What Data for This Category Looks Like
Livestock and poultry farming financial report data mainly comes from regular public reports of listed breeding enterprises, monthly monitoring data from industry associations, and statistical communiqués from livestock and poultry farming competent authorities. There are two types of data update cycles: enterprise financial reports are released quarterly and annually, while industry monitoring data is updated monthly. Document structures usually include fields such as inventory quantity, slaughter quantity, weight gain per head, feed consumption, disease prevention and control costs, revenue and cost structure, etc. Field units are mostly ten thousand heads, ten thousand birds, tons, yuan per head, yuan per bird, etc. Some documents also include structured tables and charts such as breeding cycle and regional distribution.
What Constraints These Characteristics Impose on Multi-turn Conversation and Prompting
Since data sources are scattered, multi-turn conversations must first clarify the data source type required by the querying party, to avoid confusion between internal enterprise data and public industry data. Data has a relatively high update frequency, so the conversation process must prompt the querying party to confirm the time range of the data, to prevent the use of expired information. Fields and units have industry-specific characteristics, so the prompts for multi-turn conversations must clearly require unified unit expressions, to avoid calculation errors caused by mixed use of "head" and "ten thousand heads" for inventory quantity. Financial report documents are lengthy and include structured tables, so conversation context must retain sufficient field association information, to prevent loss of data correspondence after splitting.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Structured fields in livestock and poultry farming financial reports are mostly single-paragraph descriptions. This length can fully retain the association information of core fields such as inventory and slaughter quantity, avoiding data fragmentation caused by splitting |
recallTopK | Top 6 entries | Financial report analysis needs to associate multiple dimensions such as revenue, cost, and breeding scale. Recalling a sufficient number of entries can cover all relevant data fragments |
similarityThreshold | 0.72–0.78 | Industry data keywords have high recognition. This range balances recall accuracy and coverage, avoiding omission of relevant content for segmented fields |
UPLOAD_FILE_MAX_SIZE | 500 MB | Livestock and poultry farming financial reports often include attachments such as quarterly breeding data summary tables and regional distribution charts. This upper limit supports complete file uploads |
maxConversationHistory | 10 turns | Multi-turn financial report analysis usually revolves around 3-5 core indicators. 10 turns of context can retain sufficient follow-up associated information, while avoiding redundant data occupying resources |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large financial report documents include multiple pages of charts and tables. This duration ensures complete parsing of all structured content |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: Empty content is returned during data processing after uploading a livestock and poultry farming financial report file. Cause: No custom parsing rules are configured for industry-specific fields such as inventory quantity and feed consumption. The default parser cannot recognize the structured data format.
- Phenomenon: Knowledge base search works normally in the workflow, but the AI conversation does not reference the financial report data in the knowledge base. Cause: The prompt does not clearly require priority use of recalled livestock and poultry farming industry data, or the
similarityThresholdis set too high, filtering out relevant fragments containing core fields. - Phenomenon: Conversation history cannot be viewed in MongoDB. Cause: The
enable_conversation_historyconfiguration item is not enabled, or the configured storage collection name does not match the default value, and the conversation data storage path is not correctly associated.
How to Confirm the Configuration Is Correct
- Upload a quarterly financial report PDF of a livestock and poultry farming enterprise, check the segmented results after data processing, and confirm that core fields such as inventory quantity and feed consumption are completely split.
- Initiate a multi-turn conversation: first ask about the slaughter quantity of a certain enterprise in the first half of 2024, then follow up on the year-on-year change in breeding cost per head, and confirm that the context association information is correctly retained.
- Call the conversation interface, pass in the
recallTopKandsimilarityThresholdparameters, and check whether the returned results include specific numerical values and field information in the financial report. - View the configured conversation storage collection in MongoDB, and confirm that newly initiated conversation records are correctly written, including the complete content of user questions and AI replies.
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.