What the data for this category looks like
Data related to livestock and poultry farming comes from industry monitoring institutions, feed suppliers, livestock husbandry authorities, and public technical documents. Data update cycles cover daily, weekly, and monthly levels, with significant differences in update frequencies across different data types. Document formats include structured field tables, semi-structured industry analysis snippets, and unstructured technical description texts. Fields cover specialized content such as breeding sow inventory, feed unit consumption, and slaughter cycle, with units mostly being head, kilogram, and day.
What constraints do these characteristics impose on the "model access and configuration" link
The multi-level update rhythm of livestock and poultry farming data requires that the model access link be configured with a customizable recall time window, to adapt to real-time calls of daily monitoring data and batch retrieval of monthly reports. The high proportion of structured fields in document formats requires enabling the corresponding data structured parsing switch to avoid invalid recall of unstructured text. The multi-dimensional specialized fields require configuring a field whitelist for entity extraction, to prevent irrelevant fields from interfering with model output. Large-capacity monthly industry ledger documents will lengthen parsing and loading time, requiring adjustment of corresponding timeout and upload limit parameters.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Livestock and poultry farming monthly ledger documents usually have large capacity and long parsing time, and the default timeout cannot complete full parsing |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single monthly full-industry monitoring ledger may exceed the conventional limit, requiring adaptation for large-capacity document uploads |
maxContext | 8000–12000 characters | Livestock and poultry farming research reports and technical documents are usually lengthy, requiring adaptation for long-text context processing |
Recall Count | Top 8–12 entries | Livestock and poultry farming data has multiple dimensions, and an appropriate number of recalls can cover multiple types of information such as inventory, feed, and disease |
Similarity Threshold | 0.75–0.85 | Structured fields have high matching accuracy, and the threshold can be appropriately increased to filter low-relevance unstructured snippets |
ENABLE_STRUCTURED_PARSE | Enabled | Livestock and poultry farming data contains a large number of structured table fields, and enabling this switch can improve the accuracy of field extraction and recall |
The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- A model call returns
400 Bad Requestwith a prompt about incompatible image formats. This occurs because the allowed format range has not been configured for disease diagnosis images in livestock and poultry farming scenarios, and some non-standard PNG formats are not included in the whitelist. - An empty response is returned after tool calling. This occurs because a reasonable upper limit for the
maxToolCallparameter has not been configured, or the model is not adapted to multi-round tool calling logic and cannot complete real-time retrieval of farming data. - The knowledge base text understanding model fails to recognize specialized fields. This occurs because the field extraction switch has not been enabled, and the specialized field whitelist has not been configured, resulting in specialized content such as breeding sow inventory not being correctly extracted.
How to confirm the configuration is complete
- Upload a livestock and poultry farming monthly ledger document, check whether the parsed structured fields are fully extracted, and verify whether the timeout and upload limit configurations take effect.
- Initiate a retrieval request containing specialized fields, check whether the matching farming data is included in the recall results, and verify the rationality of the recall rules and similarity threshold configurations.
- Call the configured model to complete a tool call test, check whether a normal response content can be returned, and verify the correctness of the model access and tool call configurations.
- Upload a livestock and poultry disease diagnosis image, check whether parsing can be completed normally, and verify whether the image format related parameter configurations meet the requirements.
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.