What the data for this category looks like
Crop farming financial report data primarily comes from periodic reports publicly disclosed by listed crop farming enterprises, industry statistical materials released by agricultural and rural authorities, and production and operation data compiled by industry associations. Data update cycles include quarterly (enterprise quarterly reports), annual (enterprise annual reports), and monthly (industry supply and demand data). Document structures typically include fields such as planting scale, yield per unit area, per-unit planting cost, revenue proportion, policy subsidy items, and similar metrics. Most field units use common agricultural production measurement standards including mu, kilogram, yuan/ton, ten thousand yuan, and other standard units.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-source update cycles of crop farming financial reports require dialogue systems to support data retrieval across different time dimensions, and avoid mixing data from different cycles. Specialized measurement fields require prompt engineering to pre-unify unit definitions, and prevent unit conversion errors during conversations. Multi-dimensional business fields require multi-turn dialogue to retain the statistical scope and subject of context, and avoid repeated statement of the same prerequisites. Differences between industry data and enterprise financial reports must be clearly distinguished in prompt engineering, and prevent mixing of cross-type data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 50 MB | Single crop farming financial report documents (such as annual report PDFs) typically do not exceed 50 MB, and embedded tables and long text must be fully parsed |
maxContext | 8000–12000 characters | Multi-turn dialogue needs to retain multiple sets of statistical cycle, unit, and business subject information, and avoid context overflow that causes loss of key prerequisites |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Crop farming financial reports contain a large number of structured tables and agricultural specialized terms, and parsing takes longer than general documents |
Recall Count | Top 6 entries | Core business indicators of crop farming financial reports typically number 6 to 8. Excessive recall will disrupt dialogue logic and answer accuracy |
Similarity Threshold | 0.75–0.85 | Need to distinguish similar expressions of the same field across different cycles, and avoid incorrect recall of non-target data across cycles |
Chunk Length | 1000–1500 characters | Paragraphs of crop farming financial reports mostly contain continuous production data, and chunk length adapts to context requirements for data correlation |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Issue: A
413 Request Entity Too Largeerror is returned after calling the API to upload a crop farming financial report PDF. Cause: TheUPLOAD_FILE_MAX_SIZEconfiguration was not adjusted, and the default limit is smaller than the actual size of the uploaded document. - Issue: The test conversation answer on the platform side is inconsistent with the result returned by the API call. Cause: The API call did not carry the
knowledge_base_idparameter, or the default specified knowledge base was not bound, resulting in the call using the default knowledge base instead of the one used during testing. - Issue: The statistical cycle of financial report data returned in multi-turn dialogue does not match the user's question. Cause: The
maxContextconfiguration was not set to retain statistical cycle information in the context, resulting in loss of prerequisite conditions during multi-turn dialogue.
How to Confirm Configuration is Correct
- Upload a standard crop farming financial report document, check if the parsed text contains complete tables and unit information, and verify the matching relationship between configuration items and document size.
- Initiate the same query on both the platform side and the API side, check if the content and format of the returned results are consistent, and confirm the configuration status of the knowledge base binding parameters.
- Initiate two consecutive queries: first ask for yield per unit area data for a specific cycle, then ask for corresponding cost data. Check if the dialogue automatically retains the cycle and subject information, and confirm that the context configuration is effective.
- Adjust the similarity threshold and initiate similar queries, check if the matching accuracy of recall results meets business requirements, and confirm the rationality of the threshold configuration.
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.