What the data for this category looks like
Data sources for IT service revenue rate and market daily reports include public securities trading APIs, compliant third-party financial data service providers, and revenue accounting ledgers from internal operations or business systems. Full updates run primarily after daily market close. Intraday abnormal data syncs incrementally on an hourly basis. Document structure follows a standardized format, including these fields: unique product identifier, full product name, daily net value revenue rate, cumulative annualized revenue rate, daily trading volume (lots), total daily trading amount, benchmark index price change for the day. Revenue rate fields use percentage as the unit. Trading-related fields use lots or yuan as the unit.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
Multi-source data requires explicit direction in multi-turn dialogue to prioritize core data from official trading APIs, avoiding non-compliant third-party redundant fields. Daily full updates require mandatory binding of the current dialogue's date parameter in the prompt, ensuring the model accesses the day's latest daily report document. Standardized multi-field structures require clear definition of output field order and units in the prompt, preventing the model from altering formats arbitrarily or adding unincluded content. Intraday incremental syncs require retaining data update timestamps in multi-turn dialogue context, preventing mixing of old and new market data across dialogue time periods.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | Top 10 entries | The core fields of IT service daily reports are limited in number. 10 entries can cover all necessary data and avoid introducing irrelevant content |
Similarity Threshold | 0.75–0.85 | Daily report data has a high degree of standardization. This range can accurately match market data for target products and dates, filtering low-relevance results |
maxContext | 800–1200 characters | The length of a single daily report document is typically in the thousands of characters. This range can fully load core fields without exceeding the context limit of general models |
Dialog History Retention Rounds | First 3 rounds | Multi-turn dialogue only needs to retain core context such as product code and query date. Excessive rounds will interfere with the accuracy of current queries |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Structured daily report document parsing takes relatively little time. This duration can cover network latency and parsing overhead during bulk imports |
The parameter values provided on this page are common 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
- Symptom: Results returned when calling the dialogue interface do not include the daily report data from the specified knowledge base. Cause: The
kbIdsfield was not correctly passed in the request parameters, or the passed knowledge base ID format is invalid. - Symptom: The model repeatedly references expired historical daily report data in multi-turn dialogue. Cause: The current dialogue's date parameter was not bound in the prompt, causing the model to default to calling earlier cached data.
- Symptom: A
413 Request Entity Too Largeerror occurs when parsing daily report documents. Cause: The uploaded daily report document exceeds the limit set by theUPLOAD_FILE_MAX_SIZEconfiguration, and the parameter value was not adjusted according to the document structure.
How to confirm the configuration is correct
- Manually upload a test structured daily report document, check if the knowledge base parsing result includes all preset fields, to confirm the parsing configuration is effective.
- Initiate a query that includes a specific product code and date, verify that the revenue rate data returned by the model matches the fields in the document, to confirm the recall and similarity configuration is effective.
- Initiate 3 consecutive rounds of dialogue, check if the model retains the product and date context from the first two rounds, to confirm the dialog history retention rounds configuration is effective.
- Call the dialogue interface and pass the test
kbIdsparameter, check if the returned results only include content from the specified knowledge base, to confirm the API call configuration is effective.
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.