What the data for this category looks like
Data sources for plastics and rubber research reports include domestic bulk commodity spot trading platforms, monthly statistics from industry associations, basic chemical industry research reports from securities firms, and customs import and export trade data. Update frequencies vary: spot price data updates daily, industry association statistical reports are released quarterly, and securities firm research reports update in real time alongside industry events and policy adjustments. Documents include supply and demand fundamental analysis, price trend reviews, upstream and downstream industrial chain data tables, policy interpretations, and future market outlooks. Field units include yuan/ton, ten thousand tons, tons/day, and others. Some data is marked with the delivery standards for the corresponding product.
What constraints these characteristics impose on multi-turn dialogue and prompting
Multi-source data has unit differences, so unit unification verification must be completed first during multi-turn dialogue. Prompts must explicitly require responses to strictly match original units. Single research report content is large and mixes structured and unstructured content, so multi-turn dialogue needs to support context-aware segmented recall to avoid truncating key analytical logic. Data with different update frequencies must have their call priorities differentiated during sessions, and prompts must limit priority use of daily updated spot data and newly released research report content. There are many exclusive terms for segmented product categories, so multi-turn dialogue must retain sufficient context to support coherent logical follow-up questions, avoiding analysis deviations caused by lost context.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12000–15000 token | Single plastics and rubber research report has relatively long content. Sufficient context must be retained to support logical association for multi-turn dialogue and avoid truncating critical data |
recallCount | Top 8–12 entries | Research report data has multiple dimensions. Sufficient entries must be recalled to cover multiple analysis dimensions including supply and demand, prices, policies, and others |
similarityThreshold | 0.72–0.78 | Terminology in plastics and rubber related research reports is highly professional. Balance between recall precision and coverage must be maintained to avoid missing segmented field content |
rerankCount | Top 4–6 entries | Retain core entries after reranking recall results, ensuring data called during multi-turn dialogue is accurate and not redundant |
promptTemplate | Fixed template, add "Only use plastics and rubber research report data associated with this session to respond, strictly match original units" | Restrict response scope based on category characteristics to avoid calling irrelevant data |
historyMaxTurns | 5–7 turns | Analysis logic chain for plastics and rubber is relatively long. Retaining 5 to 7 rounds of history supports coherent multi-turn follow-up questions and avoids lost context |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material forms, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: The question classification module assigns conversations related to plastics and rubber research reports to an incorrect workflow, triggering unexpected operational logic. Cause: The training samples of the classification model do not cover exclusive terms and segmented scenarios of plastics and rubber, leading to semantic matching deviations.
- Phenomenon: The conversation request returns a 504 timeout error, and research report retrieval results fail to load normally. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted to adapt to the large-volume content of plastics and rubber research reports, resulting in parsing timeout. - Phenomenon: After initiating a conversation via the API, duplicate additional historical records appear in the session history. Cause: Context cleaning rules were not configured correctly, causing duplicate request content to be written to the session context.
How to Confirm Proper Configuration
- Initiate multi-round follow-up questions containing segmented terminology for plastics and rubber, check whether the conversation context retains the association with research report data from previous questions.
- View session logs to confirm that the question classification module assigns research report-related requests to the correct workflow nodes.
- After calling the API interface, check that the returned session history has no duplicate records and complies with the configured context cleaning rules.
- Verify that the response content strictly matches the units and data fields of the original research report, with no unit confusion or irrelevant content.
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.