What Data Looks Like for This Category
Data for this category comes from road transport administration systems, the national road transport vehicle dynamic information public service platform, and vehicle and practitioner databases. Update frequency is daily. Daily documents include business license applications, vehicle technical files, driver qualification materials, safety production management systems, and road transport business licenses. Most documents combine structured fields and unstructured text. Fixed fields include vehicle identification code, qualification certificate number, business site address, and safety system clauses. A large amount of free-form text descriptions are also included.
Constraints for Vector Models and Indexing
Daily updated data sources require indexes to support incremental synchronization. This avoids computing resource consumption from full reindexing. Mixed document types require vector models to support mixed vectorization of structured fields and free text. This avoids professional field bias from single vectorization logic. Fixed fields like vehicle identification code and qualification certificate number require indexes to support filtering and recall by specific fields. This ensures precise matching of corresponding information in license applications. Free-form text descriptions of safety systems and business site information require a reasonable chunking strategy. This avoids semantic loss from truncated long text.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed structure of structured fields and free text in road transport-related documents, avoids semantic fragmentation from overly long single chunks |
| Overlap Length | 100–200 characters | Ensures semantic coherence across chunks, avoids breaks in license application logic from chunk truncation |
maxToken | 4096 token | Matches the input limit of mainstream open-source embedding models, supports long-text vehicle technical files and safety management systems |
defaultToken | 2048 token | Meets vectorization needs for basic license application text, balances computing resources and semantic completeness |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Adapts to the density of technical terms in road transport license materials, balances recall accuracy and recall scope |
| Single Search Limit | Top 10 results | Controls retrieval resource consumption, covers the range of commonly associated materials for license applications |
Three Common Misconfigurations
- Issue: No models appear in the text understanding model dropdown when creating a knowledge base, and the interface shows that the model is not loaded. Cause: The channel association between the embedding model and the language model is not configured correctly, or the model deployment path is not properly registered in the system.
- Issue: Indexing takes an excessively long time, and tasks remain queued or uncompleted for extended periods. Cause: Incremental indexing policies are not configured. Full indexing synchronizes all road transport administration data daily, leading to excessive computing resource usage.
- Issue: Retrieval results from vectorized datasets have low matching accuracy, and technical files for corresponding vehicles cannot be recalled. Cause: Chunk length settings are improper, truncating complete information for key structured fields such as vehicle identification code, resulting in semantic bias during vectorization.
How to Verify Successful Configuration
- Manually upload a single business license application document. In FastGPT v4.17.0, check that the vector generation progress completes normally with no error logs.
- Initiate a precise search for a vehicle identification code. Confirm that technical files and qualification materials for the corresponding vehicle can be recalled.
- View the indexing task logs. Confirm that incremental synchronization only updates newly added road transport data from the current day, with no full repeated synchronization.
- Verify that the configured ollama qwen2.5 and bge-m3 models appear in the model dropdown, and can be selected and used normally.
The values provided are common starting points, and should be evaluated against users’ 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.