What This Category of Data Looks Like
Hotel and catering investment research data originates from hotel PMS systems, catering POS terminals, supply ledgers, publicly available industry association operation data, and store menu ledgers. Data updates follow three patterns: Real-time operational data including store passenger flow and average customer spending updates daily. Dynamic data such as menu price changes and supplier quotes updates on an irregular basis. Industry macro operational data updates monthly. Document structures include store unique identifiers, timestamps, segmented operational fields, and supply chain-related fields. Field units include person-times, yuan per person, yuan per kilogram, hours, and other units. The data covers full-link information from front-end store operations to back-end supply chains.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Daily updated real-time operational data requires multi-turn dialogue to limit the time range and turn count of historical context. This prevents expired information from interfering with investment research judgments. The multi-dimensional segmented field document structure requires prompt engineering to clearly specify dimension boundaries for data extraction. This prevents information confusion between different fields. Cross-module data association requirements require multi-turn dialogue to retain anchor information such as store and time in context. This avoids repeated inquiries about basic parameters. Dynamically updated menu and quote data requires each dialogue to recall the latest knowledge base slices in real time. This avoids reliance on static caches.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
historyKeepTurns | 3–5 turns | Hotel and catering investment research data mostly consists of daily updated real-time operational data. 3–5 turns retains valid context while avoiding redundant expired information |
recallTopK | Top 6–8 entries | Investment research documents contain multi-dimensional segmented fields. A sufficient number of candidate segments must be recalled to cover data across dimensions such as ingredient costs, passenger flow, and supply chains |
similarityThreshold | 0.72–0.80 | Filters low-match expired inventory, historical menus, and other irrelevant documents, ensuring recalled content matches the current investment research topic |
promptTemplate | "Please answer user questions based on the following hotel and catering investment research data, combined with the store and time range from the context: {context}" | Clearly specifies data dimensions and context association rules, adapting to the multi-field document structure |
apiTimeout | 15–20 seconds | Queries for supply chain data and real-time operational data may involve cross-system calls. Reserve sufficient timeout time to avoid dialogue interruptions |
maxPromptTokens | 8000–10000 | Adapts to multi-dimensional document segments spliced during multi-turn dialogue, avoiding exceeding the model context window limit |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The model returns an authentication error. Third-party logs show the request carries the fixed string "fastgpt" as the token. Cause: The API key is not configured correctly, or the token parameter is incorrectly overwritten at the proxy layer.
- Symptom: Context turn counts are inconsistent across model nodes in multi-turn dialogue. Historical records become chaotic when accessing the same ID multiple times. Cause: The
historyKeepTurnsparameter is not uniformly configured across workflow nodes, leading to conflicting context retention rules. - Symptom: Dialogue results contain large amounts of irrelevant content to the current investment research topic, such as expired store renovation records. Cause: The
similarityThresholdis not set to filter low-match documents, or the prompt template does not limit the data time and dimension ranges.
How to Verify Correct Configuration
- Initiate a test dialogue with a request covering multi-dimensional data, check if the returned results cover relevant fields such as passenger flow, ingredient costs, and supply chains.
- Initiate multiple consecutive dialogue requests with the same ID, check if the context is retained according to the configured turn count, and no expired data is introduced.
- View API call logs, confirm that the request carries a token matching the configured value, and no incorrect replacement has occurred.
- Export dialogue history, check that only content relevant to the current investment research topic is included, with no redundant invalid data.
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.