What Data for This Category Looks Like
Livestock and poultry farming investment research data is sourced from the Ministry of Agriculture and Rural Affairs official monitoring platform, National Animal Husbandry Station industry weekly reports, regular announcements of listed breeding enterprises, satellite remote sensing monitoring data for poultry house inventory, and feed raw material spot trading databases. Update cycles differ widely: core category inventory data for pigs and white-feathered chickens updates weekly, feed raw material prices update daily, and enterprise operating data updates quarterly or semi-annually. Most documents mix structured tables and semi-structured text, including fields such as inventory volume, slaughter volume, feed conversion ratio, disease detection rate, with common units being head, kilogram, yuan/ton, and %.
Constraints on Citation Traceability From These Data Characteristics
The multi-source and multi-update-cycle nature of livestock and poultry farming investment research data creates multiple constraints for the citation traceability process. First, update cycles vary significantly across data sources. Traceability must bind timestamps for data collection or disclosure, otherwise different cycle versions of the same field cannot be distinguished. Second, documents mix structured tables and semi-structured text. When processing in chunks, original document field attribution identifiers must be retained to avoid confusion between identically named fields from different sources during recall. Traceability for satellite remote sensing monitoring data must link image capture time and analysis batch. Traceability for enterprise announcement data must label disclosure entities and disclosure dates. Traceability for cross-category linked data must associate metadata from both sources, further increasing the complexity of the traceability chain.
Configuration Settings
| Configuration Item | Recommended Value | Rationale | ||
|---|---|---|---|---|
recall count | top 8–12 entries | Livestock and poultry farming data mostly uses structured fields, with moderate single recall content length. 8-12 entries can cover core investment research logic while avoiding redundancy | ||
similarity threshold | 0.72–0.85 | Livestock and poultry farming investment research questions often involve specialized terminology. A threshold that is too low will introduce irrelevant feed or disease data, while a threshold that is too high may miss valid information in the same field | ||
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large satellite image documents or batch structured reports takes significant time. 600 seconds covers most long document processing scenarios | ||
segment length | 800–1200 characters | Most livestock and poultry farming documents mix structured tables and text. This segment length preserves field context and avoids losing field association relationships after splitting | ||
citation display format | `{{doc.source}} | {{doc.createTime}} | {{doc.title}}` | Investment research scenarios require clear traceability of data sources, times, and document titles. This format directly matches the reading habits of investment research personnel |
workflow knowledge base search node variable binding | dynamic parameters passed via API request body | Livestock and poultry farming investment research often requires switching between dedicated knowledge bases for different categories. Dynamic variable binding enables flexible invocation |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When calling the chat interface, the returned citation list does not include data collection time, and some traceability fields are empty. Cause: The
citation display formatis not correctly configured, no time or data source-related variable placeholders are added, and the document metadata extraction switch is not enabled. - Symptom: When configuring a
knowledge base searchnode in a workflow, the passed dynamic knowledge base variable does not take effect, and the node always calls the preset default knowledge base. Cause: The parameters passed via the API request are not correctly mapped to the node's knowledge base selection options, or the parameter naming format does not comply with FastGPT's variable recognition rules. - Symptom: When using
{{}}format to reference variables in an HTTP node, a400 Bad Requeststatus code is returned, and variable parsing fails. Cause: The version is not upgraded to V4.8.18-FIX2 or later, the old variable syntax is still used, and the official recommendation to replace it with the/mode for variable retrieval is not followed.
How to Verify Proper Configuration
- Upload a structured report document for livestock and poultry farming, trigger knowledge base parsing, and check if the parsed metadata includes fields such as data source, collection time, and document title.
- Initiate an investment research-related query, and check if the citation list of the returned results includes all content in the configured
citation display format. - Configure a
knowledge base searchnode in a workflow, pass dynamic knowledge base parameters via the API, and verify that the node correctly calls the specified knowledge base. - Adjust the
similarity thresholdand initiate a query, observe the change in the number of recalled results, and confirm that the threshold configuration takes effect.
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.