What the data for this category looks like
Hotel and catering financial report data primarily comes from store POS systems, third-party payment transaction records, ingredient purchase ledgers, and human resources salary reports. Data update cadence follows three levels: store-level revenue and cost details are updated daily. Monthly summary reports are generated by the 5th of the following month. Quarterly financial reports are compiled within 15 days after the quarter ends.
Document structure centers on store dimensions, splitting revenue items for catering categories including full-service restaurants, fast food, tea shops, and others. It also splits cost items including ingredient purchases, labor salaries, utilities, marketing expenses, and more. Fields include store ID, number of customer visits, average customer spending, purchase batch count, and others. Primary units are yuan, customer visits, and batches.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-dimensional, fine-grained nature of hotel and catering financial reports creates clear constraints for multi-turn dialogue and prompt configuration.
First, the split structure across multiple stores and categories requires prompts to explicitly limit query dimensions in each dialogue turn, to avoid mixing data across stores. Second, the different data update cadences require the dialogue flow to distinguish between real-time operational data and historical financial report retrieval logic. Prompts must label the time range of referenced data. Third, the large number of fields with hierarchical relationships requires multi-turn dialogue to gradually guide users to clarify specific query items—for example, confirming the store first before narrowing down to a cost type—to reduce ineffective retrieval. Additionally, scattered data sources require prompts to prioritize using uploaded structured financial report documents, rather than fragmented business transaction data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the detailed content of multi-store, multi-category hotel and catering financial reports, retains dimension-limiting context across multi-turn dialogues |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parameter logic of fastgpt-v4.9.0. Quarterly financial reports may include monthly summary files from multiple stores, leading to longer structured data parsing times |
RECALL_CHUNK_SIZE | 800 characters | Ensures a single financial report detail contains complete revenue or cost items, and segmentation preserves field relevance |
RECALL_TOP_N | Top 6 entries | Balances reference data volume for multi-store comparison and multi-category analysis, avoids retrieving redundant information |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-relevance fragmented business transaction data, prioritizes matching core fields of structured financial reports |
WORKFLOW_FORM_DISPLAY_CONDITION | Triggered based on dialogue context | In multi-turn dialogue, dynamically display corresponding form input items according to dimensions such as the store and cycle specified in user queries |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom:
Request Timeouterror appears during dialogue, with status code 504. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and document parsing or large model call time exceeds the default threshold. - Symptom: Configured form inputs in the workflow do not appear in the dialogue interface, and interactive content is empty. Cause:
WORKFLOW_FORM_DISPLAY_CONDITIONis not set to trigger based on dialogue context, or form fields are not bound to user query dimensions. - Symptom: Retrieved results from uploaded financial report documents are insufficient in number, and cannot cover the store range specified in the user's query. Cause:
RECALL_TOP_Nis set too low, orSIMILARITY_THRESHOLDis set too high, which filters out some relevant store detail data.
How to confirm the configuration is correct
- Upload a structured Excel file of a quarterly financial report, launch a multi-turn dialogue, sequentially query the daily revenue and quarterly costs of a specified store, and check whether the returned results include complete detailed items for the corresponding dimensions.
- Launch two dialogues with a 10-minute interval. The first queries the revenue of a specific tea shop category, the second queries the labor costs of that store. Check whether the dialogue context retains the store and category limiting conditions from the first query.
- View system operation logs, confirm that document parsing time does not exceed the threshold set by
PARSE_FILE_TIMEOUT_SECONDS, and there are no timeout error records. - Configure a store selection form in the workflow, enter a specified store name when launching a dialogue, and check whether the dialogue interface automatically displays the cost breakdown form fields for that store.
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.