What the data for this category looks like
Data sources for this category include the road transport administration system, the national public service platform for dynamic information of road transport vehicles, and the vehicle and practitioner database. The update frequency is daily. Daily documents cover business license applications, vehicle technical files, driver qualification materials, safety production management systems, and road transport business licenses. Most data consists of a mix of structured forms and unstructured text. Fields include business entity qualifications, vehicle technical status, practitioner qualifications, safety management system details, licensing approval process materials, and similar categories. No additional custom extended fields are included.
Constraints on Vector Models and Indexing from These Data Characteristics
Daily updated data sources require indexes to support incremental synchronization, to avoid excessive system resource usage from full reconstruction. The mixed structured and unstructured document format needs flexible chunking strategies, to ensure that approval fields in business license applications and long texts of safety management systems are split correctly. Multi-source data requires unified field mapping, to prevent field conflicts during indexing. Sensitive information in practitioner qualification materials must be desensitized before indexing, to comply with public service data security specifications. The large volume of data indexed in a single run requires reasonable configuration of chunking and recall parameters, to ensure retrieval efficiency and accuracy.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to mixed document text lengths, balances semantic completeness and retrieval accuracy |
| Overlap Length | 100–200 characters | Prevents core limiting clauses of policy terms from being truncated during chunking, ensures context coherence |
| Similarity Threshold | 0.75–0.85 | Filters irrelevant matching results, meets high confidence requirements for policy basis Q&A |
| Maximum Search Results per Query | Top 8 entries | Covers multi-clause associations of policy basis, avoids missing key approval-related information |
maxToken | 4096 | Adapts to large model context windows, fully loads retrieved policy document fragments |
defaultToken | 2048 | Balances text length of retrieval results and model processing efficiency |
Three Common Configuration Errors
- Vector indexing tasks remain pending or stuck in progress for extended periods. This occurs when the system fails to adapt to daily incremental update data sources, and full indexing tasks occupy excessive system resources.
- Retrieval results have no connection to the policy basis of the query. This happens when chunk length settings are unreasonable, truncating core content of policy terms, or when the similarity threshold is set too low, leading to retrieval of irrelevant content.
- Retrieval results contain empty fields or abnormal formatting. This is caused by failure to unify field mapping for multi-source data, resulting in field mismatches during indexing and inability to correctly extract document content.
How to Verify Proper Configuration
- Upload a road transport business license application document, view it in the chunk preview interface of FastGPT v4.17.0, and confirm that approval process and business scope fields are split completely.
- Enter a typical policy Q&A query, and verify that the similarity scores of retrieval results fall within the set threshold range.
- Simulate a daily incremental update data source, trigger an indexing task, and confirm that the task can normally update progress and generate valid indexes.
- Check retrieved document fragments, and confirm that sensitive information in practitioner qualification materials has been desensitized.
All provided configuration values are common starting points, and should be evaluated 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.