Data Characteristics for Shipping Port Research Reports
Shipping port research report data comes primarily from industry association public reports, port administration operation monthly reports, securities firm shipping sector research reports, and international shipping organization statistical documents. Update cadence: Port operation data is updated daily or weekly. Securities firm research reports have no fixed release cycle, and are concentrated around port earnings seasons and after industry policy announcements. Document structure includes structured data fields and unstructured analysis text. Field units include professional identifiers such as TEU, 10,000 tons, and shifts per day. Each document combines multiple sections of professional analysis and structured data blocks.
Constraints for Vector Models and Indexing
These characteristics create clear constraints for the vector model and indexing workflow. First, mixed structured and unstructured data structures require indexes to support field-associated hybrid retrieval. Structured fields such as throughput and port codes must be linked with semantic vector retrieval results. Second, uneven update frequencies require support for incremental indexing modes. This avoids redundant computation from full indexing when processing frequently updated operation data. Third, specialized terminology and units require vector models to adapt to shipping domain semantics. Without this, semantic matching deviations will occur for professional expressions. Fourth, multi-block document structures require a chunking strategy that preserves contextual coherence. This prevents separating professional terminology from its associated data.
Configuration Recommendations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Shipping port research reports contain specialized terminology and structured data. Excessively long chunks will lose contextual connections, while excessively short chunks will split professional expressions |
embedding_model | Calibrated via scene-specific testing (prioritize domain-fine-tuned models) | Shipping domain has specialized terms such as TEU and berth utilization rate. General-purpose models have deviations in understanding professional semantics |
retrieval_top_k | Top 8–12 results | Port research report data has multiple dimensions. A sufficient number of relevant chunks must be retrieved to cover multi-dimensional business information |
similarity_threshold | 0.72–0.85 | Filter low-relevance non-specialized matching results, and retain retrieval content strongly related to shipping port business |
chunk_overlap | 100–150 characters | Preserve contextual connections between chunks, and avoid splitting professional terminology and structured data into separate chunks |
incremental_index_enable | Enabled | Adapt to the update cadence of daily-updated port operation data and irregularly released research reports, and reduce duplicate indexing overhead |
The parameter values provided on this page are common starting points 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: Index model calls return 401 unauthorized errors, and vector embeddings cannot be generated. Cause: One API key and model endpoint are not configured correctly, and the correct embedding model name is not specified.
- Symptom: Vector retrieval matching accuracy is extremely low when using local Ollama embeddings. Cause: No shipping-domain fine-tuned Ollama model is used. Default general-purpose models cannot recognize specialized terms such as TEU and berth utilization rate.
- Symptom: Recall result count is far lower than expected after building a new index. Cause:
chunk_sizeis not adjusted to fit long research report document chunks, and incremental indexing is not enabled, causing old data to overwrite new data.
How to Verify Proper Configuration
- Upload a port research report sample, and check if segmented text fully retains professional terminology and structured field content.
- Submit a retrieval request, and verify that recalled results only include content related to the target port and shipping business.
- Submit an incremental indexing task, and confirm that the system only processes newly updated or added documents, and does not reprocess historical data.
- View embedding model call logs, and confirm that the generated vector format matches the configured index storage requirements.
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.