What the data for this category looks like
Data for automated equipment financial report analysis comes primarily from enterprise ERP systems, equipment operation data collection platforms, and public financial report disclosure documents. Data update rhythms fall into three categories: monthly operation logs, quarterly revenue reports, and annual full financial reports. Document formats include structured equipment order detail sheets, operation cost statistics tables, and unstructured financial report technical notes and equipment technical description documents. Exclusive fields include equipment model, serial number, cumulative operating hours (unit: hours), single repair cost (unit: yuan), and order delivery cycle (unit: days).
What constraints do these characteristics impose on the model access and configuration link
This category’s data contains multi-format structured and unstructured content. It requires model access to support both general document parsing and structured field extraction. Data with different update frequencies needs matching scheduled synchronization task configurations to avoid data lag or repeated parsing. The presence of exclusive fields and units requires enabling field mapping and unit verification in model configuration to prevent unit errors or missing fields in extraction results. Large equipment operation logs and annual financial report files have large file sizes. Adjust the upload and parsing timeout thresholds to avoid task interruptions.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Automated equipment financial reports include equipment technical parameters, operation records, and financial report notes. Single-segment text length is relatively high, so a sufficient context window is required |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Avoid parsing task timeouts when batch parsing large equipment operation logs and annual financial report files |
RECALL_TOP_K | Top 8–12 entries | Automated equipment financial reports are associated with a large number of equipment data entries. A sufficient number of associated data must be recalled to support accurate analysis |
FIELD_MAPPING_RULE | Match by "field name + unit" | This category’s data has clear exclusive fields and units. Strict matching is required to prevent unit errors or missing fields in extraction results |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Allow uploading large archived equipment operation log files and annual financial report documents to adapt to the size characteristics of this category’s data |
The parameter values provided on this page are common starting points 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 the configuration.
Three common mistakes
- Phenomenon: No corresponding locally deployed model appears in the text understanding model dropdown when creating a knowledge base. Cause: Document parsing permission is not enabled in the model channel configuration. Only conversation model call permission is configured.
- Phenomenon: The application automatically switches to an external model during debugging, and the selected local model is not used. Cause: The default priority configuration of the model channel is incorrect. The local model is not set to the highest call priority.
- Phenomenon: Fields such as equipment serial number and operating hours are extracted as empty when parsing equipment financial report files. Cause: Field mapping configuration is not enabled. The model cannot recognize exclusive non-general fields of this category.
How to confirm the configuration is complete
- Enter the model channel management page, verify that both the locally deployed LLM and embedding model show a "connected" status. Check that the connection return status code is 200.
- Upload a single equipment operation log file. Check whether the analysis result includes exclusive fields such as equipment model and operating hours. Confirm the integrity of field extraction.
- Create a test knowledge base, select the configured text understanding model, execute the document parsing task, and check that the task log has no timeout or format error prompts.
- Start a simple test application, select the configured model to initiate a conversation, and verify that the returned results do not include call records of non-selected models.
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.