What Data for This Use Case Looks Like
Data sources for port shipping internal office and employee services include vessel dispatch logs, crew schedules, port operation documents, equipment maintenance records, internal OA notifications, and safety reports. Operation documents update in real time with vessel berthing and departure events. Crew schedules update daily. Equipment maintenance records are generated when a fault occurs. OA documents are submitted as needed.
Data includes structured fields and unstructured text. Structured fields include berth number (unit: meters), vessel tonnage (unit: tons), and operation duration (unit: hours). Unstructured text includes long documents such as monthly safety reports and short text such as real-time operation notifications.
Constraints Imposed on Vector Models and Indexing
Mixed structured and unstructured data requires indexes to support multi-field mapping and weight configuration, to avoid semantic bias in structured fields such as berth number and vessel tonnage. Real-time generated operation documents and maintenance records require indexes to support incremental update mechanisms, reducing resource consumption and time costs of full index reconstruction. Long texts such as monthly safety reports have significant length, requiring appropriate chunking rules to avoid semantic breaks that reduce the accuracy of vector retrieval. Differences in data across business scenarios require that vector model and index configurations can be flexibly adjusted for different document types, to adapt to query needs such as crew services and equipment maintenance.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long texts such as port operation documents and safety reports, avoids content breakage in single chunks |
| Overlap Length | 100–200 characters | Covers cross-chunk context such as vessel dispatch and operation workflows, ensures semantic coherence |
maxToken | 8192 token | Adapts to the input limit of ZhiPu Embedding-3, matches the actual length of port long documents after chunking |
| Similarity Threshold | 0.72–0.78 | Distinguishes similar fault descriptions in port equipment maintenance logs, avoids mismatching unrelated documents |
| Single Search Limit | Top 10 results | Covers common multi-scenario needs in crew services such as schedule queries and equipment issue references, avoids returning excessive redundant content |
defaultToken | 1000 seconds | Adapts to vector model request timeouts during local deployment, prevents request interruptions caused by large port data volumes |
Three Common Misconfigurations
- Unable to select ZhiPu Embedding-3 as the vector model in the interface. The cause is that the API address whitelist for this model was not added in the local deployment configuration file, or the configured
defaultTokendoes not match the request header parameters required by the model. - A vector model request failure prompt appears after local deployment. The cause is that port mapping was not correctly bound in the WSL environment, or the
maxTokensetting exceeds the actual input limit of the model. - Index chunk content generated when importing port operation documents is disorganized. The cause is that chunking rules for structured fields and unstructured text were not differentiated, and uniform chunking was applied to all content without distinction.
How to Verify Correct Configuration
- Upload a single port operation document, check that the generated vector chunks cover the complete operation process description without semantic breaks.
- Initiate a crew schedule query request, verify that the number of returned search results matches the set single search limit.
- View the vector model request logs in FastGPT v4.17.0, confirm that the
maxTokenvalue does not trigger input length errors from the model. - Test importing multiple real-time updated equipment maintenance records, confirm that the index incremental update function triggers normally, with no full reconstruction prompts.
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.