What the data for this category looks like
Construction machinery industry financial data comes primarily from periodic reports of listed companies and public statistical documents released by industry associations. Quarterly reports are published each quarter. Annual reports are made public within four months after the end of the fiscal year. Document structures include consolidated financial statements, detailed operating data tables, and business segment reports. Core fields include revenue by product category, total outstanding orders, equipment production capacity, unit price per piece of equipment, and similar metrics. Units are uniformly standardized as RMB ten thousand, number of equipment units, and other standard units. No non-standard units are used.
What constraints do these characteristics impose on the model integration and configuration link
The multi-table and multi-business segment field characteristics of construction machinery financial reports require configuring a sufficiently large context window to accommodate complete report content during model integration. The fixed quarterly and annual update schedule requires configuring timed data synchronization trigger rules to ensure model calls use the latest publicly available documents. The presence of segmented category revenue fields requires the embedding model’s recall granularity to cover business segment identifiers, preventing cross-category data confusion. The coexistence of multiple units in document formats requires configuring unit standardization preprocessing parameters to ensure unified units for model input data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 1000–1500 characters | Construction machinery financial reports have dense fields. Segmentation must retain complete business field combinations to avoid losing semantic associations after splitting |
maxContext | 8000–16000 characters | The text length of a single complete financial report, including business segment reports, falls within this range. Full context must be accommodated |
embeddingRecallTopK | Top 8–12 results | Must cover recall results for multiple core business fields such as revenue by category and total outstanding orders |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Financial reports contain numerous detailed tables, which take longer to parse than general documents. The timeout period must be extended |
UPLOAD_FILE_MAX_SIZE | 500 MB | The compressed size of annual financial reports usually does not exceed this threshold, avoiding upload failures |
systemPromptTemplate | Extract core business fields from construction machinery financial report data and generate a structured analysis report | Matches the exclusive analysis requirements of this category to improve model output accuracy |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Issue: When configuring
deepseek-r1as the called model, tool call results have low matching accuracy with financial report fields. Cause: The system prompt was not adjusted for the segmented business fields of construction machinery financial reports, causing the model to fail to accurately extract target data. - Issue: Uploading financial report documents fails during parsing, returning
408 Request Timeoutor504 Gateway Timeoutstatus codes. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not increased. The default timeout cannot handle financial report files with numerous detailed tables. - Issue: Recalled financial report data only includes general financial fields, and does not cover construction machinery-specific segmented category revenue fields. Cause: The
embeddingRecallTopKparameter value is too low, failing to cover enough text segments, resulting in core fields not being successfully recalled.
How to verify correct configuration
- Upload a single quarterly financial report document, check if the parsed text segments retain complete business field combinations, and verify the actual adaptation effect of the segmentation parameters.
- Initiate a model call, check if the returned analysis results include core fields of construction machinery financial reports such as revenue by category and total outstanding orders, and verify the matching between the system prompt and configuration.
- Test the timed data synchronization task, confirm that the latest publicly available financial report documents can automatically trigger parsing and index updates, and verify the configuration effectiveness of the timed trigger rules.
- Adjust the embedding model recall parameters, verify whether the coverage of recall results meets the extraction requirements of core fields, and adjust the value based on actual recall effects.
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.