What the data for this category looks like
Dairy industry research report data primarily comes from publicly available research reports from securities brokerages, monitoring data from industry associations, quarterly financial reports of listed dairy enterprises, and public reports from third-party consumer research institutions. Update cycles include regular releases and ad-hoc updates. Regular reports are launched on a quarterly and monthly basis; ad-hoc special research reports are published when there are sudden changes in milk supply or adjustments to industry policies. Industry association data updates monitoring results for consumer-facing sub-categories each month. Documents typically include five core sections: overall industry overview, sub-category analysis, raw milk supply chain data, competitive landscape, and policy impact. Indicator fields have dedicated units, such as raw milk purchase price and terminal retail price.
Constraints for multi-turn conversation and prompting
The wide range of sub-categories and dedicated indicator units for dairy industry research reports require multi-turn conversation systems to track the sub-categories and indicator limitations specified by users, to avoid response deviations caused by lost context. Differences in update cycles require conversation systems to prioritize recalling the most recently published research report data to meet user demand for timeliness. The long sections and multi-indicator association characteristics of documents require prompts to clearly specify that the model must associate context limitations, and must not separate indicators from their corresponding categories. Additionally, the dedicated units of different indicators require prompts to strictly match the unit rules from the original research reports, to avoid incorrect indicator conversion results in outputs.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Dairy industry research reports contain long sections of sub-category analysis and indicator association content. Excessively long segments will break the correspondence between indicators and categories, while excessively short segments will lose context associations |
recall_topk | Top 6–8 entries | A single dairy industry research report covers multiple sub-categories and indicators. Too many recalled entries will introduce irrelevant data, while too few will fail to cover the detailed dimensions required by users |
similarity_threshold | 0.72–0.78 | Precise matching of dairy sub-category and dedicated indicator keywords is required. A threshold that is too low will include non-dairy industry research reports, while a threshold that is too high will miss relevant detailed data |
maxContext | 10000–14000 characters | Multi-turn conversations require tracking limiting conditions such as user-specified sub-categories and indicator types. Sufficient context can retain the exclusive rules of the conversation |
maxConversationKeepDays | Set in accordance with business compliance requirements | Some financial scenarios require retention of conversation records. The corresponding configuration file is config/production.js, and the value can be adjusted based on regulatory requirements |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single dairy industry research report typically includes detailed data across multiple sections. The default timeout duration is insufficient to complete full parsing |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing values.
Three common configuration mistakes
- Symptom: Each conversation response takes longer than 10 seconds, and the interface displays a loading timeout prompt. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, or therecall_topkvalue was set too high, resulting in excessive recalled and parsed data. - Symptom: Unit confusion appears in conversations, such as displaying the unit of raw milk purchase price as a non-dedicated unit. Cause: The prompt did not explicitly require matching the dedicated field units of dairy industry research reports, and the unit rules for indicators were not limited in the system prompt.
- Symptom: Conversation records cannot be retained as required, or a
400 Bad Requesterror appears in backend logs. Cause: ThemaxConversationKeepDaysparameter was not configured correctly, or the service was not restarted after modifying the configuration file.
How to verify successful configuration
- Initiate a multi-turn conversation that includes dairy sub-categories and specific indicators, and verify that the indicator units in the model output match the original research report.
- Access the system configuration page and confirm that the
maxConversationKeepDaysparameter value complies with business compliance requirements. - Upload a single dairy industry research report and initiate parsing, and check that no timeout errors appear in the parsing progress.
- View the conversation history list and confirm that all sessions are stored normally for the configured retention duration.
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.