Data Characteristics of This Category
Service inquiry data for fertilizers and pesticides comes primarily from internal enterprise product registration documents, instruction manuals, agricultural technology promotion manuals, and regulatory disclosure information from agricultural and rural affairs departments. Data update rhythms shift with new agricultural input product approvals and regulatory policy adjustments, with no fixed cycle. Document structures include structured parameter tables and unstructured long texts. Fields include product name, registration certificate number, active ingredient content, and similar items. Common units include grams per mu, milliliters per liter, days, and similar units.
Constraints Imposed on Vector Models and Indexes
The above data characteristics create three constraints for the vector model and index process. First, structured parameter tables and unstructured texts coexist, so field and unit information must be retained during chunking to avoid semantic confusion. Second, units of fields such as application dosage and pre-harvest interval directly impact inquiry accuracy, so the vector index must support unit semantic recognition. Third, data updates have no fixed cycle, so new data imports must support incremental indexing to reduce time costs from full reconstruction. The maximum number of characters processed per batch must be controlled via defaultToken to adapt to documents of varying lengths.
Reference only; verify against the target FastGPT release and deployment documentation.
Configuration Setup
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the dual structure of fertilizer and pesticide documents, which include both short Q&A and long usage guides. This avoids lost associated information from overly fragmented chunks or context overflow from overly long chunks. |
| Overlap Length | 100–150 characters | Retains key content from adjacent chunks, such as preceding and following instructions for application dosage, to prevent cross-chunk semantic breaks. |
defaultToken | 4096 token | Matches the average length of most fertilizer and pesticide documents, ensuring completeness of single-batch index processing. |
maxToken | 8192 token | Adapts to long texts such as quality inspection reports or policy documents, avoiding truncation of core content. |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant results, meeting the relatively high accuracy requirements of agricultural input service inquiries. |
| Reranking | Enabled, take top 3 results | Prioritizes displaying product parameters most relevant to user questions, aligning with the decision logic of service inquiries. |
Three Common Mistakes
- Symptom: A timeout status code is returned when calling the knowledge base interface to create an index. Cause: The chunking strategy was not adjusted for the length differences of fertilizer and pesticide documents, leading to excessive per-batch processing load from overly large chunks.
- Symptom: After replacing the embedding model, the previously imported knowledge base cannot return matching results. Cause: The original index was not rebuilt, and retrieval still uses the vector space from the old model.
- Symptom: Irrelevant content appears at the top of search results, and core parameters are not prioritized. Cause: Reranking functionality was not enabled, and only basic vector similarity sorting was used, without adjusting for priority rules specific to agricultural input service inquiries.
How to Verify Proper Configuration
- Import a single typical fertilizer and pesticide product instruction manual, check that the number of chunks in the index generation log matches expectations, and confirm that the
defaultTokensetting meets the document length requirements. - Submit an inquiry containing a specific application dosage, and verify that the returned results include correct unit information.
- After replacing the embedding model, perform an index rebuild operation, and verify that the vector matching results from the new model meet expectations.
- After adjusting the similarity threshold, submit a test query, and confirm that the number of returned results aligns with business requirements.
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.