Data Characteristics of This Category
Plastics and rubber investment research data primarily comes from futures contract data of the Shanghai Futures Exchange and Dalian Commodity Exchange, daily spot quotes from major domestic bulk commodity wholesale markets, public industry reports from the China Rubber Industry Association and China Plastics Processing Industry Association, and monthly import and export statistics from the General Administration of Customs. Spot quotes are updated daily, futures market data is updated every 15 minutes, industry supply and demand reports are released monthly, and customs import and export data is updated the following month. Document structures include three categories: structured market tables, supply and demand balance sheets, and industrial chain association maps. Fields cover product name, origin, transaction price, output, import volume, export volume, and more. Units follow standardized industrial measurement formats such as yuan/ton, ten thousand tons.
Constraints for Multi-turn Dialogue and Prompt Engineering Links
Multi-source data with varying real-time performance requires that context recall for multi-turn dialogue must limit time ranges. This prevents models from calling outdated spot or futures data. Structured documents with multiple fields require prompt engineering to explicitly specify required field combinations. This prevents models from confusing parameter details of different product segments between plastics and rubber. The complexity of industrial chain associations requires multi-turn dialogue to gradually decompose upstream and downstream logic. Loading full data at once would cause context overflow. Differences in update rhythms across data sources require marking data release times in dialogue context management. This avoids time misalignment issues across data sources.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContextTokens | 8000–12000 | Single structured document for plastics and rubber is generally under 5000 characters. Multi-turn dialogue needs to retain recent conversation context to avoid token overflow |
recallTopK | Top 6–8 results | There are many product segments for plastics and rubber. Excessive recall will lead to overly long prompt text and confuse field information across different product segments |
ragThreshold | 0.75–0.85 | Feature differences between product segments are small. A higher similarity threshold is needed to filter recalled data from unrelated product segments |
autoUpdateInterval | Every 6 hours | Balances the real-time performance of spot data and system performance, avoiding excessive resource usage from frequent updates |
promptTemplate | Fixed template: Combine recalled {data_source} data to answer {question} about {commodity_type}, only use information from specified fields, do not add unreached content | Explicitly limits the data sources and field ranges the model uses, to avoid generating irrelevant content |
maxParseLength | 1000–1500 characters | Adapts to segmented parsing of plastics and rubber structured tables, avoiding parsing errors caused by overly long paragraphs |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material forms, data volume and business rules. Specific issues require case-by-case analysis. Testing on local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After configuring a custom prompt template, the model’s returned content does not restrict specified fields, and confusing information about natural rubber and plastics appears. Cause: The prompt template fails to explicitly specify the product segment range for the current dialogue, and does not filter recalled data from non-target product segments.
- Phenomenon: After exceeding the preset number of multi-turn dialogue rounds, the model begins returning irrelevant futures contract details and cannot focus on spot market inquiries. Cause: The context window parameter is set too small, and insufficient historical context is retained, causing the model to forget the core theme of the current dialogue.
- Phenomenon: After switching the deployed AI model, the original configured knowledge base recall logic fails and returns empty structured data. Cause: The knowledge base recall configuration is not bound to the model, or the corresponding prompt template is not loaded in the new model.
How to Confirm Proper Configuration
- Initiate a multi-turn dialogue involving plastics and rubber product segments, and verify that the information returned by the model strictly uses specified fields and data sources, and does not include unreached content.
- View knowledge base update records to confirm that data synchronization frequency meets preset configuration requirements, and the time range of the latest data matches business needs.
- Switch between different AI models to verify that conversation context is retained normally, and that prompt templates and recall logic work correctly in the new model.
- Input questions involving complex industrial chain associations, and check whether the model can gradually associate upstream and downstream data without triggering context overload prompts.
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.