What the Data for This Category Looks Like
In marketing and customer acquisition scenarios for the finance/insurance/wealth management industry, data related to aquaculture comes from aquaculture site sensors (dissolved oxygen, water temperature, pH value), feeding equipment logs, seed batch archives, and financial institution records of farmers' insurance applications, credit, and consultations. Data update frequency varies by type: aquaculture sensor data updates every 5–15 minutes, aquaculture records update daily, and customer financial records sync in real time. The core structure of a single combined document uses breeding batch and customer ID as the core, including fields such as stocking density, feeding amount, disease records, insurance coverage, and credit limit. Fields must correspond to clear units: for example, density is measured in tail/cubic meter, feeding amount in kg/mu, and dissolved oxygen in mg/L.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Aquaculture data has real-time characteristics, batch specificity, and financial association attributes. This requires multi-turn dialogue to bind independent breeding batch and customer ID context to avoid mixing parameters across different customers or batches. The high-frequency updates of real-time sensor data require prompts to support dynamic insertion of the latest breeding environment parameters, and dialogue context must timely clean expired non-current batch data. The feature of multiple fields with dedicated units requires prompts to clearly specify unit rules to prevent the model from outputting confusing parameter descriptions. The combined document structure that integrates financial behavior data requires multi-turn dialogue to recall knowledge base content by customer and breeding batch classification, and must not mix financial and production data across customers or batches, to ensure the personalization and compliance of marketing content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Combined aquaculture and financial data includes multiple batch archives, real-time environmental parameters, and customer financial records. Excessively long context will cause the model to truncate critical information |
recallTopK | Top 6–8 entries | Financial marketing scenarios require recalling breeding production data, customer financial records, and historical dialogue content at the same time. Excessive recall will dilute core marketing information |
similarityThreshold | 0.72–0.78 | The fields of combined aquaculture and financial data are professional and have unified units. A threshold that is too low will introduce irrelevant aquaculture categories or customer data |
httpRequestTimeout | 300 seconds | Some batch aquaculture archive parsing or cross-system financial data pulling takes a long time |
Chunk size | 1000–1500 characters | A single record of combined aquaculture and financial archives includes multiple sets of parameters. Excessively long segments will cause context fragmentation during recall |
systemPromptTemplate | Integrate context in the order of customer ID, breeding batch, real-time environmental parameters, and historical financial records. When outputting marketing content, specify the corresponding parameter units | Aquaculture marketing in the financial industry requires accurate matching of the current customer's breeding status and financial needs, to avoid parameter confusion and compliance risks |
The parameter values provided on this page are common recommendations used to establish 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.
Three Common Misconfigurations
- Phenomenon: After using an HTTP request orchestration node, the corresponding output field is missing from the dialogue output result, with runtime version 4.6.9. Cause: The "Write output to dialogue context" option was not enabled in the HTTP node configuration, causing the returned breeding or financial data to not be included in the dialogue chain.
- Phenomenon: After the multi-turn dialogue completes the AI reply, it automatically enters the question classification node, interrupting the marketing dialogue process for farmers. Cause: The workflow was not configured with a jump rule after the dialogue ends, and the default returns to the initial classification node, interrupting the generation of personalized marketing content.
- Phenomenon: In one-to-many dialogue scenarios, the breeding data and financial record contexts of different farmers appear cross-mixed. Cause: Each dialogue session was not bound with an independent customer ID and breeding batch identifier, resulting in unisolated contexts and causing compliance risks for marketing content.
How to Verify Proper Configuration
- Import a single batch breeding archive and the corresponding farmer's financial record document, launch a multi-turn dialogue, and test whether it can accurately recall the corresponding customer's insurance, credit information, and breeding parameters.
- Trigger the HTTP request orchestration node, check the dialogue history, confirm that the returned breeding or financial data is displayed in the dialogue chain, and the output switch has been enabled under version 4.6.9.
- Start a one-to-many dialogue session, enter queries from different farmers separately, confirm that the contexts of each session are not cross-displayed to avoid compliance risks.
- Adjust
similarityThresholdto 0.75, test whether the recall results only include breeding and financial data that meet the matching requirements, with no irrelevant categories or customer content.
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.