What the data for this category looks like
Document data for the fertilizers and pesticides category originates primarily from internal enterprise production process documents, pesticide registration filing materials, fertilizer quality inspection reports, and compliance explanation documents. Data sources include the Ministry of Agriculture and Rural Affairs registration and publicity platform, and internal archives from enterprise production and R&D departments. Updates trigger on demand when new registration certificates are approved or production standards are revised, with no fixed cycle. Document structures include structured fields such as active ingredient content (unit: g/L or mass percentage), registration certificate numbers, and implementation standard numbers, alongside unstructured drafting documents including filing application letters and compliance self-inspection reports.
What constraints these characteristics impose on vector models and indexing
Fixed formats and unit requirements for structured fields restrict arbitrary splitting of field-related content during chunking. Splitting this content will cause loss of field semantics during vector retrieval. The length of unstructured drafting documents varies widely, from hundreds of characters for application letters to thousands of words for compliance reports. This requires indexing chunking strategies to adapt to different text lengths. Category-specific technical terms such as pesticide formulations and fertilizer nutrient ratios require vector models adapted to the agricultural field, to avoid encoding deviations of professional semantics by general models. The on-demand update feature of the data requires indexes to support flexible switching between incremental and full reconstruction, to adapt to different update scenarios.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed length of category documents, and avoids splitting that breaks the context of professional terms and structured fields |
| Maximum Paragraph Depth | 3 | Matches the hierarchical structure of filing materials, and retains the associated semantics of chapters and paragraphs |
| Index Vector Dimension | 128 | Balances encoding accuracy and retrieval efficiency for professional texts, and adapts to the semantic characteristics of category data |
defaultToken | 1000 token | Limits the maximum number of tokens for a single vector text, to avoid ultra-long texts exceeding the model encoding limit |
maxToken | 2048 token | Limits the maximum token count for a single retrieval context, to meet the needs of splicing multiple reference texts during document drafting |
| Single Retrieval Result Count | Top 5 results | Balances retrieval comprehensiveness and response speed, and meets the need for multiple references during document drafting |
Three Common Mistakes
- Phenomenon: Active ingredient content and units appear separated in retrieval results. Cause: The association between structured fields and units was not preserved during chunking, with values and units split into different chunks.
- Phenomenon: An Invalid error is returned when calling a multimodal Embedding model. Cause: A vector model adapted to the agricultural field was not used, or the submitted text contained unescaped chemical symbols, causing model parsing failure.
- Phenomenon: After upgrading the FastGPT version, retrieval results from the old vector library do not match the new configuration. Cause: Old index data was not migrated according to version adaptation rules, and vector files in the old format were reused directly.
How to Confirm Proper Configuration
- Upload a typical fertilizer registration filing material, and check if the chunked text retains the association between active ingredient content and corresponding units.
- Submit a document drafting retrieval request, and verify that the returned results contain category-specific technical terms, with coherent and uninterrupted semantics.
- Perform a full index reconstruction operation, and confirm that the index update progress is normal with no error prompts.
- Compare vector retrieval results from old and new versions, and confirm that the post-upgrade data matching logic conforms to the expected configuration.
The values provided 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.