What data belongs to this category
Data for this category primarily comes from state-owned assets supervision information systems, enterprise asset management systems, property rights exchange systems, valuation institution reports, and financial statements. The update frequency is monthly. Daily processed documents include property rights registration forms, asset valuation reports, property rights transfer plans, on-site transaction announcements, and asset disposal approval forms. Document fields include asset ownership numbers, valuation amounts, transaction base prices, approval statuses, and others. Units involve ten thousand yuan, square meters, pieces, and more. The size of individual documents varies widely. Some bulk asset registration documents have a large number of lines.
Constraints on model access and configuration
Monthly updated data requires scheduling logic for incremental synchronization or full updates to avoid repeated processing of expired data. Multi-source, heterogeneous document formats require model access to support parsing structured tables and unstructured text. This includes adapting to field mapping for property rights registration forms and professional terminology in asset valuation reports. Wide variation in individual document sizes requires limiting context length during configuration to prevent exceeding model processing limits. Processing sensitive fields such as asset ownership and transaction base prices requires enabling content desensitization rules in configuration to avoid sensitive information leaks. Serious business consequences require model output to link to original document fragments, ensuring traceability.
How to set configurations
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of splicing multiple documents such as monthly bulk property rights registration and asset valuation reports, avoiding exceeding model processing limits |
maxTokens | 2000–3000 token | Controls the output length of single-round responses, adapting to segmented verification scenarios for long texts such as asset disposal approval forms |
temperature | 0.1–0.3 | Maintains the accuracy of professional terminology such as property rights transactions and asset valuation, reducing randomness in generated content |
AIPROXY_API_ENDPOINT | Exclusive interface address matching the locally deployed model | Adapts to network call rules of state-owned assets supervision systems and property rights exchange systems, ensuring normal data access paths |
AIPROXY_API_TOKEN | Authentication key generated according to the deployment process | Ensures the security of interface calls, preventing unauthorized access to sensitive business data |
Three common configuration mistakes
- Phenomenon: The
Message field is requirederror or a 40 status code is returned when calling the interface. Cause: TheAIPROXY_API_TOKENorAIPROXY_API_ENDPOINTparameters are not configured correctly, resulting in missing authentication fields or incorrect interface paths. - Phenomenon: A locally deployed model cannot generate corresponding responses based on uploaded property rights and asset documents. Cause: The
maxContextparameter is not set correctly, causing the parsed document context to not be properly included in the model input range, or the configuration logic for linked document recall is not enabled. - Phenomenon: Adaptation issues occur when calling the model via a transit service. Cause: The model access format is incorrectly set to a non-base model format, causing the request body of the interface call to not match the model's requirements.
How to confirm successful configuration
- Call the configured interface, pass a test property rights registration form fragment, and check if the interface returns a normal business response with no required field errors.
- Upload a single asset valuation report to the platform, launch a linked question-and-answer test, and check if the model output links to content fragments of the uploaded document.
- Adjust the
temperatureparameter to different ranges, compare the rigor of the output content, and select a value that meets business requirements. - After verifying the
maxContextparameter setting, upload monthly updated asset documents in bulk, and check if the system can normally process context inputs spliced from multiple documents.
The values provided here 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.