What This Category of Data Looks Like
The due diligence data for agrochemical products mainly comes from pesticide registration announcements of the Ministry of Agriculture and Rural Affairs, agricultural means of production circulation monitoring platforms, public annual reports of agrochemical enterprises, and monthly reports of industry associations. Update cycles vary: pesticide registration information is updated alongside approval progress, market quotation data is updated monthly, and annual production capacity reports are released according to the calendar year. Most documents are in PDF format, containing fields such as product registration certificate number, active ingredient content, applicable crops, toxicity rating, production qualifications, and more. Units for active ingredient content are mostly grams per liter or percentage, while market quotation units are yuan per ton. Some documents include additional tables with product dosage forms, usage methods, and other supplementary details.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The sources of agrochemical due diligence data are scattered and their update cycles differ, which requires that the time range and source type of the data must be clearly specified in multi-turn dialogue to avoid confusion between new and old data. There are many dedicated fields and varying units, so prompt engineering must define the standard format and units of the fields in advance to prevent unit ambiguity in parameters extracted by the model. Documents are lengthy and contain a large number of professional tables, so multi-turn dialogue must retain sufficient context to connect field information across different paragraphs, avoiding loss of field information due to segment truncation.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | Previous 10 dialogues + latest 3 user inputs | Agrochemical due diligence data has many specialized fields, requiring sufficient context to calibrate unit and field ambiguities |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Agrochemical due diligence reports contain multiple types of tables and parameters, resulting in longer single-file parsing time |
Recall Count | Top 8 | Agrochemical data sources are scattered, requiring sufficient associated documents to cover multiple types of information such as registration, quotation, and production capacity |
Similarity Threshold | 0.75 | There are many specialized terms in agrochemicals, to avoid recalling irrelevant general chemical data |
Text Extraction Node Segment Length | 1000-1200 characters | Parameter paragraphs in agrochemical due diligence reports are lengthy; too short segments will damage field integrity |
UPLOAD_FILE_MAX_SIZE | 500 MB | Agrochemical due diligence reports include a large number of attached images and tables, resulting in larger single-file size |
The parameter values provided on this page are common recommended starting points for configuration. The actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test against your own samples before finalizing.
Three Common Mistakes
- Phenomenon: The generated due diligence report does not meet the preset JSON format requirements. Cause: The JSON Schema rule is not bound in the prompt, and only natural language is used to describe the format, resulting in the model generating fields in an unexpected order or format.
- Phenomenon: A format error is returned when calling a multimodal model to process agrochemical product images. Cause: The model is not restricted to only identifying dedicated areas such as registration certificate numbers and active ingredient identifiers in the images, and the model attempts to parse irrelevant content, leading to format exceptions.
- Phenomenon: The online dialogue has no response for a long time, and the knowledge base search is triggered continuously. Cause: The number of knowledge base recall entries is not limited, or the threshold is set too low, resulting in a large amount of redundant agrochemical data being recalled, exceeding the model's processing limit.
How to Confirm the Configuration Is Correct
- Upload a single complete agrochemical due diligence report, check the running logs of the text content extraction node, and verify that the extracted fields include dedicated parameters such as active ingredients and registration certificate numbers.
- Initiate a continuous multi-turn dialogue, and ask three types of questions in sequence: product registration information, market quotation, and production capacity data, to confirm that the context is correctly retained and used for subsequent responses.
- Trigger the multimodal calling process, upload the registration certificate image of an agrochemical product, and check whether the returned result conforms to the preset field extraction scope.
- Monitor the running duration of the workflow, and confirm that the parsing time does not exceed the configured
PARSE_FILE_TIMEOUT_SECONDSvalue.
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.