What data for this category looks like
Data comes primarily from public reports released by the China National Coatings Industry Association, official quotation systems of raw material suppliers, annual and quarterly financial reports of listed paint enterprises, and performance test reports issued by compliant testing institutions. Update cycles: Raw material price data is updated weekly. Industry policies and compliance standards are updated quarterly. Enterprise product formulas and performance parameters are updated on demand. Most documents use structured tables with fields including product model, raw material ratio, viscosity, weather resistance rating, and others. Additional document formats include PDF test reports and Excel supply chain data. Common fields and units: Viscosity (cps), solid content (%), adhesion (MPa), shelf life (months), raw material purchase price (yuan/ton), and more.
What constraints these characteristics impose on context and token processing
Structured multi-field documents have higher token usage per entry than generic text. Recalling multiple related documents can easily exceed the model’s context token limit. The weekly update cycle for raw material price data requires context recall to use the latest knowledge base version. Without configured context recall logic tied to versioning, outdated data may be returned, which harms investment research accuracy. When splitting long PDF test reports, failing to preserve complete table structure leads to broken context information and increased invalid token consumption. Fields with inconsistent units across different documents require extra semantic alignment processing, which further uses token quotas.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Paint and ink investment research documents often contain multiple field parameters per chunk. 8000–12000 characters can cover 3 to 5 complete entries of raw material prices or performance parameters, avoiding exceeding the model’s token limit |
chunkSize | 1000–1500 characters | The content of a single page of a structured table is approximately 1000 characters. Splitting in this way preserves table integrity and reduces invalid token consumption |
similarityTopK | Top 3–5 entries | Single paint and ink documents have high token usage. Too many recalled entries will exceed the context quota, while ensuring the relevance of recalled information |
rerankTopN | Top 2–3 entries | The reranking step only needs to filter the most relevant investment research data. Too many entries will increase token consumption and processing delay |
PARSE_TABLE_STRICT | Enabled | Enforce retention of the complete format of structured tables, avoid broken table information after splitting, and reduce invalid token consumption |
contextRefreshInterval | 7 days | Match the weekly update rhythm of raw material price data to ensure the timeliness of context recall |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: An
Invalid JSON: Bad control charactererror is returned when calling a tool node in a workflow. Cause: Special characters in paint and ink documents, such as line breaks and unit symbols within tables, are not escaped, resulting in abnormal context JSON format generation. - Phenomenon: Inconsistent units appear in investment research results returned by AI chat, such as both cps and mPa·s appearing at the same time. Cause: Context preprocessing logic for field unit alignment is not configured. Recalled parameters from different documents are not unified in units, leading to increased token consumption and mixed information.
- Phenomenon: After setting
maxContextto 16000 characters, the model returns an error that the context exceeds the limit. Cause: The token usage of single paint and ink documents is higher than that of generic text. The 16000-character context actually exceeds the model’s token limit.
How to confirm the configuration is correct
- Upload a typical paint and ink raw material quotation document, and check whether the parsed chunks retain the table structure without broken information.
- Initiate an investment research query, and verify that the units of the returned context content are unified without format confusion.
- Check the knowledge base refresh log to confirm that raw material price data is automatically updated at the set interval.
- Test the tool node call to confirm that the generated context JSON has no format errors and no parsing failures caused by control characters.
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.