What the data for this category looks like
Rail transit equipment declaration materials and file review data mainly come from enterprise PDM systems, test ledgers, qualification management systems, and compliance documents required by regulatory authorities. Data updates use batch nodes triggered by new vehicle model project initiation, and incremental nodes from daily compliance reviews. There is no demand for high-frequency real-time updates. The data includes structured fields such as vehicle model, dead weight, and inspection number, with units including tons and km/h. It also includes unstructured content such as PDF design drawings, Word test reports, and scanned archive files. A single declaration material can be up to hundreds of pages long.
Constraints imposed on the vector model and indexing link
The mixed document structure of rail transit equipment declaration materials requires vector models to handle semantic extraction for both structured parameters and unstructured text. Long documents and batch update features require indexes to support chunked storage and incremental synchronization, avoiding performance loss caused by full reconstruction. Fields with dedicated units require vector modeling to retain semantic associations related to units, preventing parameter confusion. Compliance requirements for file reviews require indexes to have high-precision recall capabilities, ensuring key approval nodes and technical parameters are not omitted.
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 | Ensures semantic integrity of single chunks for rail transit equipment test reports and drawing descriptions, avoiding separation of key technical parameters across chunks |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, preventing technical parameters in long documents from being truncated by chunking |
maxToken | 8192 | Adapts to the context window limit of the cloud-based Deepseek channel, supporting complete content of retrieval results and original documents |
defaultToken | 4096 | Default context length for basic vector generation, balancing semantic coverage of single documents and computing overhead |
| Similarity Threshold | 0.75–0.85 | Meets the strict requirements of rail transit equipment compliance reviews, filtering low-correlation non-technical documents |
| Single Search Limit | Top 10 entries | Covers associated records of multiple nodes in file reviews, avoiding omission of key approval circulation information |
Three common mistakes
- Submitting vector model configuration via the interface returns a parameter error, and logs show context length exceeded. The cause is failing to adjust the
maxTokenvalue for long-document declaration materials of rail transit equipment, using small window configurations intended for general scenarios. - No data synchronization occurs when adding an index, and the backend displays a field mismatch. The cause is failing to bind structured fields of rail transit equipment declaration materials (such as vehicle model, inspection number) to the index's metadata fields, resulting in the index being unable to recognize valid data.
- A large number of irrelevant general documents appear in retrieval results, with insufficient recall accuracy. The cause is setting the similarity threshold too low, failing to meet the strict semantic matching requirements of rail transit equipment compliance reviews.
How to confirm the configuration is properly set
- Upload a typical rail transit equipment test report PDF, perform a chunking test, and confirm that the chunked text length falls within the preset chunk range.
- Submit the vector model and index configuration, check that the interface returns a save success prompt, and no parameter verification errors occur.
- Import a historical file review record, perform index reconstruction, and confirm that the number of corresponding data displayed in the index list matches the imported quantity.
- Initiate a retrieval request for a specific vehicle model, and confirm that the returned results include associated declaration materials and review records for that model.
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.