What this category of data looks like
Maritime port investment research data originates from structured reports from port operation management systems, freight rate and capacity data published by international shipping organizations, customs clearance ledgers, channel hydrological monitoring logs, and public industry research reports. Data update frequencies cover three categories: real-time (berthing, berth occupancy), daily (schedules, clearance data), and monthly (throughput, revenue). Document structures include structured tables (with fields such as berth utilization rate, cargo-specific container volume), long-text research reports, and structured API interface data. Field units mostly use professional measurement standards such as TEU, ten thousand tons, meters, and hours.
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" link
The multi-source heterogeneous data structure, multi-timeliness update characteristics, and professional measurement fields of maritime port investment research impose clear constraints on multi-turn dialogue and prompt engineering configurations. First, the coexistence of structured tables, real-time APIs, and long-text research reports requires prompts to clearly define data call type boundaries, avoiding confusion between real-time berthing data and monthly throughput statistics. Second, data sources with multiple update frequencies require multi-turn dialogue context to track the timeliness of the currently called data source, preventing expired operational data from being output. Third, professional unit fields such as TEU, ten thousand tons, and meters require prompts to mandate attaching corresponding units when outputting, avoiding measurement deviations in investment research conclusions.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 tokens | Maritime port investment research data includes long-text research reports and structured tables, requiring retention of historical questions and knowledge base recall content in multi-turn sessions to avoid losing key field information due to context truncation |
recall_top_k | Top 6–8 entries | Port investment research data covers multi-dimensional indicators such as berths, container volume, and freight rates, requiring recall of a sufficient number of relevant fragments to cover cross-dimensional question needs in multi-turn dialogue |
prompt_template | Fixed dedicated investment research-specific template, mandatory requirement to attach data source and update time | Maritime port data has high timeliness requirements, requiring clear indication of data source timeliness in each response to avoid confusing real-time operational data with historical statistical data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Port investment research documents often contain large structured tables and long-text research reports, requiring sufficient time to complete full parsing |
similarity_threshold | 0.75–0.85 | Port investment research data includes professional terminology and cross-category indicators, requiring setting a reasonable threshold to filter low-relevance fragments while retaining associated recall across terms |
enable_chat_history | Enabled, retain context from the past 72 hours of the current session | Multi-turn investment research dialogue requires tracking context for consecutive questions, but expired session data must be cleaned regularly to avoid context overload |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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 configuration mistakes
- Phenomenon: After connecting multiple AI dialogue modules in a workflow, the final output includes all dialogue content from all modules. Cause: The
chat_history_trimparameter is not configured to truncate redundant context, or the setting to retain only the output context of the last dialogue module is not enabled. - Phenomenon: The first investment research dialogue initiates with a delay of more than 3 seconds, and subsequent dialogue speeds return to normal. Cause: The
cache_rag_resultparameter is not enabled, or the real-time port data associated with the knowledge base has not completed caching; the first call needs to pull the full data source. - Phenomenon: Investment research data output in multi-turn dialogue does not attach professional units. Cause: The prompt template does not mandate attaching corresponding units such as TEU and ten thousand tons in responses, and no unit verification rule is added to
prompt_template.
How to confirm the configuration is correct
- Initiate a question covering multi-dimensional port indicators, check whether the response attaches corresponding professional units and data timeliness information.
- Connect two AI dialogue modules and trigger the workflow, check whether the final output only includes the dialogue results of the last module.
- Initiate the first dialogue and record the delay duration, check whether the delay meets the timeliness requirements of the business scenario, and confirm that the cache configuration has taken effect.
- Bind an automatic trigger event in the workflow, check whether preset investment research question content is automatically generated after the dialogue box loads.
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.