What This Category of Data Looks Like
Agrochemical product research report data mainly comes from domestic agrochemical industry associations, R&D and marketing departments of leading agrochemical enterprises, and public reports from third-party consulting institutions. Updates follow a monthly routine schedule, with temporary supplementary documents released when raw material prices fluctuate or pesticide registration policies are adjusted. Document structures typically include four modules: core product parameters, supply chain supply and demand data, policy interpretations, and competitor dynamics. Core fields include active ingredient content (unit: % or g/L), per-mu usage amount (unit: g/mu), market wholesale price (unit: yuan/ton), release date, and document source identifier.
What Constraints These Characteristics Impose on Workflow Orchestration
The mixed multi-source formats, frequent temporary updates, differences in professional field units, and dense professional terminology of agrochemical product research reports impose multiple constraints on workflow orchestration. Multi-source data includes PDF tables, Word documents, structured Excel files and other formats. Configure nodes with adapted parsing rules to process these formats. Coexisting monthly routine updates and temporary supplementary documents require timed trigger and incremental synchronization nodes. These nodes avoid repeated import of old data. Cross-document differences exist in core field units. Add unified unit conversion rules during the data cleaning stage. The high proportion of professional terminology requires industry-specific dictionaries in the recall node. This improves semantic matching accuracy. Some documents contain internal operating data. Add permission verification nodes to filter non-public content.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Some agrochemical research report documents contain multi-page tables and long text; the standard 60-second timeout cannot complete parsing |
maxContext | 800–1200 characters | Agrochemical professional terminology is dense. Excessively long context will cause semantic confusion, while excessively short context will lose parameter association information |
Recall Count | Top 6 entries | Valid information in agrochemical research reports is concentrated in core paragraphs. Excessive recall will introduce irrelevant competitor or historical data |
Similarity Threshold | 0.75–0.85 | Semantic matching for professional terminology requires a high threshold to avoid recalling irrelevant general chemical documents |
UPLOAD_FILE_MAX_SIZE | 500 MB | Some agrochemical research reports contain high-definition supply chain charts and multi-page data tables; the standard 200 MB limit cannot accommodate complete documents |
Incremental Synchronization Trigger Cycle | Every 7 days | For research report data updated on a monthly routine basis, weekly synchronization can cover temporary supplementary documents while avoiding frequent interface calls that consume resources |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. 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 the settings.
Three Common Mistakes
- Symptom: The workflow stalls after reaching the tool call node, with no error logs. Cause: The database connection timeout parameter for the agrochemical research report data source is not configured. Reading large documents with multi-page tables triggers a timeout with no response.
- Symptom: Recall results do not display the original document details before slicing, only fragmented text appears. Cause: The configuration to retain source file metadata is not enabled during knowledge base import. Slicing removes the complete access path of the original document.
- Symptom: Professional terminology recognition errors appear in workflow-returned research report data. For example, "compound preparation" is misclassified as a general chemical raw material. Cause: No industry-specific dictionary is configured for the recall node. The general semantic model cannot recognize the professional terminology.
How to Verify Successful Configuration
- Upload an agrochemical research report with multi-page supply chain tables. Check that the parsing node completes output within the preset timeout period, with no timeout errors.
- Trigger an incremental synchronization task. Confirm that the knowledge base only adds research report documents released in the corresponding cycle, with no repeated imports of historical data.
- Submit a retrieval request for agrochemical product parameters. Verify that core field units in returned results are unified, with no cross-unit mixing.
- Click the source file link in recall results. Verify that the original document details page before slicing loads correctly.
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.