What Data for This Category Looks Like
Data sources for tourist attraction research reports include monthly scenic area operation reports publicly released by local cultural and tourism bureaus, quarterly survey documents issued by national cultural and tourism industry associations, special research reports from third-party cultural and tourism consulting institutions, and annual operation reports disclosed by scenic areas themselves. Update cadence varies by source. Official public data updates monthly, third-party research reports update quarterly or semi-annually, and scenic area annual reports release annually. Document structures typically include modules such as passenger flow statistics, revenue composition, facility operation and maintenance, surrounding business linkage, and policy compliance requirements. Field units mostly use common metrics such as person-times, yuan, and square meters. Some documents include segmented operation data broken down by time period and sales channel.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Decentralized access to multi-source data requires configuring multiple workflow nodes to pull documents from different sources separately. This prevents timeouts caused by a single node processing all data sources. Different update cycles require matching trigger rules. Using a unified full synchronization frequency will generate duplicate data or miss recently updated documents. Segmented fields in documents need to be mapped to a standard format. Otherwise, subsequent AI Q&A nodes cannot accurately identify similar data from different sources. Time-segmented data requires configuring time range filter nodes in the workflow. This prevents recalling historical data outside the query range, which would interfere with final Q&A results.
Configuration Settings
| Config Item | Suggested Value | Rationale |
|---|---|---|
recall count | Top 8-12 entries | Scenic area research reports have lengthy individual content. Too many recalled entries will exceed the context window limit. Too few will miss core operation data |
similarity threshold | 0.72-0.85 | Scenic area research reports have highly recognizable exclusive keywords. A threshold that is too low will mix in general cultural and tourism industry documents. A threshold that is too high will miss relevant segmented research reports |
incremental sync cycle | Match by data source type: official data daily, third-party reports quarterly, annual reports annually | Different data sources have different update frequencies. Matching cycles avoids duplicate pulls or missed latest data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Scenic area research reports include multi-page operation reports and segmented data. Parsing takes longer than general documents |
maxContext | 8000-12000 characters | Needs to cover content from three core modules: passenger flow, revenue, and facilities. Ensures AI nodes can obtain complete context |
field mapping rules | Unify "visitor volume" to totalvisitors, map "ticket revenue" to ticketrevenue`` | Unify data formats to facilitate subsequent nodes to uniformly process research report data from different sources |
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 values.
Three Common Misconfigurations
- Phenomenon: After the workflow runs, the knowledge base search returns results that do not match scenic area-specific research report content. Logs show a large number of general cultural and tourism documents are recalled. Cause: The precise filtering of
similarity thresholdis not configured, or the threshold is set too low, causing irrelevant documents to be recalled. - Phenomenon: After calling the MCP tool to obtain scenic area passenger flow data in the workflow, subsequent nodes cannot read the returned value, and the variable field is empty. Cause: The output of the MCP tool is not bound to the workflow's context variables, causing data transfer interruption.
- Phenomenon: The content output by the AI conversation node mixes context storage and AI reply information, causing subsequent processing nodes to fail to correctly read the retrieved research report content. Cause: The usage scenarios of the two node outputs
context storageandAI reply outputare not distinguished. The reply content is incorrectly used as the input for subsequent processing.
How to Confirm Proper Configuration
- Run a single-step test of the workflow to verify that the recall results of the knowledge base match the input scenic area research report query keywords as expected. Adjust the values of corresponding configuration items.
- Trigger an incremental sync task to check that the number of synced documents matches the number of documents updated by the data source, with no duplicates or omissions.
- Check that the system prompt word of the AI node matches the preset scenic area research report Q&A rules, and does not include additional irrelevant requirements.
- View the workflow execution logs to confirm that the returned value after the MCP tool call is correctly bound to the input variables of subsequent nodes, with no missing fields.
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.