What the data for this category looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints these characteristics impose on the vector model and indexing process
Shipbuilding document data has multiple long paragraphs, dense professional terminology, and mixed structured and unstructured content. These characteristics create multiple constraints for the vector model and indexing process. Long text paragraphs require chunking strategies that balance context completeness and retrieval accuracy. Overly small chunks risk splitting professional terms, while overly large chunks may exceed model context limits. The presence of structured fields requires distinguishing indexing logic for structured metadata and unstructured text. This prevents incorrect vectorization of metadata. Batch update scenarios require indexes to support incremental synchronization, reducing resource consumption from full index rebuilding. Additionally, the specificity of industrial terminology requires vector models to adapt to professional semantics in the equipment manufacturing field. This improves the accuracy of retrieval matching.
How to set configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average single-paragraph length of ship technical documents, avoids splitting professional terms |
| Overlap Length | 100–150 characters | Connects context between adjacent chunks, prevents key information from being broken |
| Similarity Threshold | 0.75–0.85 | Calibrated based on actual measurements, filters low-match irrelevant documents |
| Single Search Limit | Top 10 results | Adapts to the information volume of shipbuilding documents, controls total context length |
| defaultToken | 8192 | Adapts to the basic context length of general industrial documents |
| maxToken | 16384 | Handles complete content of long inspection reports and technical specifications |
Three common mistakes
- Vectorization tasks take too long, and logs show low character counts processed per request. Cause: Batch vectorization trigger logic is not configured, and only single text slices are submitted for vectorization processing.
- A large number of irrelevant results appear in retrieval outputs, with returned matches deviating significantly from shipbuilding-related topics. Cause: The similarity threshold is set too low, and low-match irrelevant documents are not filtered.
- Context overflow errors occur, with a return status code of 413. Cause: The maxToken setting does not match the total token count of long documents, exceeding the model's context limit.
How to confirm the configuration is properly set
- Upload a standard shipbuilding instruction manual, and check if the chunk length and overlap parameters in the chunk preview interface match the configured values.
- Initiate a ship material selection or process query, and verify if the number of retrieval results matches the single search limit setting.
- Check the vectorization service call logs, and confirm if text data is submitted in batch slices within requests.
- Manually adjust the similarity threshold, and confirm if low-match irrelevant documents are correctly filtered.
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.