What the data for this category looks like
Investment research data for the paint and ink category primarily comes from industry association public reports, quality inspection reports from raw material suppliers, formula development documents, and compliance testing documents. The data update cadence has multiple tiers: raw material pricing and inventory data is updated weekly, industry trend reports are released monthly, and customized formula documents and compliance testing reports are updated quarterly.
Document structures typically combine structured tables and long text, including fields such as component proportions, production process parameters, and compliance indicators. Standard units for indicators include percentage for solid content, millipascal-seconds (mPa·s) for viscosity, and micrometers (μm) for fineness. Some overseas documents include a CAS registry number field.
Constraints imposed by these characteristics on conversation logs and auditing
The multiple update cadences of paint and ink investment research data require conversation logs to mark traceable timestamps by data type, distinguishing call records for weekly raw material data, monthly industry reports, and quarterly compliance documents.
The large number of structured component fields with unified units means the auditing link must verify unit consistency for matched parameters in conversations, avoiding unit confusion for indicators like solid content and viscosity.
Recall of long-text process documents and customized formulas requires logs to fully record recalled context fragments, preventing key production process parameters from being truncated.
Calls for compliance testing data must retain operator information, call time, and returned content to meet traceable requirements for compliance auditing.
Configuration settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
LOG_RECORD_CONTENT_LENGTH | 800–1200 characters | Paint and ink documents mostly contain long-text component tables and process parameters. This length can fully record key recalled fragments and avoid truncating core data |
AUDIT_RETENTION_DAYS | 180 days | Chemical industry compliance audits require retaining operation records for at least six months, covering the full lifecycle of quarterly updated compliance documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large formula documents and process flowcharts takes a long time; this setting prevents log recording from being interrupted by timeouts |
MAX_CONTEXT_RECALL_NUM | Top 6 entries | Paint and ink investment research needs to cover multi-dimensional data including components, prices, and compliance. Too many recalls increase log redundancy, while too few fail to cover all scenarios |
LOG_UPLOAD_PLUGIN_PARAMS | Only record required parameters | When calling custom plugins, paint and ink formula parameters are numerous and complex. Only recording required items simplifies the readability of audit logs |
ERROR_LOG_TRIGGER_LEVEL | WARNING and above | Common issues in investment research scenarios such as parameter matching errors and unit confusion must be captured to facilitate subsequent audit troubleshooting |
The parameter values provided on this page are all conventional recommendations used as 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 misconfigurations
- Phenomenon: After adding a custom formula parsing plugin to the workflow, no content is displayed in the input and output parameter panel. Cause: No field mapping rules unique to the paint and ink category are bound in the plugin configuration, causing FastGPT to fail to recognize the structure of parameters such as solid content and viscosity. In the open-source version v4.8.20-fix2, this configuration item does not adapt to basic chemical sub-categories by default, so this issue is likely to occur.
- Phenomenon: The console reports
bootstrap-legacy-autofill-overlay.js:6247 Uncaught TypeErrorerror, and the page fails to load after spinning. Cause: FastGPT static resources and custom plugin script paths are not isolated during deployment, causing script conflicts that block the initialization process. - Phenomenon: After deploying via Docker, the page fails to load after spinning, with no clear error logs. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted to adapt to large paint and ink documents, and container resources are occupied for a long time, triggering timeout restart.
How to verify successful configuration
- Initiate a conversation targeting paint and ink raw materials, check if the update cycle and unit information of the corresponding data are marked in the logs.
- Trigger a custom plugin call, confirm that the parameter configuration items corresponding to the category are loaded in the workflow panel.
- Query conversations related to compliance testing, check that the audit logs retain complete operation records and returned content.
- Test the copy function of long-text formula documents, confirm that there is no interaction blocking.
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.