What data in this category looks like
Shipping port financial report data primarily comes from port official quarterly/monthly operation announcements, listed company financial report disclosure documents, and berth and route operation statistical reports. Two update cycles apply: quarterly financial reports update per fiscal quarter, monthly operation data updates per calendar month, and some core berth data updates daily. Document structures include structured statistical tables and unstructured operation explanations. Structured fields cover container throughput (TEU), berthing trips, cargo throughput (10,000 tons), berth operating hours (hours), and more. Unstructured content includes route adjustment explanations, operation risk reminders, and other business details.
What constraints do these characteristics impose on the "vector models and indexing" workflow
Vector processing workflows separate structured fields and unstructured text for shipping port financial reports’ mixed data format, avoiding splitting that breaks business associations of statistical data. Switching between incremental indexing and full indexing adapts to data sources with multiple update cycles, balancing index freshness and computing resource usage. Index sharding and recall thresholds adjust for datasets that reach 100,000 entries, preventing index construction timeouts or redundant recalled data. Standardization processing for specialized units such as TEU and 10,000 tons runs before vectorization, ensuring the accuracy of semantic retrieval.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Shipping port financial reports include long operation explanations and text transcribed from structured tables. This range preserves the integrity of single-segment business logic and avoids splitting that breaks statistical data associations |
recall_top_k | Top 10–15 entries | A single financial report links to many operation data entries. Too many recalled entries increases inference latency. Too few fails to cover all key indicators |
index_refresh_interval | Every 24 hours | Monthly operation data updates daily or every other day. Quarterly financial reports update less frequently. This interval balances index freshness and resource usage |
vector_embedding_dim | 768–1536 dimensions | Financial report text includes specialized terminology and unit identifiers. This dimension range preserves semantic features of units and business scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single financial report may include multi-page structured tables and long-text annotations. Sufficient time must be reserved for parsing and vectorization |
hybrid_search_weight | 0.3–0.5 | Keyword matching for structured fields and vector semantic retrieval need balanced weights. This adapts to the mixed retrieval needs of indicators and explanations in financial reports |
The parameter values provided on this page are all conventional recommendations, used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: After uploading 100,000-entry CSV financial report data, the number of valid entries after vectorization is thousands fewer than the original data. Cause: Some rows contain non-standardized shipping-specific units or invalid format fields. The vectorization workflow automatically filters entries that do not meet format verification rules.
- Phenomenon: Creating a financial report analysis collection returns a successful creation prompt, but the page index status remains unestablished for a long time. Cause: Batch vectorization tasks do not trigger background queue scheduling, or index sharding configurations do not adapt to 100,000-level data volume, leading to blocked index construction tasks.
- Phenomenon: Vector retrieval results work normally locally, but after packaging as a Docker image, retrieval scores are consistent and results deviate. Cause: The image does not correctly configure access permissions for the model API, leading to vectorization calls using the default low-quality model, or environment variables do not sync local vectorization parameter configurations.
How to confirm correct configuration
- Run a vectorization test for a single financial report text. Check the field integrity and unit identifier retention of the vectorization result, and confirm that the
chunk_sizeconfiguration adapts to text length. - View index construction logs. Confirm the execution progress of batch vectorization tasks and the number of filtered entries, and check the matching relationship between
recall_top_kand dataset scale. - Trigger a hybrid retrieval test. Compare the result weights of vector retrieval and keyword retrieval, and confirm that the
hybrid_search_weightconfiguration meets business retrieval needs. - Check the environment variable configuration of the Docker image. Confirm that the model API key and vectorization parameters are consistent with the local runtime environment, to avoid runtime parameter deviations.
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.