What the data for this category looks like
Logistics investment research data sources include port operation real-time ledgers, trunk line transport timeliness reports, weekly warehouse inventory turnover reports, industry freight rate index API interfaces, and more. Data update frequencies fall into three categories: port dynamics update every 15 minutes, warehouse ledgers update daily, and industry research reports and freight rate indexes update weekly. Document structures include structured CSV tables, semi-structured PDF research reports, and JSON-formatted API-retrieved data. Core fields include cargo category, transport distance, timeliness cycle, freight unit price, and warehouse turnover times. Their units are category code, kilometer, hour/day, yuan/ton, and times/month respectively.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Real-time updated port and transport data requires multi-turn dialogue to recall the latest dataset in real time, to avoid using expired data that reduces analysis accuracy. Structured tables and multi-field features require prompt engineering to clearly specify field extraction and unit conversion rules, to prevent data confusion. Long-text research reports and segmented API data require limiting the length of multi-turn context, to avoid exceeding LLM window limits. Multi-dimensionally associated logistics data requires prompt engineering to distinguish similar fields across different business scenarios, such as distinguishing timeliness units between trunk line transport and same-city delivery.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Logistics investment research data includes long-text research reports and multi-segment structured reports, and needs to accommodate multi-turn dialogue context and segmented recall content |
recallNum | Top 6–8 entries | Logistics data has multi-dimensional similar fields, and sufficient associated datasets need to be recalled to cover investment research analysis requirements |
similarityThreshold | 0.72–0.80 | Distinguish similar data across detailed dimensions such as freight routes and freight rate types, filter low-relevance recalled content |
chunkSize | 1000–1500 characters | Logistics operation reports are mostly tabular content, and the segment length adapts to single-table or single-chapter data, avoiding splitting that destroys business logic |
multiRoundMaxTurn | 5 turns | Investment research multi-turn dialogue is mostly progressive analysis, limiting the number of turns to avoid model confusion caused by redundant context |
apiSyncInterval | 15 minutes | Match the real-time data update frequency of ports and trunk line transport, ensuring the timeliness of recalled data |
promptTemplate | Structured prompt engineering with multi-task branches | Support the same input to trigger multiple sets of prompt engineering for parallel processing and result merging, adapting to the multi-dimensional analysis needs of logistics investment research |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Only a single set of prompt engineering is triggered for the same input, and merged multi-dimensional analysis results are not generated. Cause: The multi-task branch rule of
promptTemplateis not configured, and only a single set of prompt engineering is used to process the input. - Phenomenon: A 400 status code is returned when calling the API dialogue, and the returned result is empty. Cause:
apiSyncIntervalis not set to match the data update frequency, and expired empty datasets are recalled. - Phenomenon: The output of the program execution module cannot be used as background knowledge input to subsequent AI dialogue modules. Cause: The external variable binding configuration of the dialogue context is not enabled, and the execution result is not written to the specified context field.
How to confirm the configuration is correct
- Upload a logistics freight rate report, trigger multi-turn dialogue, and verify that the recalled segmented content matches the chapters of the original document.
- Input a query for the same freight line, trigger multiple sets of progressive questions, and verify that the returned results cover all expected analysis dimensions.
- Call the API with test parameters, verify that the returned status code is 200, and the result fields include expected logistics data.
- View the dialogue log, verify that the number of context turns does not exceed the value set by
multiRoundMaxTurn.
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.