What the data for this category looks like
Core data for semiconductor packaging and testing scenarios comes from MES systems, bonding and molding equipment, ATE testers, X-ray/SAM detection equipment, and customer specification documents. Daily maintained documents include packaging process cards, bonding and molding parameter tables, wafer incoming material drawings, test program version lists, yield reports, and failure analysis reports. Data update frequencies cover per wafer batch, per chip test, and daily summaries. Fields include industrial parameters with clear units such as packaging temperature, bonding pressure, and test bin codes. These data have clear differences from the microfabrication data of front-end wafer manufacturing processes, and focus on the electrical testing and grading link after bare dies are packaged into mountable devices.
What constraints these characteristics impose on the deployment and upgrade link
Data sources for this scenario are mostly industrial equipment and business systems. The system must support unified access to multi-source heterogeneous data. Therefore, the deployment phase needs to adapt to industrial-grade database and storage configurations. High-frequency update requirements mean the Q&A system must support near-real-time document index synchronization to avoid data lag affecting Q&A accuracy. Structured industrial parameters and batch-associated data require the knowledge base to retain metadata association relationships. The upgrade phase must be compatible with the index structures of old-version documents. At the same time, errors can lead to chip packaging failures and defective products being shipped. Deployment must ensure high availability, and upgrades must minimize business interruption risks.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
PG_URL | postgresql://fastgpt:your_secure_pass@192.168.1.20:5432/fastgpt?sslmode=disable | Structured documents for packaging and testing scenarios, such as test program versions and yield reports, require transactional storage from relational databases to support associated queries for multiple batch data sets. |
REDIS_URL | redis://:redis_secure_pass@192.168.1.21:6379/0 | Low-latency memory storage is needed for session caching of high-frequency Q&A and vector retrieval acceleration, to adapt to data update frequencies at the per-chip test level. |
STORAGE_S3_ENDPOINT | http://192.168.1.22:9000 | Documents such as packaging process cards and failure analysis reports have large file sizes, so distributed storage is required to support batch uploads and cross-node reading. |
DB_MAX_LINK | 200–300 connections | Concurrent connection requirements when multiple packaging and testing devices synchronize data, to avoid service interruptions caused by exhausted database connections. |
ROOT_KEY | Randomly generated 32-character string | Permission verification for the admin backend, to prevent unauthorized access to configuration and knowledge base data. |
Three common mistakes
- Symptom: Docker local deployment of FastGPT v4.17.0 throws database connection errors every few hours. Logs show "FATAL: too many connections". Service can only be restored by deleting the PostgreSQL container and redeploying. Cause: The
DB_MAX_LINKparameter is not configured, and the concurrent connection count exceeds the default limit of PostgreSQL, causing the database container to exit abnormally. - Symptom: The Q&A system fails to recall wafer incoming material drawings for a specific batch, and the returned results do not include the corresponding batch information. Cause: The
STORAGE_S3_ENDPOINTis not configured as a storage endpoint compatible with the packaging and testing MES system, causing a mismatch between document metadata and actual storage paths. - Symptom: After an upgrade, Q&A for existing test program versions cannot recall correct parameters. Cause: A cross-version upgrade was performed directly without running the knowledge base metadata migration script, causing the index structure of old-version documents to be incompatible with the new system.
How to confirm the configuration is correct
- Log in to the database monitoring panel in the FastGPT admin backend, check the connection status of the
PG_URLandREDIS_URLconfigurations, and confirm there are no timeout or connection refused errors. - Upload a packaging process card document, and verify that the storage path configured for
STORAGE_S3_ENDPOINTcan normally read file content and metadata. - Submit a test Q&A about bonding parameters, and confirm that the returned document metadata matches the batch information from the MES system.
- Submit 100 concurrent Q&A requests, and confirm that the connection count configured for
DB_MAX_LINKdoes not trigger database connection exhaustion errors.
The values provided here are common starting points, and should be evaluated against the reader's own samples.