What the data for this category looks like
Hotel and catering investment research data mainly comes from store operation ledgers, supply chain procurement records, public industry research reports, user review texts, and business district passenger flow monitoring ledgers. Update rhythms vary significantly: store operation data is updated daily or weekly, public research reports are released irregularly alongside industry dynamics, and user reviews are added in real time.
Document structures include structured reports with fields such as store ID, revenue, average customer price, using yuan and person-times as units; semi-structured review texts with rating and consumption time tags; and unstructured industry analysis documents.
What constraints these characteristics impose on the vector model and indexing link
The multi-structure and update differences of hotel and catering data bring multiple constraints to the vector model and indexing link.
Structured reports have clear numerical fields and units. Chunking must retain the semantic association of fields to avoid breaking business logic. Real-time new user reviews and periodically updated operation data require the index to support switching between incremental and full updates, adapting to different data rhythms.
The coexistence of long documents such as industry research reports and short texts such as user reviews requires adapting to the input length limits of vectorization models of different lengths, while avoiding loss of cross-segment business associations when splitting long texts.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_length | 800–1200 characters | Hotel and catering documents include short customer reviews and long research reports. This range balances text semantic completeness and chunk granularity, and adapts to the input length limits of most open-source embedding models |
chunk_overlap_chars | 100–150 characters | Prevents loss of cross-segment business associations after long document splitting, and adapts to semantic continuity of cross-row data such as store revenue and passenger flow |
embedding_batch_size | 32–64 items/batch | Reduces the risk of exceeding embedding rate limits, adapts to the total number of documents uploaded in a single batch for hotel and catering knowledge bases, while ensuring vectorization efficiency |
index_shard_num | Calibrated by cluster node count | Adapts to the data volume of multiple stores and business districts that may exist in hotel and catering knowledge bases, balancing retrieval concurrency and storage overhead |
recall_top_k | Top 10–15 items | Covers multi-dimensional information including operations, reviews, and research reports associated with a store, avoiding missing business-related content from single-dimensional retrieval |
similarity_threshold | 0.72–0.85 | Filters low-related unstructured reviews and structured reports, adapting to the semantic judgment standard of "association" in hotel and catering business |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and testing on relevant samples prior to final setup is recommended.
Three Common Mistakes
- Phenomenon: The knowledge base remains in "Indexing" status for a long time after upload, with no progress updates. Cause: Incremental index trigger rules are not configured, and full index processing times out when handling batch data from multiple stores.
- Phenomenon: Text chunks are lost after setting
chunk_lengthto 3000 characters. Cause: The maximum input length limit of some embedding models is lower than 3000 characters, and ultra-long text chunks cannot complete vectorization and are not properly included. - Phenomenon: Rate limit error is triggered during vectorization, and adjusting
embedding_thread_numdoes not resolve the issue. Cause: Rate limits are caused by platform API call quota restrictions. Adjusting thread count only affects local processing efficiency and does not touch the quota threshold.
How to Confirm Proper Configuration
- Upload a single long document such as an industry research report, and check if the chunking results retain core business fields and units, with no obvious semantic breaks.
- Simulate 100 real-time new user reviews, verify that the index can complete incremental updates within the preset time, and that retrieval results include the new content.
- Run a batch vectorization test, observe whether the platform API call rate meets the preset quota, and no limit error is triggered.
- Retrieve associated data for a specified store, and check that the recall results cover multiple types of documents including operation reports, user reviews, and industry research reports.
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.