What the data for this category looks like
Data for this category primarily comes from state-owned assets supervision information systems, enterprise asset management systems, property rights exchange systems, evaluation agency reports, and financial statements, with a monthly update frequency. Daily processed documents include property registration forms, asset evaluation reports, property rights transfer plans, on-site transaction announcements, and asset disposal approval forms. Most documents contain structured fields such as ownership numbers, original asset values, evaluation values, and transaction statuses, along with lengthy business description text. Fields mostly involve compliance identifiers related to state-owned assets and transaction process nodes.
Constraints imposed by these characteristics on model access and configuration
The monthly updated data scale is relatively stable but has a large single batch processing volume, requiring adaptation to mixed parsing needs for multiple document types. The proportion of structured fields and business text in property rights documents is balanced, so the model must support both structured information extraction and natural language question answering. Data related to state-owned assets has high compliance requirements; model calls must ensure that sensitive information is not leaked in the context, and must strictly match the field logic of business documents. In addition, process nodes in asset disposal documents are strongly correlated, so the model must be able to associate ownership and transaction information across documents. Therefore, configuration must balance context length and the rationality of associated recall.
How to set up the configuration
This document adapts to the configuration logic of FastGPT v4.17.0. The following are configuration recommendations for this category:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context carrying requirements of long documents such as property rights transfer plans and asset evaluation reports, avoiding truncation of key business process descriptions |
maxTokens | 2000–3000 token | Controls the output length of a single round of responses, adapts to the detail requirements for policy interpretation and process descriptions in party building learning Q&A, and avoids redundant output |
quoteMaxToken | 1500–2000 token | Limits the total length of quoted document fragments, ensuring that quoted content is strongly related to the party building learning theme, while avoiding occupying too many context quotas |
temperature | 0.1–0.3 | Reduces the randomness of responses, ensuring that the content of party building learning Q&A complies with the compliance requirements of state-owned assets supervision, and reduces ambiguous expressions |
AIPROXY_API_ENDPOINT | Fill in according to the interface of the deployed model service provider | Adapts to the call paths of different models, ensuring connectivity for model access |
AIPROXY_API_TOKEN | Fill in according to the key provided by the service provider | Completes identity verification for model calls, ensuring the security of interface access |
Three common misconfigurations
- A 404 status code (no body) is returned when calling the model. The cause is an incorrect interface path configured in
AIPROXY_API_ENDPOINT, orAIPROXY_API_TOKENnot filled in correctly, resulting in failure to connect to the model service. - After deploying the model locally, it is unable to answer questions based on document content. The cause is that
maxContextis configured too small, truncating the context fragments after document parsing, causing the model to fail to obtain sufficient reference information; or the document parsing results are not correctly associated with the model call process. - A "Message field is required" error is returned during model testing. The cause is that the parameter configuration format does not meet the model requirements, or the exclusive prompt word for party building learning Q&A is not correctly passed, resulting in the interface being unable to recognize the request parameters.
How to verify successful configuration
- Call the model test interface, check the returned HTTP status code, and confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. - Upload a single property rights document, initiate a party building learning-related question and answer, verify that the model output content includes the core business information in the document, and confirm that context loading is working properly.
- Adjust the
temperatureparameter and initiate multiple tests, observe the consistency of responses, and confirm that the parameter configuration meets business compliance requirements. - Batch upload monthly updated asset disposal documents, test the context carrying capacity of the configuration, and confirm that the configuration can cover the total document length of a single batch processing.
The values given 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.