What data for this category looks like
Agrochemical product investment research data mainly comes from pesticide registration announcements, monthly reports from agrochemical industry associations, raw drug price monitoring platforms, patent databases, and field trial reports. Update cadences vary: raw drug market quotes are updated daily or weekly, pesticide registration announcements are released irregularly following approval progress, and industry research reports are updated quarterly or annually.
Two types of document structures exist: One is structured parameter documents, which include fields such as active ingredient CAS number, registration certificate number, dosage form, application rate per mu, toxicity grade, etc. Common units include yuan/ton, mg/kg, hectare, and similar metrics. The other is semi-structured long-text reports, which include trial data, market analysis, and policy interpretations.
What constraints do these characteristics impose on model access and configuration
The multiple data types and differing update rhythms of agrochemical product data create clear constraints for model access and configuration. Standardized field requirements for structured parameters mean the context retrieved by the model must cover multi-dimensional associated fields, to avoid information loss from single-field matching. The length and complexity of long-text trial reports require adjustments to document segmentation and context window parameters, to prevent key trial data from being truncated. Data with different update frequencies require configured incremental indexing trigger rules, to adapt to daily-updated price data and irregularly updated announcement data. For intranet deployment scenarios, if agrochemical data involves industry-sensitive information, model interfaces must be accessed via intranet mapping. This requires correct network proxy rule configuration, to avoid security risks and connection failures caused by public network access.
How to set configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
chunkSize | 800–1200 characters | Agrochemical product documents include long-text trial reports and structured parameters. This length balances context completeness and retrieval accuracy, and avoids truncation of key information such as active ingredients and registration certificate numbers |
recallTopK | Top 6–8 entries | Agrochemical investment research data includes multi-dimensional associated fields. A sufficient number of retrieved contexts covers multiple types of information such as active ingredients, prices, and toxicity, to improve the comprehensiveness of responses |
similarityThreshold | 0.72–0.78 | A large number of standardized fields and similar product parameters exist in agrochemical data. An overly high threshold will miss relevant data, while an overly low threshold will introduce irrelevant competitor information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Agrochemical registration announcements are mostly multi-page PDFs containing a large number of tables and structured content, which take a long time to parse. This duration avoids parsing failures mid-process |
ollamaNumGpuLayers | 30–40 layers (AMD GPUs) | Adapts to the memory allocation of AMD GPUs, avoids model loading failures caused by insufficient memory, and meets the context requirements of large agrochemical models |
proxyUrl | Public network address mapped via intranet | Adapts to the access requirements of locally deployed intranet models, and resolves model connection issues in intranet environments |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to conduct tests on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: The knowledge base consistently shows an indexing in progress status, and backend logs return the
ETIMEDOUTerror code. Cause: In a Docker deployment environment, the container network has not been configured with correct proxy rules, making it impossible to access external model interfaces. - Symptom: An "access point configuration error" prompt pops up when configuring a model. Cause: The official standard API access address is not used, or valid API keys and corresponding model version identifiers are not filled in the configuration items.
- Symptom: An error is reported when executing model calls after deploying Ollama on an AMD GPU, and logs show
cudaErrorUnknown. Cause: The ROCm runtime environment for AMD GPUs is not properly adapted, or theollamaNumGpuLayersparameter is set beyond the available memory of the graphics card.
How to confirm successful configuration
- Navigate to the model configuration page of the knowledge base, and check whether parameters such as
proxyUrlandapiKeymatch the official documents provided by the model service provider. - Upload a pesticide registration announcement PDF for agrochemical products, trigger manual parsing, and check whether the parsed segments include complete active ingredient and registration certificate number information.
- Initiate a test call, input "Current market price of a certain agrochemical raw drug", and check whether the returned results include the corresponding structured fields and data sources.
- View the model call logs, and confirm that there are no error codes such as
timeoutorconnection refused.
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.