What data for this category looks like
Data sources include public monitoring data from the Ministry of Agriculture and Rural Affairs Animal Husbandry and Veterinary Bureau, weekly survey results from industry associations, special research reports from securities firm agriculture, forestry and fishing teams, and quarterly operation disclosures from large-scale breeding enterprises. Update frequency: industry monitoring data is updated weekly, securities firm research reports are released per project cycle, and enterprise operation data is published quarterly. Each individual research report typically includes four parts: core indicator tables, supply and demand balance analysis, price trend charts, and policy impact interpretation. Core indicator tables mostly contain structured data, while chart sections mostly use visual formats. Core indicators include inventory, slaughter volume, feed raw material prices, and disease monitoring data. Inventory is measured in ten thousand heads, and feed prices are measured in yuan per ton.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
The weekly update rhythm of livestock and poultry farming research reports requires multi-turn dialogue chains to support real-time recall of the latest monitoring data, to avoid conclusion bias caused by relying on expired cached data. The multi-unit system of core indicators requires prompts to clearly specify unit unification rules, such as unifying slaughter volume to the ten thousand heads unit and marking the original data source. The document structure that coexists with structured tables and unstructured analysis requires multi-turn dialogue to first trigger structured data recall, then initiate unstructured interpretation follow-up questions based on the results. Differences in data accuracy across sources require prompts to limit the trusted scope of data, only using official public monitoring data as the analysis basis.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Livestock and poultry farming research reports have relatively long individual lengths, and need to accommodate multi-turn dialogue context and multiple recalled research report contents |
recallTopK | Top 8–12 results | Research report data density is high; too many recalls will exceed the context window, while too few will lead to insufficient coverage |
rerankTopN | Top 4–6 results | Focus on research report fragments related to core indicators, to avoid redundant information interfering with dialogue |
promptTemplate | "Based on livestock and poultry farming data in {context}, answer user questions, uniformly use ten thousand heads and yuan/ton as units, mark data sources" | Adapt to unit and traceability requirements of livestock and poultry farming, clarify data usage rules |
conversationMaxTurns | 5–7 turns | Avoid context overflow caused by overly long dialogues, align with conventional question rounds of industry analysts |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Research reports contain charts and complex tables, which take longer to parse, so the timeout period needs to be extended |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: After enabling the dialogue log annotation function, exclusive data fields of livestock and poultry farming research reports cannot be correctly marked. Cause: Core fields to be annotated (such as inventory, feed price) are not specified in the prompt, resulting in the annotation logic not matching the category characteristics.
- Phenomenon: After enabling AI dialogue streaming output, fragments received by the front end are not spliced as expected, with missing fields or disordered order. Cause:
responseStreamis not set to disabled, and text splicing trigger conditions are not specified, causing the splicing logic to trigger during generation. - Phenomenon: After passing knowledge base retrieval results to an HTTP request, the AI dialogue cannot correctly identify referenced data, returning "no relevant data found". Cause: The passed retrieval results do not carry the three required fields
source,content, andtitle, which do not meet the knowledge base reference format requirements of AI dialogue.
How to Confirm Configuration is Correct
- Initiate a query that requires unit conversion, and check if the unit of the AI's reply conforms to the preset rules.
- Adjust the recall count parameter, observe changes in the number of retrieval results to confirm that the parameter takes effect.
- Simulate continuous multi-turn queries, check if the context is correctly retained without content loss or overwriting.
- Upload a livestock and poultry farming research report, check if structured fields are correctly extracted after parsing with no format errors.
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.