What the data for this category looks like
Data sources for auto service intelligent due diligence reports include maintenance systems of auto service shops, transaction records from second-hand vehicle circulation platforms, and claim ledgers of insurance institutions. Update rhythm is flexible. Updates can be completed in real time after a single service is finished, or synced in batches per business requirements. The main content consists of structured tables, including fields such as vehicle identification number, maintenance items, consumable specifications, settlement amount, and service shop information. A small amount of unstructured maintenance description text is also included. Field formats are fixed: vehicle identification number is a fixed-length string, working hour unit is hour, settlement amount unit is Chinese Yuan, and consumable model includes manufacturer code string.
Constraints imposed on model access and configuration
Mixed data format requires model access to support mixed encoding of structured tables and unstructured text, to avoid field identification misalignment. Fixed-format fields require configuration of field validation rules during access, to filter invalid data such as vehicle identification numbers and amount units. Flexible update rhythm requires support for both real-time invocation and batch task access modes, covering different due diligence trigger scenarios. Fields with units such as settlement amounts must be associated with unit validation logic in model configuration, to ensure output results match the units of input fields.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embeddingModel | multimodal-embedding-v1 | This model supports mixed encoding of structured tables and unstructured text, adapting to the mixed data format of auto service due diligence reports |
apiRequestTimeout | 600 seconds | Auto service due diligence reports include multi-dimensional maintenance and transaction data, which require long model processing time. 600 seconds covers the complete inference and data return cycle |
chunkSize | 800–1200 characters | Structured tables in auto service due diligence reports have many fields per row. Field relevance must be preserved after splitting. This range balances context completeness and model input constraints |
structuredParseMode | strict | Field formats for auto service due diligence reports are fixed. Strict mode ensures accurate identification and encoding of structured data, avoiding field misalignment |
fieldValidationEnabled | Enabled | Fixed-format fields such as vehicle identification numbers and amount units require validation to filter invalid inputs and improve model invocation accuracy |
rerankTopN | Top 5 entries | Auto service due diligence reports have many associated data entries. Retrieving and reranking the top 5 entries covers core relevant information while reducing model inference load |
The parameter values provided on this page are common starting recommendations for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis. Testing on local samples is recommended before finalizing values.
Three Common Misconfigurations
- A
400 Bad Requesterror occurs when invoking themultimodal-embedding-v1model. The cause is that the exclusive interface domain name and authentication parameters of this model were not correctly added in the FastGPT model channel configuration. - Model return results do not include chain-of-thought content. The cause is that the chain-of-thought output configuration item of the model channel was not enabled, or the selected model does not declare support for chain-of-thought output.
- After creating two channel models in FastGPT 4.9.7, the application's AI model settings cannot correctly switch the target model. The cause is that the channel model name was not set as a unique identifier, causing the system to fail to distinguish different model channels.
How to Confirm Configuration Is Complete
- Enter the FastGPT model channel management page, check the
embeddingModelconfiguration item, confirm that the correct interface information ofmultimodal-embedding-v1has been filled in. Test the interface to return vector data to verify configuration effectiveness. - Submit standard auto service due diligence report data, check the chunked results after model processing, confirm that the
chunkSizeconfiguration matches the actual chunk length, with no excessive splitting or missing fields. - Enable the
fieldValidationEnabledconfiguration, input a vehicle identification number with incorrect format, confirm that the system returns a field verification failure prompt to verify configuration effectiveness. - Trigger a batch due diligence task, check whether the
apiRequestTimeoutparameter in the task log matches the configured value, and confirm that there are no timeout interruptions.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.