What the data for this category looks like
Crop farming investment research data covers multiple sources: public agricultural situation monitoring data from the Ministry of Agriculture and Rural Affairs, crop trial reports from local agricultural academies, variety approval announcements from seed enterprises, real-time observation records from national meteorological stations by time period, and trading data from agricultural product futures markets.
Update cycles vary significantly: meteorological data is updated hourly or daily, variety approval announcements are released irregularly along with annual approval batches, agricultural situation monitoring weekly reports are updated every ten days, and futures data is updated in real time during trading days.
Document structures include structured fields (such as yield, rainfall, accumulated temperature, with corresponding units of kg/mu, mm, °C·day), semi-structured trial records (including trial location, variety parameters, growth period), and unstructured farmer survey texts.
What constraints these characteristics impose on the "knowledge base retrieval and recall" link
Multi-source heterogeneous data structures require the retrieval system to support cross-type matching, and need to distinguish recall weights for structured numerical data, semi-structured trial records, and unstructured text.
Differences in update cycles require setting timeliness filtering rules by data source type, to avoid recalling expired historical variety data or outdated meteorological records.
Exclusive fields and units (such as kg/mu, effective accumulated temperature) require the retrieval system to support unit normalization and entity recognition, to prevent recall failure caused by unit mismatches or incorrect term splitting.
The span of document lengths, from hundred-word meteorological warnings to thousand-word trial reports, requires balancing semantic integrity during segmentation and rationality of merging.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Segment Length | 800–1200 characters | Crop farming documents range from thousand-word trial reports to hundred-word meteorological warnings. This range balances semantic integrity for long documents and rationality of merging for short documents |
Similarity Threshold | 0.65–0.75 | Crop farming investment research data contains a large number of exclusive professional terms such as "grains per ear" and "effective accumulated temperature". A threshold that is too low will introduce irrelevant results, while a threshold that is too high will miss relevant professional literature |
Number of Recalled Entries | Top 15–20 entries | Crop farming investment research requires reference to multi-source data including trial data, meteorological records, and policy documents. A sufficient number of recalled entries can cover multi-dimensional information |
Number of Reranked Returned Entries | Top 5–8 entries | Investment research scenarios require precise core reference content. Too many entries will increase the burden of context processing |
Incremental Update Frequency | Match by data source type | Meteorological data is updated daily, agricultural situation monitoring is updated every ten days, and variety approval announcements are updated by annual batches. Setting corresponding frequencies by data source ensures data timeliness |
Text Understanding Model | Model that supports agricultural domain word segmentation | Crop farming has a large number of exclusive terms. This model can improve the accuracy of keyword matching and entity extraction |
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 test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: No English literature in the knowledge base is returned after a Chinese query. Cause: Multi-language retrieval configuration is not enabled, or no corresponding language tag is added when importing English literature, resulting in only Chinese indexes being matched during retrieval.
- Phenomenon: After custom separator configuration is completed, segmentation results either merge multiple natural paragraphs or split single paragraph content. Cause: The segmentation rules are not adjusted based on the chapter line break characteristics of crop farming documents, and only relying on custom separators without setting a minimum length threshold for paragraph merging.
- Phenomenon: Retrieval results cannot accurately match professional trial data, and core entities are not correctly recalled. Cause: The text understanding model is not enabled, so variety names, planting areas and other exclusive entities in the document cannot be extracted, and only literal keyword matching is relied on.
How to confirm the configuration is correct
- Upload a crop farming trial report containing English content, initiate a query with the corresponding English keywords, and check whether the retrieval results include the content of this report.
- Upload a document segmented with custom separators, view the segmentation preview interface, and confirm that the segmentation results meet the expected length and chapter division.
- Initiate a query containing professional terms such as "effective accumulated temperature" and "grains per ear", and check whether the retrieval results include matching entity tags.
- View the knowledge base update log, confirm that the update frequencies of different data sources meet the preset configuration, and expired data is not recalled.
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.