What data for this category looks like
Hotel and catering industry research reports originate from public industry association reports, third-party consumer research institution data, regular financial reports of listed catering and hotel enterprises, and OTA platform operation analysis documents. Update cycles differ by report type. Industry overview reports update monthly. Segmented category special reports release quarterly. Enterprise case research reports update alongside financial report deadlines. Document structures usually include overall industry overview, segmented track operation data, regional market distribution, typical enterprise operation models, policy impact analysis, and future trend forecasts. Fields include customer unit price, table turnover rate, number of stores, and sales per square meter. Corresponding units are yuan/person-time, times/day, stores, and yuan/square meter/month.
Constraints imposed on model access and configuration
The multi-source, heterogeneous nature of hotel and catering research reports requires model access links to support parsing of PDF tables, structured financial reports, and semi-structured OTA analysis documents. Corresponding format-specific parsing adaptation rules must be configured. Different report update cycles require distinct full and incremental sync trigger logic. This prevents repeated loading of expired data. Unique business fields for segmented tracks differ from general metrics. Entity extraction field mapping rules must be configured to ensure accurate matching of business requirements during retrieval. Long-form special reports carry context overflow risks. Segment length and recall parameters must be adjusted to fit document characteristics. High-frequency mentions of regional and brand names require custom entity dictionaries to optimize recall accuracy. This avoids recognition biases from general models for segmented industry terminology.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Hotel and catering research reports often contain multi-page tables and long text passages, requiring sufficient time for document parsing |
maxContext | 8000–12000 characters | Segmented long-form research reports must retain complete business logic connections to avoid breaking semantic coherence |
embedding_model | bge-large-zh-v1.5 or 百度embedding-v1 | Adapts to professional terminology in Chinese catering and hotel fields, improving semantic recall accuracy |
retrieval_top_k | Top 6–10 entries | Balances relevance coverage for research report retrieval and model inference load, avoiding interference from redundant information |
classify_model | deepseek-distill-qwen-32b or same-scale lightweight models | Adapts to the speed and accuracy balance requirements of research report classification tasks, reducing inference latency |
UPLOAD_FILE_MAX_SIZE | 500–1000 MB | Supports batch upload and parsing of large-scale industry research report documents |
The parameter values provided on this page are general recommendations that serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Configuring
embedding_modelas百度embedding-v1orollama-deployed bge-large-zh-v1.5returns a 404 error. This occurs when the API gateway address and key permissions for model access are not properly configured, or when the third-party model service has not opened corresponding interface permissions. - Enabling research report classification functionality results in overly varied classification results. This occurs when custom classification tags and entity dictionaries are not configured for hotel and catering segmented business types. General models cannot accurately recognize segmented category characteristics.
- Question classification task execution speed is too slow. This occurs when an overly large model is selected that exceeds current computing power load, and the setup does not adapt to lightweight inference requirements for segmented scenarios.
How to confirm successful configuration
- Upload a single long-form hotel and catering research report document. Check that parsing progress completes without error logs, confirming that document parsing parameters are properly adapted.
- Submit a research report retrieval request. Verify that the number of returned recall entries matches the range set in the
retrieval_top_kconfiguration, confirming that recall parameters are active. - Submit a classification request for a segmented category research report. Verify that classification results match custom tags, confirming that entity dictionaries and classification model configurations are correct.
- Review model inference time logs. Confirm that classification task time meets current business response requirements, confirming that the lightweight model configuration fits the scenario needs.
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.