What the data for this category looks like
Port intelligent due diligence data sources include port operation logs, vessel scheduling system records, customs clearance data, berth usage ledgers, and maritime special reports. Operational data is synced daily, with special analysis reports generated monthly. Document structures include structured CSV batch job statistics and unstructured PDF special due diligence reports. Fields include character identifiers (such as berth numbers), physical quantity values (throughput measured in tons, operation duration measured in hours, vessel draft measured in meters), and text description content.
Constraints on vector models and indexing
Multi-source heterogeneous data formats require the indexing system to support mixed parsing and vectorization of structured CSV files and unstructured reports. High-frequency updated business data requires incremental indexing logic to avoid resource consumption from full reindexing. Fields contain numerical values with different physical units, requiring vector models to support numerical feature encoding to prevent semantic drift. Single data entries vary widely in length, from short job records to dozens of pages of special reports. Adaptive variable-length chunking rules are needed to avoid losing key business information during splitting.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Adapts to the mixed length of port operation records and special reports, avoiding over-splitting short records or losing context in long reports |
VECTOR_MODEL_NAME | text-embedding-ada-002 or a local open-source numerical-compatible model | Supports vector encoding of numerical fields such as throughput and operation duration, adapting to multi-type field features of port business |
INDEX_INCREMENTAL_ENABLE | Enabled | Adapts to the daily update rhythm of port data, reducing resource consumption and time spent on full indexing |
RECALL_TOP_K | Top 6–10 results | Balances recall coverage and retrieval efficiency for due diligence reports, avoiding excessive redundant results interfering with analysis |
SIMILARITY_THRESHOLD | 0.72–0.85 | Filters low-relevance port operation records, ensuring recalled content matches business requirements for due diligence |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of large port special reports, avoiding timeout interruptions of the indexing process |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common misconfigurations
- Issue: A
413 Request Entity Too Largeerror occurs when uploading port CSV batch statistics files after a version upgrade. Cause: The new version adjusted the default value ofUPLOAD_FILE_MAX_SIZE, which does not match the file size limit of port batch data. - Issue: The knowledge base indexing progress gets stuck above 90% without completing, and the interface displays "Indexing Exception". Cause:
INDEX_INCREMENTAL_ENABLEis not enabled, and full indexing of historical port data exhausts system resources. - Issue: Recalled port operation records have significant semantic deviation from the query, and some numerical fields are not matched correctly. Cause: A vector model compatible with numerical features is not used, and the original model only supports plain text encoding logic.
How to confirm configuration is correct
- Upload a single port special report file, check if the number of parsed segments matches the actual document length to confirm the
PARSE_CHUNK_SIZEconfiguration takes effect. - Trigger an incremental indexing task, check the indexing logs to confirm only new port data entries are displayed, verifying the
INDEX_INCREMENTAL_ENABLEconfiguration is correct. - Switch the vector model, submit a business query containing throughput and operation duration, check if recalled results include semantically matched content for the corresponding fields to confirm the
VECTOR_MODEL_NAMEconfiguration takes effect. - Check indexing resource usage in the system backend, confirm no continuous full indexing tasks are running, verifying the actual operating effect of
INDEX_INCREMENTAL_ENABLE.
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.