What This Category's Data Looks Like
Public transit service inquiry data primarily comes from operation scheduling systems, route logs, passenger inquiry history databases, and temporary operation adjustment notices. Structured data includes route numbers, station names, operating hours, and fare standards. Unstructured data includes temporary route change notices and ticketing policy explanations. Data update cycles vary: real-time scheduling and temporary adjustments are updated immediately as operational status changes, route timetables are calibrated quarterly, and the passenger common questions database is updated monthly. Documents include two types: plain text descriptions and structured tables, with fields using standardized units such as time, mileage, and price.
Constraints Imposed on Vector Models and Indexing Workflows
The mixed structure and varied update cycles of public transit data create multiple constraints for the vector indexing process. Real-time temporary adjustment data requires incremental indexing to avoid resource occupation from full reindexing. The need for precise matching of structured fields such as route numbers and station names requires indexes to cover both semantic vectors and keyword recall. Coexisting short texts such as station names and route numbers, and long texts such as several-hundred-word route change notices, requires adaptive text chunking logic for variable lengths. High-frequency colloquial inquiry content requires vector models to adapt to semantic understanding in daily commuting scenarios to avoid matching deviations. FastGPT v4.17.0’s indexing module supports configuration adjustments to fit these characteristics.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the common length of public transit announcements and inquiry responses, avoids overly long chunks that lose semantic connections |
| Overlap Length | 100–150 characters | Connects context between adjacent chunks, prevents semantic breaks in long texts such as route change notices |
defaultToken | 8192 | Fits the context window of most open-source vector models, covers the total token count after combining multiple public transit fields |
| Similarity Threshold | 0.72–0.78 | Balances recall accuracy for colloquial passenger inquiries, avoids mismatches with similar routes or stations |
| Single Search Limit | Top 8 results | Answers to transit inquiries usually focus on a small number of relevant routes or notices, excessive results will interfere with user judgment |
| Maximum Context | 3000–4000 characters | Integrates relevant chunks and the original query, ensures responses cover complete information such as routes, times, and adjustment details |
Three Common Configuration Errors
- The knowledge base upload status remains stuck in indexing for an extended period. Cause: The incremental indexing switch is not enabled, and full indexing processes a large volume of historical transit operation data, resulting in task backlog.
- Text chunks are lost when the chunk length is set to 3000 characters. Cause: The paragraph separation logic for long public transit announcements is not adapted, and chunking truncates unclosed semantic units, causing some content to fail indexing.
- Vector model calls throw timeout errors. Cause: Correct model call parameters are not configured, and an incompatible proxy service address is used, leading to failed request completion.
How to Verify Proper Configuration
- Upload a typical public transit route change announcement document, check the chunk preview after indexing is complete, and confirm the chunk length and overlap settings match expectations.
- Submit a query aligned with daily inquiry scenarios, verify the relevance of returned results, and adjust the similarity threshold to the range that meets business requirements.
- Check the vector model call logs, confirm that the
defaultTokensetting does not trigger model context overflow errors. - Upload batch historical operation data, verify the completion time of indexing tasks, and confirm that the incremental indexing switch is enabled as needed.
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.