What Data Looks Like for This Category
Data sources for agrochemical product research reports include industry association public reports, securities firm chemical industry research reports, corporate compliance disclosure documents, and agricultural materials professional media content. The update rhythm adjusts with industry policy releases, quarterly supply and demand data updates, and new product registration progress. There is no fixed weekly frequency, but special documents are published 1-3 days after key nodes. Document structures center on core product production capacity, raw material costs, market prices, and policy compliance requirements. Fields include active ingredient percentage, per-acre application rate, production capacity scale, registration certificate number, and more. Units involve percentage, g/L, yuan/ton, ten thousand tons/year, and similar units.
What Constraints Do These Characteristics Impose on the "Model Access and Configuration" Link
Agrochemical product research reports contain a large number of specialized chemical terms, multi-dimensional unit fields, and non-standardized supply and demand data modules. This requires the accessed model to have semantic understanding capabilities in the professional chemical field. Therefore, domain-fine-tuned models must be configured, or term alignment prompts must be added to the prompt. Research report updates have no fixed cycle and scattered nodes, so dynamic recall trigger rules need to be configured to avoid invalid recalls. Long-text production capacity analysis and tabular data require adapted context window and segmentation processing parameters to prevent key information from being truncated. Some fields such as registration certificate numbers and application rates require precise format matching, so structured output constraint parameters must be configured.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000-16000 characters | Single agrochemical product research reports often contain long supply and demand analysis and embedded tables, requiring coverage of complete core information |
segment length | 1000-1500 characters | Balances the integrity of single-segment information and context window utilization, avoids long text truncation |
RECALL_TOP_K | Top 8-12 entries | Research report data for segmented agrochemical categories is scattered across multiple documents, requiring recall of a sufficient quantity of relevant content |
SIMILARITY_THRESHOLD | 0.75-0.85 | Filters low-relevance general chemical documents, retains research report content that strongly matches agrochemical products |
FUNCTION_CALL_ENABLE | Enabled | Requires tool calls to extract structured fields such as production capacity, price, application rate and other professional data from research reports |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Agrochemical research report PDFs often contain multi-page charts and vector graphics, resulting in longer parsing time |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to conduct tests on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Units for fields such as active ingredient content and application rate are mixed up, and some fields are empty in the research report data returned by the model. Cause: The prompt template does not clearly require the unified use of agrochemical industry standard units, and the model does not impose constraints on field formats.
- Phenomenon: After configuring
FUNCTION_CALL_ENABLE, the function call trigger success rate of the Qwen2-72B-Instruct-Int8 model is low, and the returned results do not meet expectations. Cause: The trigger threshold for function calls is not adjusted for agrochemical professional terms, and the model has insufficient understanding of segmented domain instructions. - Phenomenon: After uploading JPG format chart files of agrochemical research reports, the model cannot extract production capacity and price data from the charts. Cause: The OCR function for file parsing is not enabled, or relevant parameters for image parsing are not configured.
How to Confirm Proper Configuration
- Upload a single agrochemical product research report file, check whether the parsed text completely retains professional fields and corresponding units, and confirm that the parsing configuration takes effect.
- Initiate a query containing keywords for segmented agrochemical categories, check whether the content returned by the model matches the recall rules, and confirm that the recall parameter configuration is reasonable.
- Test the function call function, check whether the model can correctly extract structured data from research reports, and confirm that the function call configuration is enabled and adapted to domain requirements.
- Upload JPG format research report chart files, check whether the text content extracted by OCR is accurate, and confirm that the image parsing configuration is normal.
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.