What this category's data looks like
Data for this category comes from the road transport management system, national road transport vehicle dynamic information public service platform, and vehicle and practitioner database. Daily documents include business license application materials, vehicle technical files, driver qualification materials, safety production management systems, and road transport business licenses. The data update frequency is daily. Document structures are a mix of structured tables and paragraph text, with fields such as license number, vehicle license plate number, qualification certificate number, and safety system clauses. There is no unified fixed format.
What constraints do these characteristics impose on the vector models and indexes link
High-frequency daily updated data requires indexes to support incremental synchronization, avoiding full reindexing that takes too long and impacts business response speed. Mixed structured tables and paragraph documents need to support both table field vectorization and paragraph semantic vectorization, avoiding loss of structured business information or long-text semantics with a single chunking method. Fields include short business identifiers and long system texts, requiring a balance between vector accuracy for short texts and chunking rationality for long texts. The party building learning Q&A scenario needs to associate policy clauses with licensing business, requiring indexes to match semantic associations across documents, avoiding the limitations of single-document recall.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed structure of long system texts and short business fields in this category, avoids losing semantics from overly long chunks and damaging field integrity from overly short chunks |
| Overlap Length | 50–100 characters | Connects semantic associations between chunks, adapts to cross content between vehicle information and system texts |
maxToken | 8192 | Adapts to long-text recall of party building policies and licensing documents, meets context requirements for cross-document stitching |
| Single Search Limit | Top 8 entries | Covers high-frequency updated data from multiple sources, avoids missing key licensing information due to insufficient recall |
defaultToken | Calibrated via actual testing | Adapts to token limits of different embedding models, compatible with input requirements of multimodal embedding models |
| Question Optimization | Enabled | Enhances semantic matching for party building learning Q&A, associates policy clauses with licensing business scenarios |
Three Common Configuration Errors
- A prompt reading "No available index model detected" appears after refreshing the page. The cause is failure to associate documents related to road transport business licensing with the party building learning Q&A knowledge base, or failure to save and activate the index model configuration.
- Table data cannot enable multi-vector support. The cause is failure to enable the multi-vector recognition switch of the knowledge base, or failure to process table fields according to structured chunking rules.
- An error occurs when testing access to a multimodal embedding model. The cause is failure to match the input token limit of the model, or failure to format request parameters according to model requirements, resulting in an invalid interface response.
How to confirm the configuration is complete
- Enter the knowledge base settings page of FastGPT v4.17.0, confirm that the multi-vector support switch for the corresponding documents has been enabled, and the index model status shows normal.
- Create a new test conversation, enter a road transport licensing question related to party building, and check whether the number of recalled documents matches the configured single search limit.
- View the chunking logs of the knowledge base, confirm that both table data and text data have generated corresponding vector indexes.
- Test the interface call for accessing the multimodal embedding model, confirm that the returned vector data format meets the configuration requirements.
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.