What Data Looks Like for This Category
The entities involved in this category include transportation bureau approval departments, road transport management agencies, and transport enterprises. Documents generated and processed daily include business license applications, vehicle technical files, driver qualification materials, safety production management systems, road transport business licenses, and similar documentation. Data mainly comes from the road transport administration system, the national road transport vehicle dynamic information public service platform, and the vehicle and personnel database. The update frequency is daily. Each individual document includes structured fields such as main body qualifications, vehicle parameters, personnel qualifications, and safety specifications, alongside unstructured text content. Most fields are related to road transport business qualifications, and there are no universal standardized field units across categories.
What Constraints These Characteristics Impose on the Vector Model and Indexing Link
Daily updated business data requires vector indexes to support incremental update mechanisms, preventing full reconstruction from consuming excessive computing resources. Mixed storage of multiple document types, including structured forms and non-standard text, requires indexes to distinguish the business attributes of different documents, reducing interference from cross-type matching. Large differences in data formats from different sources require vector models to adapt to diverse text encoding logic, ensuring consistent feature extraction across different documents. The approval link has high requirements for data accuracy. Index recall results must be linked to the complete path of the original document, facilitating subsequent verification and traceability, and avoiding situations where licenses are rejected.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average paragraph length of road transport-related documents, avoiding including too many unrelated business contents in a single chunk |
| Overlap Length | 100–150 characters | Ensures key information such as vehicle license plates and driver IDs is not truncated during chunking, preserving context coherence |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Meets the semantic similarity requirements for road transport approval materials, filtering low-correlation recall results |
| Single Search Limit | Top 6 results | Adapts to the verification information volume of the approval link, avoiding excessive results increasing the burden on review staff |
maxToken | 4000 token | Matches the context window limit of mainstream large models, carrying recalled document text and query content |
defaultToken | 2000 token | Serves as the default single-round interaction context length, balancing information completeness and response speed |
Three Common Misconfigurations
- Phenomenon: The vector model option in the interface is empty or cannot be selected, and an error "No valid index model selected" is reported after submitting the configuration. Cause: The deployment path of the local vector model is not configured correctly, or FastGPT has not completed the container mounting and initialization of the vector model.
- Phenomenon: The recalled text blocks are not linked to the source information of the original document, and the document source cannot be displayed in the response. Cause: The "Retain Document Metadata" configuration item is not enabled, or the path, name and other meta fields of the document are not extracted synchronously during chunking.
- Phenomenon: After deploying a DeepSeek-related model, the vector recall results do not meet expectations, or incremental index updates cannot be completed. Cause: The daily updated road transport administration system data is not converted to a unified text format, causing the vector model to fail to encode features correctly.
How to Confirm Proper Configuration
- Upload a typical road transport business license application document, perform chunking and indexing operations, and check whether the chunking results match the preset length and overlap settings.
- Initiate a query related to road transport approval, verify that the number of recall results conforms to the single search limit configuration, and that the original document metadata is linked.
- Test the incremental update function, upload new vehicle technical file data, and confirm that the index can automatically identify the new content and complete the update.
- Check the FastGPT system logs to confirm that there are no error messages in the vector model call and index construction process.
The values provided here 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.