What the Data for This Category Looks Like
The data for dairy product intelligent due diligence reports primarily comes from ranch milk source inspection ledgers, factory production batch records, third-party authoritative quality inspection reports, and supply chain traceability systems. Data is synchronized according to production batches or daily updates. A single batch report typically includes milk source traceability information, physical and chemical indicators, microbial test results, compliance inspection items, and other content. Core fields include batch number, milk source location, milk fat content, total bacterial count, somatic cell count, and inspection qualification status. Common units include mg/kg, cfu/mL, percentage, and similar units.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The multi-batch nature of dairy product due diligence data requires multi-turn dialogue to retain the batch number context bound to the current session, to avoid mixing data across batches. Unit differences in professional physical and chemical indicators require prompts to predefine interpretation rules for corresponding field units, preventing the model from outputting incorrect conversion results. The verification needs of multi-source data require multi-turn dialogue to guide users to supplement missing data source types step by step. For example, when only quality inspection reports are obtained, proactively ask whether traceability information needs to be synchronized. The high-frequency data update feature requires prompts to call the latest batch dataset by default, avoiding the generation of reports using expired data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Dairy product due diligence reports include multi-batch traceability data, requiring complete batch-related context to be retained |
system_prompt_template | Preset dedicated template for dairy product due diligence, bound to batch field verification rules | Clarify unit interpretation requirements for professional physical and chemical indicators, preventing the model from confusing data dimensions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Third-party quality inspection reports include multiple detection tables, which take a long time to parse |
recall_top_k | Top 6 entries | Dairy product due diligence needs to cover three core links: milk source, production, and quality inspection. A maximum of 2 related records should be recalled for each link |
UPLOAD_FILE_MAX_SIZE | 500 MB | Full-link traceability data for a single batch includes multi-dimensional attachments, allowing large file uploads |
chunk_size | 1000 characters | Dairy product detection indicators have many professional fields. Excessively long segments will make it difficult for the model to capture key information |
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 settings.
Three Common Mistakes
- Generated due diligence reports are associated with incorrect production batches. Phenomenon: The batch number included in the report does not match the final provided batch number. Cause: The
maxContextconfiguration value is too small, failing to retain complete batch-related context. - Parsed quality inspection data has incorrect units, such as labeling total bacterial count as mg/kg. Phenomenon: The units of detected indicators in the output do not match actual testing standards. Cause: The system prompt does not clearly specify standard unit interpretation rules for each detection item.
- After advanced orchestration executes an SQL query, the results cannot be displayed in the AI dialogue window. Phenomenon: Structured data returned by the workflow is not synchronized to the dialogue window. Cause: The
workflow_output_bindparameter is not configured, and the query results are not bound to the dialogue output node.
How to Verify Correct Configuration
- Upload a dairy product quality inspection report, check whether the parsed fields cover batch numbers, core physical and chemical indicators, and other content, and verify the matching between parsed results and preset business fields.
- Initiate two linked dialogue sessions. First, provide a specified batch number, then supplement supplementary detection data for that batch, confirm that the model can retain and associate information from both dialogue sessions.
- Run a workflow that includes data queries, confirm that the structured content generated by the query is automatically synchronized to the dialogue output window without additional configuration conversion steps.
- Enter a query about detection indicators with professional units, confirm that the interpretation results output by the model comply with standard unit rules for the dairy industry.
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.