What the data for this category looks like
Data for tourism attraction research reports mainly comes from financial institution cultural tourism industry research reports, public disclosure documents from cultural tourism authorities, monthly operation briefings from scenic spot operators, and survey documents from third-party cultural tourism consulting institutions. The primary update cycle is quarterly. Passenger flow and revenue data are updated monthly. Annual overall planning documents are updated once per year. Common document structures include passenger flow statistics tables, daily carrying capacity calculation tables, and supporting facility parameter lists. Fields include instantaneous carrying capacity, average daily passenger volume, and per-customer consumption amount. Common units are person-times, ten thousand yuan, and square meters.
What constraints do these characteristics impose on the document parsing and chunking link?
The mixed typesetting structure of tourism attraction research reports creates multiple constraints for the document parsing and chunking process:
- Monthly passenger flow and revenue documents often contain statistical tables with merged cells. Conventional parsing logic may incorrectly split field data.
- Annual overall planning documents have relatively long lengths. Long text chunking may break context connections.
- Supporting facility parameter lists are mostly structured data. The binding relationship between fields and their corresponding units must be strictly preserved.
- Some documents embed scenic spot real-shot images. These images must be synchronized with associated main text paragraphs to avoid disconnection between images and text during retrieval.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxChunkSize | 800–1200 characters | Adapt to the mixed typesetting of long text paragraphs and structured tables in scenic spot research reports, avoid losing context due to overly long single chunk content |
chunkOverlap | 150–200 characters | Retain overlapping content between adjacent chunks, connect the logical association between monthly passenger flow tables and subsequent analysis paragraphs |
parse_table_merge_cell | true | Process statistical tables with merged cells in scenic spot research reports, restore the corresponding relationship between fields and values |
enable_image_attach | true | Bind embedded scenic spot real-shot images in documents to corresponding main text paragraphs, support synchronous retrieval of text and images |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapt to the parsing duration of single annual overall planning documents, avoid parsing timeout for long documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodate single multi-page scenic spot overall planning documents, meet batch upload requirements |
The parameter values provided on this page are common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the settings.
Three common misconfigurations
- Phenomenon: Some structured tables cannot be chunked normally, and parsed fields are empty or misplaced. Cause: The
parse_table_merge_cellconfiguration is not enabled, or the used parsing version does not support the merged cell table format of scenic spot research reports. - Phenomenon: Out-of-vRAM error is reported after deployment, and logs show GPU resources are not detected. Cause: GPU drivers and CUDA environment are not mounted during Docker deployment, and the
CUDA_VISIBLE_DEVICESparameter is not specified, causing the parsing service to fail to call graphics card resources. - Phenomenon: Embedded scenic spot real-shot images in documents cannot be associated with main text during retrieval, or image links are lost. Cause: The
enable_image_attachconfiguration is not enabled, or the original path and domain name binding relationship of images is not retained when converting to markdown.
How to confirm the configuration is correct
- Upload a scenic spot monthly research report containing merged cell statistical tables, check the parsed chunk content, and confirm that table fields are not misplaced or missing.
- Check the system logs of the deployment node, confirm that the parsing service has successfully detected and called GPU resources, with no vRAM-related errors.
- Upload a document containing embedded scenic spot real-shot images, verify whether the binding relationship between images and corresponding main text paragraphs in retrieval results is normal.
- Upload a single long annual overall planning document, confirm that the parsing process does not trigger timeout interruption.
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.