What the Data for This Category Looks Like
Paper industry investment research data mainly comes from industry reports published by the China Paper Association, regular financial reports of listed companies, customs import and export statistics, spot and futures quotation platforms for raw materials such as wood pulp, and production and circulation data from upstream and downstream of the paper industry chain. Update cycles are divided into multiple tiers: raw material spot quotations are updated daily, industry supply and demand balance sheets are released quarterly, listed company financial reports are updated quarterly or annually, and industrial policy documents are released irregularly. Document structures are mostly split by specific paper types, and include sections such as prices, production volume, cost breakdown, and supply and demand gap. Field units mostly follow industrial measurement standards such as yuan/ton and ten thousand tons.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Configuration
The multi-source update cycles and specific paper type characteristics of paper industry data impose clear constraints on multi-turn dialogue and prompt configuration. Daily updated raw material price data requires the dialogue system to recall real-time latest information to avoid returning expired quotations. Differences between specific paper types require prompts to bind the specific paper type currently being discussed to prevent confusion of cross-category data. Structured documents with multiple sections require multi-turn dialogue to track contextual discussion topics and automatically associate categories and links from historical questions. Differences in update cycles across documents require configuring differentiated recall time rules to ensure timeliness matches between industry reports and spot data.
How to Set Configuration Values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Paper industry documents have relatively long lengths. Multi-turn dialogue must retain 5 to 7 rounds of industry chain discussion context to prevent answer interruptions caused by context overflow |
conversationHistoryMaxTurns | 5–7 turns | Multi-turn questions for paper industry investment research usually focus on a single industry chain link. 5 to 7 turns are sufficient to cover a complete discussion and reduce redundant historical information occupying context space |
recallTopK | Top 8–12 results | There are many specific paper types and scattered data. A sufficient number of relevant documents must be recalled to avoid missing key data for specific paper types |
similarityThreshold | 0.72–0.78 | Paper industry content has dense professional terminology. A relatively high similarity threshold can filter out generally irrelevant documents and only retain content strongly related to paper industry topics |
rerankTopN | Top 4–6 results | Retain industry chain and category data most relevant to the current query after reranking, to improve answer accuracy and conciseness for multi-turn dialogue |
promptTemplate | Prompt template that binds the current specific paper type and contextual topic | Clearly specify the paper type and industry chain link being discussed to prevent cross-category data confusion during multi-turn dialogue |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Symptom: Long unresponsive periods occur when using a large language model with a local paper industry knowledge base for dialogue. Cause: The
recallTopKandrerankTopNparameters are not adjusted, and too many redundant paper industry documents are recalled and passed in, leading to excessive model computing load. - Symptom: Chart data from the paper industry cannot be returned during multi-turn dialogue. Cause: The knowledge base's image parsing function is not enabled, and the prompt does not specify that answer content can be returned in image format.
- Symptom: After configuring a judge node in advanced orchestration, the initial user query content cannot be obtained. Cause: The
userQueryglobal variable is not bound to the judge node's input parameters, causing the initial query to not be passed to subsequent AI dialogue nodes.
How to Confirm Proper Configuration
- Initiate a query that includes a specific paper type, check the category matching degree of recalled documents, and verify the effect of
recallTopKandsimilarityThreshold. - Initiate 5 to 7 consecutive progressive queries focused on the paper industry chain, confirm that answers can associate topics and data from historical queries with no context loss.
- Test the judge node in advanced orchestration to verify whether the initial user query is correctly passed to subsequent AI dialogue steps.
- Initiate a query that includes industry charts, confirm that the dialogue can return corresponding image content, and verify the effectiveness of image parsing and display configuration.
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.