What the data for this category looks like
Financial report data for the decoration and renovation industry mainly comes from publicly disclosed periodic reports, special audit reports, and project settlement documents. The core update cycles are annual and quarterly reports, with supporting temporary announcements disclosing major contracts and completion settlement matters. Document structures include fields such as revenue breakdown (home decoration, public decoration, project subcontracting), operating cost composition (raw materials, labor, subcontracting payments), accounts receivable aging, and details of under-construction projects. Most documents use ten thousand yuan as the pricing benchmark, and some project files add detailed fields like individual contract amount and construction period nodes.
What constraints do these characteristics impose on model integration and configuration
The multi-dimensional detailed fields, long project detail documents, and non-fixed-cycle temporary announcements of decoration and renovation financial reports create multiple constraints for model integration and configuration. Split revenue and cost breakdowns require the model to support multi-class field extraction, with pre-configured classification mapping rules. Long-text project detail documents need adjusted context window and segmentation parameters to prevent information truncation. Temporary announcements with non-fixed updates require configured incremental data pull strategies to adapt to sudden data access. The uniform ten thousand yuan pricing rule needs configured numerical unit verification parameters to avoid cross-unit calculation errors.
How to set the configurations
| Configuration Item | Recommended Value | Basis for This Value |
|---|---|---|
chunk_size | 800–1200 characters | Project detail paragraphs in decoration and renovation financial reports are relatively long, to avoid losing context association during single-paragraph splitting |
maxContext | 12000–16000 characters | A single financial report contains multiple project details and financial data, requiring sufficient context for the model to conduct integrated analysis |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large decoration and renovation financial report documents include extensive project settlement data, leading to longer parsing times |
field_extract_tags | Configured as home decoration revenue, public decoration revenue, subcontracting cost, accounts receivable aging | Matches the core detailed fields of decoration and renovation financial reports, enabling accurate model extraction |
similarity_threshold | 0.75–0.85 | Differentiates similar project names and aging categories to avoid extraction errors |
retrieve_top_k | Top 8–10 entries | Decoration and renovation financial reports have a large number of project details, recalling an appropriate number of entries to ensure analysis accuracy while avoiding redundancy |
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 specific analysis, and it is recommended to conduct actual tests on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: The oneAPI channel test passes normally, but the FastGPT interface returns a model call failure with the prompt
500 Internal Server Error. Cause: The exclusivefield_extract_tagsfor decoration and renovation financial reports is not configured, and the model cannot identify detailed fields, leading to parsing failure. - Phenomenon: After switching the model to Deepseek in the application, the actual call still returns responses from Qianwen. Cause: The API key and model identifier of the corresponding channel are not updated in
model_channel_config, only the drop-down option in the interface is modified. - Phenomenon: The DingTalk robot prompts "message address verification failed" when publishing a message. Cause: The callback address of FastGPT is not configured as the HTTPS port specified by the DingTalk Open Platform, and no scene-specific field verification rules are added.
How to Confirm the Configuration is Complete
- Upload a quarterly financial report document of a decoration and renovation enterprise, and check whether the parsed and extracted fields include the preset tag content.
- Call the model to test the financial report analysis function, and verify whether the returned results include cost breakdown and aging analysis for detailed projects, and do not include general financial conclusions.
- Test the incremental data pull function, upload a temporary announcement document, and confirm that the system only synchronizes new content without repeatedly loading full financial report data.
- Check the channel configuration page to confirm that the currently used model API key and identifier are consistent with the preset parameters.
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.