What this type of data looks like
Shipping port research report data primarily comes from industry association public reports, securities firm transportation sector special research reports, official port monthly operation disclosures, and international shipping freight rate index databases. Update rhythms vary:
- Port throughput and container volume data is updated weekly or monthly
- Freight rate indices are updated daily
- Securities research reports are released on event-driven timelines
Document structures include abstracts, core data tables (covering throughput, route rates, and similar content), industry trend analysis, and policy interpretations. Exclusive units include TEU, 10,000 tons, and USD per ton. Some long documents span dozens of pages.
Constraints on Knowledge Base Retrieval and Recall
Dispersed multi-source data requires the knowledge base to support cross-platform, cross-format content integration. Content with different update frequencies needs differentiated synchronization strategies to prevent daily-updated freight rate data from becoming invalid due to delayed synchronization. Incorrect splitting of the large number of structured tables in documents will damage data association relationships and reduce retrieval accuracy. Exclusive units and industry terms require semantic matching during the recall stage, otherwise result relevance will decline. A high proportion of long documents requires a reasonable chunking strategy to avoid context overflow.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Shipping port research reports contain a large number of structured throughput and container volume data tables. Enabling this option preserves data association relationships |
UPLOAD_FILE_MAX_SIZE | 20 MB | Individual research report PDFs typically do not exceed 10 MB, with redundant space reserved for batch upload scenarios |
chunk_size | 800–1200 characters | Research reports contain long paragraph analyses and table fragments. This range preserves the integrity of business logic |
recall_top_k | Top 6–8 results | Shipping industry research reports have concentrated data dimensions. Retrieving too many results will introduce irrelevant analysis content |
RAG_INCREMENTAL_SYNC_INTERVAL | 1 hour | Daily-updated data such as freight rates requires high-frequency synchronization to prevent recalling outdated information |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing long PDF research reports requires sufficient time to prevent upload failures due to timeouts |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: After adding research reports to the knowledge base, querying analysis for a corresponding port returns no matching results. Cause: Incremental synchronization configuration is not enabled, or the synchronization interval is set too long, so new data is not included in the retrieval scope in a timely manner.
- Symptom: When uploading a text dataset using version v4.14.3, the
failed to create post perror occurs. Cause: S3 storage configuration has abnormal access keys or bucket permissions, or storage mapping was not reconfigured after upgrading the version. - Symptom: Recall results include a large number of general industry research reports unrelated to shipping ports. Cause: Industry keyword filtering rules are not configured, or the number of recalled results is set too high, and domain-level reranking is not enabled.
How to Confirm Configurations Are Correct
- Manually upload a latest port monthly operation data document. Wait for synchronization to complete. Query core data keywords within the document to confirm matching content can be recalled.
- View the knowledge base synchronization logs to confirm that incremental synchronization tasks run according to the preset cycle with no failed records.
- Test the parsing results of research reports containing structured tables to confirm that table content is fully split into retrievable segments.
- Adjust retrieval relevance threshold parameters to verify that recall result relevance meets business expectations.
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.