What the data for this category looks like
Data mainly comes from production batch ledgers, quality inspection department test reports, agricultural technology promotion agronomic guidance documents, warehouse inventory records, and internal employee training materials. Update rhythms vary widely. Production ledgers update daily with production progress. Quality inspection reports update after each batch’s testing is completed. Public agronomic documents are updated and revised quarterly.
The document structure includes both structured fields and unstructured content. Structured fields include batch number, active ingredient percentage, production/inspection date, storage location, and similar items. Units include percentage, grams per liter, tons, and others. Unstructured content mostly consists of long texts such as product usage instructions and field operation guides, with wide variation in single-document length.
What constraints these characteristics impose on vector models and indexing
The mixed data structure of this category requires indexes to support both precise matching of structured fields and semantic retrieval of unstructured text. Index rules adapted to multiple data types must be configured.
Differences in update rhythms across data sources require incremental index refreshes for high-frequency updated production ledgers, and full regular refreshes for low-frequency updated agronomic documents. This avoids index lag or resource waste.
The wide variation in single unstructured text length requires chunking strategies that balance semantic integrity of long texts and retrieval accuracy of short texts. The strategy must also adapt to field semantics including percentage and concentration units, to avoid losing key unit-related information during vector encoding.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semantic integrity of long texts such as agronomic guides and product manuals, avoid breaking single chunk content |
| Overlap Length | 100–150 characters | Compensate for semantic gaps after long text chunking, improve retrieval accuracy for cross-chunk content |
| Similarity Threshold | Calibrated based on actual testing | Adapt to scenarios with many professional terms and high semantic accuracy requirements in fertilizer and pesticide data, avoid false or missed retrieval |
| Single Search Limit | Top 10 results | Balance retrieval efficiency and content relevance, adapt to the information density needs of employee queries in internal office work |
defaultToken | 8000 | Adapt to context splicing needs after long document chunking, avoid truncating key professional content |
maxToken | 16000 | Cover the total token limit after multiple documents are spliced, meet the needs of multi-content reference in complex office scenarios |
Three common mistakes
- When entering a query unrelated to fertilizer and pesticide professional content, relevant fragments from the knowledge base are still returned. The cause is failure to adjust the similarity threshold for the professional term density of this category, resulting in low-semantic matching content being incorrectly retrieved.
- A circular execution error occurs during vector index construction, with logs showing duplicate overlapping chunk content. The cause is that the chunk overlap length is set too large, causing overlapping content of adjacent chunks to exceed a reasonable range and triggering the cyclic verification logic.
- Directly using the full database table content as the knowledge base index fails to accurately match employees' office query needs. The cause is failure to set targeted index rules for structured fields of fertilizer and pesticide data such as batch number and active ingredient percentage, leading to generalized retrieval content.
How to confirm the configuration is correct
- Enter a common internal office query related to fertilizers and pesticides, check the relevance of the retrieval results, and adjust the similarity threshold to meet business requirements.
- Import a long agronomic document, check whether the chunked content fully retains professional terms and core semantics without obvious breaks.
- After configuring the incremental index, submit newly uploaded production ledger data, verify whether the index completes the update within the set refresh cycle.
- Test the context length after multiple documents are spliced, confirm that the
maxTokenlimit is not triggered to cause content truncation.
The values listed above are common starting points and should be evaluated 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.