What Data for This Category Looks Like
Shipbuilding service consultation and response data primarily comes from shipbuilding industry service guidelines, classification society survey specifications, production node approval documents, and government service window consultation records. Data update cadence aligns with industry regulations. Classification society specification documents are updated quarterly or annually, while daily service consultation records are added in real time. Most documents are a mix of structured and semi-structured content, including fields such as item number, handling department, handling time limit (unit: working days), required submission materials list, and ship parameter fields (e.g., ship length, gross tonnage).
What Constraints These Characteristics Impose on the Vector Model and Indexing Link
Shipbuilding service consultation data has a high proportion of structured content, including standardized fields and numerical parameters. This requires vector models to support joint encoding of structured fields and associated text, to avoid losing field correspondence during splitting. Industry specification documents have fixed update cycles, so a matching full index refresh cadence must be configured to adapt to quarterly-level data changes. Daily service consultation records are added in real time, so the index must support incremental synchronization to avoid performance losses from full index rebuilding. Ship parameter units are bound to numerical values, so the association between units and values must be retained during chunking to prevent parameter confusion during retrieval.
Recommended Vector Model and Indexing Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Shipbuilding service documents mostly contain standardized processes and parameter descriptions. This length can cover a complete single service item or parameter group, avoiding semantic fragmentation after splitting |
| Overlap Length | 100–150 characters | Retains overlapping content between adjacent chunks, ensuring that cross-chunk process descriptions or parameter associations are not truncated, improving retrieval accuracy |
| Similarity Threshold | 0.72–0.80 | Shipbuilding service consultation has high requirements for semantic matching accuracy. This range can filter low-relevance consultation content while retaining valid retrieval results |
| Single Search Limit | Top 8 entries | Valid answers to service consultations are usually concentrated in a small number of items. Limiting the number of search entries reduces context redundancy and improves response efficiency |
maxToken | 4000 token | Shipbuilding documents have moderate content length. This parameter can accommodate complete retrieval results and user questions, avoiding context overflow |
defaultToken | 1024 token | Adapts to the token count of single-chunk text from shipbuilding documents, ensuring the embedding model can complete encoding normally |
Three Common Configuration Mistakes
- Phenomenon: The default index generated for manually uploaded shipbuilding service documents in the knowledge base automatically disappears after several hours. Cause: No index persistence storage policy is configured, or the refresh interval for incremental indexes is set too short, causing temporarily generated indexes to not be persistently saved.
- Phenomenon: An error occurs when batch re-embedding service documents after replacing the embedding model, with some documents failing to embed. Cause: Chunking rules are not adjusted for shipbuilding's structured fields, causing text containing long numerical parameters to be incorrectly split, triggering the embedding model's length limit.
- Phenomenon: Search retrieval results only contain ship parameter values, with no associated service process descriptions. Cause: The vector model's structured field joint encoding function is not enabled, and only plain text content is embedded, losing the association information between parameters and service processes.
How to Verify Proper Configuration
- Upload a single shipbuilding service guideline document, check that the chunked text retains complete item fields and process content with no semantic fragmentation.
- After replacing the embedding model, run a batch embedding task, check that there are no chunking errors or embedding failure error messages in the task logs.
- Submit a shipbuilding-related service consultation question, verify that the retrieval results include the corresponding service process and parameter descriptions, and that the index does not have the automatic disappearance anomaly.
- Adjust the similarity threshold, verify that the relevance of search results meets the needs of the actual consultation scenario. No fixed values are required, only alignment with business expectations.
The provided values are common starting points and should be assessed against samples used in actual deployment.
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.