What the data for this category looks like
Data sources for tourist attraction smart due diligence reports include official scenic spot operation ledgers, filing and public documents from cultural and tourism authorities, third-party passenger flow monitoring data, business lease contracts and compliance qualification documents. Update rhythms vary significantly: operational data such as passenger flow and revenue is updated daily, lease and qualification documents are updated quarterly or annually, and filing documents are only updated after scenic spot rating assessments.
The data structure includes standardized fields and unstructured text. Standardized fields include scenic spot grade, location coordinates, daily passenger trips, quarterly revenue amount, and merchant per-area efficiency, with units of "A-level", "longitude and latitude", "people/day", "yuan", and "yuan/㎡" respectively. Unstructured text includes long-form content such as scenic spot planning plans and compliance rectification reports.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The multiple update rhythms of tourist attraction due diligence data require indexes to support incremental triggering, to avoid resource waste caused by full reindexing. The data includes both structured fields and unstructured text, so support for both semantic vector retrieval and exact matching is required. A single index type cannot cover all retrieval needs. Long-form compliance documents and short-form operational data coexist, so a chunking strategy must balance context completeness and retrieval granularity. This avoids losing key information in long text or over-splitting short entries. Cross-source data association requirements mean indexes must support cross-data-source field binding, to ensure retrieval results can link to complete operational information for the corresponding tourist attraction.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Tourist attraction due diligence reports include long-form compliance documents and short-form passenger flow data. This range balances context completeness and retrieval accuracy |
chunk_overlap | 50–100 characters | Avoid losing cross-segment associations after long text chunking, and adapt to coherent descriptions of tourist attraction business formats |
index_type | Hybrid index (vector + structured) | Tourist attraction data includes structured passenger flow and revenue fields, plus unstructured documents. Hybrid indexes support both semantic retrieval and exact matching |
recall_top_k | Top 8–12 results | Tourist attraction due diligence reports need to cover multi-dimensional data. Too many recalled results increase context load, too few will miss key business format information |
similarity_threshold | 0.72–0.85 | Distinguish similar descriptions of the same tourist attraction business format, avoid recalling irrelevant data with low matching scores |
incremental_index | Enabled | Tourist attraction passenger flow data is updated daily, and lease information is updated quarterly. Incremental indexing reduces repeated calculation overhead |
The parameter values given on this page are all common recommendations used to determine the starting point 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.
Three Common Configuration Mistakes
- Phenomenon: After uploading tourist attraction compliance qualification documents, knowledge base retrieval results are empty. Cause:
incremental_indexis not enabled, and the full index has not completed synchronization updates. - Phenomenon: A large number of irrelevant merchant information is returned when retrieving tourist attraction passenger flow data. Cause:
similarity_thresholdis set too low, and non-associated text with low matching scores is mistakenly recalled. - Phenomenon: The index model cannot be selected to initiate retrieval in version V4.14.3. Cause: The index model channel is not configured in AIProxy, and channel binding for
index_modelis not completed.
How to Confirm Proper Configuration
- Upload a single tourist attraction business lease document, check the chunked results after parsing by the knowledge base, confirm that the chunk size matches the configured range.
- Initiate a retrieval test, input keywords for the tourist attraction's exclusive operational description, check that the returned result fields match the tourist attraction's data characteristics.
- Upload a new monthly passenger flow report, check the knowledge base index update status, confirm that the incremental index triggers normally.
- Check the AIProxy configuration page, confirm that the channels for
index_modelandvector_modelhave been bound.
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.