What the Data for This Category Looks Like
Investment research data for tourist attractions comes primarily from official operation ledgers, filings from cultural and tourism authorities, real-time passenger flow monitoring systems, partner merchant agreements, and emergency response plans. Update frequencies vary widely: passenger flow and ticket data refresh daily or in real time. Policy announcements and partner agreements update when changes are made. Emergency plans are revised once per year. Document fields include unique attraction identifier, regional area, peak passenger capacity, per-customer consumption unit price, policy document number, and more. Units include square kilometers, passenger trips, yuan, and others.
Constraints Imposed by Data Characteristics on Vector Models and Indexing
Tourist attraction data characteristics impose multi-dimensional constraints on the vector model and indexing workflow. High-frequency updated passenger flow and ticket data require incremental indexing to avoid excessive time spent on full rebuilds, which would harm real-time access to investment research data. Mixed structured and unstructured documents from multiple sources need vector extraction logic adapted to different fields. For example, numerical passenger flow data must be normalized before vectorization to ensure consistent feature representation. Documents include metadata such as unique attraction identifiers, which must be bound to corresponding fields during indexing to enable precise recall by individual attraction. Some documents contain sensitive operational data, so additional permission filtering rules must be configured during indexing to ensure compliance with data access regulations. Document length varies widely, from short announcements of a few dozen words to thousands of words in emergency plans. Flexible segmentation rules are needed to prevent key information from being truncated.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
INDEX_INCREMENTAL_ENABLE | true | Scenic spot data includes high-frequency incrementally updated passenger flow and ticket data. Full index rebuilds take too long. Incremental indexing only syncs changed content, improving construction efficiency |
CHUNK_SIZE | 800–1200 characters | Scenic spot documents include short announcements (dozens of words) and long plans (thousands of words). This range balances segmentation information integrity and recall accuracy |
RECALL_TOP_K | Top 10–15 results | Investment research for scenic spots requires covering multi-dimensional information including passenger flow, policies, and business formats. Too many recall results increase context redundancy; too few fail to cover complete investment research dimensions |
VECTOR_MODEL_EMBEDDING_DIM | 768 dimensions | Most scenic spot data fields are structured numerical values and short text. 768-dimensional vectors can effectively carry feature information with moderate computational overhead |
PARSE_CHUNK_OVERLAP | 50–80 characters | Segmented long documents for scenic spots need retained overlapping content to prevent key information from being truncated at segmentation boundaries, improving recall coherence |
INDEX_PERMISSION_FILTER | Filter by attraction ID | Scenic spot investment research data is divided by individual attractions. The corresponding attraction identifier must be bound during indexing to enable precise recall by dimension |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing values.
Three Common Misconfigurations
- Symptom: A 60-second timeout occurs when switching the knowledge base index, and a gateway timeout error is returned on the interface. Cause: The incremental indexing function is not enabled. A full index rebuild is performed on the scenic spot knowledge base containing high-frequency updated passenger flow data, and the time exceeds the system default threshold.
- Symptom: The knowledge base only contains a small number of scenic spot documents, but remains in the training or rebuilding state for more than one day without completion. Cause: The incremental indexing switch is not configured, and a full index rebuild is accidentally triggered. Some documents are emergency plans with thousands of words, leading to excessively long processing time for a single batch of segments.
- Symptom: A parameter verification failure is returned when adding indexes in batches, and index ingestion cannot be completed. Cause: The
batch_sizeparameter is not correctly specified in the request body, or the attraction ID metadata field is not bound, resulting in the inability to aggregate indexes by attraction dimension.
How to Verify Correct Configuration
- View the index construction log to confirm that the incremental indexing switch is active, only syncing changed documents without performing a full rebuild.
- Perform a small-scale document upload test, and verify that the segmented character count falls within the preset range, with no obvious key information truncation.
- Initiate a retrieval request to verify that results can be filtered and recalled by attraction ID, and the number of recalled results matches the preset configuration.
- Check the index permission configuration item to confirm that the attraction ID metadata has been bound, enabling permission filtering by individual attraction.
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.