What the data for this category looks like
Paper-making investment research data mainly comes from light industry manufacturing industry association monthly reports, listed companies' quarterly/annual financial reports, wood pulp and other raw material spot and futures quotation platforms, and environmental protection regulatory policy documents. Data update rhythms vary: raw material quotes update daily, industry capacity data is released monthly, and listed company financial reports are updated quarterly/annually. Document structures include structured time-series tables (such as wood pulp prices, monthly capacity), long-text analysis sections (such as industry supply and demand interpretations), and standardized financial fields (revenue, gross margin, capacity). Field units include dedicated identifiers such as yuan/ton, ten thousand tons, 100 million yuan, tons/day.
What constraints do these characteristics impose on context and token management
The multi-structure, varied update frequency, and dedicated field characteristics of paper-making investment research data create multiple constraints for context and token management. Mixed data sources of structured tables and long text require the context to carry both numerical indicators and interpretive text, increasing token usage per round of conversation. Data with different update frequencies needs to match the knowledge base refresh rhythm. If the context cache fails to synchronize the latest quotations in time, the model will reference outdated information. Dedicated units and field identifiers need to retain complete semantics in the context, avoiding index association breaks caused by splitting, which increases token consumption for segmentation and context splicing. The recall requirements for multi-dimensional data sources also expand the total token scale of the context, requiring more precise filtering mechanisms.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the typical paragraph length of paper-making financial reports and industry reports, avoids splitting financial indicators and associated text |
overlapRatio | 10%–15% | Retains association information between raw material prices and capacity between segments, reduces the probability of context association breaks |
similarityTopK | Top 10–15 entries | Covers the multi-dimensional data dimensions of paper-making investment research (prices, capacity, policies), ensures comprehensiveness of recalled information |
rerankTopN | Top 5–8 entries | Filters redundant recall results, controls context token consumption, while retaining core investment research information |
maxContext | 8000–12000 token | Adapts to the context splicing requirements of single-round paper-making investment research conversations, matches the basic context window threshold of mainstream large models |
tokenLimitPerRequest | 16000 token | Limits the total token scale of a single request, avoids token limit errors triggered by model interfaces |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing the settings.
Three Common Misconfigurations
- Phenomenon: The interface displays 30 context recall entries, but the actual number of context entries sent to the model does not match the interface display, and the number of valid information entries referenced by the model exceeds the interface display value. Cause: The values of
similarityTopKandrerankTopNare not synchronized. The entries filtered in the reranking stage are not updated synchronously on the interface, resulting in inconsistency between the interface and actual request parameters. - Phenomenon: Segmented paper-making financial reports show wood pulp price and corresponding capacity data split into different segments, and the context cannot associate the two indicators. Cause:
chunkSizeis set too small, and a reasonableoverlapRatiois not configured, causing splitting to destroy the semantic association between indicators. - Phenomenon: A 413 status code is returned when initiating an investment research conversation, prompting a token limit exceeded. Cause: The value of
maxContextortokenLimitPerRequestexceeds the support limit of the currently called model, triggering interface interception.
How to Verify Proper Configuration
- Upload a single sample of a paper-making industry financial report, check the segmentation preview interface, and confirm that core financial indicators and associated text are not forcibly split.
- Initiate a round of investment research conversation targeting paper-making raw material prices, check the interface request log, and confirm that the carried context token count matches the
maxContextsetting. - Test multi-data source recall scenarios, check the number of returned context entries, and confirm that they comply with the configuration rules of
similarityTopKandrerankTopN. - Check the execution log of the knowledge base update task, confirm that the update frequency matches the actual update rhythm of paper-making data, and avoid outdated context cache.
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.