What the data for this category looks like
Data for this category comes from customer drawings and special characteristic lists, ERP/MES systems, measuring tools, equipment process parameters, and supplier quality certificates. Daily documents include control plans, process flowcharts, work instructions, first article and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data is collected and updated per batch, per shift, or continuously. Fields include part number, process parameters, inspection results, batch number, and others. Units cover industrial standard units such as millimeters, newtons, and rotational speed.
What constraints these characteristics impose on deployment and upgrade
Data sources are scattered and mostly require integration with industrial systems, so deployment must support access to multi-source heterogeneous data. Update frequency is high and batch-oriented, so a high-frequency synchronization mechanism must be configured to avoid data lag. Document types are diverse and have fixed formats, so corresponding parsing rules must be preset to adapt to different industrial documents. Restricted by vehicle customer quality systems, post-deployment permissions and data traceability must meet audit requirements. During upgrades, data consistency must be maintained without breaking existing traceability links.
How to set configurations
This document is written based on FastGPT v4.17.0. The following configuration items are adapted for the batch automotive component manufacturing scenario:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://fastgpt_user:secure_pass@internal-db:5432/fastgpt?sslmode=disable | Stores structured relational data for automotive component batch ledgers, supports high-performance multi-condition queries |
MONGODB_URI | mongodb://fastgpt_mongo:mongo_pass@internal-mongo:27017/fastgpt?authSource=admin | Stores unstructured industrial documents such as SPC reports and PPAP materials, supports batch indexing |
REDIS_URL | redis://:redis_pass@internal-redis:6379/0 | Caches frequently read process parameters and batch statuses, reduces database access pressure |
DB_MAX_LINK | 20–30 connections | Matches concurrent data write and query requirements in batch manufacturing scenarios, prevents connection exhaustion |
STORAGE_S3_ENDPOINT | http://internal-s3:9000 | Stores industrial documents with a maximum single file size of 1000 MB, supports batch file upload requirements |
FILE_TOKEN_KEY | 32-digit randomly generated hexadecimal string | Encrypts and stores sensitive process parameters and customer special characteristic data in ledgers |
Three common mistakes
- Symptom: After deploying FastGPT and connecting to an external database, the knowledge base does not synchronize automotive component ledger data, or key fields such as part number and batch number are missing. Cause: The database connection address was not configured correctly, or database permissions were not opened to the FastGPT deployment node, resulting in failure to pull ledger data from ERP/MES systems.
- Symptom: Connection timeout errors occur when generating SPC reports in bulk. Cause: The maximum database connection count is set too low, which cannot support concurrent data read and write requests in batch manufacturing scenarios.
- Symptom: Generated reports do not meet vehicle customer quality system requirements after upgrade. Cause: The original data traceability configuration was not retained, resulting in lost batch association relationships in reports, which cannot meet customer audit requirements.
How to confirm the configuration is correct
- Access the FastGPT backend, view the database connection status panel, confirm that the connection address, port, and permissions are configured correctly, and adjust thresholds according to business concurrency requirements.
- Upload a control plan document, verify that fields such as part number and process parameters can be extracted after parsing, and confirm field integrity meets the category document structure requirements.
- Initiate a batch ledger generation task, check that the task log has no connection errors or data missing, and confirm task success rate meets business batch scale requirements.
- Check the object storage directory, confirm that industrial documents such as SPC reports and PPAP materials have been successfully uploaded and can be downloaded normally.
The values provided are common starting points and should be measured against the reader's own samples.