What data for this category includes
Typically includes business data from natural resource authorities, state-owned asset supervision institutions, property transaction institutions, and public resource trading centers. Daily documents include transfer announcements, transaction notices, bid application materials, margin vouchers, transaction confirmation documents, listing result announcements, and property right transfer specifications. Data sources are the national public resource trading platform, natural resource trading systems, property exchange systems, and state-owned asset supervision systems, with a daily update frequency. Core data fields include transaction subject ownership information, margin amounts, bidding rules, transaction confirmation details, and similar items. Document formats and field order vary across different exporting systems.
Constraints on model access and configuration
Multi-source heterogeneous data sources require configuring compatible interface formats for different systems to prevent data pull failures. Daily updated business data needs appropriate context window lengths to ensure full coverage of all transaction documents generated that day. Diverse document structures require configurations that support automatic identification and extraction of different fields, reducing manual verification workload. Errors in transaction processes have severe consequences, so ledgers and reports generated by the model must have high confidence levels, requiring strict control of generation parameter stability.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Proxy access address corresponding to each transaction system. For Ollama scenarios, set to http://localhost:11434 | Adapts to data pull and model calls for multi-source transaction systems |
maxContext | 8000–12000 token | Covers the full content of multiple daily transaction documents, avoiding truncation of critical ownership and transaction information |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring accuracy of core data such as ownership information and amounts in ledgers |
quoteMaxToken | 4000–6000 token | Fully retains referenced content from core documents such as margin vouchers and transaction confirmation documents |
toolChoice | auto | Automatically calls tools to complete multi-source data aggregation and format conversion |
vision | false (default), set to true when visual analysis is required | Adapts to the text-dominant transaction documents in this scenario; enable visual recognition functionality as needed |
Three common configuration errors
- A 404 error occurs when calling a local Ollama model. This is caused by failing to correctly configure
AIPROXY_API_ENDPOINTto point to the Ollama proxy address, or failing to open the corresponding port permissions. - Connection failure occurs when an Embedding model is forwarded via OneAPI. This is caused by failing to separately configure the access path for the Embedding model from the Chat model, or failing to match the model mapping parameters of OneAPI to the calling format of FastGPT.
- No response is received when attempting to access a multimodal visual analysis model. This is caused by failing to enable the
visionconfiguration item, or failing to adapt to the visual input format requirements of the model.
How to confirm successful configuration
- Import one typical transfer announcement document, trigger the ledger generation task, and check whether the output includes core fields such as subject ownership information and margin amounts.
- View the model call logs to confirm that the request parameters for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the configured values. - Run the daily incremental data aggregation task three consecutive times, and check the field completeness and consistency of the output results.
- If the visual analysis function is enabled, upload one scanned copy of a margin voucher, and confirm that the model can correctly extract key text information from the voucher.
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.