Data Characteristics of This Category
Livestock and poultry farming investment research data comes from multiple sources. These include publicly available industry association survey data, daily operation ledgers of large-scale farms, regional meteorology and disease monitoring platforms, and transaction data from feed ingredient supply chains. Data update frequencies cover three ranges: real-time (farm site temperature and humidity, feeding records), weekly (regional stock and slaughter statistics), and monthly (full industry supply and demand reports). Document formats include structured statistical tables, semi-structured survey interview summaries, and unstructured disease prevention and control operation guidelines. Core fields include stock scale, daily weight gain per animal, feed conversion rate, and disease prevention batch. Most units use concrete measurement units such as head, ton, day, and week.
Constraints for Knowledge Base Retrieval and Recall
High proportions of structured statistical tables require retrieval to support field-level precise matching. This prevents incorrect associations caused by semantic ambiguity. Data with multiple update cycles coexists. Recall workflows must distinguish priorities between full existing documents and incremental real-time documents. This stops outdated data from being returned first. Multi-format documents are mixed. Differentiated recall weights must be configured for different content types. For example, disease monitoring data must prioritize matching timeliness tags. Fields include dedicated measurement units. Retrieval must automatically associate unit verification. This avoids invalid recall results across categories. Investment research scenarios require linking time-based trend data. Recall workflows must support filtering and sorting by update cycle.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | Top 10-15 entries | Livestock and poultry farming investment research requires covering multi-dimensional statistical data. Excessive entries increase context redundancy, while insufficient entries fail to cover all relevant core fields. |
similarity threshold | 0.75-0.85 | Livestock farming data has high standardization of field naming. A threshold that is too low introduces irrelevant feed or disease documents. A threshold that is too high may miss precise content for niche categories. |
chunk length | 800-1200 characters | Structured tables have long single-page content. Too short chunks destroy the semantic integrity of tables. Too long chunks fail to adapt to context window limits. |
incremental update frequency | Every 6 hours | Real-time farm ledger data has a high update frequency. Full updates consume excessive computing resources. Incremental synchronization ensures data timeliness. |
field filter toggle | Enabled | Precise recall must be performed based on fields such as stock type and statistical cycle. This avoids invalid cross-category results. |
reranked return count | Top 5 entries | Investment research scenarios require prioritizing highly relevant core data. Reranking filters out content that is semantically similar but has no investment research value. |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: After enabling tool calling, no knowledge base content is returned via the chat window, and the interface shows empty results. Cause: The knowledge base retrieval node is not added to the tool calling workflow configuration, or the node's permissions are not enabled for the current session.
- Symptom: Knowledge base retrieval results include content from other livestock and poultry categories, and filtering by farming category fails. Cause: The field filter configuration is not enabled, or dedicated category fields are not marked when importing documents. This prevents retrieval from distinguishing data across different categories.
- Symptom: Documents pushed via external systems are not parsed and stored in the knowledge base, and cannot be called during retrieval. Cause: The trigger callback for external pushes is not configured, or the pushed file size exceeds the
UPLOAD_FILE_MAX_SIZElimit.
How to Verify Successful Configuration
- Upload a test document containing structured farming data, initiate a retrieval request, and check whether the returned results include the core field content of the document.
- Enable the field filter toggle, enter a search term containing category-specific keywords, and check whether the returned results only include documents from the corresponding category.
- Call the incremental update API to push a test document, wait for the configured update cycle to end, initiate a retrieval request, and check whether the document is recalled.
- Enable the tool calling workflow, add the knowledge base retrieval node, initiate a test request, and check whether knowledge base content is returned normally.
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.