What this category of data looks like
This category of data typically comes from national public resource trading platforms, natural resource trading systems, property right exchange systems, and state-owned asset supervision systems, with a daily update frequency. Daily documents include transfer announcements, trading notices, bid application materials, margin vouchers, transaction confirmation documents, listing result announcements, property right transfer specifications, and more. Core fields cover transaction subject ownership information, restriction conditions, bidding rules, margin amount, transaction confirmation clauses, and others. The document structure mainly combines structured forms and unstructured announcement text.
What constraints these characteristics impose on model access and configuration
Data sources for this category are scattered and updated daily. Model access must support interface compatibility for pulling data from multiple systems to avoid parsing failures caused by differences in data source formats. Daily documents contain both structured forms and unstructured announcement text, so context splitting and recall rules adapted to mixed formats need to be configured. Core fields involve sensitive information such as ownership and bidding rules, so model output scope must be restricted to meet compliance requirements. Errors in the transaction process lead to serious consequences, so strict context verification and result tracing mechanisms must be configured to ensure the accuracy of business links.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the context requirements of multi-document splicing for this category, avoid omission of ownership information due to insufficient context |
maxTokens | 2000–3000 token | Match the output length requirements of transaction documents, ensure complete output of core content such as transaction confirmation and bidding rules |
quoteMaxToken | 1500 token | Limit the length of referenced documents, focus on key information such as transaction subject ownership and margin requirements |
temperature | 0.1–0.3 | Reduce randomness, ensure accuracy and compliance of business guidance content, avoid generating content that does not conform to transaction rules |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Adapt to the dedicated pulling and processing link for this category of data, ensure data security |
toolChoice | auto | Allow the model to automatically select tools to handle parsing requirements for structured data and unstructured text |
Three common configuration errors
- An error "No language model configured" appears when entering the workspace edit page. The cause is that the
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTparameters are not filled in correctly, causing the platform to fail to recognize available models. - The AI model option is empty in the workflow problem classification. The cause is that the basic model access configuration is not completed, or the configured model has not passed the platform's validity verification.
- A prompt "Model stream response is empty, please check whether the model stream output is normal" appears during conversation. The cause is that
maxTokensis set too low, causing the model output to be truncated to empty, or data source pulling fails resulting in no valid context being passed in.
How to confirm the configuration is complete
- Check whether the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters match the deployment environment, and test connectivity by calling the basic interface. - Test calling the model to generate a piece of guidance content for trading notices, verify whether the output includes accurate expressions of the core fields of this category.
- Import a typical document of this category, test the context recall and reference functions, confirm that the recalled content is consistent with the core information of the document.
- Simulate the conversation scenario after migrating the knowledge base, verify whether the model can correctly read the transaction subject information in the knowledge base.
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.