What the Data for This Category Looks Like
Coatings and inks investment research data is primarily sourced from public technical reports from industry associations, quality inspection reports from raw material suppliers, Material Safety Data Sheets (MSDS) from production enterprises, compliance regulatory documents, and process R&D documents. The update rhythm varies: raw material prices and performance parameters are updated monthly based on market changes, industry process standards are revised quarterly or annually, and compliance documents are updated irregularly per regulatory requirements. Document structures include structured parameter tables, semi-structured process flowcharts, and unstructured technical descriptions. Core fields include viscosity, fineness, solid content, and drying time, with corresponding units of mPa·s, μm, mass percentage, and hours.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
A high proportion of structured parameter tables requires multi-turn dialogue to track contextually linked raw material batches and parameter dimensions, to avoid mixing indicators from different samples. Large volumes of long documents and semi-structured content require the context window to adapt to long text loading, while limiting invalid context redundancy to avoid excessive computational resource consumption. Fine-grained differences exist in unit systems, requiring prompt engineering to clearly specify unit rules to prevent the model from outputting confusing measurement standards. Layered update frequencies require the knowledge base to regularly refresh the latest parameters and compliance content, while multi-turn dialogue must support the ability to call the latest data sources to meet the timeliness requirements of investment research.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the context requirements of long coatings and inks documents and multi-turn parameter comparison |
recallTopK | Top 6–8 results | Covers core structured parameters and associated process information, avoids redundant recall |
similarityThreshold | 0.75–0.85 | Improves the accuracy of fine-grained parameter matching, filters low-relevance document fragments |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing time requirements of long MSDS and process documents |
chatHistoryTTL | Calibrated per business needs | Investment research scenarios require retaining historical conversation records, with specific duration configured per the traceability cycle |
promptTemplate | Must explicitly specify parameter units, e.g., viscosity is measured in mPa·s | Adapts to the fine-grained unit system of coatings and inks, prevents the model from confusing measurement standards |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific scenarios require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Conversation response times exceed 10 seconds. Cause:
maxContextis set too large andrecallTopKuses a high value, resulting in excessive context and recalled data being loaded for each conversation. - Phenomenon: Conversation records are lost prematurely. Cause:
chatHistoryTTLis incorrectly configured to0 daysor session storage is not enabled, resulting in automatic clearing of conversation data. - Phenomenon: Returned parameter units are inconsistent. Cause: The prompt does not explicitly specify unit rules, resulting in the model outputting mixed measurement standards for different parameters.
How to Verify Proper Configuration
- The system accepts a complete MSDS document upload, then checks parsing completion status and response time to confirm alignment with business expectations.
- Two progressive queries are initiated. First, the viscosity parameter of a specified raw material is queried, followed by a query for the drying time of the same batch. The system verifies that the model associates context and maintains conversation logic.
- The
chatHistoryTTLparameter in the system configuration is reviewed to confirm it matches the business traceability cycle. - A cross-raw material parameter comparison query is initiated. The system checks returned results to confirm each parameter’s corresponding unit is clearly labeled.
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.