What this category's data looks like
Core data for this category comes from the enterprise registration full-process electronic system, the national enterprise credit information disclosure system, the electronic business license system, and the residence information database. Update frequency is daily. Daily processed documents include establishment registration applications, articles of association, shareholder resolutions, residence certificates, business licenses, food business license applications, and other materials. These cover the full process of subject qualification applications and business license processing. Data fields include subject name, residence information, shareholder information, license type, and more. There is no unified fixed single-document length standard.
Constraints on model access and configuration
Multiple data sources require model access to support output formats from different systems, to prevent recall errors caused by field mismatches. The daily update frequency requires configuring incremental synchronization logic to keep the vector database consistent with data sources. Diverse document structures need context and recall parameter adjustments to adapt to application materials and policy Q&A content of varying lengths. Errors directly impact the access progress of market entities, so model recall accuracy and context completeness must be strictly controlled. This avoids incorrect responses caused by improper configuration.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total context length of party building Q&A and market entity registration documents, to avoid truncation of key information |
temperature | 0.1–0.3 | Ensures accuracy of policy responses and process guidelines, meeting the rigor requirements of regulatory scenarios |
quoteMaxToken | 2000–3000 characters | Matches the average length of single registration application materials, ensuring completeness of recalled content |
toolChoice | auto | Automatically determines whether to call tools to verify shareholder information, residence information, etc., adapting to verification needs for registration and licensing |
vision | disabled | Core data of this category is in text format, no need to enable visual processing capabilities, reducing resource consumption |
AIPROXY_API_ENDPOINT | Fill in according to the locally deployed model address | Connects to locally deployed large models and vector models to enable private deployment |
AIPROXY_API_TOKEN | Fill in according to the model deployment key | Completes identity verification for model calls, ensuring access security |
Three common configuration mistakes
- Symptom: Vector model access fails, with a prompt indicating no available channels. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, or the interface format does not match the vector model. - Symptom: Local model tokens per second cannot be obtained, causing abnormal context truncation. Cause: Model rate parameters are not correctly configured in FastGPT, relying on default values that do not match the actual processing capabilities of the model.
- Symptom: No response after uploading scanned copies of paper registration materials. Cause: The model’s support for visual processing is not confirmed, or the
visionconfiguration item is not enabled.
How to confirm successful configuration
- On the model management page of FastGPT v4.17.0, initiate a model test call. Confirm the return status code is 200.
- Upload an establishment registration application, then initiate a vector recall test. Check that returned associated documents come from the specified data sources.
- Enter party building learning-related registration policy questions. Verify that model responses meet preset rigor requirements with no obvious deviations.
- Review system operation logs. Confirm there are no error messages related to model access, vector recall, or context truncation.
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.