What the data for this category looks like
Data sources for agrochemical products intelligent due diligence include agricultural inputs industry regulatory public platforms, public annual reports of agrochemical enterprises, pesticide registration certificate databases, field test reports, and supply chain transaction data. Update rhythm adjusts based on regulatory approval progress, industry quarterly announcements, and enterprise annual report releases, with no fixed cycle. Document content falls into three categories: compliance qualification, product parameters, and supply chain. Compliance qualification content includes registration certificate numbers and validity periods. Product parameter content includes active ingredient content, applicable crops, and toxicity levels. Supply chain content covers information related to production capacity and shipment volume. Field units include mass percentage or volume concentration for active ingredient content, uniform encoding format for registration certificate numbers, and internationally recognized grading standards for toxicity levels.
What constraints these characteristics impose on the vector models and indexing link
Agrochemical due diligence data contains mixed structured fields and unstructured text. It needs to support both professional term vectorization and structured field association retrieval, so hybrid indexing configuration must be enabled. Some compliance data is updated unexpectedly, with no way to plan a full indexing cycle in advance. Incremental index trigger rules must be configured to avoid resource consumption from full reconstruction. Document lengths vary widely: qualification certificates as short as hundreds of characters and test reports as long as thousands of characters coexist. Adaptive segmentation rules are needed to prevent professional term combinations from being truncated. Multi-source data formats include PDF, Excel, HTML, etc. Multiple document parsing logic must be adapted to ensure agrochemical data in different formats can be correctly vectorized.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Professional term combinations in agrochemical test reports and compliance documents are often long. This range avoids cross-segment truncation of core information |
top_k | Top 8–12 entries | Agrochemical due diligence needs to cover compliance, parameter, and supply chain information. An appropriate number of recalls prevents omission of key content |
similarity_threshold | 0.72–0.80 | Distinguish similar active ingredients and formulations of agrochemical products, avoiding false association of due diligence data from different categories |
embedding_api_timeout | 30 seconds | Adapt to interface latency for batch vectorization of agrochemical documents, preventing index construction failures caused by timeouts |
parse_multi_format | Enable PDF, Excel, HTML parsing | Cover multi-source data formats including regulatory announcement webpages, capacity statistics Excel files, and test report PDFs |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- A 404 error is returned when configuring
embedding-v1. Cause: The access domain name and authentication parameters of the embedding interface are not correctly configured, resulting in failure to correctly route interface requests. - The m3e model cannot be added to the platform after deployment without a GPU environment. Cause: The model port is not mapped to the outside of the container, or the access address and port of the local model are not specified in the platform configuration.
- The number of vector recall results does not match the configured
top_k. Cause: A reasonablesimilarity_thresholdis not set, resulting in fewer results meeting the threshold than the configured number, or some documents failed to generate vectors.
How to verify successful configuration
- Upload a single agrochemical compliance qualification document, check the parsed text segments, and confirm the segment length falls within the configured
chunk_sizerange. - Input a query containing the name of an agrochemical active ingredient, verify that the number of vector recall results matches the configured
top_kparameter. - Check the vector index update records, confirm whether newly added regulatory announcement data is automatically synchronized to the index.
- Test the request response of the external embedding interface, confirm that the interface can normally return vector data that meets the dimension requirements.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.