What the data for this category looks like
Documents and materials for urban public transit originate primarily from daily operation ledgers, route adjustment notices, safety inspection reports, passenger service specifications, ticketing policy documents, and similar sources. Data sources cover frontline stations, dispatch centers, and administrative departments. Update cadences combine on-demand and scheduled updates. Route adjustment files are updated immediately when routes change. Operation reports are archived weekly or monthly. Most documents use structured formats, with fields including route number, effective date, matter content, approval node, and others. Units involved include passenger trips, operating kilometers, service duration, and additional metrics.
Constraints on vector models and indexing workflows
Structured transit documents and varied update cadences create multiple constraints for vector models and indexing workflows. When a scenario has many structured fields, associated information must be retained to avoid semantic fragmentation. Mixed temporary on-demand updated files and regularly archived reports require indexes to support flexible switching between incremental refresh and full reconstruction. Document lengths vary widely, from tens-of-character route notices to thousands-word safety reports. Adaptable chunking logic is required for these scenarios. Content involving multiple units such as passenger trips and kilometers needs vector normalization configuration to match the output characteristics of different embedding models.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the length range of urban public transit documents, from short route notices to long safety reports, and avoids semantic breaks in single chunks |
| Overlap Length | 100–200 characters | Preserves contextual connections between adjacent chunks, and prevents semantic loss caused by structured fields being split across chunks |
| Similarity Threshold | 0.72–0.85 | Matches the semantic distinction between technical terms and general expressions in transit documents, and filters low-relevance search results |
| Single Search Limit | Top 8 results | Covers the number of core paragraphs in a single transit document, while controlling token consumption for a single retrieval |
| Maximum Context | 30000 token | Aligns with the general limit of FastGPT v4.17.0, and supports the splicing needs of multiple transit documents |
defaultToken | Calibrated via actual testing | Meets the field embedding requirements of urban public transit documents, and matches vector normalization configuration requirements |
Three Common Misconfigurations
- Scenario: Retrieval returns matching urban public transit ticketing policy documents, but the large language model outputs that no relevant content was found. Cause: Field association configuration for context splicing is not enabled, causing retrieved chunks to be disconnected from the field information of the original document, and the model cannot recognize content ownership.
- Scenario: Basic index retrieval recall rate is low, and cannot cover cross-route operation notices. Cause: Dedicated index mapping is not set for structured fields, leading to insufficient vector matching accuracy for technical terms.
- Scenario: Knowledge base query traffic is high, and token consumption for a single request exceeds the threshold, causing timeout. Cause: The maximum context value is not restricted, and a high default limit is used directly, leading to too much document content being pushed per request.
How to Verify Proper Configuration
- Upload a typical urban public transit safety inspection report, and manually verify that chunked text retains core fields and complete semantics.
- Simulate a multi-concurrent query scenario, and observe whether the number of retrieved chunks matches the single search limit configuration.
- Call an unnormalized embedding model, verify whether the vector normalization configuration takes effect, and check whether the vector magnitudes of embedding results are unified.
- Upload a new route adjustment notice, and confirm that the incremental index refresh completion time meets expectations.
The values provided here are common starting points, and should be evaluated against the user’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.