What the Data for This Category Looks Like
Tourist attraction financial report data primarily comes from publicly disclosed periodic reports of attraction operating entities, internal operation ledgers, and passenger flow statistical reports. Update cycles fall into two categories: quarterly public financial reports and monthly internal operation data. Document structures include fields such as revenue breakdown (ticket, catering, accommodation, cultural and creative, and other segments), passenger flow headcount, labor and operation and maintenance costs, cash flow, and liability details. Core measurement units are ten thousand yuan and passenger trips. Some data includes a cross-cycle business association analysis module.
Constraints on Vector Models and Indexing
The multi-dimensional detailed categories, differentiated update cycles, and mixed measurement units of tourist attraction financial reports impose multiple constraints on the vector model and indexing process. A single financial report document contains multiple business segments and has a relatively long overall length. It is necessary to reasonably control the segmentation granularity to avoid semantic fragmentation. Public financial reports and internal operation data have significantly different update cycles. Differentiated incremental index triggering rules need to be configured. Different business segments use different measurement units. Field metadata must be retained during indexing to avoid semantic confusion. Some cross-segment association data also requires support for cross-paragraph semantic recall.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Financial reports include multiple business segments. This range preserves semantic integrity for individual segments and prevents loss of associated information due to excessive segmentation |
chunk_overlap | 100–150 characters | Financial reports have frequent business associations across paragraphs. The overlap length ensures semantic coherence and prevents recall gaps |
top_k | Top 6–8 results | Associated information for attraction financial reports is scattered across multiple segments. An appropriate number of recall results covers the full range of business dimensions required for complete analysis |
index_update_strategy | Trigger incrementally by update time | Public financial reports and internal data have different update cycles. Incremental updates reduce server load and improve indexing efficiency |
embedding_model | bge-large-zh-1.5 | This model adapts to Chinese financial report professional terminology. Its embedding dimension is compatible with most mainstream retrieval frameworks, ensuring semantic recall accuracy |
similarity_threshold | 0.72–0.78 | Financial report terminology is highly specialized. This threshold filters low-relevance non-business text and retains valid recall results |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: A single financial report file remains in the "indexing" state for a long time with no progress updates. Cause: The
chunk_sizeparameter is not configured correctly, theUPLOAD_FILE_MAX_SIZElimit is exceeded, or there are unclosed rich text formats in the document, causing parsing blockage. - Phenomenon: Vector models in different deployment environments cannot reuse the same index document. Cause: Vectors are not generated using a unified
embedding_model. Different models have different embedding dimensions or semantic encoding logic, leading to inability to match vector spaces during retrieval. - Phenomenon: Results are not returned according to preset priorities during cross-knowledge base question answering. Cause: The
knowledge_base_priorityconfiguration switch is not enabled, or priority weight parameters are not bound to each knowledge base.
How to Confirm Correct Configuration
- Upload a financial report fragment from a single business segment. Check the segmentation results to confirm that each segment retains complete business semantics, with no truncation or redundant concatenation.
- Trigger an incremental indexing task. Check the progress log to confirm that only newly added or modified financial report data is updated, and no full documents are processed repeatedly.
- Send a cross-knowledge base question answering request. Verify that retrieval results preferentially return content from knowledge bases with higher preset priorities, in line with business call rules.
- Call the vector generation interface. Check that the generated vector dimensions match the currently deployed
embedding_modelparameter, ensuring cross-environment reusability.
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.