What the data for this category looks like
Data sources for hotel and catering intelligent due diligence reports include industrial and commercial registered licenses, store operation ledgers, supply chain procurement records, offline customer review summaries, and more. Data update cycles vary: store business licenses and food business permits are updated quarterly, monthly operating revenue is archived by natural month, and supply chain procurement data is synchronized weekly. A single due diligence document usually contains multiple types of sub-files, with a structure divided into license verification page, operating data page, supply chain details page, and risk reminder page. Fields include "average daily customer traffic per store" (unit: person-times), "ingredient procurement unit price" (unit: yuan/kg), "license validity period" (unit: days), "business area" (unit: square meters), and more. Some unstructured customer review data requires field extraction.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Multi-source and heterogeneous data sources require multi-turn dialogue to first clarify the specific data type being queried, to prevent the model from confusing different categories of documents. Data with different update cycles requires specifying a time range in the prompt. For example, when retrieving the latest monthly operating data, the time limit of "last 30 days" must be clearly marked. The specificity of fields and units requires mandatory matching of exclusive field names and units in the prompt, to prevent the model from generating outputs that do not comply with industry standards. The lengthy document structure requires retaining sufficient context during multi-turn dialogue, to avoid losing prior query requirements during subsequent reasoning.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Hotel and catering due diligence data is mostly spliced from multiple documents, and context for multi-turn queries must be retained to avoid losing prior requirements mid-process |
json_schema | Defined in the format {"store name":"string","average daily customer traffic":"number","license status":"string","procurement unit price":"number"} | Due diligence reports require structured output to match import requirements of downstream reporting tools |
similarity_threshold | 0.72–0.85 | Hotel and catering have numerous fields with special units, so matching accuracy must be improved to avoid confusing fields like "person-times" and "revenue" |
recall_top_k | Top 7–9 results | Hotel and catering data has numerous scattered fields, so a sufficient number of matching documents must be recalled to avoid missing key information |
workflow_debug_timeout | 600 seconds | Due diligence data requires cross-document recall and multi-round retrieval, so sufficient time must be reserved for the full workflow during debugging |
prompt_template | "First confirm the hotel and catering store type to be queried, then retrieve data of the corresponding category in sequence, and the output must strictly follow the specified JSON format" | Adapt to pre-guidance for multi-turn dialogue, clarify query order and format requirements |
The parameter values provided on this page are common starting points for configuration setup. 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 Misconfigurations
- Empty dialogue response with no clear error log. Cause: Failure to explicitly specify hotel and catering-specific field units in the prompt, resulting in retrieved documents not matching query requirements.
- Workflow runtime significantly exceeding debug phase duration. Cause: Failure to limit the reasonable value range of
maxContext, with excessive redundant context accumulated during multi-turn dialogue, leading to increased model inference time. - Output JSON format does not meet preset standards, with extra fields or incorrect data types. Cause: Incorrect configuration of the
json_schemaparameter, or failure to enforce strict adherence to the schema format in the prompt, resulting in the model generating non-standard content.
How to Confirm Proper Configuration
- A complete multi-turn dialogue query can be triggered, context snippets in the conversation history reviewed, and prior query requirements confirmed to be fully retained.
- A single test output can be generated, the output content compared with the
json_schemadefinition, and field names and data types checked for compliance. - The workflow can be run and runtime records viewed, compared with debug phase duration, and delays caused by unnecessary context accumulation confirmed to be absent.
- Data for a single hotel and catering-specific field can be retrieved, the original document and output content compared, and field units confirmed to comply with industry standards.
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.