What the data for this category looks like
Livestock and poultry farming marketing content data primarily comes from daily ledgers of farming entities, data collected by IoT farming equipment, and publicly available standard documents from local animal husbandry departments. Data update cycles fall into two categories: real-time and scheduled. Equipment-collected data such as feeding volume and indoor temperature and humidity is updated in real time. Inventory volume, slaughter plans, and disease prevention and control records are updated daily or weekly. The structure of a single document includes fields such as livestock and poultry breed, breeding shed number, animal age, feed type, feeding weight, health status, and warning flags. Units are mostly basic measurement units such as kilograms, degrees Celsius, and days.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Real-time collected short-cycle farming data requires multi-turn dialogue contexts to retain the latest records such as feeding volume and indoor temperature and humidity, to avoid using outdated information that affects the accuracy of marketing content. Structured multi-field data requires prompts to clearly specify the order of extracted fields and their corresponding units, to prevent large models from confusing measurement methods and causing unit errors in marketing content. Regularly updated industry standard documents need to be configured for regular recall and updates, to ensure that marketing content matches the latest farming epidemic prevention and feeding standards. Subtle differences between livestock and poultry breeds require prompts to bind exclusive parameters for corresponding categories, to avoid misalignment of marketing content across categories.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Livestock and poultry farming marketing requires retaining associated information such as breed, animal age, and feeding records from multi-turn dialogues. A sufficient context window prevents loss of key information |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-similarity irrelevant farming category data, ensuring recalled content matches the current marketing livestock and poultry breed and scenario |
RECALL_TOP_N | Top 6–8 entries | Covers multi-field information of farming data, providing sufficient structured support for marketing content |
RERANK_TOP_N | Top 3–4 entries | Streamlines recalled results, focuses on core farming data, and avoids redundancy in marketing content |
PROMPT_TEMPLATE | Fixedly bind the current livestock and poultry breed and animal age, extract fields in the order of [breed, animal age, feeding weight, health status], and ensure output content complies with the latest farming standards | Clarifies extraction rules for structured data, preventing large models from confusing fields and units |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing duration of multiple batches of farming ledger documents, ensuring complete loading of historical data required for marketing |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: A
connection errormessage appears when calling the model, and the issue persists after enabling a proxy. Cause: ThePROXY_URLparameter is not configured correctly, or the proxy address and port are entered incorrectly, preventing connection to the target model interface. - Phenomenon: Keywords and variables such as livestock and poultry breed, animal age, and feeding volume cannot be accurately extracted during multi-turn dialogue, resulting in marketing content that does not match the actual farming scenario. Cause: The prompt does not clearly specify field extraction rules, nor does it limit the scope and order of extracted fields, making it impossible for large models to accurately locate key information.
- Phenomenon: Image materials for farming scenarios cannot be uploaded in the dialogue interface, and images returned by the knowledge base cannot be displayed normally in the dialogue. Cause: The
ENABLE_IMAGE_UPLOADconfiguration item is not enabled, or relevant parameters for image storage and rendering are not configured.
How to confirm that configurations are properly set
- Initiate a dialogue that includes livestock and poultry breed and animal age, check whether the context retains key information from the previous round, to confirm that the configuration is effective.
- Upload a farming ledger document, initiate a dialogue that requires field extraction, and check whether the number of recalled documents and reranking results match the configuration parameters.
- Input knowledge base content that includes image links, initiate a dialogue, and check whether the large model converts image links into displayable formats, to confirm that the prompt rules are effective.
- Test dialogue requests for different farming categories, check whether the generated marketing content matches the exclusive parameters of the current category, to confirm that the binding rules are effective.
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.