What the Data for This Category Looks Like
Rail transit equipment policy and regulatory basis data comes primarily from publicly available official documents of the National Railway Administration, the Ministry of Industry and Information Technology, and the China Rail Transit Equipment Industry Association. It also includes internal corporate compliance documents. Policy documents are updated irregularly to align with national regulatory requirements. Industry-wide standards are revised every 1 to 2 years. Internal corporate documents are adjusted alongside product iterations. Most documents use structured clause formatting, with fields including release number, effective date, applicable scenario, and specific requirements. Units of measurement used include kilometers, hours, units, batches, and others.
Constraints for Vector Models and Indexes
Multi-source data from rail transit equipment policy documents creates differences in data formats. Some files are scanned PDFs, requiring additional OCR parsing workflows. Field naming varies across documents from different sources. Field standardization processing must be completed before indexing. Policy updates follow no fixed cycle. Incremental indexing must support synchronization triggered by release date, to avoid wasting resources on full reindexing. Most documents are long structured clauses. Text splitting must preserve clause integrity, to prevent semantic fragmentation after splitting. Specialized terms such as "running kilometers" and "maintenance cycle" require vector models to support professional semantic embeddings for the rail transit equipment field.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the clause length of rail transit equipment policy documents, avoids splitting individual complete clauses |
| Overlap Length | 100–150 characters | Preserves contextual connections across chunks, ensures semantic coherence for long clauses |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters low-match irrelevant policy clauses, adapts to semantic matching accuracy for specialized terminology |
| Single Search Limit | Top 8 results | Balances recall scope and context window limits, meets the precision requirements for policy Q&A |
maxToken | 4000 token | Adapts to the context window limit of FastGPT v4.17.0, ensures recalled content can be fully included in inference |
defaultToken | 200 token | Controls the output length of a single recalled entry, avoids redundant information interfering with Q&A logic |
Three Common Mistakes
- Scenario: After deploying FastGPT locally, no image index configuration option appears when creating a new knowledge base. Uploading policy documents with images fails to extract image text. Cause: Image index-related modules are not enabled in deployment configuration, or the local deployment image does not include dependent components for this feature.
- Scenario: Recalled policy clauses have poor matching with query questions. Generic policy content unrelated to rail transit equipment is returned. Cause: The similarity threshold is set incorrectly, and matching accuracy is not adjusted for specialized terminology.
- Scenario: When batch uploading annual policy documents, indexing tasks throw timeout errors, and task status shows failure. Cause: Chunk length is set too large, exceeding the processing limit corresponding to
maxToken, leading to excessive single-processing time that triggers a timeout.
How to Verify Configuration is Active
- Upload a rail transit equipment policy document containing scanned images. Check if the knowledge base’s parsed result includes text from the images. This confirms OCR and image index configurations are active.
- Submit a query targeting a specific policy clause. Verify that the number of recalled results matches the single search limit setting. This confirms search configurations are active.
- Adjust the chunk length and overlap length parameters, then upload a long clause document. Check if chunked results preserve the integrity of individual clauses. This confirms chunking configurations are active.
- View the FastGPT v4.17.0 system logs. Confirm that the vector model loads normally, with no error messages about failed specialized term embeddings.
Additional Note
The values provided in this guide are common starting points. They should be evaluated against the specific samples and requirements of the target 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.