Multi-turn Dialogue and Prompt Engineering for Condiment Intelligent Due Diligence Reports

Condiment due diligence data sources include publicly available industry association data, listed company operating disclosures, supermarket POS sales

What Data for This Category Looks Like

Condiment due diligence data sources include publicly available industry association data, listed company operating disclosures, supermarket POS sales details, and raw material supplier supply ledgers. Data updates follow three cycles: daily (sales details), monthly (industry operations), and quarterly (enterprise financial reports). Document structure is divided into four categories: basic information (category name, implementation standards), cost module (raw material unit price, packaging material cost), sales module (channel proportion, average price per channel), and compliance module (additive usage, inspection item labeling). Most field units use physical and financial measurement standards such as kilograms, yuan, and batch numbers.

Constraints Imposed on Multi-turn Dialogue and Prompt Engineering

Multi-source data with inconsistent update cycles requires explicit specification of the reference data time range during multi-turn dialogue, to avoid mixing cross-cycle data. Daily sales details have a large number of entries. A time window for recall must be defined in the prompt to prevent context overload. The compliance module links implementation standards to inspection items. The prompt must require association with corresponding labeling fields, rather than vague references to compliance. Cost structures vary significantly across condiment categories. Multi-turn interaction must first confirm the target condiment category before retrieving corresponding module data, to avoid mixing parameters from different categories.

Configuration Settings

Configuration ItemRecommended ValueRationale
maxContext8000–12000 charactersCondiment due diligence data includes multi-module details. Longer contexts preserve category restriction and time range parameters across multi-turn interactions
Recall CountTop 6–10 entriesCondiment sales detail data has a large number of entries. Too many recalls cause context redundancy, too few fail to cover core cost and sales data
Similarity Threshold0.75–0.85Field matching accuracy requirements for compliance inspection data are high. Too low a threshold will introduce inspection results from unrelated categories
PARSE_FILE_TIMEOUT_SECONDS300 secondsLarge supermarket POS detail files take a long time to parse. Sufficient parsing time must be reserved
Segment Length1000–1500 charactersCompliance documents for condiments have long paragraphs. Reasonable segmentation improves recall accuracy
UPLOAD_FILE_MAX_SIZE2000 MBCondiment due diligence includes multiple supply chain and sales documents. Larger file upload support is required

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Configuration Errors

  • Symptom: Uncaught exception logs are returned when calling the AI dialogue component. Cause: MODEL_API_BASE and FASTGPT_API_KEY environment variables are not configured correctly, causing an abnormal model call chain.
  • Symptom: Dialogue initiated via Python code does not reference condiment data from the knowledge base. Cause: Knowledge base recall is not enabled in the dialogue configuration, or associated parameters for the corresponding knowledge base are not specified.
  • Symptom: The dialogue page or knowledge base page crashes after deploying version 4.8.20. Cause: A known front-end resource loading conflict exists in this version. Configuration adjustments or version upgrades are required.

How to Verify Successful Configuration

  • Initiate a single-turn test dialogue, enter a due diligence query for a specified category and time range, and verify that the reply associates with corresponding category data in the knowledge base.
  • View the dialogue interaction log to confirm that the context is not abnormally truncated, and meets the context window requirements of the current configuration.
  • Upload a condiment supply chain detail file, and confirm that the parsing process completes normally without triggering timeout errors.
  • View the knowledge base recall log to confirm that the number of recalled entries matches the configured recall rules, and that fields meet category restriction requirements.

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.