What data for this category looks like
Data related to tourist attraction revenue yield comes primarily from park ticketing systems, offline POS consumption terminals, online booking platforms, and park passenger flow counting devices. Data update rhythms fall into two categories: core metrics such as daily total revenue and average order value are updated on a natural day basis. Real-time passenger flow and per-hour consumption data are updated hourly.
Each individual data entry includes the unique attraction identifier, statistical date, total revenue, total visitor count, category-specific revenue (tickets, catering, amusement rides), and average order value. Units are RMB yuan and passenger trips.
Constraints imposed on multi-turn dialogue and prompt engineering by these characteristics
The two update frequencies (daily and hourly) require multi-turn dialogue to guide users to clarify the time granularity of their query. Prompts must mandate a specified statistical period to avoid returning mixed datasets.
The multi-field structure of category-specific revenue requires prompts to proactively ask for the specific query category if the user does not specify one, to avoid returning vague aggregate data.
Different data sources correspond to different tool calls. Multi-turn dialogue must match the corresponding data interface based on user needs. Prompts must also clearly specify field units to prevent output with mixed currency units.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Multi-turn dialogue for attraction revenue yield needs to retain multiple conditions such as time range and query category. This range covers 3-5 valid interactions and avoids context overflow. |
maxToolCalls | 3 times | Single-round queries need to call two data sources: ticketing and consumption. 3 retries cover common network fluctuation errors and avoid infinite loops. |
toolCallTimeout | 60 seconds | Attraction data requires aggregation of multi-dimensional revenue information. 60 seconds covers normal data pulling and calculation durations, avoiding timeout interruptions. |
systemPromptTemplate | Fixed template: As an attraction revenue query assistant, first confirm the user's query time granularity (daily/hourly) and specific category, then call the corresponding tool to obtain data, and annotate units in returned results | This template guides the interaction logic of multi-turn dialogue, clearly constrains the output format of prompts, and adapts to the multi-dimensional characteristics of attraction data. |
recallTopK | Top 3 entries | There are many similar data entries in the attraction knowledge base. Recalling the top 3 entries focuses on core revenue metrics and filters irrelevant information interference. |
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
- Phenomenon: Returns 400 status code (no body) when calling MySQL tools related to attraction data. Cause: Did not specify attraction-specific query table names and fields in tool configuration, causing the parameter format received by the interface to not meet backend verification rules.
- Phenomenon: Cannot switch models or call online tools to obtain real-time passenger flow data in the multi-turn dialogue interface. Cause: Did not configure model switching nodes and tool trigger controls in the workflow, or the system prompt did not grant call permissions for corresponding tools.
- Phenomenon: Cannot clear historical dialogue records after local Docker Compose deployment. Cause: Did not correctly mount the storage volume for dialogue records, or did not execute data cleaning instructions inside the container, causing historical data to persist.
How to Confirm Proper Configuration
- Initiate a multi-turn dialogue including time range and query category, check whether the system proactively asks for unclear information, and confirm that the interaction logic matches the configured prompt template.
- Call tools related to attraction data to obtain test data, check whether the returned fields and units meet preset requirements, and confirm that the timeout and retry configurations for tool calls take effect normally.
- Try to initiate queries with different models, check whether the interface provides model switching options, and confirm that the model configuration items in the workflow have been correctly deployed.
- Execute the dialogue record cleaning operation for local deployment, check whether historical data in the storage path has been cleared, and confirm that the storage mounting logic of the deployment configuration is correct.
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.