What this type of data looks like
Logistics investment research data mainly comes from transportation authority monthly operation bulletins, public financial reports of logistics enterprises, international shipping manifest data, trunk line transport timeliness monitoring reports, and express delivery end-point station filing information. It supports scenarios such as credit granting for the logistics industry and evaluation of wealth management targets by financial institutions. Update rhythms vary significantly: authority bulletins are updated monthly, corporate financial reports are updated quarterly, and manifests and real-time transport data can be updated daily or hourly. Documents include two categories: structured statistical tables and semi-structured industry analyses. Core fields include transport volume (units: TEU, tons, pieces), transport timeliness (units: hours, days), coverage area, publishing entity, and publishing time.
What constraints do these characteristics impose on the citation source and traceability link
Logistics investment research data has scattered sources and inconsistent standards. Units for transport volume and timeliness fields differ across publishing entities. The traceability link must complete cross-source field mapping and unit unification. Significant differences in data update rhythms require differentiated timestamp verification logic. Real-time transport data must retain original publishing timestamps, while monthly bulletins must mark their release cycles. Documents include two types: structured statistical tables and semi-structured analysis reports. Citation snippet extraction must match the document type. Structured data requires specific row number annotations, while semi-structured content needs clear paragraph range definitions. Cross-regional and cross-entity logistics data characteristics require traceability information to link both operating entities and coverage areas, to avoid data confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | Top 10-15 | Logistics investment research data has a large volume, with significant differences in single-data length. Too many recalls will exceed token limits, while too few will miss key transport timeliness data. |
Similarity Threshold | 0.75-0.85 | Logistics data has many professional terms. A threshold that is too low will introduce irrelevant general industry data, while a threshold that is too high will fail to match precise data for specific shipping routes. |
maxContext | 8000-12000 characters | Logistics investment research reports often contain multiple sets of structured tables, and single-segment reference content is long. Sufficient context space must be reserved to ensure complete traceability information. |
Reference Snippet Length | 300-500 characters | Core information of logistics data is often concentrated in specific paragraphs or table rows. An overly long snippet will introduce redundant content, while an overly short snippet will fail to cover complete transport volume and timeliness information. |
Traceability Information Display Fields | Publishing Entity, Publishing Time, Document Location | Traceability of logistics data requires clear publishing entities and time, to facilitate verification of data timeliness and authority. |
The parameter values provided on this page are common starting points for configuration setup. Actual values are influenced 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 settings.
Three Common Misconfigurations
- Phenomenon: When the
Reference Upper Limitis set to 2000, the per-round answer token count exceeds 3000. Cause: Single reference snippets for logistics investment research data are long. Recalling 2000 entries will splice a large amount of redundant content, exceeding the model's context window limit. - Phenomenon: The knowledge base reference prompt is configured in English, and cannot match Chinese logistics data sources. Cause: The prompt template is not adjusted according to the data source language, causing the model to fail to correctly parse Chinese professional terms.
- Phenomenon: The answer does not use the top-ranked reference result from the knowledge base. Cause: The reranking logic is not enabled, or the
Rerank Return Countconfiguration is too low, causing the recalled results to not be reordered by relevance.
How to Confirm Proper Configuration
- Check the knowledge base search configuration page to confirm that parameters such as
Recall CountandSimilarity Thresholdhave been adjusted according to the characteristics of logistics data. - Initiate a test query to check whether the traceability information in the generated answer includes core fields such as publishing entity, publishing time and document location.
- Check the log file to confirm that the per-round answer token consumption does not exceed the model's supported range, and that the reference snippet length meets the configuration requirements.
- Compare the recalled results with the original data to confirm that the sorting logic has taken effect, and high-correlation data is prioritized for inclusion in the answer.
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.