Deployment and Upgrade for Paint and Ink Yield and Market

What the Data for This Category Looks Like

Paint and ink yield and market data comes primarily from public monitoring datasets released by the China Coatings Industry Association, and listed quotes from domestic bulk commodity spot trading platforms. Daily full-category data aggregation for the current day is completed after 16:00, and publicly released in the early morning of the next day. Each data document uses a structured format, and includes fields such as product category (e.g., resin-based coatings, solvent-based inks), origin marker, specification model, daily listed price, wholesale reference price, and inventory turnover reference value. Price-related fields use units of yuan per kilogram or yuan per ton. No additional derived statistical fields are included.

Constraints on Deployment and Upgrade

Scattered data sources, multiple classification dimensions, and fixed update schedules for the paint and ink category create multiple constraints for deployment and upgrade. First, pull data from multiple sources: Configure corresponding interface authentication and format conversion rules to avoid data parsing failures caused by field name differences. Second, daily fixed-time update tasks must align with data source release schedules. Avoid pulling incomplete temporary data. Third, data volume fluctuations caused by category segmentation require pre-configured vector database sharding and index expansion thresholds. During upgrades, compatibility with new field index rules is required to prevent service interruptions from full index reconstruction.

How to Set Configurations

Configuration Item	Recommended Value	Rationale
`DATA_SYNC_CRON`	`0 0 17 * * *`	Matches the paint and ink data source release schedule after 16:00 daily. Pulling data at 17:00 ensures access to complete daily datasets
`FIELD_MAPPING_RULES`	Set based on actual testing	Different data sources have varying field naming conventions. Mapping must be completed for paint and ink-specific fields such as `specification model` and `origin marker`
`VECTOR_DB_SHARDS`	`8`	Pulled paint and ink data entries per batch are relatively numerous. Sharding configuration improves indexing and retrieval efficiency
`PARSE_DATA_TIMEOUT`	`600 seconds`	Structured data requires processing multi-category field mapping and format conversion. A longer timeout prevents parsing interruptions
`INDEX_COMPATIBILITY_MODE`	`enabled`	New fields added during version upgrades must be compatible with legacy index rules. This avoids service interruptions caused by full index reconstruction

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. It is recommended to test on local samples before finalizing settings.

Three Common Misconfigurations

Symptom: When executing docker-compose up, the error Error response from daemon: pull access denied for redis, repository does not exist or may require 'docker login': denied: requested access to the resource is denied appears. Cause: Aliyun image acceleration address is not configured, or the official Aliyun redis image address is not specified in docker-compose.yml.
Symptom: Retrieval result fields are missing after index construction completes. Cause: FIELD_MAPPING_RULES is not configured, leading to incorrect mapping of paint and ink-specific fields. Data is not fully indexed.
Symptom: After starting FastGPT, the retrieval model returns a model not found error. Cause: The interface address and API key for qwen3-embedding-8b are not correctly configured in the configuration file for version v4.9.11 and above.

How to Confirm Configuration Is Successful

Access the FastGPT backend data source management page, view the latest synchronization task logs. Confirm that the task started and completed successfully at the configured synchronization time.
Randomly select one pulled paint and ink data entry, verify the field mapping results. Ensure specific fields are correctly identified and entered.
Access the vector database management interface, check that the number of index shards matches the configured sharding parameter. Confirm that indexes were created normally.
Run a manual synchronization task, compare the number of data entries before and after synchronization. Confirm that data pulling and indexing processes have no abnormalities.

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.