What data for this category looks like
Refractory material investment research data primarily comes from raw material quality inspection reports, kiln operation logs, industry standard specifications, and downstream application case documents. Data is updated following three cadences: raw material price and supply and demand data is updated weekly, kiln operating conditions are collected in real time, and standard specifications and case documents are revised as needed. Individual documents include physical and chemical indicator fields such as Al₂O₃ content and bulk density, with units of percentage and grams per cubic centimeter, usage scenario parameters, and lifecycle data. Some long documents contain multiple consecutive sections of condition monitoring records.
What constraints do these characteristics impose on the database and operations link
The multi-source heterogeneous nature of the data requires the database to support mixed storage of structured and unstructured data. High-frequency writing of real-time operating condition data requires configuration of high-throughput storage nodes. Unit and precision requirements for different fields are strict, so unified field mapping rules must be established to avoid data confusion. Segmented storage and vector retrieval for long documents need to adapt to the maximum length limit of a single document to avoid losing key condition association information during splitting. At the same time, historical data retention periods for investment research scenarios are long, so a reasonable data archiving strategy must be configured to balance storage usage and query efficiency.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Refractory material single quality inspection report or kiln log document typically does not exceed 800 MB, with reasonable buffer space reserved |
VECTOR_SEARCH_TOP_K | Top 10–15 results | Investment research scenarios require balancing recall coverage and query efficiency; 10–15 results can cover most key physical and chemical indicators and operating condition data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Long documents containing multiple sections of condition monitoring records require longer processing time for splitting and vectorization |
MONGO_CONNECTION_POOL_SIZE | 50–80 | High-frequency real-time operating condition data writing requires sufficient database connection pool support to avoid connection exhaustion |
DB_BACKUP_INTERVAL | 2:00 AM daily | Investment research data requires regular backups; performing backups during off-peak hours reduces impact on business operations |
VECTOR_DB_MAX_CONNECTIONS | 30–50 | Concurrent requests for vector retrieval need to match the database connection upper limit to avoid overload |
The parameter values provided on this page are all common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Sustained high disk space usage and service errors indicating insufficient disk space after deployment. The cause is failure to configure an automatic archiving strategy for long documents, and frequently written kiln operating condition logs are not regularly cleaned or migrated to cold storage.
- Error
getaddrinfo EAI_AGAIN mongoafter service startup. The cause is domain name resolution timeout for database connection configuration, and failure to configure local hosts mapping or insufficient connection pool parameters leading to resolution request backlog. - Prompt
column vector does not existafter vector retrieval. The cause is failure to run the initialization script after deleting and reinstalling the vector extension, and failure to rebuild indexes and mapping rules for vector fields.
How to confirm the configuration is correct
- Upload a typical refractory material quality inspection report document, verify that the upload progress is not stuck, and that physical and chemical indicator fields can be retrieved normally after parsing is complete.
- Simulate high-frequency writing of 100 operating condition log entries, check that there are no errors in the database connection pool, and that query response times meet preset thresholds.
- Perform a manual database backup, verify that the backup file is generated completely, and that all configurations and data can be restored using the backup recovery tool.
- Initiate a vector retrieval request, confirm that the returned results include target physical and chemical indicators and operating condition data, with no missing fields or format errors.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.