What the data for this category looks like
Intelligent due diligence report data for the planting industry comes from four primary sources: public planting zoning documents released by agricultural and rural competent authorities, field trial archives from local agricultural science academies, planting ledger records from agricultural input suppliers, and hourly monitoring data from local weather stations.
Update frequency varies by data dimension:
- Planting plan data is updated in a single batch in early each year.
- Field growth monitoring data is synchronized every ten days.
- Pest and disease records and agricultural input usage logs are updated in real time alongside the farming cycle.
Most documents are structured tables and semi-structured reports. Core fields include plot code, crop variety, sown area, yield per unit, and pest and disease severity level. Standard agricultural units are used, such as mu, kilograms per mu, millimeters, and degrees Celsius.
What constraints do these characteristics impose on knowledge base retrieval and recall?
The multi-source heterogeneous nature of planting industry data requires retrieval systems to support normalized matching across source fields. This prevents recall gaps caused by inconsistent field naming, such as "sown area" and "planted area".
The mixed format of structured tables and semi-structured reports demands targeted parsing rules. These rules must separately adapt to structured table field extraction and paragraph semantic extraction.
Differential update frequencies across data dimensions require retrieval systems to support incremental update tasks configured by data type. This avoids resource consumption from full index refreshes.
Unique agricultural measurement units and professional terminology require unit validation and term expansion during the recall stage. This ensures semantic consistency across retrieval results.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Planting industry due diligence reports often include multi-page trial data and multi-sheet ledgers. Single file sizes are generally larger than those in general scenarios. |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Parsing structured tables and long-form trial reports requires longer processing times. This prevents parsing interruptions caused by file complexity. |
chunk_length | 1000–1200 characters | Preserves complete semantics of agricultural professional terms, avoiding semantic breaks during recall caused by term splitting. |
recall_count | top 8 results | Planting industry due diligence requires coverage of multi-dimensional information including field data, meteorological data, and trial results. A higher recall count can cover multi-source associated content. |
similarity_threshold | 0.72–0.78 | Agricultural professional terms have relatively high semantic similarity differentiation. A moderate threshold can filter irrelevant results while retaining relevant information. |
reranked_return_count | top 4 results | Due diligence reports need to focus on core and accurate information. Reranked results are streamlined to improve reading efficiency. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: After uploading a structured PDF field ledger, knowledge base search tests return empty results, with the interface displaying "No relevant knowledge matched". Cause: Table parsing configuration is not enabled. Structured fields within the document are not extracted as indexable text fragments.
- Phenomenon: After splitting a long farming cycle document by fixed length, retrieval only returns partial paragraphs and fails to cover complete farming process information. Cause: Context overlap parameters are not configured during splitting. Professional terms and farming steps are split into different paragraphs, preventing the retrieval system from matching coherent query intent.
- Phenomenon: API models can normally return results related to planting industry professional terms, but local models fail to match similar queries. Cause: Local models have not loaded agricultural domain fine-tuned corpora. Semantic vectors cannot accurately map the semantic features of planting industry-specific terms.
How to Confirm Configuration Is Properly Set Up
- Upload a single planting industry trial report that includes structured tables and professional terms. Check if parsed text fragments fully extract core fields, confirming that parsing rule configurations are active.
- Initiate a retrieval test that includes agricultural professional terms. Verify the number of recall results and semantic matching degree, adjusting parameters to meet business requirements.
- Perform a splitting test on long documents. Check if adjacent chunks have a reasonable context overlap amount, avoiding semantic breaks caused by split professional terms.
- Call different models to run the same retrieval. Compare semantic consistency of recall results, adjusting model adaptation parameters to match the business scenario.
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.