What the data for this category looks like
Hotel and catering financing daily report data comes from in-store POS terminals, supply chain management systems, catering industry filing data from local commercial authorities, and internal enterprise financing docking ledgers. Data is updated daily at midnight with full data from the previous day. Each individual document includes these fields: unique store identifier, store business type, same-day revenue amount, food material procurement expenses, same-day in-store customer traffic, associated financing application ID, approval progress, and credit limit range. Field units are uniformly Renminbi yuan, person-times, and ten thousand yuan. No custom units are used.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The daily updated data source requires multi-turn dialogue contexts to bind to the current day’s data timestamp, preventing calls to expired historical data. The binding relationship between unique store identifiers and financing application IDs requires multi-turn dialogues to automatically associate previously mentioned stores with their corresponding financing applications, avoiding cross-store query errors. The multiple field classifications require prompts to clearly distinguish calling rules between operational data and financing approval data, preventing misclassification of revenue data as credit limits. Differences in field units require prompts to include built-in conversion rules for yuan and ten thousand yuan, avoiding incorrect numerical display. The large number of fields per document requires limiting the total information recalled in a single turn during multi-turn dialogue, preventing context overflow.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
max_history_tokens | 4000–6000 tokens | Adapts to the multi-turn context length of hotel and catering financing daily reports, accommodating dialogue information across multiple stores and fields |
recall count | Top 6 entries | Matches the number of fields per document, limits the total information recalled in a single turn to avoid context overflow |
similarity threshold | 0.75–0.85 | Filters low-relevance store financing data, reducing interference from irrelevant information in dialogues |
rerank return count | Top 3 entries | Focuses on same-day financing and operational data for core stores, simplifying information display in multi-turn dialogues |
prompt_template | Calibrated based on actual testing | Adapts to the financing daily report scenario for the hotel and catering industry, clearly distinguishing calling rules between operational data and financing approval fields |
file_parse_chunk_size | 800–1200 characters | When splitting financing daily report documents, preserves complete binding relationships between stores and financing applications, avoiding information breakage across chunks |
The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After adjusting
max_history_tokensto above 2000 tokens, the large language model output does not reference any knowledge base-recalled financing daily report content. Cause: The matching relationship betweenrecall countand the context window was not adjusted synchronously. Recalled content exceeds the context carrying limit and is automatically truncated, making it unrecognizable by the model. - Phenomenon: Retrieved financing daily report content includes store number and financing application ID fields. Cause: The prompt template does not explicitly require filtering non-business display fields, resulting in identification fields from the original document being returned to the dialogue interface.
- Phenomenon: After custom model deployment, model response results between the dialogue interface and workspace are inconsistent. Cause:
prompt_templateandmax_history_tokensparameters for the two scenarios were not unified, leading to differences in pre-configured model calling rules.
How to Confirm Proper Configuration
- Initiate a multi-turn dialogue, query the same-day revenue and financing progress for two different stores in sequence, and verify that the model automatically associates previously mentioned store information without cross-store query errors.
- View the knowledge base recall log, and verify that the number of recalled contents each time matches the configured
recall count, with no required non-display identification fields included. - Adjust the context window parameter, test continuous multi-turn dialogue, and confirm that the model can retain previously mentioned store and financing application information.
- Initiate the same financing daily report query request in both the dialogue interface and workspace, and verify that the output content and rules are consistent between the two.
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.