What the data for this category looks like
Feed profitability and market daily report data mainly comes from domestic livestock industry public monitoring platforms and daily sampling data from local agricultural and rural affairs departments. Same-day market data is released the next morning. The data supports filtering and querying by producing area and feed category. Most documents use structured CSV or JSON format. Each single record includes the following fields: product name, producing area, wholesale unit price (unit: yuan/kg), retail unit price (yuan/kg), market volume (unit: ton), monitoring date. There is no additional unstructured text content. Field definitions are consistent and unambiguous.
Constraints from data characteristics during model access and configuration
The multi-field structured nature of feed market data requires precise field mapping rules during model access. This ensures the model correctly identifies each data dimension and avoids confusing information across different categories or producing areas. The daily update rhythm requires scheduled fetch tasks to match the T+1 update cycle. This prevents frequent requests from exceeding data source interface limits. The structured CSV/JSON format requires enabling a dedicated structured data parsing switch. Do not rely on general text parsing workflows. Also configure unit standardization processing steps to unify measurement rules for different fields. This prevents the model from generating incorrect calculations due to unit differences. Feed categories have large field differences from other agriculture, forestry, animal husbandry and fishery categories. Do not directly reuse field mapping configurations from other categories.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
field_mapping | Map the data source's "product name" to product_name, "producing area" to producing_area, "wholesale unit price" to wholesale_price, "retail unit price" to retail_price, "market volume" to market_volume, "monitoring date" to monitor_date | Match the standard field names of feed market data to ensure the model correctly identifies each data dimension |
parse_structured_data | true | Feed market data uses standardized CSV/JSON format. Enabling this switch allows direct extraction of field values without additional text cleaning |
api_request_timeout | 30 seconds | Batch fetching structured data may require longer processing time. This setting prevents request interruptions due to timeout |
scheduled_fetch_interval | 86400 seconds | Feed market data updates once daily. Daily fetching ensures data timeliness and avoids repeated requests |
system_prompt_template | This model acts as a feed market daily report assistant. It must generate content based on the provided {context} data, organizing daily market information by category and producing area | Clarify the model's task scope, limit it to using only the incoming feed data for content generation, and avoid interference from irrelevant information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Model API calls return a 404 status code (no body). Cause:
API_BASE_URLis not correctly configured to the official access address of the corresponding model, or the correct interface path suffix is not added. - Phenomenon: Model-generated content does not reference incoming feed market data, only outputs generic content. Cause: The
parse_structured_dataswitch is not enabled, or thefield_mappingconfiguration is incorrect, preventing the model from identifying valid data fields. - Phenomenon: Local deployed model testing displays the "Message field is required" error. Cause: The
messagesfield is not correctly populated in the request parameters, or the field format does not match the required array structure of the model.
How to confirm successful configuration
- Users navigate to the FastGPT model testing interface, enter a query containing feed category keywords, and verify whether returned results include the configured field mapping information.
- Users check the scheduled task log to confirm whether daily data fetch requests successfully return structured data with no timeout or format errors.
- Users verify the API key configuration items to confirm the key has not expired and has access permissions for the corresponding data source.
- Users adjust the system prompt template to verify whether the model strictly generates content according to the template requirements and does not introduce irrelevant information.
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.