What the data for this category looks like
Feed research report data comes primarily from public statistical materials released by agricultural and rural affairs department feed industry management bodies, industry associations, and industry analysis reports from professional consulting firms. Regular monthly and quarterly reports are the primary update format. Temporary supplementary documents are released when industry policies change or raw material prices fluctuate. Document structures include modules such as industry supply and demand data, raw material price trends, policy explanations, and key enterprise updates. Fields include raw material unit prices, livestock and poultry inventory numbers, policy document numbers, survey sample sizes, and more. Individual documents range from 3,000 to 10,000 words in length.
What constraints these characteristics impose on tool calling and plugins
Data from multiple sources requires permission configuration to distinguish between public and paid data sources when calling tools. This prevents call failures due to insufficient permissions. Fixed monthly and quarterly update cycles require scheduled tool tasks to match these cycles. This prevents repeated calls of expired data. Complex document structures and multi-unit fields require configured field extraction rules to adapt to parsing of different numerical units. Wide variation in individual document word counts requires adjusting chunking parameters for vector models. This avoids truncation of long texts that would lose core information, while also controlling the number of documents called per batch to prevent interface timeouts.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Feed research reports have high information density per segment. This range fully covers core fields such as raw material prices and supply and demand data, and avoids information truncation |
recallTopK | Top 8–12 results | Feed research reports involve multiple raw materials and multi-dimensional data. Too many recalled results increase context pressure, while too few fail to cover all core information |
vectorModel | Determined through actual testing | Feed research reports contain a large number of professional terms and unit fields. A vector model that supports agricultural domain word segmentation must be matched. Specific model selection is determined by testing recall accuracy |
pluginApiTimeout | 60 seconds | Some third-party consulting firm research report APIs have high response delays. This duration covers most normal call scenarios, and prevents premature timeouts |
parseFieldMapping | Configure unit conversion rules for fields such as raw material unit prices and inventory numbers | Feed research reports contain multi-unit numerical values such as yuan/kilogram and yuan/ton. This configuration is required to unify output to standard units |
scheduleCron | 0 2 1 * * * | Matches the regular monthly update cycle of feed research reports, ensuring scheduled calls obtain the latest data |
The parameter values provided on this page are all conventional recommendations, used as a starting point for determining configurations. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- A third-party multi-dimensional table HTTP plugin call returns a 401 status code, and node output variables cannot be written successfully. Cause: The plugin access token is not configured in environment variables, or the configured key permissions are insufficient, leading to interface authentication failure.
- After switching the vector model, there is no obvious change in knowledge base recall results. Cause: The model identifier was not updated synchronously in the
vectorModelconfiguration item, and knowledge base reindexing was not triggered. This results in the original model still being used to generate vectors. - Unit fields in research report data returned by tool calls are empty or formatted chaotically. Cause: The
parseFieldMappingrule was not configured, and numerical values with different units were not uniformly converted, leading to parsing failure.
How to Confirm Configurations Are Set Correctly
- Trigger a single tool call, check if the returned research report data fields include the configured unit conversion results, and verify that the numerical format is unified.
- Check the environment variable list, confirm that all keys and API addresses required by the plugin are correctly configured, with no empty values or formatting errors.
- Manually modify the
vectorModelconfiguration item, trigger knowledge base reindexing, then check if the semantic matching degree of recall results meets expectations. - View the scheduled task execution logs, confirm that the call time matches the trigger time configured in
scheduleCron, with no abnormal delays or repeated calls.
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.