What the Data for This Use Case Looks Like
Smart due diligence reports for tourist attractions draw data from official cultural and tourism department filing records, the attraction’s own ticketing and passenger flow systems, surrounding business operation data, and publicly available tourist review text. Data update frequencies vary: Operational data such as passenger flow and revenue updates daily. Information such as business adjustments and rating changes updates weekly or monthly. Tourist reviews are added in real time. Document structures include structured tables (such as daily passenger volume and single-day revenue statistics), semi-structured weekly operation reports, and unstructured tourist comments and inspection records. Fields include scenic spot level, daily passenger volume, safety inspection ID, business proportion, and more, with corresponding units such as person-times, yuan, and percentage.
Constraints Imposed on Model Access and Configuration
These characteristics impose constraints on the model access and configuration link. Multi-source, heterogeneous data formats require the access link to support parsing and adaptation of multiple file types, preventing field loss from single parsing rules. Frequently updated operational data requires configuring timed synchronization trigger parameters to match the attraction’s data update rhythm. Fields have clear units and exclusive identifiers, so model access must retain metadata information to ensure traceability of data in due diligence reports. The length of unstructured tourist comments varies widely, requiring adjustment of context segmentation parameters to fit the input window limits of the model. Sensitive safety inspection data also requires configuring access permission verification parameters to ensure data compliance.
How to Set Configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300-600 seconds | Annual attraction operation reports are typically lengthy. An overly short timeout will cause parsing failures for large files. |
maxContext | 8000-12000 characters | Tourist comments and inspection records for attractions vary widely in length. This range adapts to the input window limits of mainstream large language models. |
RECALL_TOP_K | Top 8-12 entries | Scenic spot due diligence requires covering multi-dimensional data such as passenger flow, revenue, and public opinion. Excessive recall will increase model computing load. |
UPLOAD_FILE_MAX_SIZE | 500-1000 MB | Historical operation data files for attractions are typically large in size. An overly small limit will prevent core data from being uploaded. |
RERANK_THRESHOLD | 0.65-0.75 | Large volumes of similar reviews exist in scenic spot public opinion data. An overly low threshold will introduce irrelevant content, while an overly high threshold will miss valid information. |
SYNC_INTERVAL | Every 6 hours | Scenic spot passenger flow data updates daily. This timed synchronization frequency matches the data update rhythm to avoid excessive requests. |
The parameter values provided on this page are all conventional recommendations, serving as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: An
Invalid URL (POST /v1/rerank)error occurs when calling the reranking model. Cause: The correct interface address for the reranking model was not configured, or the deployment port of the reranking model was not exposed publicly. - Phenomenon: Uploaded scenic spot operation data in the chat window is not referenced by the model, and the uploaded file’s content is not mentioned in the reply. Cause: The switch for associating uploaded files with the knowledge base was not enabled, or the uploaded file was not correctly parsed into retrievable knowledge base entries.
- Phenomenon: Reply speed remains consistently slow after deploying a 70B parameter model. Cause: The
max_batch_sizeparameter was not configured to limit concurrent request volume, or the model’s quantized acceleration configuration was not enabled.
How to Confirm Configuration Is Complete
- Upload a scenic spot monthly operation report. Verify that the parsing task status shows "Completed", and that parsed fields include preset entries such as
daily passenger volumeandsingle-day revenue. - Initiate a scenic spot due diligence query. Confirm that the model reply references the uploaded operation data content, and that core data mentioned matches values in the uploaded file.
- Test a reranking model call. Confirm that the number of returned results matches the
RERANK_TOP_Nparameter setting, and that sorting results align with the relevance of input content. - Check the system monitoring panel. Confirm that model request response times fall within a reasonable range, with no continuous timeouts or error logs.
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.