What this category of data looks like
Shipping port research report data mainly comes from domestic shipping exchanges, port group public operation reports, industry association monthly monitoring data, and third-party shipping consulting firm in-depth analysis. The update rhythm follows a monthly basis. Core operation data updates synchronize with operation cycles, and in-depth strategic research reports are released quarterly. Document structures mostly include structured data tables, policy interpretation paragraphs, and route capacity analysis modules. Fields include container throughput (unit: TEU), cargo throughput (unit: 10,000 tons), number of covered routes, number of ship calls. Some research reports also include port berth utilization rate and hinterland foreign trade import and export related data.
What constraints do these characteristics impose on deployment and upgrade?
The high proportion of structured data, layered update frequencies, and specialized units in shipping port research reports create multiple constraints for deployment and upgrade work. A vector retrieval engine that supports structured field filtering must be configured, and semantic embedding adapted for specialized units such as TEU and 10,000 tons to avoid retrieval bias. Monthly high-frequency operation data requires upgrade processes to support incremental synchronization mode, reducing resource consumption from full data reimport. Long-text in-depth research reports need to adapt to model context windows, retaining chapter logical connections during splitting to avoid damaging the complete analysis logic of reports. Additionally, embedding dimensions of specialized fields must match the feature complexity of business data, preventing reduced retrieval accuracy caused by insufficient dimensions.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Shipping port research reports often contain long tables and multi-chapter content, with long parsing duration, to avoid timeout interrupting the parsing process |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some in-depth research reports include high-definition charts and structured data sets, with large single-file size, so upload limits need to be relaxed |
maxContext | 8000–12000 characters | After splitting long-text research reports, sufficient context must be retained to maintain analysis logic, adapting to context windows of mainstream large models |
Recall count | Top 8–12 results | Structured data and analysis content of port research reports are highly correlated; too many recalled results will introduce irrelevant information, while too few will fail to cover core data |
Similarity threshold | 0.72–0.85 | Semantic similarity of specialized units and structured fields requires precise matching to avoid recalling low-correlation research reports |
Incremental Sync Trigger Interval | 2 times per day | Matches the update rhythm of monthly operation data; high-frequency synchronization ensures data timeliness while avoiding excessive resource occupation |
The parameter values provided on this page are common recommended starting points for configuration setup. 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 settings.
Three common mistakes
- Symptom: A
404 no bodyerror is returned when testing model calls in a local deployment environment. Cause: API keys and interface addresses for model access are not correctly configured, or container network policies restrict access permissions for model interfaces. - Symptom: After entering the pgvector container in Docker, SQL queries cannot be executed, returning a
connection refusederror. Cause: Container ports are not correctly mapped to the host machine, or database initialization scripts were not executed, resulting in the service failing to start normally. - Symptom: When parsing research reports containing multi-page tables, returned structured fields are empty. Cause: The table structured extraction switch for document parsing is not enabled, or splitting parameters are set incorrectly, resulting in table content being truncated.
How to confirm configurations are correct
- Upload a standard port monthly research report, check if the parsed structured fields include core business data such as throughput and number of routes, to confirm that the parsing switch is correctly enabled.
- Trigger an incremental synchronization task, check the index update logs of the vector database, to confirm that only research report data updated on the same day was added, and no full data reimport was triggered.
- Initiate a research report retrieval test, verify that the number of returned results matches the configured
Recall count, and that the results include relevant data with specialized units of measurement. - View container runtime logs, confirm that there are no error messages such as
解析超时or连接失败, and that the service is running normally.
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.