What the data for this category looks like
Daily documents for this category include transfer announcements, transaction notices, bid application forms, margin vouchers, transaction confirmation letters, listing result announcements, property transfer descriptions, and more. Data primarily comes from the national public resource trading platform, natural resource trading systems, property exchange systems, and state-owned asset supervision systems, with an update frequency of once per day. Most documents use structured formats, containing fields such as subject ownership, restriction conditions, transaction processes, and margin rules. Some documents involve cross-system associated data, and the overall length of documents varies widely.
What constraints these characteristics impose on the "model access and configuration" link
Configure a scheduled synchronization mechanism for daily updated multi-source data to ensure the model uses the latest ownership and transaction information when called. Ensure the model accurately identifies fixed fields in structured documents, so restrict the rigor of generated content. Adjust parameters to adapt to long text input, as cross-system data association increases context length. Include a data verification link in the configuration to avoid generating incorrect content, as document errors can lead to invalid transactions and asset loss.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxTokens | 8000-12000 token | Core documents such as property transfer descriptions and transaction confirmation letters have relatively long lengths, and need to cover complete subject information |
temperature | 0.2-0.4 | Documents must maintain rigor and standardization, to avoid generating casual or non-compliant expressions related to transaction rules |
quoteMaxToken | 3000-5000 token | Need to reference ownership data and historical transaction records from the trading system, so the effective length of referenced content must be limited |
AIPROXY_API_ENDPOINT | Fill in the locally deployed model service address | Adapt the call link between privately deployed FastGPT and local models |
AIPROXY_API_TOKEN | Configure the access key for private models | Ensure identity verification security for model calls and prevent unauthorized access |
toolChoice | auto | Call built-in tools as needed to complete auxiliary operations such as subject ownership verification and format alignment |
Three common configuration mistakes
- A 404 no body error occurs during model testing. This happens when
AIPROXY_API_ENDPOINTis not configured correctly, or the privately deployed model service has not opened the corresponding access port. - An unmarshal_resp error triggers during speech-to-text conversion. This happens when no adaptation is made for the structured fields of property transaction documents, leading to format exceptions when the model parses multi-segment data.
- Local Ollama models cannot be accessed after Docker private deployment. This happens when the port of the model service is not opened in the container configuration, or
AIPROXY_API_ENDPOINTis not correctly filled in as the model access address within the container.
How to confirm the configuration is complete
Call the model interface corresponding to AIPROXY_API_ENDPOINT. A valid response without identity verification errors confirms correct API configuration. Generate a draft transfer announcement, and verify that core fields such as subject ownership and transaction conditions are included. This confirms maxTokens and quoteMaxToken configurations adapt to document lengths. Submit batch test data, and observe that the model output format aligns with property transaction document specifications. This confirms the rigor of the temperature parameter meets requirements. Review the logs of the privately deployed FastGPT container. Absence of parameter parsing exception errors confirms correct overall link configuration.
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.