What the data for this category looks like
Logistics research report data comes from public reports from transportation industry associations, regular filings of listed logistics companies, special analyses from third-party logistics research institutions, and public operational data from ports and freight hubs. Update cycles cover monthly, quarterly, and annual. Monthly data mostly includes high-frequency metrics such as trunk line freight volume and freight rate indices. Quarterly reports include regional logistics chain analysis. Annual reports cover full industry trends and policy interpretations. Document structures typically include core business metrics, regional distribution details, policy impact analyses, and corporate operation cases. Fields use specialized units such as ten thousand tons, TEU, and yuan/ton-kilometer, with attached time periods and month-over-month, year-over-year comparison dimensions.
Constraints on multi-turn dialogue and prompt engineering
The high-frequency, multi-metric, multi-unit characteristics of logistics research reports impose specific constraints. Multi-turn conversations must retain the metric scope and regional context specified by users, to avoid repeated inquiries about basic information. Prompts need clear calibration rules for multi-unit fields, to prevent confusion between measurement dimensions such as ten thousand tons, TEU, and yuan/ton-kilometer. The dialogue system must prioritize recalling the most recently released knowledge base entries for frequently updated monthly data, to avoid citing outdated information. The long document structure requires multi-turn conversations to accurately locate targeted sections and fragments, to reduce ineffective recall content. Users often ask follow-up questions across metrics, such as shifting from freight rate indices to regional freight volume. The system must automatically associate filtering conditions from the context to ensure consistent answers.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Multi-turn dialogue for logistics research reports often involves context association across multiple metrics and regions, requiring sufficient window space to retain historical conversations and recalled report fragments |
RECALL_TOP_N | Top 6–8 entries | Logistics research reports have numerous specialized metrics. Too many recalled entries increase context load, while too few fail to cover the multi-dimensional data required by users |
SIMILARITY_THRESHOLD | 0.75–0.85 | Metric names in logistics research reports have similar expressions, such as "trunk line freight volume" and "road freight volume", requiring a balance between recall precision and coverage |
PARSE_CHUNK_SIZE | 1000–1500 characters | Single sections of logistics research reports are lengthy. Too long segments split metric associations, while too short segments increase recall redundancy |
CHAT_LOG_RETENTION_DAYS | 30–90 days | Industry analyses in logistics research reports often require reviewing metric records from historical conversations. Too long logs increase storage load, while too short fails to meet cross-session needs |
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 targeted analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: A prompt indicates no knowledge base selected, but the function works normally in debug preview. Cause: The knowledge base association configuration in the production environment was not synchronized, or the multi-turn dialogue triggered automatic knowledge base switching logic without retaining the current session's knowledge base binding relationship.
- Phenomenon: Unit-confused answers appear in multi-turn dialogue, such as converting TEU to tons. Cause: The prompt did not specify calibration rules for logistics-specific units, and did not retain the user-specified measurement dimension in the context.
- Phenomenon: Chat logs cannot be automatically cleaned, or some session records remain after cleaning. Cause: The
CHAT_LOG_RETENTION_DAYSconfiguration value was set incorrectly, or the automatic cleaning task did not trigger per the set cycle.
How to Verify Successful Configuration
- Initiate a multi-turn conversation involving multiple metrics and regions, verify that the system retains the filtering conditions from historical conversations without repeated inquiries about basic information.
- Verify that the returned research report data in the conversation matches the units specified in the current session, such as confirming that container volume returns use TEU without confusion with tons when the user specifies TEU.
- Check that the chat log retention duration meets business requirements, and confirm that expired logs are automatically cleaned per the set rules.
- Adjust different recall count configurations, verify that the returned research report fragments cover the core metrics of the user's question without redundant irrelevant content.
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.