Reference only; verify against the target FastGPT release and deployment documentation.
What data for this category looks like
Fertilizer and pesticide ledger data mainly comes from production records of manufacturing enterprises, sales data from agricultural input circulation platforms, and filing archives from agricultural and rural affairs departments. Data update frequency changes with production batches and sales cycles: production records are updated in real time per batch, and sales ledgers are summarized monthly. Each single record is mostly in structured format, including fields such as product name, pesticide registration certificate number, active ingredient content, manufacturer, sales flow direction, recommended application dosage, etc. Units involved include kilograms, hectares, milliliters, percentage concentration, and others.
Constraints for knowledge base retrieval and recall
The structured fields of fertilizer and pesticide ledgers are numerous, and units vary widely. The retrieval process must precisely match both fields and units to avoid returning invalid results due to inconsistent units. Data update frequency shifts with production batches and sales cycles. The recall process must support incremental synchronization configuration to prevent the use of expired production or sales data. Single records are well-formatted but cover a broad set of fields. The number of recalled results per query must be limited to avoid context overload that disrupts subsequent ledger summary and report generation logic.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Fertilizer and pesticide ledgers are structured data; filtering low-match irrelevant entries ensures retrieval accuracy |
limit | Top 3–5 results | Single records cover a wide range of fields; too many recalled results cause context overload and disrupt subsequent summary logic |
searchMode | Precise matching | Ledger data has clear fields; precise matching avoids returning content unrelated to target fields |
usingReRank | Enabled | Secondary sorting of field relevance for structured data optimizes the accuracy of recalled field matches |
datasetSearchUsingExtensionQuery | Disabled | Ledger data has clear fields; no need to expand query terms, to prevent introducing irrelevant non-category data |
collectionIds | Bind exclusive fertilizer and pesticide ledger knowledge base collection ID | Isolate cross-category retrieval data, ensuring recalled results only come from the target dataset |
Three Common Mistakes
- Issue: No optional values are displayed when selecting a knowledge base for variable reference. Cause: The variable node that outputs the knowledge base collection ID was not configured first in the workflow, or the variable type was not set to the knowledge base selection type.
- Issue: Custom delimiters fail to correctly split file lines when uploading CSV-format ledger data. Cause: The set delimiter does not match the actual delimiter used in the file, or the configuration option to enable custom delimiters was not selected.
- Issue: Retrieval results return irrelevant non-fertilizer and pesticide category data. Cause:
collectionIdswas not configured to bind the exclusive dataset, causing the retrieval scope to cover knowledge base content from other categories.
How to Confirm Correct Configuration
- Execute a test retrieval, input a query that includes the target fertilizer or pesticide product name, verify that returned results only originate from the configured exclusive ledger knowledge base collection.
- Adjust the retrieval matching degree parameter, confirm that the relevance of returned results aligns with business requirements and the parameter values are reasonable.
- Upload a single test structured data record, verify that the custom delimiter correctly splits file lines without format errors.
- Test the authentication configuration, use an unauthorized access credential to attempt retrieval, confirm that the authentication logic functions as expected.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.