What data for this category looks like
Tourist attraction research report data primarily comes from public statistics released by cultural and tourism authorities, operational reports disclosed by scenic spot official channels, and on-site survey data from third-party cultural and tourism monitoring institutions. The core update cycle is quarterly. Temporary supplementary documents are released during statutory holidays, major cultural and tourism events, or sudden passenger flow changes. Typical document modules include scenic spot overview, time-split passenger flow statistics, revenue composition, surrounding business layout, policy impact analysis, and more. Standard metrics include passenger volume (unit: ten thousand person-times), daily revenue (unit: ten thousand yuan), land area (unit: square kilometers). Some detailed research reports also include split data for sub-scenic spots.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering?
The update frequency and multi-dimensional field characteristics of tourist attraction research reports create multiple constraints for multi-turn dialogue and prompt configuration. The quarterly update requirement means prompts must clearly indicate the latest data release cycle to prevent users from accessing outdated information. The multi-field structure requires multi-turn dialogue to support users in gradually refining query dimensions—for example, first querying overall passenger flow, then following up with revenue data for sub-scenic spots. The long-text characteristic requires the context window to retain key data from earlier retrieval sessions while limiting interference from redundant history to avoid the model mixing up retrieval results from different turns. Additionally, some research reports contain bulk tabular data, so the retrieval process must accurately match the association between fields and values.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single tourist attraction research report text is lengthy, and multi-turn dialogue needs to retain key data such as earlier retrieved passenger flow and revenue to avoid context loss |
recallTopK | Top 6–8 results | Tourist attraction research reports contain multi-dimensional data; excessive recall will introduce redundant information, while insufficient recall will miss retrieval results for detailed fields |
similarityThreshold | 0.72–0.78 | Detailed fields of tourist attraction research reports (such as passenger flow time periods, business format proportions) have high semantic similarity, so balance between precision and recall range is needed |
chunkSize | 1200–1500 characters | Paragraphs of tourist attraction research reports often contain associated data (such as quarterly passenger flow + corresponding revenue); overly long chunks will split associated information, while overly short chunks will lose context |
maxConversationHistory | Previous 5–7 turns | Multi-turn queries for tourist attraction research reports mostly focus on specific dimensions (such as passenger flow changes, policy impacts); excessive history will interfere with the precision of current retrieval |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Tourist attraction research reports mostly contain bulk passenger flow data tables, which take longer to parse, so avoid interrupting parsing tasks due to timeout |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis, and testing on local samples is recommended before finalizing settings.
Three Frequently Made Configuration Mistakes
- Issue: After deleting a single dialogue turn, the scenic spot passenger flow field in the corresponding retrieval log is empty, and previous query content cannot be traced back via historical records. Cause: The
conversationHistoryPersistenceparameter is not enabled, and dialogue history is bound to retrieval logs for deletion, resulting in loss of context-associated data. - Issue: When referencing scenic spot revenue data from multi-turn dialogue in a prompt, the model returns results that do not match the previously queried scenic spot. Cause: Context information from multi-turn dialogue is not explicitly anchored in the prompt, causing the model to mix up retrieval objects from different turns.
- Issue: After uploading a screenshot of scenic spot passenger flow statistics to a dialogue node, the model prompts that it cannot provide image content. Cause: The
imageOcrEnablesub-configuration under themultimodalEnabledparameter is not enabled, and the image parsing task is not bound to the research report retrieval node.
How to Verify Correct Configuration
- Initiate a query for quarterly passenger flow of a specified scenic spot, delete that dialogue turn, then check if the retrieval log retains the basic data fields of the scenic spot to confirm that the dialogue history persistence configuration is effective.
- Add an instruction to reference multi-turn dialogue context in the prompt, initiate two consecutive research report queries for different scenic spots, and verify that the model can distinguish the retrieval scope of each turn and return correct data for the corresponding scenic spot.
- Upload a screenshot of scenic spot passenger flow statistics to a dialogue node, check if the model can extract numerical information from the image, and verify that the multimodal configuration and binding of the image parsing task to the retrieval node are correct.
- Adjust the
recallTopKparameter to different values, initiate a query containing multi-dimensional fields, and verify that the number of returned results matches the value range of the current configuration.
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.