What this category’s data looks like
Dairy industry investment research data sources include upstream breeding logs, dairy enterprise production and quality inspection archives, category dynamic data released by industry associations, sales ledgers from offline supermarkets and online e-commerce platforms, and customs import and export clearance records. Update frequency varies by data type. Breeding and production quality inspection data updates with each production batch. Sales data updates weekly. Industry research reports are released monthly. Documents include structured batch inspection tables, unstructured in-depth analysis documents, and standardized quality inspection certificate files. Fields cover raw material indicators, production parameters, sales pricing, circulation cycles, and more. Units include mass concentration, production capacity weight, retail unit price, and more.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering?
The multi-type, batch-bound, and update-frequency-diverse characteristics of dairy industry investment research data impose multiple constraints on multi-turn dialogue and prompt settings. First, coexistence of structured inspection data and unstructured research reports requires prompts to clearly distinguish output logic for different document types, to avoid format confusion. Second, fields tied to production batches and sales cycles require multi-turn dialogue contexts to retain the currently discussed batch number or time window, to prevent incorrect cross-batch data calls. Third, differences in update frequencies across data sources require prompts to specify rules for calling the past 7 days of sales data or monthly industry reports, to ensure returned content matches the timeliness required by queries. Fourth, cross-data-source association analysis needs require dialogue contexts to retain data source identifiers, to facilitate accurate traceback of corresponding data sources during subsequent follow-up questions.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | Last 10 turns of dialogue context | Dairy industry investment research often involves batch association and cross-data-source follow-up questions. Retaining 10 turns of context allows complete tracking of the discussed batch number and time window, preventing context loss. |
recall_top_k | Top 8 recalled results | Dairy industry investment research data includes multi-dimensional fields. 8 recalled results can cover core query dimensions such as raw material indicators, production parameters, and sales data, avoiding interference from redundant information. |
rerank_top_k | Top 3 reranked results | For precise queries tied to batches, retaining 3 most relevant results after reranking allows quick location of target data, adapting to the rapid decision-making needs of investment research scenarios. |
prompt_template | {input}\nPlease base on the batch number {context_batch_id} from the current context, prioritize calling inspection data and corresponding period sales data for that batch. Disable Markdown formatting in output results | Dairy industry investment research requires fixed batch association. Specifying the batch variable and disabling Markdown can match users' standardized output requirements, reducing format adjustment costs. |
enable_context_history | Enabled | Multi-turn dialogue needs to track batch numbers and time windows. Enabling context history can retain key parameters from each dialogue turn, avoiding repeated questions. |
file_parse_chunk_size | 800–1200 characters | Dairy industry research reports and quality inspection documents are mostly long texts. Segmentation of 800-1200 characters can retain complete indicator descriptions and analysis logic, improving recall accuracy. |
The parameter values provided on this page are all conventional recommendations used as 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 the settings.
Three Common Configuration Mistakes
- The symptom is that dialogue results are displayed in the sidebar by default instead of the main dialog box. The cause is that the
enable_dialog_main_outputconfiguration item is not enabled, causing results to be classified as auxiliary information. - The symptom is that a new dialogue session is created for each question. The cause is that
enable_context_historyis not enabled. The system does not retain dialogue context, and starts a new session by default. - The symptom is that AI replies force Markdown formatting. The cause is that
prompt_templatedoes not explicitly specify disabling Markdown. The system enables format rendering by default, which does not meet the plain text output requirements of investment research reports.
How to Verify Correct Configuration
- Initiate a first round of query that includes a specific batch number, then initiate a second round of follow-up questions for corresponding data of that batch. Check if results are displayed directly in the main dialog box instead of the sidebar.
- Launch 3 consecutive rounds of queries targeting different dimensions of the same batch. Check if the system retains context and associates the corresponding batch parameters.
- Create a custom prompt and launch a test. Check if the AI reply does not use Markdown formatting and only outputs plain text content.
- Upload a single dairy enterprise quality inspection report, launch a segmented recall test. Check if the system splits the document according to the configured segment length and accurately recalls target fields.
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.