What the data for this category looks like
Data related to aquaculture comes primarily from online monitoring equipment at ponds, daily breeding records, sales delivery documents, and disease diagnosis records. This data supports the generation of marketing content for financial institutions targeting aquaculture entities. Monitoring equipment data updates hourly or in real time. Records and sales documents update daily. Diagnosis records are archived immediately after completion. Structured data includes fields such as pond ID, monitoring time, dissolved oxygen concentration, water temperature, and pH. Units are mg/L, ℃, and dimensionless respectively. Unstructured data includes disease photos and feeding log documents. Fields include pond number, feeding period, feed type, and feeding weight.
What constraints these characteristics impose on model integration and configuration
High-frequency updated monitoring data requires model integration to support real-time data pulling or short-cycle synchronization. This prevents generated marketing content from mismatching actual breeding conditions due to delayed data. Multiple structured data fields and clear units require unified field mapping rules during configuration. This avoids model inference bias caused by mixed units. Unstructured disease photos and long documents require adjusting segment parsing parameters to fit the length characteristics of aquaculture documents. It also requires optimizing recall rules for breeding-specific terminology. Additionally, mixed access to multi-source data requires configuring data validation links to filter invalid pond IDs or abnormal monitoring values.
How to set configuration parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Collections of aquaculture disease photos and monthly feeding log documents typically do not exceed 200 MB per file. Exceeding this threshold causes upload timeouts |
maxContext | 8000–12000 characters | Aquaculture professional documents contain many technical terms and long paragraphs. This range retains complete context information and avoids truncation of critical content |
RECALL_TOP_K | Top 6–8 results | Aquaculture marketing content requires combining multi-dimensional data such as pond environment and disease status. Too many recalled results increase inference load, while too few fail to cover necessary information |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing long documents requires sufficient time for text splitting and term recognition. 300 seconds covers parsing requirements for most aquaculture documents |
SIMILARITY_THRESHOLD | 0.72–0.85 | Aquaculture professional terms have high semantic similarity. This range filters irrelevant recalled results while retaining content related to the professional field |
TOOL_CALL_MAX_STEPS | 3 steps | Aquaculture marketing content generation typically only requires three tool links: data pulling, term verification, and content generation. Too many steps cause context overflow |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- When configuring
PROXY_CHANNEL_KEY, fail to add an aquaculture-specific model call whitelist. This causes a 403 error when calling the Huoshan Doubao model. The root cause is that separate configuration for call permissions targeting aquaculture-specific data is not completed, and the default whitelist does not cover call requests for this specialized scenario. - Tool calls return a context overflow error, shown as truncated response content or the error prompt
context length exceeded. The root cause is failure to adjust themaxContextparameter based on the length characteristics of aquaculture long documents, resulting in insufficient context window. - When accessing the MiniMax model, knowledge base content fails to load normally, shown as empty knowledge base field mappings. The root cause is failure to configure field parsing rules adapted to the MiniMax model, which cannot recognize specialized fields in aquaculture documents such as pond ID and feeding weight.
How to confirm proper configuration
- Upload a standard aquaculture feeding log document. Check that the parsing result correctly identifies specialized fields such as pond number and feeding weight, to confirm field mapping configuration is active.
- Initiate a model call request. Verify that the returned marketing content includes aquaculture professional terms and has no context truncation, to confirm the
maxContextandPARSE_FILE_TIMEOUT_SECONDSconfigurations are reasonable. - Call the model via the connected MiniMax or Huoshan Doubao channel. Check that responses based on aquaculture data are returned normally, to confirm channel permissions and parameter configurations are correct.
- Simulate a high-frequency data pulling scenario. Check model call response speed and success rate, to confirm
UPLOAD_FILE_MAX_SIZEandRECALL_TOP_Kconfigurations fit business rhythm.
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.