What data for this category looks like
Feed industry data sources include feed ingredient purchase ledgers, terminal livestock and poultry farming feedback data, public industry association reports, feed formula standard libraries, and commodity spot and futures market data. Ingredient price data is updated daily. Industry analysis reports are updated monthly or quarterly. Formula standard libraries are updated irregularly alongside adjustments to industry standards.
Each individual document includes fields such as ingredient identifier, composition parameters, procurement cycle, price range, and applicable livestock and poultry farming categories. Composition parameters include content values for crude protein, crude fiber, lysine, and similar metrics, with units of grams per kilogram. Price fields use yuan per ton.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Feed industry data contains a large volume of professional composition parameters and price fields. Multi-turn dialogue must continuously associate context information such as ingredient categories, composition values, and procurement prices, otherwise responses will be off-topic.
Update cycles vary significantly across different data sources. Price data requires daily refreshes. Industry reports require monthly retrieval. Prompts must clearly specify the update scope and source type of the data.
There are many specific feed document categories, with notable differences in formula parameters across categories. Multi-turn dialogue must support users in gradually clarifying the retrieval document scope to avoid retrieving irrelevant feed category data.
Professional composition parameters have high precision requirements. Prompts must constrain the model to return accurate values and their corresponding units, avoiding vague statements.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
max_history | First 3–5 turns of dialogue | Feed investment research dialogue focuses on ingredients, prices, and formulas. 3–5 turns covers core context association needs, avoiding model confusion from overly long context |
rerankTopN | Top 4–6 results | There are many specific feed document categories. 4–6 results balances retrieval coverage and context load, avoiding retrieval of irrelevant cross-category data |
similarityThreshold | 0.70–0.82 | Semantic differences between feed professional terms are subtle. This threshold filters low-match documents and retains core relevant ingredient composition and price data |
conversationRetentionDays | 30–90 days | Feed industry data update cycles range from daily to monthly to quarterly. 30–90 days retains sufficient historical dialogue for review while avoiding excessive storage load |
systemPrompt | Prioritize retrieving documents for the feed category specified by the user, and return content that includes accurate values and units for corresponding parameters | Feed data has high requirements for professional fields, and system prompts constrain the model to clarify retrieval scope and return format |
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 settings.
Three common mistakes
- Phenomenon: Starting from the second question in multi-turn dialogue, responses are off-topic and cannot associate previously mentioned feed ingredient categories or breeding requirements. Cause: The
max_historyparameter is not configured, or the value only retains 0–1 turns of dialogue, failing to load key previous context information. - Phenomenon: When searching a feed knowledge base containing multiple documents, results mix data from different feed categories, failing to accurately retrieve formula or price information for a specified category. Cause: The
systemPromptis not used to constrain the model's retrieval scope, and in the open source version V4.8.22, custom prompt templates cannot be configured, making it impossible to clearly specify the target document category and fields. - Phenomenon: Dialogue logs cannot be cleaned as required, or storage space usage exceeds expectations. Cause: The
conversationRetentionDaysparameter is not configured, or the value does not match the feed industry data update cycle, resulting in old dialogue not being cleaned in a timely manner.
How to confirm correct configuration
- Initiate two consecutive conversations. First, ask for the composition parameters of a specific feed category, then ask for the corresponding procurement information for that category. Verify that the model associates the feed category mentioned in the first conversation, and that no off-topic responses occur.
- Explicitly specify a specific feed category in a query. Verify that returned results only include documents for that category, with no irrelevant category data retrieved.
- Check the conversation archive parameters in system configuration, and verify that the value of
conversationRetentionDaysmatches the feed industry data update cycle. - Use test conversations to verify the constraint effect of
systemPrompt, and verify that returned content includes accurate parameter values and their corresponding units.
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.