What the data for this category looks like
Data sources include public monitoring datasets from livestock and poultry regulatory authorities, production management system export files from on-site farms, and livestock and poultry health reports from third-party testing institutions. Basic farm archives are updated quarterly, production ledgers are synced daily, and quarantine and slaughter data are updated in real time per production batch. Document structure is a multi-sheet spreadsheet file with four modules: basic farm information, batch-specific production data, disease prevention and control records, and cost accounting. Fields include: breeding category, inventory scale, total feed usage, average daily weight gain, number of vaccinations, and slaughter quantity. Corresponding units: breeding category, head/feather, kilogram, kilogram per day, count, head/feather.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Scattered data sources with inconsistent update frequencies require gradual guidance of users to supplement missing data for different modules during multi-turn dialogue, to avoid interactive confusion caused by requesting too much information at once. Documents contain multiple sheets. Prompts must explicitly specify which sheet’s fields to read, to avoid mixing up production data across different breeding categories. Field units vary. Multi-turn dialogue needs a unified unit conversion logic, to prevent calculation errors from mismatched units. Real-time updated slaughter data requires dialogue to support real-time calls to the latest batch data, and cannot rely on static knowledge base content.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000–6000 characters | Single-turn dialogue context for livestock and poultry farming due diligence data is lengthy, containing multi-field production data, requiring sufficient context to retain historical conversation information |
recallTopK | Top 8–12 entries | Due diligence documents contain multiple modules of data, requiring sufficient relevant fields to be recalled to cover complete production ledger information |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | A single due diligence document contains multiple sheet tables, which takes longer to parse, to avoid early timeout causing file parsing failure |
conversationMaxRound | 10–15 rounds | Due diligence requires gradual collection of information across multiple modules; too many rounds will lead to redundant context |
similarityThreshold | 0.75–0.85 | Distinguish relevance between different breeding categories and production data, to avoid recalling irrelevant document content |
promptTemplate | Explicitly specify the fields to read from the corresponding sheet, unify units according to breeding category, and gradually guide users to supplement missing data | Adapt to the requirements of multi-sheet documents and multi-field units, standardize the interactive logic of multi-turn dialogue |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Conversation responses automatically include a citation file list, which cannot be hidden via the interface. Cause: The default enabled state of the
enableCitationconfiguration item was not turned off, and the system appends citation source content by default. - After the 8th round of multi-turn dialogue, the system prompts that it cannot respond to new queries. Cause: The
conversationMaxRoundparameter was set to 7, exceeding the preset maximum number of dialogue rounds. - Queries about disease prevention and control are incorrectly routed to the cost accounting workflow. Cause: The trigger rules of the question classification module are not bound to the corresponding relationship between breeding categories and modules, leading the classification logic to confuse different business modules.
How to confirm the configuration is correct
- Initiate a test query containing multiple fields, check whether the returned results include a citation list, and adjust the switch state of
enableCitationbased on whether citations are required for the business. - Conduct multiple consecutive test dialogues, confirm that the dialogue does not interrupt after the preset number of rounds, and adjust the value of
conversationMaxRoundbased on the required dialogue length for the business. - Import multiple due diligence documents of different categories, initiate classification queries, verify that the classification results match the business modules, and adjust the trigger rules of the question classification module based on classification accuracy.
- Initiate a test query involving unit conversion, verify that the units of the returned fields are unified, and adjust the rules of the prompt template based on the unit requirements of the breeding category.
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.