What the data for this category looks like
Data sources include daily spot quotes from domestic professional chemical fiber industry statistical institutions, ex-factory price announcements from upstream petrochemical enterprises, and matched transaction data from regional spot markets. The update schedule is as follows: mainstream category data is updated by 17:00 daily, and niche category updates are delayed until 10:00 the next day. The document structure is a structured two-dimensional table, with each row corresponding to a single chemical fiber sub-variety. Fields include: standard variety name, daily spot settlement price, daily ex-factory guide price, daily price change from the previous day, daily industry operating rate proportion, and daily total social inventory. Units: prices and price changes are yuan/ton, total inventory is tons, and operating rate proportion is a ratio value.
What constraints do these characteristics impose on deployment and upgrade
The data source update windows for chemical fiber categories are concentrated, with delayed updates for niche categories. When deploying, configure staged pull tasks to avoid excessive server resource usage from full synchronization. There are many categories, including delayed niche ones, so pre-set a dynamic category whitelist to filter non-existent category data and avoid task execution failures. There are many structured fields with unified units, so pre-configure field mapping rules during deployment to avoid data anomalies caused by field misalignment across different data sources. During upgrades, retain historical scheduled pull and broadcast configurations to avoid interrupting the daily fixed yield and market trend broadcast process. Additionally, each data entry has many fields, so the context length requirement for model inference is high. Adjust batch processing parameters based on deployment hardware.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SCHEDULER_MAX_WORKERS | 2–4 | The update windows for chemical fiber data are concentrated, avoiding excessive server load from too many threads |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Structured parsing of full-category chemical fiber data takes a long time, preventing task interruption from early timeout |
maxContext | 8000–12000 characters | A single data entry contains multiple fields, requiring sufficient context to fully present yield and market trend content |
UPLOAD_FILE_MAX_SIZE | 500 MB | Full-category summary data files typically do not exceed this size, preventing invalid large file uploads |
PYTHON_RUN_TIMEOUT | 300 seconds | The data pull and cleaning process involves multiple rounds of API calls, requiring sufficient execution time |
VERSION_UPGRADE_BACKUP_PATH | /data/fastgpt/backup | The official recommended persistent storage path, which can fully retain pre-upgrade configurations and data |
The parameter values provided on this page are common recommended starting points 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.
Three common errors
- Phenomenon: When deploying a 16B scale model on a 4070Ti graphics card to process chemical fiber data, single inference takes more than 3 minutes. Cause: The
maxContextparameter was not adjusted to match the hardware's video memory, resulting in excessive video memory usage during model loading, triggering memory swapping and extending inference time. - Phenomenon: The model backend can normally respond to test requests in a Docker environment, but the workflow conversation node shows execution failure. Cause: Network interconnection parameters for the model service were not configured in
docker-compose.yml, causing the workflow node to fail to access the model interface. - Phenomenon: After upgrading from V4.9.3 to V4.12.2, all historical scheduled broadcast tasks are lost. Cause: The
VERSION_UPGRADE_BACKUP_PATHparameter was not configured, and the original configuration files were not backed up during the upgrade, resulting in configuration loss.
How to confirm the configuration is correct
- Execute a scheduled pull task once, check the logs for errors related to field misalignment or missing data, and adjust the corresponding configuration parameters based on the errors.
- Call the model interface to test the inference response of a single chemical fiber data entry, adjust
maxContextand batch processing parameters based on the hardware's video memory to ensure the response time meets business requirements. - Before performing a version upgrade operation, check whether a complete configuration backup file is generated under the
VERSION_UPGRADE_BACKUP_PATHpath, and confirm the backup is correct before performing the upgrade. - Start the Python code node, pass test parameters, check whether the node output normally receives and prints external parameters, and confirm that the
PYTHON_RUN_TIMEOUTconfiguration is reasonable.
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.