What the Data for This Category Looks Like
Data for baijiu research reports primarily comes from domestic securities research institutes, industry consulting firms, and public periodic reports of listed liquor enterprises. Update frequency adjusts based on listed liquor enterprises' financial report disclosure cycles and major industry events. Updates are typically concentrated after quarterly and annual financial reports are released, with temporary updates triggered by dynamic events such as channel surveys and product price hikes.
Document structure includes modules such as core viewpoint summaries, corporate financial metrics, channel coverage, production and sales analysis, and risk disclosures. Fields include revenue per kiloliter of liquor (unit: yuan/kiloliter), terminal selling price per bottle (unit: yuan), production capacity (unit: 10,000 tons), number of covered channel stores, as well as research report issuing institution, release date, and rating information.
How These Characteristics Impact Multi-turn Dialogue and Prompt Engineering
The characteristics of baijiu research reports impose multiple constraints on multi-turn dialogue and prompt engineering workflows. First, reports include specific numerical indicators such as finance, production capacity, and channels broken down by individual liquor enterprises. Multi-turn dialogue must maintain continuous association with the user's numerical query context, and prompts must clearly limit the retrieval scope to listed entities in the baijiu sector and their corresponding research reports to avoid cross-category confusion. Second, update frequency adjusts dynamically based on financial reports and industry events, so retrieval logic must prioritize the latest published report content, and prompts must include time range restriction rules. Third, industry ratings and channel survey details have specific contextual meanings, so prompts must guide the system to retain original wording without arbitrary rewriting or simplification.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single baijiu research report is typically 3000–8000 characters long. Multi-turn dialogue requires retaining 2–3 rounds of context and retrieved report content to avoid context overflow |
recallTopK | Top 6–8 results | There are many segmented liquor enterprise dimensions in baijiu research reports. Too many retrieved results will cause context overload, while too few will fail to cover multi-enterprise comparison data required by users |
similarityThreshold | 0.75–0.85 | Baijiu research reports contain a large number of professional terms. A threshold that is too low will introduce irrelevant research reports from other food and beverage categories, while a threshold that is too high may miss accurate segmented content |
promptTemplate | Precisely retrieve research report content based on the user's queried baijiu segment, liquor enterprise name, and time range. Prioritize displaying the latest published reports, and retain the unit expressions of original ratings and financial values | The professional context of baijiu research reports requires strict restriction of retrieval dimensions to avoid generalized retrieval leading to results deviating from requirements |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Single baijiu research report often contains multi-page charts and detailed data, which takes a long time to parse. This avoids research reports failing to be retrieved due to timeout |
enableLatexRender | Enabled | Baijiu research reports often contain financial formulas, production capacity calculation formulas and other LaTeX-formatted content. The publishing environment must support normal rendering |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfiguration Issues
- Symptom: Prompt configuration has no effect, and user queries fail to retrieve targeted baijiu research reports. Cause: The prompt template fails to clearly restrict the retrieval scope to the baijiu sector, or the template format does not comply with system parsing rules, resulting in the system being unable to correctly match retrieval conditions.
- Symptom: LaTeX-formatted content displays correctly in debug preview, but only shows raw LaTeX code after publishing. Cause: The
enableLatexRenderconfiguration item is not enabled, or the rendering plugin in the publishing environment is not activated synchronously, resulting in LaTeX syntax failing to be parsed and rendered. - Symptom: After Docker deployment, dialogue remains stuck and fails to return normal research report retrieval results. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is set too low, resulting in timeout when parsing single baijiu research reports, or insufficient container memory allocation to load parsing caches for multiple reports.
How to Verify Successful Configuration
- Initiate a multi-turn dialogue, first ask "2023 baijiu industry revenue status", then ask "What is the share of a listed baijiu enterprise in this total". Verify that the returned results are linked to specific research report data for that listed baijiu enterprise, and that the context is coherent.
- Upload a public baijiu research report, check that all preset financial and production capacity fields are included in the parsed content, and that LaTeX-formatted formulas display correctly.
- Adjust the value of
similarityThreshold, test the number of retrieved results under different thresholds, and confirm that the retrieval accuracy meets business requirements. - Check system operation logs to confirm that there are no timeout errors during the research report parsing process, and that the release dates of retrieved reports match the expected update cycle.
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.