What Data for This Category Looks Like
Data sources for hotel catering yield and daily market reports include store operation management systems, aggregated payment interfaces, and online booking channel data interfaces. Data is divided into two categories: Complete daily reports for the previous natural day are generated and updated at 2 AM daily. Real-time passenger flow and revenue snapshots are synchronized every 30 minutes. Documents use structured CSV or JSON format, and include the following fields: unique store identifier, store business category, daily operating hours, in-store customer count, takeout delivery order count, total revenue amount, per capita consumption amount, available guest room count, table turnover times during daily dining hours. Corresponding units are person-times, orders, yuan, yuan/person, rooms, times.
Constraints for Multi-turn Dialogue and Prompting
This category’s data has two types: T+1 fixed daily reports and real-time snapshots. The difference in update rhythms requires clear specification of the data time range in multi-turn conversations. This avoids confusion between historical daily reports and temporarily collected real-time data. The data includes multiple fields related to hotel guest rooms and restaurant dine-in services. Users must be guided to clarify the specific query segment during the initial conversation stage. This prevents returning irrelevant vacant room count or table turnover data. Single daily report documents have a large number of fields. If a user adds query conditions during multi-turn conversations, the field scope of the current context must be verified first. This avoids field matching errors caused by context overflow.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Hotel catering daily reports contain multiple segmented fields. Multi-turn conversations need to retain complete historical query conditions and data results, to avoid field omissions caused by context truncation |
rag_top_k | Top 6–8 entries | A single daily report has a large number of structured fields. A sufficient number of associated data entries must be retrieved to cover core query dimensions such as store, time period, and revenue |
relevance_threshold | 0.75–0.85 | Filter low-relevance historical data, avoid mixing irrelevant store data in multi-turn conversations, while retaining sufficient valid context |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large daily report documents takes a long time, to avoid document parsing failure caused by timeout |
multi_round_turn_limit | 12–15 turns | Hotel catering queries usually involve multi-dimensional month-over-month and year-over-year analysis. Limiting the number of turns avoids logical confusion caused by context overflow |
response_max_tokens | 4000 characters | Control the length of single-round replies, to ensure users can fully view the analysis results of yield and market data |
The parameter values provided on this page are common recommended starting points 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 Misconfigurations
- Phenomenon: After configuring the proxy, model thinking process text appears in the reply, and the content is not hidden in the historical conversation. Cause: No instruction to hide the thinking process was added to the system prompt, or the corresponding shielding switch was not enabled in the proxy configuration.
- Phenomenon: After adding a revenue month-over-month query condition in a multi-turn conversation, the returned results include irrelevant vacant guest room data. Cause: The specific query segment was not clearly defined in the initial prompt, causing the model to confuse fields related to hotels and catering.
- Phenomenon: A
504 Gateway Timeouterror occurs when calling the data parsing tool. Cause: The value ofPARSE_FILE_TIMEOUT_SECONDSwas not adjusted to match the parsing time required for large daily report documents.
How to Verify Successful Configuration
- Initiate a conversation with multi-dimensional queries, verify that the reply only includes data within the specified time range and specific segment.
- View the historical conversation record, confirm that the model's thinking process is not displayed in user-visible conversation content.
- Submit a daily report document that exceeds the preset document size threshold, verify that parsing completes successfully without timeout errors.
- Add multi-round query conditions, verify that the context fully retains historical query parameters and data results.
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.