Vector Models and Indexing for Shipping and Port Investment

What this category of data looks like

Shipping and port investment research data sources include port operation logs, container throughput reports, route schedules, maritime weather reports, industry research reports, berth scheduling records, and freight rate index data. Update frequencies vary: berth scheduling and real-time throughput data update at minute-level intervals. Industry research reports and rate adjustment documents update weekly or monthly. Document formats include structured time-series tables, semi-structured PDF research reports, and structured data pulled via APIs. Core fields include berth numbers, container TEU units, navigation durations, port rates, and more. Some data includes supplementary investment research reference information such as longitude and latitude, and available berth durations.

Constraints on Vector Models and Indexing

Multi-source data with mixed update frequencies requires indexes to support hot and cold data tiered storage. This prevents frequent full indexing of real-time data from consuming excessive system resources. A high proportion of structured fields requires vector encoding to support specified field filtering. This stops non-business fields from polluting vector semantics. Long-text research reports and short time-series data coexist. This requires splitting granularity to balance semantic integrity and retrieval accuracy. The maritime field has many specialized terms. Vector models must adapt to encoding logic for industry-specific vocabulary to ensure accurate retrieval matching. Real-time data updates frequently. Indexes must support incremental triggering mechanisms to reduce duplicate computing overhead.

Configuration Settings

Configuration Item	Recommended Setting	Rationale
`embedding_model`	`bce-embedding-v1`	Adapts to multi-modal and structured field encoding, covering port research report and operation data scenarios
`chunk_size`	800–1200 characters	Balances semantic integrity of long-text research reports and splitting accuracy of structured data
`index_type`	`HNSW`	Supports high-dimensional vector fast retrieval, adapting to batch query requirements of port time-series data
`recall_top_k`	Top 20 results	Covers multi-dimensional investment research reference data, meeting multi-factor analysis needs for shipping and port scenarios
`incremental_index_interval`	60 seconds	Adapts to update frequencies of real-time data such as berth scheduling and throughput
`filter_fields`	`port_id, throughput_teu, vessel_eta`	Only generates vectors for core business fields, reducing interference from irrelevant information on retrieval accuracy

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require case-by-case analysis, and testing against local samples is recommended before finalizing settings.

Three Common Configuration Mistakes

After uploading data using the chunk-mode push_data API, the interface displays an "Indexing" status for an extended period with no progress updates. The incremental_index_batch_size parameter is not configured, leading to an overly large single batch of data and blocking the indexing process.
When using the bce-embedding-v1 model, some structured fields show deviations in vector similarity matching results. Target business fields are not specified in filter_fields, causing non-business fields to be included in the vector encoding workflow.
Irrelevant port business data appears in index recall results, and the number of recalled entries does not meet expectations. A reasonable threshold for recall_top_k is not configured, or a port_code retrieval filter condition is not added.

How to Verify Correct Configuration

View vector generation logs to confirm that only fields specified in filter_fields are used for vector generation.
Submit a retrieval request for a specified port, and verify that the number of recall results matches the recall_top_k configuration.
Upload a single piece of real-time berth scheduling data, and confirm that the index update completes within the set incremental_index_interval.
Test the splitting result of a long-text research report, and confirm that chunk splitting does not damage core semantic units.

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.