What data for this category looks like
Data sources for crop farming industry research reports include public monitoring data from the Ministry of Agriculture and Rural Affairs’ Crop Farming Management Department, monthly monitoring reports from provincial agricultural and rural departments, special research reports from institutions such as the Chinese Academy of Agricultural Sciences, production and marketing analysis documents from industry associations, and survey data from third-party agricultural information platforms. Update cycles vary: monthly monitoring reports update monthly, weekly agricultural situation bulletins update weekly, and special research reports release on demand. Document structures typically include industry overview, crop-specific planting status, supply and demand analysis, pest and disease monitoring, agricultural material price trends, and policy explanations. Fields include planting area (unit: thousand hectares), yield per unit area (unit: kg/mu), total output (unit: ten thousand tons), agricultural material price (unit: yuan/kg), pest and disease occurrence area (unit: thousand hectares), and more. Some documents include additional fields for policy document number, issuing authority, and release date.
Constraints on workflow orchestration
Individual crop farming research reports have long lengths and multiple segmented chapters. Adjust parameters in the workflow text splitting step to avoid truncating critical professional information. Data sources are scattered and have inconsistent update cycles. Configure multiple data source pull nodes to separate pull frequencies for monthly, weekly, and special reports. Field units vary across documents. Some use mixed units such as mu and hectare, yuan/kg and yuan/ton. Configure a unit mapping node to standardize units. Content includes dense professional terminology. Adjust recall and filtering parameters to ensure retrieval result accuracy.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Crop farming research reports have long individual lengths. Standard timeout durations are insufficient and will cause parsing failures. |
segment length | 1500–2000 characters | Adapt to long document splitting and avoid truncating critical professional information. |
number of recall entries | Top 8 | Crop farming research reports have multiple segmented content dimensions. Sufficient recall volume covers relevant segmented content. |
similarity threshold | 0.72–0.80 | Filter low-match irrelevant content while retaining valid segments related to crops. |
scheduled pull cycle | Daily, monthly | Adapt to different pull frequencies for weekly agricultural situation bulletins and monthly monitoring reports. |
CONTEXT_CLEAR_TRIGGER | Triggered when new crop keywords are detected | Reset context when asking questions about different crop research reports to avoid redundant content interfering with responses. |
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. Analyze specific issues on a case-by-case basis, and test on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: After SQL query results are returned, they cannot be displayed in the AI dialog box. Cause: No result output node is added to the workflow, and structured data returned by tools is not converted to natural language format.
- Phenomenon: In an offline private deployment environment, the workflow editing interface displays the error
Application error: a client-side exception. Cause: The external resource pull node relied on by the workflow is not configured with local access permissions, causing the interface to fail to load. - Phenomenon: When a question matching the specified conditions is triggered, the context history is not cleared, resulting in redundant conversation context. Cause: The
CONTEXT_CLEAR_TRIGGERparameter is not configured, and the judgment logic for clearing the context is not set.
How to confirm successful configuration
- Upload a public crop farming industry research report document, run the workflow, check that the split length of the parsed text matches the configured values, and confirm that critical professional information is not truncated.
- Configure a scheduled pull task, wait for the specified data source update cycle, and check whether the pulled research report data is updated at the monthly and weekly frequencies.
- Trigger a question containing the specified crop keywords, check whether the context history is cleared, and confirm that the context reset logic takes effect.
- Run the workflow containing SQL queries, check whether the query results are correctly displayed in the AI dialog box.
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.