What the data for this category looks like
Port and shipping business data comes primarily from port operation scheduling systems, berth application documents, ship customs declaration guides, fee schedule documents, voyage log ledgers, and similar sources. Update cycles fall into two groups: Voyage logs and real-time operation documents are updated per voyage or daily. Fixed documents such as process guides and fee schedules are updated quarterly or annually. Document structures include structured form fields and unstructured process descriptions. Structured fields often include voyage numbers, berth IDs, operation durations, declaration numbers, and similar items, with dedicated units such as hours and yuan. These fields link data across different business links.
Constraints on Vector Models and Indexes
The update frequency of port and shipping business data varies widely. High-frequency voyage logs and real-time operation documents require support for incremental indexing and fast updates. Many structured fields have dedicated units. Vector indexes must balance metadata association and text semantic matching. There are many long-text process description documents. Chunking must not disrupt business logic. Business terminology is highly specialized, such as berth mooring plan and customs inspection process. The model must accurately identify semantic associations. Some data is linked to unique identifiers such as voyages and berths. Indexes must support filtering search results by business identifiers to improve guidance accuracy.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Port and shipping process documents are mostly paragraph-style descriptions. Most single paragraphs fall within this range, avoiding disruption of business logic during chunking |
| Overlap Length | 100–200 characters | Key information for voyage scheduling and berth applications often appears at paragraph starts and ends. Overlapped chunking preserves context connections |
defaultToken | 1024 | Serves as the base token quota, supporting retrieval needs for short port text data such as voyage numbers and berth IDs |
maxToken | 8192 | Matches the input upper limit of multimodal Embedding models supported by FastGPT v4.17.0, aligning with token requirements for port long-text chunks |
| Similarity Threshold | 0.72–0.80 | Port business terminology is highly specialized. Low-match irrelevant results must be filtered. This range balances recall rate and accuracy |
| Single Search Limit | Top 8 entries | The number of optional process steps for port business handling is limited. Excessive results will reduce guidance accuracy |
Three Common Mistakes
- When testing a multimodal Embedding model, an error with the format
{"error":{"code":"Invalid"}}is returned. The cause is a mismatch between the chunk length of port documents and the model's input token limit, resulting in chunked text exceeding the model's supported range. - After upgrading to a new FastGPT version, old vector database data cannot be properly linked to business process guidance. The cause is failure to retain the original chunk metadata identifiers, leading to incompatible field formats between new and old indexes.
- After rebuilding the knowledge base index, duplicate results appear in business guidance. The cause is failing to clear original index data before rebuilding, resulting in overlapping old and new index data.
How to Verify Correct Configuration
- Upload a single port business process document, and confirm that the length of chunked text matches the preset chunk length range.
- Initiate a port business query such as "How to apply for a berth", and confirm that the number of returned results matches the single search limit setting.
- Perform an incremental index operation, upload a new voyage log document, and confirm that the new data can be retrieved normally.
- View the index's metadata fields, and confirm that they include dedicated port business identifiers such as voyage numbers and berth IDs.
The values provided here are common starting points, and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.