What This Category's Data Looks Like
Data for this category comes primarily from store operation systems, including menus, member consumption records, in-store time slot statistics, and user review snippets from third-party review platforms. Menu data includes fields such as dish category, name, selling price, ingredient composition, and applicable meal periods. Selling price uses yuan as the unit, and time slot uses hours as the unit. Marketing campaign materials include copy, launch channels, and validity period. Validity period uses days as the unit. Data update rhythm varies by business scenario: Menus are updated when new dishes launch or quarterly adjustments are made. Member data is synced in real time. Temporary marketing campaign materials are updated temporarily when the campaign launches. Review comments are mostly short text snippets, ranging from tens to hundreds of characters in length.
How These Characteristics Impose Constraints on Model Integration and Configuration
High-frequency menu updates require the knowledge base sync cycle to align with business rhythms, preventing generation of outdated marketing content. Marketing materials include both structured fields and unstructured copy, so the model must handle both structured parsing and natural language generation. Appropriate context window configuration is required. Real-time data such as in-store time slots and customer unit price requires access to real-time APIs. The timeout setting for model calls must adapt to fast response requirements. Short text snippets from review comments need matching chunking parameters to avoid truncating key information. Additionally, scattered data across multiple stores requires configuring multi-source recall rules to ensure coverage of business information from different stores.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_DOC_CHUNK_SIZE | 800–1200 characters | Menu entries and marketing copy for hotel and catering businesses mostly fall within this length range, preventing truncation of dish descriptions and campaign rules |
KNOWLEDGE_SYNC_INTERVAL | 1 hour (can be adjusted to 15 minutes during periods of intensive new dish launches) | Regular menu update frequency is quarterly or monthly. Shortening the sync cycle for temporary marketing campaigns ensures content timeliness |
MAX_CONTEXT_TOKENS | 8000–16000 | Generating marketing content requires combining menus, campaign rules, and historical user feedback. Sufficient context is needed to support logical coherence |
RECALL_TOP_N | Top 6 entries | Customer groups in the hotel and catering industry have diverse needs. A small number of precise recalls can cover most user consultation scenarios |
API_REQUEST_TIMEOUT | 30 seconds | Real-time in-store data queries require fast responses. Excessively long timeouts will negatively impact user experience |
VECTOR_SIMILARITY_THRESHOLD | 0.72–0.78 | Filters low-relevance menu entries and review snippets, avoiding invalid recalls that interfere with generation results |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Issue: A
413 Request Entity Too Largeerror occurs when uploading menu files. Cause: TheUPLOAD_FILE_MAX_SIZEparameter is not adjusted. The default value cannot accommodate menu files that combine dish images and long text descriptions. - Issue: The first AI conversation response delay exceeds 3 seconds, while subsequent conversations run at normal speed. Cause: No model warm-up mechanism is configured. The cold start time of the external API is not avoided, which does not meet the first-packet response requirements for real-time hotel and catering consultations.
- Issue: Generated marketing copy does not include correct dish selling prices. Cause: Structured field recall is not enabled. Only unstructured menu descriptions are recalled, and accurate price field information is not associated.
How to Verify Correct Configuration
- Upload a menu file containing complete dish information, and check that the parsed segments retain core fields such as dish name, selling price, and ingredients, with no obvious truncation.
- Initiate a consultation covering store location, dish prices, and campaign rules, and check that the first response time meets the preset timeout requirements.
- Compare generated marketing copy with original menu data, and confirm that price and ingredient information referenced in the copy matches the original data.
- Add a new dish and adjust the sync interval, then check if the new dish information is automatically updated in the knowledge base list to confirm that the sync process works correctly.
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.