Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data sources include toll station toll transaction records, road segment traffic flow monitoring, daily maintenance inspection records, road administration incident ledgers, service area operation data, and more. Update cadence varies by business type: systems update toll transaction records hourly, refresh maintenance inspection records daily, and push road administration incidents in real time. A single ledger document includes fields such as event ID, occurrence time, point coordinates, disposal process, and expense details. Units include kilometers, hours, yuan, vehicle trips, and others. Some long documents include multi-page disposal steps and on-site photo annotations.
What constraints these characteristics impose on the vector model and indexing link
Hourly updated toll transaction data creates pressure for high-frequency incremental indexing, so the number of documents indexed per batch must be limited. The multi-field mixed ledger structure requires retaining business-related fields during chunking to avoid semantic fragmentation. Real-time pushed road administration incidents require support for incremental indexing. Numeric fields from different sources need unified semantic encoding to prevent deviations in similarity calculations. Long documents such as maintenance inspection reports require reasonable chunking parameter settings to avoid context loss.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the average semantic unit length of single expressway ledger documents, avoid overly scattered content in a single chunk |
| Overlap Length | 100–200 characters | Retain semantic association between adjacent chunks, prevent breakage of cross-chunk business logic |
| Similarity Threshold | 0.72–0.85 | Distinguish semantic similarity between different business types such as road administration incidents and toll transaction records, avoid mismatching |
| Single Search Limit | Top 10 entries | Adapt to the core associated data volume during ledger summary, reduce interference from redundant results |
maxToken | 8000 | Adapt to context carrying for long maintenance report documents, comply with token limits of mainstream external APIs |
defaultToken | 2000 | Used for vector encoding of single ledger entries, balance semantic completeness and computing overhead |
Three common mistakes
- Phenomenon: The exported knowledge base backup file cannot be split by business category, only the entire knowledge base can be exported. Cause: The configuration to create sub-knowledge bases by ledger business type is not enabled, resulting in a backup granularity that does not match business requirements.
- Phenomenon: When connecting an external vector model API, soft route deployment encounters response timeouts. Cause: The
maxTokenparameter is not adjusted to adapt to the network bandwidth limit of the soft route, causing the single request data volume to exceed the hardware processing capacity. - Phenomenon: Excel ledger files and multi-dimensional documents in Feishu folders cannot be imported. Cause: The adaptation switch for Feishu document parsing is not enabled, resulting in ledger files in non-general formats not being correctly identified and indexed.
How to confirm the configuration is correctly set
- Upload a single toll transaction ledger, check the semantic integrity after chunking, confirm that adjacent chunks retain associated business fields.
- Perform an incremental indexing operation, verify that only newly added ledger data is included in the index, and no full reconstruction is triggered.
- Initiate a ledger summary query, check that the similarity matching of returned results meets business classification requirements, with no cross-type interference.
- Export the current knowledge base configuration log, confirm that the
maxTokenanddefaultTokenparameters match the preset values.
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.