What the data for this category looks like
Data sources are statutory shipbuilding specifications released by the competent industry authorities of the shipbuilding industry and maritime management agencies, as well as internal compliance documents for shipbuilding operations of state-owned and state-controlled enterprises. Update frequency is irregular, aligned with adjustments to industry policies and the launch of new shipbuilding standards. Most individual documents are structured text, containing fields such as document number, issuing authority, effective date, clause number, specific technical requirements, etc. The units involved include standard measurement units related to ship tonnage, main engine power, and construction cycle.
What constraints these characteristics impose on vector models and indexes
The structured nature of the data requires indexes to support filtered retrieval based on metadata such as clause number and effective date, to ensure accurate positioning of corresponding policy clauses. The presence of specialized terminology (such as ship tonnage, main engine power) requires vector models to adapt to industrial domain semantics, to avoid semantic deviation. The irregular update feature requires indexes to support incremental synchronization, to avoid resource consumption caused by full reconstruction. Individual documents have relatively long lengths; when chunking, the integrity of semantic units must be preserved to prevent policy clauses from being split.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the structured length of shipbuilding policy documents, preserve complete semantic meaning of individual clauses |
| Overlap Length | 200–300 characters | Prevent semantic breakage after chunking, ensure contextual coherence for cross-chunk policy clauses |
maxToken | 4096 token | Match the input upper limit of most open-source vector models, cover core content of a single policy clause |
defaultToken | 70% of the model's maximum input | Reserve redundant space for model processing, avoid truncation of critical policy information |
| Similarity Threshold | 0.75–0.85 | Filter low-correlation results, ensure accuracy of retrieved policy clauses |
| Single Search Limit | Top 10 results | Control the scale of retrieved results, avoid context overload that affects response quality |
Three Common Misconfigurations
- Issue: Returns a 404 error when configuring baidu-embedding-v1. Cause: The model's calling key and interface address are not configured correctly, preventing normal requests to the model service.
- Issue: Fails to add the vector model to the platform when deployed without a GPU environment. Cause: The CPU-compatible running mode of the model is not enabled, or the system dependencies required for the model to run are not installed correctly.
- Issue: Encounters a startup timeout error after adding a locally deployed index model. Cause: Insufficient resources required for model initialization, or the memory quota set for the Docker container is too low.
How to Verify Successful Configuration
- Upload a single typical shipbuilding policy document, check that the chunked text retains complete clause semantics without obvious truncation or breakage.
- Submit a query regarding shipbuilding compliance requirements, verify that the metadata of the retrieved results matches the query content.
- Check the index management interface update log in FastGPT v4.17.0, confirm that incremental update tasks can be normally triggered and completed with no failed records.
- Test the vector model's calling interface, confirm that the dimension of the returned embedding vector matches the configured model parameters.
The values provided here are common starting points, and should be validated using samples relevant to the target deployment scenario.
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.