What the data for this category looks like
Data sources for hotel and catering research reports include public reports from catering industry associations, operational announcements disclosed by chain catering enterprises, third-party catering consumption survey datasets, and desensitized aggregated data from store POS systems. Regular industry research reports are updated quarterly, store-level operational data is updated weekly, and temporary reports will be added when food material price fluctuations or industry policy adjustments occur. Most documents combine structured tables and analytical paragraphs, including fields such as store identification, revenue amount, average customer spending, table turnover rate, and proportion of food material costs in revenue. The corresponding units for these fields are yuan, yuan/person-time, times/day, and proportional values respectively.
What constraints do these characteristics impose on the knowledge base retrieval and recall link?
The high proportion of structured fields requires retrieval to prioritize precise field matching instead of full-text broad matching, to avoid mixing irrelevant results. Data sources with multiple update frequencies require distinguishing recall priorities between regular research reports and real-time operational data, to prevent old data from overwriting new information. The large variation in document length requires segment length to adapt to different content types: excessively long analytical paragraphs need to be split reasonably, and excessively short structured entries should avoid excessive splitting. Fields with exclusive units require associating unit matching during retrieval, to prevent confusion of results with different valuation calibers.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
RECALL_TOP_K | Top 10 results | Hotel and catering research reports contain multi-dimensional operational data. Too many recall results will interfere with LLM output logic, while too few will fail to cover full-dimensional retrieval needs |
SIMILARITY_THRESHOLD | 0.72–0.78 | There are many research report fields and similar expressions exist. A threshold that is too low will introduce irrelevant results, while a threshold that is too high will fail to recall precisely matched fine-grained data |
PARSE_SEGMENT_LENGTH | 800–1200 characters | Hotel and catering research reports contain both long analytical paragraphs and structured tables. This segment length balances the integrity of table splitting and context coherence |
UPLOAD_INCREMENTAL_TRIGGER | By update timestamp | Store-level data is updated weekly. Triggering incremental uploads by timestamp avoids repeated processing of full historical documents |
FIELD_WEIGHT_RATIO | Revenue:2, Average customer spending:1.5, Table turnover rate:1.2 | Hotel and catering retrieval needs mostly revolve around core operational indicators. Setting higher weights for high-priority fields improves retrieval precision |
PARSE_FILE_TIMEOUT_SECONDS | 90 seconds | Large chain catering research report documents have relatively long length. This duration ensures complete parsing without timeout interruptions |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After importing documents, the page number keeps refreshing and the target page cannot be selected. Cause: Hotel and catering research reports mostly contain multi-page structured tables. The table pagination nodes are not correctly identified during parsing, leading to abnormal pagination parsing logic.
- Phenomenon: API call response delay exceeds 30 seconds. Cause: Incremental update rules are not configured, full retrieval of all historical research report data is performed, and a separate recall index pool is not set for frequently updated store-level data.
- Phenomenon: LLM response content exceeds the scope of the knowledge base. Cause:
PROMPT_CONTROL_MODEis not correctly configured for strict knowledge base matching, or recall results are not properly adapted to context window limits.
How to Verify Proper Configuration
- Upload a test hotel and catering research report document, check whether the parsed segmented content retains the integrity of core operational fields.
- Initiate a retrieval request containing specific operational indicators, verify whether the field weights of the recall results match the preset configuration.
- Test the incremental upload function: upload a research report with an update timestamp later than existing documents, confirm that only the new document is parsed.
- After configuring strict prompt matching, initiate a retrieval request outside the scope of the knowledge base, confirm that the LLM cannot generate content outside the knowledge base.
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.