What this category's data looks like
Data related to specialty chain store profit margins comes from three sources: POS transaction systems of individual chain stores, cost collection modules of headquarters ERP, and regional operation management ledgers. A full operational summary for all stores is generated each day. Full updates complete in the early hours of the next business day. Documents are grouped by administrative region, and sorted by store name within each group. Each record includes these fields: store unique identifier, business address, total daily revenue, total daily direct costs, total daily indirect costs, total daily gross profit, and operation days. Units follow these rules: revenue, costs, and gross profit use Chinese Yuan. Operation days are counted as natural days. No percentage-based metrics are included.
Constraints on Multi-turn Dialogue and Prompting
These data characteristics create clear constraints for multi-turn dialogue and prompt configuration. First, data is layered by region and store. Multi-turn dialogue must explicitly ask for unspecified dimensions to avoid mixing data from different stores. Second, data only includes the latest updated daily summary for the current day. Prompts must limit use to the latest daily report data associated with the current session, and must not reference outdated historical data. Third, no percentage-based metrics exist in the fields. Prompts must avoid automatically generating percentage conversions, and retain original numerical values and units. Fourth, data is collected from multiple systems. Dialogue context must retain store filtering conditions to avoid cross-session dimension confusion.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
maxContext | 25-35 conversation context entries | Specialty chain profit margin conversations often require multi-turn inquiries about stores and regions. Sufficient context preserves filtering conditions, and aligns with the session storage logic of FastGPT 4.10.0 and newer versions. |
contextRecallCount | 8-12 historically associated conversation entries | Specialty chain data is layered by region and store. Excessive context interferes with dimension filtering in current sessions. This range balances context relevance and query efficiency. |
systemPrompt | Only use same-day updated specialty chain store profit margin daily report data, only include fields listed in the documentation, prohibit generating percentage-based metrics, and ask for unspecified store/region dimensions | Matches the field restrictions, update schedule, and layered structure of this category's data, to prevent the model from generating invalid or incorrect content. |
promptTemplate | Preserve original text spaces, match data using full store name/ID, prohibit automatic unit conversion | Resolves issues with prompt recognition of spaces, aligns with unit requirements for data fields, and prevents matching errors caused by splitting store names. |
LOG_RETENTION_DAYS | 30-90 days | Specialty chain operation data requires retention of at least one monthly cycle for review. This range matches user needs for restoring lost logs, and aligns with docker deployment configuration logic. |
apiConversationMode | Persistent session mode | Ensures multi-turn dialogue context is maintained during API calls, prevents session reset on each call, and adapts to cross-terminal query requirements. |
The parameter values provided on this page are common starting points 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 Misconfiguration Issues
- Symptom: After setting
maxContextto 30 in FastGPT 4.10.0 and newer versions, only 2 context entries are displayed in conversation details, and replies fail to associate historical store filtering conditions. Cause: Persistent storage of conversation context is not enabled, or thecontextRecallCountvalue is much smaller thanmaxContext, resulting in only a small amount of historical content being recalled. - Symptom: Prompts fail to correctly identify store names with spaces, such as "Shanghai Nanjing West Road Store", which are split into separate words leading to incorrect data matching. Cause: The prompt template does not explicitly preserve original text spaces, and does not specify matching data using full store names.
- Symptom: Conversation logs are lost when calling the application via API, and previous profit margin query records cannot be reviewed. In docker deployment environments, logs cannot be restored via configuration. Cause: The
LOG_RETENTION_DAYSparameter is not set in the docker configuration file, or the configured retention days are shorter than the business review cycle, resulting in automatic log cleanup.
How to Verify Proper Configuration
- Initiate a multi-turn conversation that includes store name and region dimensions. Check that replies associate with the current session's filtering conditions, and no context loss occurs.
- Enter a store name that includes spaces. Check that the model correctly matches the corresponding store's profit margin data, and no splitting errors occur.
- View the conversation log list. Confirm that saved historical conversation records are not automatically cleaned up, and meet the business review cycle requirements.
- Initiate two consecutive API calls for conversations. Check that the second reply associates with the first round's store filtering conditions, and no session reset occurs.
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.