What the data for this category looks like
Paint and ink industry market and yield rate data primarily comes from public quote databases of domestic basic chemical industry associations and public data sources from bulk commodity spot trading platforms. Data is updated every morning with complete statistical results for the previous trading day, including three core indicators: daily ex-factory price, spot transaction price, and raw material purchase price.
Data documents are usually in CSV or Excel format. Each data entry contains the following fields: product identifier, specification parameters, origin, quote unit, transaction price, and statistical date. The quote unit is uniformly yuan/ton, the transaction price field uses yuan as its unit, and the statistical date follows the YYYY-MM-DD format.
What constraints do these characteristics impose on the "model access and configuration" link
The structured multi-source nature of category data requires the model access link to support batch pulling from multiple data sources and unified format parsing, to avoid data confusion caused by differences in data source formats. The daily update rhythm requires the configured dataset refresh frequency to align with the data source update rhythm, preventing the use of expired market data.
The field design for products with the same name and multiple specifications requires configuring data filtering rules to distinguish products of the same category with different specifications and origins, avoiding confusion in matching results. The unified unit field requires the model to strictly follow preset units when processing data, eliminating the need for additional conversion and reducing data verification costs.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Paint and ink industry quote Excel files usually contain hundreds of category data entries; 300 seconds is sufficient for complete parsing, avoiding parsing interruptions caused by large data volume |
rag_relevance_threshold | 0.75–0.85 | Required to filter low-match results with the same product name but non-matching specifications, while retaining valid match results for products of the same specification from different origins |
dataset_refresh_interval | 86400 seconds | The data source updates previous trading day's data once daily; this interval aligns with the update rhythm, preventing use of expired market data |
UPLOAD_FILE_MAX_SIZE | 50 MB | Single-batch paint and ink industry quote Excel files usually do not exceed 20 MB; this value reserves sufficient space for batch upload scenarios |
schedule_trigger_cron | 0 8 * * * | The data source completes previous day's data update before 8 AM every morning; this trigger time allows dataset refresh immediately after data update |
maxContext | 8000–12000 characters | Required to accommodate multi-category historical quote data and current broadcast content simultaneously, avoiding model call failures caused by context overflow |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Model calls return empty results, and logs show field matching failures. Cause: Secondary filtering by product specification and origin is not configured, leading to matching invalid data with the same name but different specifications.
- Symptom: Model tests prompt a 404 no body error. Cause: The locally deployed model service is not correctly bound to the API address configured in FastGPT, or the port has not been granted access permissions.
- Symptom: Model errors are triggered after voice input is converted to text, and logs show an unmarshal_resp field error. Cause: Strict verification of the model return format is not configured, causing unstructured voice transcription content to fail to be correctly parsed by the model.
How to confirm the configuration is complete
- Manually upload a single paint and ink quote file, check if the parsed dataset fields correspond to the source file, confirming that the parsing configuration adapts to the category data structure.
- Trigger a manual dataset refresh, compare the dataset update time before and after the refresh, confirming that the scheduled refresh configuration takes effect.
- Enter a query containing a specific specification paint and ink product, check if the returned results include matching specification and origin information, confirming that the similarity threshold configuration is reasonable.
- Call the text query converted from voice transcription, check if the model can normally return structured market results, confirming that the format verification configuration is correct.
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.