What data for this category looks like
Business data for the sheet metal cutting and bending category comes primarily from CAD/CAM software, order BOM systems, sheet material inventory ledgers, laser cutting machines, bending equipment, and workshop MES systems. Daily generated documents include part unfolding drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece inspection records, and process transfer cards. Data update rhythms align with order delivery, daily production scheduling, and each equipment changeover adjustment. Core fields include sheet thickness, bending coefficient, cutting size, bending sequence, leftover material specifications, and others. No fixed unified statistical caliber exists, and values change dynamically with production batches.
This document was written based on FastGPT v4.17.0.
Constraints imposed by these characteristics on deployment and upgrade
Data sources are scattered across multiple production systems. Connection parameters for multiple data sources must be configured during deployment. Different types of business documents must be synced to the knowledge base. Data update frequency is high. New data is generated with every order and equipment changeover. Sufficient storage capacity and connection pool resources must be reserved. Knowledge base synchronization delays must be avoided. Most category documents are engineering drawings and process texts. Parsing rules for unstructured files must be adapted during deployment. Content such as unfolding drawings and nesting diagrams must be extracted correctly. The multi-variety, small-batch production model requires the Q&A system’s recall logic to flexibly adapt to parameters of different models. Space for configuration adjustments must be reserved during deployment. Hard-coding restrictions on business expansion must be avoided. Production errors in this category directly lead to material waste of entire sheets. The Q&A system’s configuration must comply with production safety requirements during deployment. Invalid inspection and maintenance guidance caused by configuration errors must be avoided.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:pass@your-host:27017/fastgpt?authSource=admin | Connects to structured business data such as order BOMs and process transfer cards, and stores historical interaction records for Q&A |
PG_URL | postgresql://fastgpt:pass@your-host:5432/fastgpt | Stores text parsing results of CNC cutting programs and bending process cards, and supports quick retrieval by process number |
REDIS_URL | redis://:fastgpt-redis@your-host:6379/0 | Caches recall results for high-frequency inspection Q&A, and reduces concurrent access pressure on databases |
DB_MAX_LINK | 20–30 | Adapts to concurrent scenarios where multiple workshop stations initiate inspection requests simultaneously, and prevents database connection pool exhaustion |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Protects access permissions for uploaded production files such as unfolding drawings and nesting diagrams, and prevents unauthorized download |
STORAGE_S3_ENDPOINT | Fill in the address of the enterprise’s self-built object storage, such as https://oss.example.com:8080 | Stores large quantities of engineering drawings and process documents, and reserves more than 1000 MB of storage space to avoid insufficient capacity |
ROOT_KEY | Complex key generated according to enterprise internal permission rules | Manages the highest permission configuration for the system, and is only available to authorized operation and maintenance personnel to prevent unauthorized system modifications |
Three common errors
- Phenomenon: Garbled content appears after importing nesting diagrams or unfolding drawings in the online environment. No abnormality occurs in local deployment. Cause: The online environment fails to properly configure
STORAGE_S3_ENDPOINT, leading to abnormal encoding parsing when reading files. OrFILE_TOKEN_KEYconfiguration is inconsistent, leading to file transfer corruption. - Phenomenon: Unable to edit the request body content when calling the equipment inspection Q&A API. Cause: Incorrect
FE_DOMAINconfiguration. Front-end cross-domain restrictions block body editing operations. - Phenomenon: When entering a dozen-word inspection question, the answer first returns the full result at once, then switches to streaming output. Cause: Improper
DB_MAX_LINKconfiguration, leading to database connection pool fluctuations. The recall logic fails to trigger step-by-step output as expected.
How to confirm proper configuration
- Access the domain configured in
FE_DOMAIN. Check if the front-end page loads normally and displays the equipment inspection Q&A entry. No loading failures or cross-domain errors appear. - Upload a test bending process card. Check if the file can be parsed normally and stored in the storage service corresponding to
STORAGE_S3_ENDPOINT. No garbled code or upload failure prompts appear. - Log in to the system backend using
ROOT_KEY. Check if the connection status ofMONGODB_URIandPG_URLshows normal. No connection timeout or authentication failure prompts appear. - Send a simple inspection question request. Check if the response body content can be edited normally. No missing fields or garbled code appear.
The values provided here are common starting points. They should be evaluated against the reader's own samples.