access security for internal system interfaces, prevents unauthorized configuration modifications |
Three common mistakes
- Phenomenon: When parsing PDF-format furnace temperature curves or metallographic reports, the interface returns a timeout error, but the parsing service log shows execution successful. Cause: The file parsing timeout threshold is not configured to match the duration required for large-volume heat treatment documents. The default threshold is insufficient to cover long document parsing processes.
- Phenomenon: Docker-deployed FastGPT instances trigger abnormal restart prompts every 30 minutes. Manual restart restores normal operation. Cause: Persistent caching is not configured for
REDIS_URL, leading to session state loss that triggers system self-check restarts. - Phenomenon: Search results do not associate with the process card data for the corresponding heat. Field matching accuracy is low. Cause: Precise recall rules are not configured for exclusive heat treatment data fields such as furnace temperature and hardness values. General recall parameters lead to insufficient matching accuracy.
How to confirm the configuration is correct
- Upload a standard furnace temperature curve PDF. Wait for parsing to complete, then check if the parsed fields include core business parameters such as furnace temperature and holding time.
- Perform a batch import operation for 10 heat treatment process cards. Confirm that the parsing queue has no backlogs and no error logs.
- Check the storage bucket corresponding to
STORAGE_S3_ENDPOINT. Confirm that the uploaded metallographic report files have been successfully stored to the specified path. - Check database connection pool monitoring data. Confirm that the current active connection count does not exceed the configured value of
DB_MAX_LINK.
The values provided are common starting points and should be measured against the reader's own samples.