What Data Looks Like for This Category
Rail transit equipment internal office and employee service data mainly comes from equipment operation and maintenance ledgers, monthly maintenance records, employee post training manuals, office process specification documents, and spare parts inventory lists. Data update rhythms change with business nodes: maintenance records are entered in real time after each equipment maintenance is completed. Training documents are updated when new vehicle models are developed or positions are adjusted. Inventory lists are synchronized weekly. Most document structures combine structured fields and unstructured paragraphs. Fields include equipment number, maintenance period, fault description, treatment plan, and spare parts model. Field units are mostly standard industrial codes, timestamps, and physical measurement units.
Constraints for Vector Models and Indexes
Rail transit equipment data has many structured fields, high professional term density, and some data such as maintenance records have unstable update frequencies. These factors create multiple constraints for the vector model and index workflow. Structured fields must be separately mapped to retrievable vector dimensions. This prevents general models from misencoding professional industrial codes. Long paragraph fault analysis documents require adaptation to long text chunking rules. This stops key operation and maintenance information from being truncated. Incremental update demands require indexes to support incremental synchronization. This avoids extra resource usage caused by full index reconstruction.
Configuration Settings
The following table lists recommended configuration values and their rationales:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the long paragraph structure of rail transit equipment documents, retain core information such as complete fault analysis and operation procedures |
| Overlap Length | 100–200 characters | Avoid truncating continuous business context, such as coherent maintenance step descriptions |
| Similarity Threshold | 0.72–0.85 | Distinguish subtle differences in professional industrial terms, reduce false recalls of irrelevant equipment records |
| Single Search Limit | Top 10 entries | Match employee query needs in internal office scenarios, avoid excessive recall results increasing context processing pressure |
maxToken | 8000–12000 token | Adapt to vector indexing and context recall of long documents, meet query needs for complex operation and maintenance issues |
defaultToken | 500 token | Control resource occupation for single vector generation, balance retrieval accuracy and cost |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Imported knowledge bases only contain partial document content and do not cover all expected business entries. This occurs when custom chunking rules are not enabled. The system splits documents by fixed length by default, truncating structured fields such as equipment numbers and spare parts models.
- A large number of irrelevant non-rail transit documents appear in retrieval results. This happens when the similarity threshold is not adjusted for industrial professional terms. The general vector model falsely recalls business documents from other categories.
- Vector generation tasks frequently time out. This is caused by the
maxTokensetting exceeding the computing power limit of the current deployment hardware. This leads to excessively long time for single vector generation.
How to Verify Proper Configuration
- Upload a single typical equipment maintenance record document, check the chunk preview interface, and confirm each chunk retains core fields such as equipment number and fault description.
- Initiate a query targeting a specific equipment number, check the number and matching degree of retrieval results, and adjust the similarity threshold to a range that meets business needs.
- Check the storage space usage of the vector database, confirm the total data volume generated by the index matches the estimated knowledge base capacity, with no abnormal overflow.
- Test the incremental update function, upload a new maintenance record, and confirm the system only synchronizes newly added content and does not trigger full index reconstruction.
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.