This document is written based on FastGPT v4.17.0.
What This Category of Data Looks Like
Data comes from material grade standards, customer technical requirements, furnace temperature control instruments, furnace temperature uniformity test reports, hardness tester and metallographic equipment test results, and workshop MES systems. Update frequency is per furnace load 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 sheets. Document fields include furnace temperature range (unit: ℃), holding time (unit: minutes), hardness requirements (unit: HRC), batch number, furnace loading position parameters, and other fields. Some documents include visual charts and binary detection files.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The multi-source heterogeneous nature of heat treatment data requires deployment to adapt to multiple document parsing formats: structured tables, visual curves, and binary detection files. High-frequency per-furnace updates require vector indexes to support incremental synchronization, avoiding performance loss from full index reconstruction. Process parameters have strict precision requirements. Retrieval results must strictly match the process specifications corresponding to material grades. Deployment requires configuration of high-confidence retrieval matching rules. The need for associated retrieval between exception handling sheets and equipment inspection forms requires the knowledge base to support cross-document associated queries. Upgrade must adapt to vector storage logic for multi-document associations.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 | Heat treatment data is updated in batches per furnace load. Connection count must adapt to scenarios with parallel import of multiple documents. |
REDIS_URL | redis://localhost:6379/0 | Cache frequent retrieval results of process specifications to reduce resource consumption from repeated calculations. |
STORAGE_S3_ENDPOINT | Fill in according to the internal network object storage address | Store large-volume documents such as furnace temperature curves and metallographic reports to ensure internal network access speed. |
MONGODB_URI | Fill in according to the internal network MongoDB instance address | Store vector indexes and metadata of process documents, supporting cross-document associated retrieval. |
FILE_TOKEN_KEY | Custom 32-bit encryption key | Protect access permissions for heat treatment process documents, avoiding unauthorized leakage of parameters. |
ROOT_KEY | Configure according to administrator permissions | Manage permissions for the deployed knowledge base and models, restricting unauthorized configuration modifications. |
Three Common Mistakes
- When deploying with Docker, running
docker buildthrows theEMFILE: too many open fileserror. The cause is failure to adjust the maximum file handle limit for the Docker container. Most heat treatment documents are large-volume furnace temperature curves and metallographic reports. Batch parsing uses excessive file handles. - After deploying with Docker Compose on an internal network, importing process cards as text without adjusting segment word count leads to low retrieval result matching accuracy. The default segment length fails to cover complete process parameter segments, causing key information to be split and lost.
- Voice input on iOS devices cannot trigger Q&A interactions. The cause is failure to configure an HTTPS certificate for the frontend domain name. For internal network deployments, port permissions for voice recognition are not opened, leading to abnormal transmission of voice data.
How to Verify Successful Configuration
- Run the
docker pscommand. Confirm all service containers are in normal running state, with no restart or abnormal exit records. - Upload a single heat treatment process card. Verify that core fields such as furnace temperature range, holding time, and hardness requirements can be extracted after parsing.
- Initiate a query related to process specifications. Verify that retrieval results include associated content of corresponding furnace temperature curves and hardness reports.
- Check the connection logs for
REDIS_URL. Confirm that cache hits are normal, with no connection timeout errors.
The values provided here are common starting points. They should be evaluated against the reader's own samples.