What this category’s data looks like
The data for hotel and catering intelligent due diligence reports mainly comes from industrial and commercial public disclosure systems, store operation ledgers, health supervision department public notices, supply chain procurement records, and offline passenger flow statistical reports. Data update cycles vary: store passenger flow and daily revenue data is updated daily, while health rating and industrial and commercial qualification data is updated quarterly or annually. Documents are primarily structured tables, containing fields including store unique identifier, business address, opening time, average daily passenger flow, ingredient purchase unit price, business area, and more. Most field units are such as person-times/day, yuan/kilogram, square meters.
What constraints these characteristics impose on the knowledge base retrieval and recall link
The data characteristics of this category create multiple constraints for the retrieval and recall process. A high proportion of structured fields requires support for field-level precise matching and numerical range retrieval, to avoid result deviations caused by fuzzy matching. Multi-source data has inconsistent update cycles, so incremental synchronization tasks must be configured to distinguish high-frequency and low-frequency data sources, preventing expired information from being returned in recall results. A single due diligence report document usually includes associated data for multiple stores, so chunk splitting must be done by store dimension to avoid cross-store information interfering with retrieval results. Fields include dedicated units, so unit information must be bound during preprocessing to prevent recall failures caused by unit mismatches.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | top 10-15 | Hotel and catering due diligence data contains multi-dimensional structured fields. Too many recalled entries increases context processing load, while too few fails to cover all associated information |
similarity threshold | 0.72-0.85 | Balance precise matching of structured fields and coverage of semantic recall, to avoid missing valid data with an overly high threshold, or introducing irrelevant results with an overly low threshold |
chunk split length | 800-1200 characters | Core field information for a single store’s due diligence data is approximately 500-800 characters. Splitting at this length retains complete field association logic and avoids chunk splitting breaks |
incremental synchronization interval | High-frequency data sources: once daily; low-frequency data sources: once every 7 days | Matches update cycles for high-frequency data such as passenger flow and revenue, and low-frequency data such as health ratings and industrial and commercial information, preventing expired content from being recalled |
field matching weight | 0.3-0.5 | Balances priority between semantic recall and field precise matching, adapting to this category’s high proportion of structured data |
The parameter values provided on this page are 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 it is recommended to test on your own samples before finalizing settings.
Three common errors
- A
quote type erroroccurs when referencing configuration variables, the interface displays format verification failure, and the log returns a400 Bad Requeststatus code. The cause is failure to add a reference identifier in accordance with the structured field naming format of this category, causing the system to fail to recognize the variable type. - When retrieving a query that exactly matches a store’s due diligence data, the corresponding chunk is not recalled. A newly created knowledge base with the same configuration can recall results normally. The phenomenon is zero retrieval results or return of irrelevant store data. The cause is that the original knowledge base’s
chunk split lengthconfiguration does not match the store data’s field length, causing core field association information to be lost during chunk storage. - No feedback appears when adding new due diligence data to the knowledge base during a conversation, and the new entry is not displayed in the interface. The cause is failure to enable the
incremental synchronization trigger switch, or failure to configure the correct data source update path, meaning new content is not synchronized to the knowledge base index.
How to confirm configuration is set correctly
- Upload a single store’s due diligence document, view the
chunk previewinterface, and confirm split chunks retain complete store field information without breaks or loss. - Enter a test query containing structured fields, verify the
similarity scoreof retrieval results falls within the configured threshold range, and adjust parameters to match retrieval needs. - After configuring the incremental synchronization task, wait for the preset interval, view the knowledge base’s
update log, and confirm high-frequency and low-frequency data sources completed synchronization as planned. - Test the variable reference function, enter a query containing store fields, confirm the interface does not trigger format errors, and returned results include correct field information.
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.