Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Category Looks Like
Data for urban public transit policies and regulatory references mainly comes from bus operation regulations released by local transport bureaus, internal management documents of urban public transit groups, and national and industry-standard passenger service specifications. The update rhythm adjusts alongside policy revisions. It is synced immediately after local new rules are released. National standard documents are updated every 2 to 3 years. Each individual document includes policy document number, issuing body, effective date, clause entries, and applicable area. Fields mainly use numbers and text descriptions, with no complex numerical units.
Constraints on Vector Models and Indexing
The structured fields and clear clause hierarchy of urban public transit policy data require the vector model and indexing process to separate metadata from main text content. This prevents non-core Q&A content such as document numbers and effective dates from being mixed into semantic vectors. Long clauses in policy documents must retain context when split. This avoids loss of clause association during Q&A. Differences in issuing bodies across policy levels require indexing to be partitioned by jurisdiction and policy level. This prevents confusion between cross-regional policies. The non-fixed update rhythm requires support for incremental indexing. This reduces resource consumption from full reindexing.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Most single clauses in urban public transit policies are 500-1000 characters long. This retains context while avoiding semantic fragmentation within chunks |
| Overlap Length | 100–200 characters | Covers context continuity after clause splitting, prevents semantic breaks between chunks |
| Similarity Threshold | 0.75–0.85 | Distinguishes similar phrasing across policy clauses, prevents irrelevant policy entries from being recalled |
| Single Recall Limit | Top 8 entries | Covers multi-clause requirements for single-round Q&A, while controlling context length |
defaultToken | Calibrated based on actual testing | Adapts to locally deployed embedding models, avoids third-party token permission issues |
maxToken | 4096 token | Matches input length limits of mainstream open-source embedding models, avoids truncation of core policy content |
Three Common Mistakes
- Phenomenon: The interface prompts no available embedding channel, or reports the error "This token does not have permission to use the model". Cause:
defaultTokenis not configured correctly, or a third-party proxy channel is used directly without matching the corresponding model permissions. - Phenomenon: Knowledge base search takes more than 30 seconds, with excessive single-round Q&A latency. Cause: Chunk length is set too large, or single recall limit is set too high, leading to excessive document chunk loading during vector retrieval.
- Phenomenon: Recalled policy entries have low relevance to the query, with irrelevant clauses appearing. Cause: Similarity threshold is set too low, or indexing partitions are not configured by policy level, leading to accidental recall of cross-category policies.
How to Confirm Correct Configuration
- Upload a single urban public transit policy document, check if the number of chunks and single chunk length match the preset configuration.
- Initiate a query related to policies, verify that the number of retrieved results matches the single recall limit.
- Check the embedding model call logs, confirm that
defaultTokendoes not trigger third-party permission errors. - Compare retrieval results for policies from different jurisdictions, confirm that indexing partitions are effective and no cross-regional policy confusion occurs.
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.