What the Data for This Category Looks Like
The core data for this category typically originates from the national public resource trading platform, natural resource trading systems, property right exchange systems, and state-owned asset supervision systems, with updates occurring daily. Teams process documents including transfer announcements, trading notices, bid applications, margin vouchers, transaction confirmation letters, listing result announcements, and property right transfer specifications each day. Most documents are structured text with fixed fields such as trading subject ownership information, bidding rules, margin amounts, and transaction confirmation clauses. No unified unit specification exists, and some fields require confirmation based on regional trading rules.
What Constraints Do These Characteristics Impose on the "Model Access and Configuration" Link
Access to multiple data sources requires the configuration to support connections to API endpoints of multiple heterogeneous systems, preventing data pulling exceptions. The wide variety of document types and inconsistent fields requires context configuration to adapt to text content of different lengths, stopping key ownership and bidding information from being truncated. Daily updated data requires consistent vector database synchronization to ensure the model uses the latest transaction information during calls. Transaction scenarios have extremely high accuracy requirements: any incorrect ownership judgment or process description may lead to invalid transactions or state-owned asset disputes, so model parameters must prioritize rigor.
How to Set the Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Trading documents for this category often include long texts such as property right transfer specifications and listing result announcements. Complete context is needed to accurately extract key information |
temperature | 0.1–0.3 | Transaction scenarios require strict rigor to prevent the model from generating incorrect ownership judgments, bidding rules, or process descriptions |
toolChoice | auto | The system must automatically call tools based on transaction scenarios to complete tasks such as ownership verification, margin calculation, and transaction clause verification |
quoteMaxToken | 2000–3000 token | Key fields must be extracted from multiple documents, such as margin amount, subject location, and transaction confirmation time. Complete referenced content must be retained |
AIPROXY_API_ENDPOINT | Fill in the docking endpoint corresponding to the trading system or public resource platform | Core data comes from this type of system. Direct access to self-owned data sources ensures data timeliness and accuracy |
AIPROXY_API_TOKEN | Fill in the docking key assigned by the system | Used for identity verification to ensure normal pulling of transaction data |
Three Common Misconfigurations
- When batch uploading documents such as bid applications and margin vouchers, only the first dozen entries process normally, with no results returned for subsequent entries. This happens because
maxContextis not adjusted to adapt to the context limit for multi-document batch input, causing later documents to exceed the threshold and be truncated. - After configuring
toolChoiceasauto, the function calling effect of Qwen2-72B-Instruct-Int8 is poor, and generated calling parameters have deviations. This happens becausetemperatureis not set below 0.3, leading to excessive randomness in the model's generated content. - When attempting to access Casdoor identity verification,
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not filled correctly, causing identity verification to fail. This happens because the docking key and endpoint are not configured according to system requirements, and the identity verification process cannot be completed.
How to Confirm the Configuration is Complete
- Import a single transfer announcement document, and verify that the ownership description, bidding rules, and other content output by the model match the original text.
- Call tools for testing, confirm that the model can automatically extract core fields such as margin amount, trading subject type, and transaction confirmation clauses.
- Check the connectivity of
AIPROXY_API_ENDPOINTto confirm that the latest transaction system data can be pulled normally. - Adjust
temperatureto a reasonable range, generate outputs for the same scenario multiple times, and confirm that result consistency meets business requirements.
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.