What This Category's Data Looks Like
Core data for aquaculture comes from daily production logs of on-site breeding operators, environmental monitoring data from regional aquatic technology promotion stations, and daily purchase quotes from bulk aquatic product wholesale markets. Update schedule: Core market quote and profit calculation data is updated daily. Real-time monitoring data for breeding water bodies is collected hourly. Daily reports integrate daily average values. Documents are organized by breeding region, breeding species, and production cycle. Included fields are breeding area, species name, daily feed input, water dissolved oxygen and water temperature, harvest weight, purchase unit price, and allocated upfront fixed costs. Units include square meters, kilograms, ℃, mg/L, yuan/kg, and others. Field details vary slightly across different breeding operators.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Data sources are scattered and field formats are inconsistent. Multi-turn dialogue must gradually guide users to clarify breeding region, species, and production cycle, to prevent the model from accessing irrelevant data. Daily report data is updated daily. Multi-turn dialogue must proactively inform users of the current data update date, to avoid use of outdated information. Profit rate calculation relies on multiple related fields. If a user does not provide all required parameters, multi-turn follow-up questions must be used to collect missing information, rather than generating results directly. Market price fluctuations vary significantly across different breeding species. Prompts must be limited to the species specified by the user, to avoid mixing up quote data for different categories.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 15000–20000 characters | Aquaculture daily report data includes multiple types of production and market fields. A longer context must retain sufficient historical dialogue and knowledge base content, to ensure the model can associate key information across multiple rounds of questions |
maxHistory | Previous 6–8 dialogue turns | Aquaculture dialogue must track core parameters such as user-specified species and region. Excessive historical dialogue will distract from the current request's context focus |
RECALL_TOP_N | Top 8–12 entries | Aquaculture market data has differences across detailed species and regions. Too many recalled entries will cause redundant information interference; too few will miss key quote and cost data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Breeding logs may include multi-page daily production records. The parsing process requires a longer time to complete field extraction and integration |
systemPrompt | Only calculate profitability based on that day's aquaculture market data and the corresponding breeding operator's log data, according to the user-specified breeding species, region, and production cycle. First confirm any missing key parameters | Guide the model to proactively ask for necessary information that the user has not provided, to avoid generating results that deviate from actual data |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Scenario: After calling the API to start a dialogue, the
Humanfield in the response preview is empty. Cause: The user's input content for thecontentfield was not correctly included in the API request parameters, resulting in missing dialogue context. This issue is common in FastGPT V4.9.1. - Scenario: Dialogue records from different users are visible to each other. Cause: An independent
chatIdwas not generated for each user, or the corresponding user's unique identifier was not bound in the API request, resulting in failed session isolation. - Scenario: Profitability data for different breeding cycles is mixed up in multi-turn dialogue. Cause: The prompt did not explicitly require the user to specify the current production cycle node being discussed, resulting in the model accessing historical data from different cycles.
How to Verify Correct Configuration
- Initiate a test dialogue, enter basic information for a specified breeding species, and confirm whether the model proactively asks for missing profitability calculation parameters, such as harvest weight and purchase unit price.
- Call the API to start two independent dialogues, use different user identifiers, and confirm that the historical records of the two dialogues do not overlap.
- Upload an aquaculture daily report document, and confirm that the parsed fields include core information such as breeding region, species, and price.
- View the dialogue response preview, confirm that the
Humanfield always carries the user's input content, with no empty values.
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.