What Data for This Category Looks Like
Aquaculture investment research data comes primarily from four sources: pond environment monitoring sensors, daily logs from farming entities, market reports from industry associations, and compliance records from fishery administration departments. Sensor data updates hourly, and includes structured metrics such as dissolved oxygen, water temperature, and pH, with fixed units of mg/L, ℃, and pH. Daily logs from farming entities are semi-structured documents containing fields like pond ID, farming cycle, feed dosage, and disease records. Market reports from industry associations and compliance archives from fishery administration departments are unstructured documents that include content such as monthly price trends and farming permission scope.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Multi-turn dialogues for structured real-time monitoring data must accurately bind pond IDs to avoid mixing metrics from different ponds. Sensor data updating hourly expires quickly; overly long conversation contexts include invalid historical data, so the effective duration of the context window must be limited. Semi-structured farming logs have cross-section pond association information, so sufficient overlapping content must be retained during chunk processing. Investment research scenarios require querying both structured metrics and unstructured disease solutions, so prompts must clearly distinguish data types and unit requirements.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Aquaculture data includes real-time monitoring and historical logs. Excessively long contexts will introduce expired pond data, reducing the accuracy of current dialogues |
recallCount | Top 6 entries | Individual farming logs are medium-length. Too many recalled entries will cause context overload and reduce model response speed |
similarityThreshold | 0.72–0.80 | Need to distinguish monitoring data from the same pond across different cycles. A threshold that is too low will recall historical records from unrelated ponds |
chunkSize | 800–1000 characters | Farming monitoring data has many structured fields. Chunks that are too long will lose field association information, reducing model extraction accuracy |
promptPrefix | "Please combine the historical monitoring data and real-time environmental indicators of the currently specified pond ID, and organize the results according to the required fields and units" | Aquaculture investment research requires accurate association with pond identifiers. Prompts must clearly specify field and unit requirements to avoid mixing data from different ponds |
parseChunkOverlap | 100–150 characters | Farming logs have cross-section pond ID association information. Overlapping chunks retain context coherence and prevent the model from fragmenting information |
The parameter values provided on this page are conventional recommendations used as a starting point 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 settings.
Three Common Misconfigurations
- Phenomenon: After 5 consecutive multi-turn dialogues, dissolved oxygen data queries for the same pond show unit confusion (for example, both mg/L and ppm appear at the same time). Cause: Unit formats are not strictly specified in the prompt, and
maxContextis not configured to only retain context for the current pond, so old historical data with inconsistent units is included. - Phenomenon: Structured data returned after executing an SQL query via a workflow cannot be displayed correctly in the AI dialogue window. Cause: Rendering rules for structured data are not configured in
promptPrefix, and the automatic adaptation switch formarkdownRenderis not enabled. - Phenomenon: When configuring advanced orchestration to send two investment research briefing messages, the second message is intercepted by the system and not sent. Cause: A reasonable delay for
workflowMessageIntervalis not set. FastGPT's message sending queue defaults to limiting single message intervals; without configuration, two messages will be merged.
How to Confirm Proper Configuration
- Import one real aquaculture pond monitoring log, initiate a multi-turn dialogue, and verify that each answer is associated with the currently specified pond ID.
- Trigger a workflow to run an SQL query and return results, and verify that the format displayed in the AI dialogue window complies with preset Markdown rules.
- Initiate more than 5 consecutive investment research dialogues, and verify that the accuracy of responses remains stable with no unit or field confusion issues.
- Configure advanced orchestration to send two test messages, and verify that both messages are sent normally in order with no truncation or merging.
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.