What the data for this category looks like
Data related to shipping port yield rates primarily comes from official port management authorities, international shipping trading platforms, and vessel operation statistics systems. The core update cycle is daily; long-term freight rate benchmarks for some long-haul routes are updated weekly. Individual data documents use a flat structured format, containing fields such as universal port code, berthing route name, operation category, benchmark rate, daily operation volume, and fluctuation range. Field units include yuan per standard container, ten thousand tons, calendar day, etc., with no nested levels.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
The flat structured nature of this category’s data requires prompts to clearly define the scope of field calls, preventing the model from extracting undefined non-standard fields. The daily update rhythm mandates that every data query triggered during multi-turn dialogue processes must call the latest daily dataset, and reusing cached results older than 24 hours is prohibited. The variation in field units requires prompts to explicitly specify the corresponding unit for each output item, avoiding unit confusion across different data categories. The unique identification attribute of port codes requires that the first round of multi-turn dialogue must clearly request the target port code, preventing cross-port data confusion.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Shipping port yield rate data includes multiple fields, and multi-turn dialogue needs to retain multi-turn query requirements and historical data. This length can cover the context requirements of conventional multi-turn interactions |
recall_top_k | Top 6–8 entries | Individual port data entries are numerous. Excessive recall will exceed the context window, while insufficient recall will lose key data related to operation categories and routes |
prompt_template | Specify target port code + query field scope + output unit requirements | Flat structured data requires clear limitations on called fields to prevent the model from extracting non-standard fields, while unifying unit output rules |
temperature | 0.1–0.3 | Yield rate data requires factual accuracy; a lower temperature reduces the probability of the model generating irrelevant content |
datasource_tag_filter | ["shipping port yield rate", "updated today"] | Accurately filter the latest data of the corresponding category, avoiding mixing in data from other industries or outdated historical data |
workflow_token_limit | 15000 characters | Sufficient space must be reserved for the cumulative token usage of multi-model nodes in multi-turn dialogue, preventing token overflow errors |
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 testing on internal samples is recommended before finalizing settings.
Three common mistakes
- When calling the
api/v1/chat/completionsinterface, thedatasource_tag_filterparameter is not specified, resulting in non-shipping port yield rate data being included in the returned results. Without configuring a knowledge base tag filter, the interface by default recalls all knowledge base content, failing to accurately match target category data. - Calling the interface after configuring the prompt template returns a
400 Bad Requeststatus code. This may be due to unclosed quotation marks or invalid characters in the prompt template, or the knowledge base fields referenced in the template are not defined in the dataset. - Multiple AI dialogue nodes are orchestrated in a workflow, and the total token usage per interaction exceeds the limit, causing the workflow to interrupt. The
workflow_token_limitparameter is not configured, and unified control of token usage across multiple nodes is not implemented. Each node calculates tokens independently without shared restrictions.
How to confirm that configurations are correctly set
- Call the
api/v1/chat/completionsinterface, pass the preset port code and query requirements, and check whether the returned result fields and output format meet the prompt template requirements. - Check the knowledge base configuration items, confirm that the corresponding category tags have been added, and verify that the correct tag filter parameters are included in the interface call.
- Run multi-turn dialogue test cases, confirm that the model can retain port identifiers and query conditions from historical interactions, with no context confusion or loss.
- Run a workflow containing multiple AI nodes, monitor the token consumption of each node, and adjust the corresponding parameters to fit the current workflow scope.
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.