What the data for this category looks like
Data sources for baijiu intelligent due diligence reports include publicly disclosed annual/quarterly financial reports of baijiu manufacturers, production and sales monitoring data released by industry associations, third-party beverage quality inspection reports, product distribution information from official brand channels, and product parameter documents. Data update frequency varies by source type: financial reports are updated quarterly and annually, industry monitoring data is updated monthly, and inspection reports are updated upon completion of batch inspections. Document structures are mixed, including structured tables, long-form professional descriptions, and standardized parameter fields. Field units include professional measurement standards such as alcohol content %vol, production capacity in kiloliters, and revenue in ten thousand yuan. Some fields require differentiation based on production year and brand tier.
Constraints on Multi-turn Dialogue and Prompt Engineering
Dispersed data sources require multi-turn dialogue to sequentially guide the model to confirm compliance and time dimensions of different data sources, to avoid mixing cross-cycle data. Mixed document lengths and formats require the context window to cover sufficient original fragments, preventing truncation of professional terms and structured data. Differences in units for professional fields require the prompt to clearly constrain output formats, to avoid the model mixing measurement standards. Multi-time dimension data requirements require the dialogue flow to actively confirm the statistical cycle of data, to prevent mismatched time ranges in output results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000-12000 characters | Baijiu due diligence reports contain multiple types of document fragments, and need to cover core content of at least 3 complete data sources to avoid loss of professional fields due to context truncation |
streamResponse | false | Baijiu due diligence results need to be integrated with multi-dimensional data before unified output; streaming output will interrupt the splicing logic of professional data |
promptTemplate | "For baijiu due diligence scenarios, first confirm the time dimension and source compliance of the data. Outputs must clearly mark field units, such as alcohol content as %vol and production capacity as kiloliters" | Baijiu due diligence involves professional measurement and multi-time dimension data, and needs to constrain model output format and information verification logic |
contextRecallCount | Top 6 | Baijiu due diligence data is scattered across multiple documents such as financial reports, industry reports, and inspection reports. Enough relevant fragments need to be recalled to cover core dimensions including production capacity, channels, and inspections |
similarityThreshold | 0.75-0.85 | Baijiu industry terminology has high semantic similarity. Need to balance recall precision and coverage to avoid missing relevant data fragments |
fileParseChunkSize | 1000-1500 characters | Baijiu due diligence documents contain long paragraphs of professional descriptions such as blending processes and production capacity explanations. Too short chunk size will destroy the contextual association of professional terms |
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 settings.
Three Common Mistakes
- Phenomenon: A non-streaming result is returned when the API is called, but the frontend still displays segmented loading error messages. Cause: The
streamResponseparameter was not correctly set tofalse, and the frontend was not adapted to the non-streaming response format. - Phenomenon: AI dialogue output in the workflow is directly displayed on the page, and cannot be uniformly output after splicing via subsequent components. Cause: The
autoShowDialogconfiguration item was not disabled, causing the model output to be directly attached to the dialogue panel and skipping the intermediate processing link of the workflow. - Phenomenon: Annotated dialogue logs cannot be associated with corresponding due diligence data fragments. Cause: The
logContextLinkparameter was not enabled in the dialogue configuration, causing annotations to only record text content without binding position information of the original data source.
How to Verify Correct Configuration
- Upload 1 annual financial report of a baijiu enterprise and 2 monthly industry association reports. Initiate a dialogue with the prompt "Sort out the production capacity and channel proportion of this enterprise for the specified cycle". Check whether the model output includes clear unit markings.
- Call the API to initiate a dialogue. Check whether the response format is a complete JSON object with no segmented streaming chunk fields.
- Add a text splicing component in the workflow, connect the AI dialogue node and the output node, initiate a test dialogue, and confirm that only the spliced content is displayed in the final output panel.
- Enter the dialogue log page, select any piece of dialogue content for annotation, and check whether the annotation is associated with the uploaded original document fragment.
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.