What this category of data looks like
Plastics and rubber investment research data is sourced primarily from official delivery data of commodity exchanges, industry research reports from industry associations, real-time spot data from commodity quotation platforms, customs import and export statistics, and financial reports of industrial chain enterprises. Data update cycles cover daily, weekly, monthly, and quarterly/annual intervals. Document structures include structured quotation tables (with fields such as delivery grade, specification, origin, price, and more), semi-structured research reports (divided into supply and demand, policy, and market modules), and unstructured industry news. Field units mostly use industrial standard units such as yuan/ton, 10,000 tons/year. Some physical property indicators include dedicated test units.
What constraints these characteristics impose on the context and token workflow
The multi-field structure of structured quotation tables increases token consumption per data entry. Total token volume must be controlled during batch recall to avoid overflow. Semi-structured research reports have large variations in module length. Overlong reports must be properly chunked to retain business logic, while overly short chunks will break context coherence. High-frequency updates of real-time spot data require regular refreshes of knowledge base slices. Otherwise, old data will contaminate current context. Strong correlations between industrial chain upstream and downstream links lead to increased recall data volume. A balance must be struck between recall count and token window to avoid redundant data occupying too many tokens and causing core information truncation.
How to configure parameters
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContextToken | 8000–12000 | Single plastics and rubber industrial chain research reports often exceed 5000 tokens. Combined with recall volume of upstream and downstream associated data, reserve sufficient context window to avoid truncation of key information |
chunkSize | 3000–4000 characters | Matches average module length of structured quotation tables and semi-structured research reports for plastics and rubber. Avoids single-chunk token overflow while retaining complete business logic |
recallTopK | Top 6–8 entries | Balances recall volume of industrial chain associated data and token consumption. Too few entries will lose key supply and demand information, while too many will exceed the context window |
responseMaxToken | 2000–3000 for standard queries, 4000–6000 for deep analysis | Adapts to different complexity levels of investment research questions. Avoids truncation of complete derivation processes in short replies |
tokenStatisticSwitch | Enabled | Monitors token consumption of context and replies in real time to troubleshoot abnormal restriction issues |
parseFileChunkOverlap | 200–300 characters | Compensates for strong field correlations in plastics and rubber data, avoiding context breaks after chunking |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon:
responseMaxTokenis set above 3000, but some question replies only reach 200 tokens. Cause: The question triggers the system's built-in context verification logic, or the total token volume of recalled associated data exceeds themaxContextTokenlimit, resulting in automatic truncation of reply length. - Phenomenon: Token-related parameters cannot be configured after connecting WeChat Official Accounts to cloud space. Cause: The WeChat Official Account access module for cloud space encapsulates underlying token configurations. Unified control must be performed through the project-level
tokenAuthconfiguration item. Direct modification in the Official Account module is not supported. - Phenomenon: In FastGPT 4.6.7, the knowledge base chunk size is set to 5000 tokens, and the recall limit is set to 1500, but excess chunks are still retrieved. Cause: The
recallTokenLimitparameter is not enabled, or there is a deviation between actual token conversion and character count forchunkSize, causing single-chunk tokens to exceed the recall limit.
How to confirm proper configuration
- Enter the project's token statistics page, check the real-time data of the
global.workerPoll.countGptMesfield to confirm that token consumption of context and replies matches the preset configuration. - Launch an investment research question that includes upstream and downstream industrial chain information, and verify that the reply length matches the preset value of
responseMaxTokenwith no abnormal truncation. - Upload a plastics and rubber spot quotation table, and check that the parsed chunk length falls within the preset range of
chunkSize, with no excessive splitting or merging. - Test conversations after connecting to the Official Account, confirm that reply token consumption matches the project backend statistics, with no abnormal restrictions caused by parameter conflicts.
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.