Multi-turn Dialogue and Prompt Engineering for Aquaculture

What the data for this category looks like

Aquaculture research report data is primarily sourced from publicly available monitoring data from provincial aquatic technology promotion stations, the national aquaculture industry association, and production logs submitted by aquaculture entities in major production areas. Update frequency falls into two categories: daily production dynamics for major production areas are updated daily, while in-depth industry reports are updated weekly or monthly. Document structure includes fields such as aquaculture stock volume, feed consumption, disease occurrence records, finished product purchase prices, and regional policy adjustment content. Units include tons, individual specimens, yuan per kilogram, aquaculture acreage, water volume in cubic meters, and others.

What Constraints These Characteristics Bring to Multi-turn Dialogue and Prompt Engineering

The real-time nature and segmented field characteristics of aquaculture research reports require multi-turn dialogue to retain context parameters specified by users, such as production area and aquaculture breed, to avoid repeated questions. Daily updated production dynamics data requires the dialogue API to support real-time recall of the latest content, without relying on static knowledge base caching. The multi-field, multi-unit document structure requires prompts to clearly define the statistical caliber and units of fields, to prevent parameter confusion. Segmented processing of long in-depth reports must adapt to the length limits of the dialogue context window, to avoid information loss from content truncation. Multi-turn interactions must automatically track the segmented focus areas users care about, to avoid answer deviations caused by lost context.

How to Set Configurations

Configuration Item	Recommended Value Range	Rationale
`maxContext`	`8000–12000 characters`	Adapts to the length of single aquaculture research reports (5000–10000 characters) while retaining context content for multi-turn interactions
`recall_top_k`	`Top 6–8 results`	Covers multi-dimensional monitoring data from aquaculture research reports, avoiding overly scattered or redundant recalled content
`similarity_threshold`	`0.72–0.78`	Filters low-relevance general agricultural data, accurately matching content from the aquaculture segment
`stream_response_interval`	`800–1200 milliseconds`	Adapts to front-end display rhythm, balancing data return speed and user experience
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Provides sufficient processing time for multi-field parsing of long in-depth reports
`enable_history_tracking`	`Enabled`	Retains key context parameters specified by the user, such as production area and breed, to avoid repeated questions in multi-turn dialogue

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.

Three Common Configuration Errors

After calling the streaming dialogue API, returned external links only work for the current page and cannot open in a new window. This occurs when the link_target parameter is not set to _blank, as the front end uses the default current-page jump logic.
After multiple conversation rounds, the detailed content in the conversation log does not match the actual returned answer content, and the log content repeats consistently. This happens when the enable_detailed_log parameter is not enabled, or when log storage is not associated with real-time conversation context updates.
The interval for streaming output returned data is fixed at 4 seconds, and cannot be adjusted to 1 or 2 seconds. This occurs when the default configuration of the stream_response_interval parameter is not modified, or when the configuration is not correctly synchronized to API service nodes.

How to Confirm Configurations Are Properly Set

Initiate a query that includes a specific production area and aquaculture breed, then follow up with a second query related to the same parameters. Verify that the conversation context retains the key information from the first query.
Call the streaming dialogue API, observe whether the front-end returned external links support opening in a new window, and check that the link_target parameter configuration matches expectations.
View the detailed page of the conversation log, verify that the returned answer content matches the content recorded in the log, confirming that the log tracking function operates normally.
Adjust the stream_response_interval parameter, then use an API debugging tool to observe whether the returned data interval falls within the configured value range.

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.