What the data for this category looks like
Data for fertilizers and pesticides policy and regulatory basis comes from multiple sources. These include pesticide registration regulations and fertilizer reduction and efficiency enhancement action plans released by the Ministry of Agriculture and Rural Affairs’ Agricultural Inputs Regulatory Bureau, local agricultural input support policies issued by local agricultural and rural departments, and industrial guidance documents for the chemical sector related to agricultural inputs released by the National Development and Reform Commission. Updates follow two patterns: full updates trigger when new policies are published, and incremental updates run when policy interpretations or detailed supplements are released on an ongoing basis. Most documents are official PDF files, with fields including document numbers, issuing authorities, effective dates, clause numbers, and applicable scope. Some files include testing standards and dosage tables, with professional units such as hectares, kilograms, and active ingredient percentage.
What constraints these characteristics impose on the vector model and indexing workflow
Fertilizers and pesticides policy documents mostly use long clause structures, and contain large amounts of professional terminology and numerical content with units. If chunks are split too finely, the semantic integrity of clauses will be damaged. This requires reasonable control of chunk length. Policy updates are irregular and use incremental update methods. This requires the indexing system to support incremental construction with breakpoint resume, to avoid resource waste caused by full reconstruction. Some policy files have bilingual versions. If the embedding model does not support multilingual features, cross-language recall results will have deviations. In addition, semantic alignment of professional terms requires the model to have a corpus foundation in the agricultural and chemical fields. Otherwise, term matching errors will occur.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the long clause structure of fertilizers and pesticides policy documents, preserves complete semantics of individual policies, and avoids breaking contextual connections of professional terms |
| Overlap Length | 100–150 characters | Compensates for semantic gaps in long chunks, ensures that policy-related content across clauses is not truncated by chunking |
| maxToken | 4096 | Adapts to the input length limit of most open-source embedding models built into FastGPT v4.17.0, prevents embedding failures caused by overly long text |
| defaultToken | Calibrated via actual testing | Corrects semantic deviations during embedding by mapping common professional abbreviations for policy documents (such as NY/T, FAO) by default |
| Similarity Threshold | 0.72–0.78 | Filters low-relevance policy clauses while retaining valid recall results for professional term matches |
| Single Search Limit | Top 8 results | Matches the multi-clause reference needs of policy Q&A, avoids returning too many irrelevant documents that interfere with result interpretation |
Three Common Mistakes
- Phenomenon: After manually uploading policy documents, the number of index entries displayed in the interface drops to 0 several hours later. Cause: Persistent storage configuration for incremental indexing is not enabled. FastGPT’s default temporary index cache is not mounted to a persistent volume, and indexes are lost after restart or cache clearance.
- Phenomenon: After switching the embedding model, search recall rates for bilingual policy files drop significantly. Cause: No language tagging is applied to multilingual documents, and features are not aligned by language dimension during embedding, leading to failed cross-language semantic matching.
- Phenomenon: Searching for "fertilizer reduction and efficiency enhancement policies" returns scattered numerical fragments, with no connection to complete policy requirements. Cause: Chunk length is set too small, damaging the complete semantic structure of policy clauses, leading to recall results lacking contextual connections.
How to Confirm Proper Configuration
- Upload a single complete pesticide registration policy document, check the chunk preview interface, confirm that chunks do not cross complete clauses, and that overlapping portions match the set length requirements.
- After switching the embedding model, upload a bilingual policy file, search for corresponding bilingual keywords, confirm that returned results include relevant policy clauses in both languages.
- Manually insert a test policy document, wait 12 hours and check the index list, confirm that no entries are lost.
- Enter a professional term (such as "imidacloprid active ingredient percentage requirements"), confirm that returned search results include complete relevant policy clauses with no scattered fragments.
The values listed here 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.