What the data for this category looks like
Data sources for crop farming intelligent due diligence reports include public monitoring data from agricultural and rural affairs departments, production records from planting bases, real-time meteorological APIs, soil test reports, buyer quotation records, and satellite imagery materials. Update frequencies vary significantly: meteorological data is updated hourly, production records are updated monthly, purchase prices are updated weekly, and annual due diligence summary data is updated quarterly.
The data includes structured fields and unstructured content. Structured fields include plot ID, planting area, yield per mu, and similar items, with units such as kg/mu, days, and others. Unstructured content includes planting logs, scanned contracts, and on-site investigation reports.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-dimensional nature and varying update frequencies of farming due diligence data require multi-turn dialogue to first anchor the core query scope, to avoid mixing invalid data across cycles or plots. Differences in units and statistical standards for structured fields require prompts to clearly define field definitions, to prevent the AI from confusing yield per mu standards across different production areas.
The combination of long documents and multi-source data requires context management to limit redundant information while retaining the associated relationships of key due diligence indicators. Multi-round follow-up questions need to gradually narrow the query scope, so prompts must include pre-validation steps: first confirm crop type, time range, and target plot, then retrieve the corresponding data sources.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Crop farming due diligence data includes multi-dimensional structured fields and long documents. Excessively long contexts will dilute core due diligence indicators |
recall_top_k | Top 6–10 entries | Farming data has scattered dimensions. Too many recalled entries will introduce redundant information from irrelevant plots or cycles |
similarity_threshold | 0.75–0.85 | Farming data has differences in units and statistical standards. The threshold must balance recall accuracy and coverage |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Crop farming due diligence includes satellite imagery, multi-page contracts, and soil test reports. Parsing takes a long time |
prompt_template | First confirm the due diligence crop, time range, and target plot, then retrieve the corresponding data sources to generate the report | Farming data is scattered across multiple types of records and APIs. The core query scope must first be anchored |
UPLOAD_FILE_MAX_SIZE | 500 MB | Annual crop farming due diligence packages include multiple documents and image files. This accommodates large-volume upload requirements |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After configuring a custom prompt template, the AI directly generates general due diligence content without first confirming the crop, time range, and plot as required. Cause: The prompt template is not correctly linked to the trigger logic of the dialogue application, or the template does not explicitly include pre-validation steps.
- Phenomenon: When calling a configured dialogue application with an API key, an unauthorized prompt is returned, and the API key configuration has been confirmed to be correct. Cause: The knowledge base bound to the API key does not include crop farming due diligence-specific data sources, or the interface permission for multi-turn dialogue functions has not been enabled.
- Phenomenon: During a multi-turn dialogue, switching the AI model causes the previous conversation context to be lost. Cause: Cross-model context synchronization configuration is not enabled, or the context cache of the current conversation is not retained when switching models.
How to Confirm Proper Configuration
- Initiate a test dialogue, enter "Help me generate a crop farming due diligence report", and observe whether the AI first asks for crop type, time range, and target plot to confirm that the prompt logic is active.
- Upload a crop farming annual record document, initiate multi-round follow-up questions, and observe whether the AI can correctly associate context and retrieve corresponding fields to generate content.
- Call the API interface of the dialogue application, pass the configured API key, and check whether the returned results include correct context association and data source matching.
- Attempt to switch the AI model used for the dialogue, check whether the context is retained, and whether the new model can correctly continue the previous dialogue logic.
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.