What the data for this category looks like
Port and shipping business consultation and response data mainly comes from port government public service guides, customs declaration specifications, berth booking procedures, dangerous goods management rules, as well as real-time port announcements, vessel schedule adjustment notices, and frequently asked question summaries. Most documents are structured text, containing fixed fields such as handling item name, handling entity, required materials, and handling time limit. Common units include workdays, copies, berth quantity, and vessel tonnage. Business guide documents are updated quarterly or semi-annually. Port announcements and vessel schedule information are updated daily.
What constraints these characteristics impose on the vector model and indexing link
Port business consultation data has a high proportion of long procedural texts. When chunking, avoid cutting complete handling steps. Otherwise, retrieval results cannot provide coherent handling guidance. Real-time port announcements and vessel schedule information require incremental indexing. Full indexing causes unnecessary resource consumption. Documents contain dedicated fields such as berth numbers and vessel type requirements. Indexing must preserve field semantics to avoid generic matching that leads to retrieval results deviating from port-specific scenarios.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to long procedural texts in port business guides, preserve semantic integrity of individual handling steps |
| Overlap Length | 100–150 characters | Avoid cutting procedural connections, ensure cross-chunk semantic coherence |
| Similarity Threshold | 0.72–0.80 | Match the accuracy requirements of port business consultation, filter low-relevance generic government content |
| Single Search Limit | Top 8 entries | Cover multi-scenario related needs of port business, avoid missing relevant handling guidance |
maxToken | 4096 token | Adapt to long-context question answering for port consultations, ensure complete return of handling procedures and material requirements |
defaultToken | 2048 token | As the length limit for default embedding requests, balance model performance and embedding accuracy |
Three Common Mistakes
- Phenomenon: The knowledge base deployed via Docker shows stuck indexing progress, with no completion after a long period of time. Cause: No incremental indexing strategy is configured for the high-frequency updates of real-time vessel schedules and port announcements, resulting in repeated loading of large amounts of historical full data.
- Phenomenon: The embedding model call returns a 503 status code, prompting that no available text-embedding model exists under the default group. Cause: No dedicated embedding model group is bound to the port consultation scenario in FastGPT configuration, and the default group has insufficient resources or no corresponding model configured.
- Phenomenon: Retrieval results contain a large amount of generic government content, failing to match port-specific business needs. Cause: No retrieval filtering is configured for dedicated fields in port documents (such as berth numbers, vessel type requirements), relying only on global similarity matching.
How to Verify Correct Configuration
- Manually upload a port berth booking business guide, check that the chunked text retains complete booking procedure steps without mid-cut truncation.
- Initiate a simulated consultation such as "How to complete vessel entry declaration", verify that the semantic matching degree between the retrieved documents and the consultation question conforms to the preset matching rules.
- Simulate adding a real-time port announcement, check that the indexing system completes indexing loading of new data within the set update cycle with no delay.
- View the embedding model call logs, confirm that the token count of each request does not exceed the configured
maxTokenanddefaultTokenlimits, with no error returns.
The values provided are common starting points and should be measured 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.