What the Data for This Category Looks Like
Research report retrieval data for tourism attractions targeting the financial industry primarily comes from public disclosure information of cultural and tourism authorities, official operational announcements of attractions, special research results from third-party cultural and tourism consulting institutions, and operational data from online travel platforms. Two update cycles apply:
- Real-time data such as passenger flow and reservation volume updates daily.
- Research report content such as annual passenger flow and revenue structure updates quarterly or annually. Temporary passenger flow forecast documents are added before statutory holidays.
Each document typically includes fields such as basic attraction information, core business proportion, annual reception scale, tourist profile tags, and policy impact analysis. Standard units of measurement include ten thousand person-times, ten thousand yuan, square kilometers, and similar standardized units.
Constraints on Citation Source and Attribution
The multi-source, layered update characteristics of tourism attraction research reports for the financial industry impose three constraints on the attribution process. First, data with different update frequencies must be paired with corresponding timeliness tags. This avoids confusing quarterly research report passenger flow data with real-time daily reservation data, which reduces the accuracy of financial analysis. Second, the multi-field, multi-unit document structure requires precise matching of field names and measurement units during attribution. This prevents analysis errors caused by incorrect unit labeling. Third, dispersed data sources must clearly mark the source entity in attribution information. This distinguishes the authority hierarchy of data from different channels, ensures the credibility of cited content, and meets compliance requirements in financial scenarios.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity_top_k | Top 6-8 results | Tourism attraction research reports often include segmented business and passenger flow layered data. Too many retrieved results introduce redundant content from unrelated businesses. Too few fail to cover core analysis dimensions. 6-8 results balance retrieval scope and precision. |
score_threshold | 0.55-0.65 | Attraction research report content is structured analytical text with high semantic similarity differentiation. A threshold that is too low introduces irrelevant research report fragments. A threshold that is too high may miss core relevant content. This range fits most attraction research report semantic characteristics. |
max_reference_token | 1200-1500 characters | The core analytical paragraphs of a single attraction research report are around 1000 characters. Setting 1200-1500 characters fully retains the contextual logic of cited fragments, avoiding truncation of key analytical basis. |
reference_mode | "Full source + fragment marker" | Data sources for attraction research reports are dispersed. This mode clearly marks the source entity, update time, and starting position of the cited fragment, to allow easy tracing of specific analytical content. |
update_time_range | Last 12 months | Attraction passenger flow and revenue data have strong timeliness. Research report data older than 12 months has reduced reference value. Limiting the time range filters outdated content. |
data_source_priority | Cultural and tourism department disclosures > Attraction official announcements > Third-party research | The authority hierarchy of attraction research report data is clear. Prioritizing high-credibility data sources improves the reliability of attribution content. |
The parameter values provided on this page are common starting points for configuration. Actual values vary based on material form, data volume, and business rules. Specific cases require targeted analysis. Testing on independent samples is recommended before finalizing settings.
Three Common Mistakes
- Phenomenon: Setting
score_thresholdto 0.4 results in a large number of irrelevant attraction passenger flow data in retrieved results. Reason: Attraction research reports have high semantic similarity differentiation. A threshold that is too low introduces fragments of similar-category research reports unrelated to the target attraction. - Phenomenon: After calling the API to obtain knowledge base corpus, the AI response does not include source identifiers. Reason: The
reference_modeconfiguration is not enabled, or the field for returning attribution information is not specified in the API request parameters. - Phenomenon: Attempting to modify the
similarity_top_kparameter triggers a prompt that the configuration cannot be saved. Reason: The advanced settings page of the corresponding knowledge base is not accessed, or the current account does not have knowledge base configuration permissions.
How to Confirm Configuration Is Successful
- Upload a test attraction research report document, initiate a retrieval query covering attraction passenger flow and business analysis, and check whether the source entity, update time, and starting position of the cited fragment are displayed below the response.
- Adjust the
similarity_top_kparameter, observe changes in the number of retrieved results, and confirm that the configuration takes effect. - Switch test documents from different data sources, verify that the
data_source_priorityconfiguration takes effect, and that content from high-priority data sources is retrieved first. - Set
update_time_rangeto a limited period, retrieve old research reports exceeding that period, and confirm that they are filtered out.
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.