What Data for This Category Looks Like
Core data for crop farming comes from plot planting ledgers, soil test reports, agricultural input purchase receipts, meteorological observation records, and agricultural product acquisition vouchers. These materials serve as the foundation for marketing content used by financial institutions when offering products such as crop insurance and agricultural input loans to farmers. Data updates follow the farming cycle, with new records generated during planting, growing, and harvesting phases. Document structures primarily use structured tables, supplemented by unstructured planting logs. Fields include plot ID, crop variety, planting date, plant height, soil pH value, fertilization amount, pest and disease records, and more. Most fields have associated units, such as kilograms per mu, millimeters, degrees Celsius, and others.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
As a marketing content and customer acquisition tool for financial institutions targeting farmers, the mixed structured and unstructured nature of crop farming data requires multi-turn dialogue to align with both structured fields and unstructured log content. Prompts must clearly specify field units and business scenarios. The fact that data updates with the farming cycle means multi-turn dialogue must link to historical record timelines. This prevents confusion between planting data from different cycles, which would reduce the accuracy of marketing content. A single document may contain long-term records for multiple plots. The context length of a single round of input can easily exceed model limits, so long documents must be properly chunked to ensure efficient marketing content generation. Marketing content must align with specific crop farming scenarios, so multi-turn dialogue must accurately match identifying information such as crop and plot provided in user input to improve customer acquisition conversion effectiveness.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Aligns with the mixed context length of single plot planting ledgers and marketing copy for crop farming, prevents context overflow |
chunkSize | 800–1000 characters | Preserves complete single-cycle planting data for one plot while controlling per-chunk length, supports splitting long documents with multiple plots |
recallTopK | Top 3–5 entries | Accurately matches historical data for specific plots or crops, avoids excessive recall that disrupts marketing content generation |
similarityThreshold | 0.75–0.85 | Filters low-match irrelevant planting records, fits the characteristic that crop farming data fields have clear units |
conversationHistoryMaxCount | 10–15 entries | Retains recent farming operations and dialogue records, balances context relevance and model load |
UPLOAD_FILE_MAX_SIZE | 200 MB | Supports uploading large files such as remote sensing images for multiple plots and long-term planting logs |
Three Common Mistakes
- Phenomenon: Multi-turn dialogue fails to provide relevant answers starting from the second question, with returned results unrelated to historical planting records. Cause: Conversation context association configuration is not enabled, or the
conversationHistoryMaxCountvalue is too low, failing to retain sufficient historical dialogue and planting data. - Phenomenon: After uploading bulk planting ledger documents, context overflow prompts appear, or some plot data is missing from recall results. Cause: The
chunkSizeparameter is not configured, or its value is outside the reasonable range, failing to effectively split long documents. - Phenomenon: In a multi-knowledge-base scenario, specific planting documents cannot be targeted for recall, with returned results mixing content from multiple documents. Cause: Document identifying fields are not clearly specified in the prompt, or document metadata retrieval rules are not configured.
How to Confirm Configuration Is Correct
- Upload a complete quarterly planting ledger document, initiate multi-turn questions, verify that the second question can link to fertilization records for the historical plot, confirming that context association is active.
- Upload a document exceeding the preset size, verify that the system automatically triggers chunking with no context overflow error, confirming that chunking configuration is active.
- Specify the document identifying field for a specific crop in the prompt, initiate a question, verify that recall results only include planting data for that crop, confirming that document targeting rules are active.
- View the dialogue log management interface, verify that historical records are automatically cleaned according to the preset duration, confirming that log configuration is active.
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
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.