What the data for this category looks like
Data sources include state-owned asset ledgers required for submission by state-owned asset supervision departments, property right change documents, annual operation reports, internal workflow manuals, archived inquiry tickets, and public state-owned asset policy documents. Asset ledgers update in real time when property rights change. Operation reports update monthly. Policy documents update irregularly per regulatory requirements. Archived inquiry tickets are saved in real time as requests are submitted. Document formats are mostly structured tables, long-text policy documents, and short-text service guides. Fields include asset number, book value, approval node, inquiry item category, and reply time limit requirements. Common units include ten thousand yuan, square meters, and person-times.
What constraints these characteristics impose on the model access and configuration link
The mixed document structure of structured asset tables and long-text policy documents requires models to support both structured information extraction and long context understanding, to avoid field identification errors. Real-time updated asset ledgers and irregularly updated policy documents require model access to support dynamic loading of the latest data, to avoid using stale information. The multi-field, multi-unit document characteristics require clear field mapping rules during configuration, to ensure model output aligns with standardized units and classification standards for state-owned asset management. High-frequency real-time inquiry tickets require the model call chain to have low latency, to ensure reply timeliness meets regulatory requirements.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to mixed context requirements of long-text policy documents and multi-page asset ledgers |
maxTokens | 2000–3000 token | Controls the output length of service inquiry replies, meets the concise and rigorous requirements of state-owned asset services |
quoteMaxToken | 1500–2000 token | Limits the maximum length of quoted document fragments, avoids redundant content interfering with model understanding |
temperature | 0.1–0.3 | Reduces randomness in model output, ensures compliance and accuracy of state-owned asset service replies |
toolChoice | auto | Automatically selects whether to call tools for structured asset queries based on the type of inquiry item |
AIPROXY_API_ENDPOINT | Fill in the model node address for private deployment | Connects to locally deployed large language model services, ensures data privacy |
Three common configuration errors
- After adding
ollama qwen2.5andbge-m3as model channels, the corresponding options do not appear in the text understanding model dropdown menu when creating a knowledge base. The cause is failure to enable index permissions for the corresponding models in system configuration, or incorrect association of the model deployment path with the FastGPT service. - Timeout errors occur during model calls, with a 504 status code returned. The cause is an excessively large
maxContextconfiguration value, which exceeds the context capacity limit of the model service, leading to excessive request processing time. - Model calls fail after configuring
AIPROXY_API_TOKEN. The cause is that the key permission is not bound to the corresponding model, or the API endpoint address is filled incorrectly and does not point to the actually deployed model service.
How to confirm the configuration is complete
- Navigate to the model channel management page, confirm that the status of added models shows normal operation.
- Upload a state-owned asset ledger document, test whether the model can accurately extract asset number and book value fields.
- Submit a test service inquiry request, confirm that the model's reply includes compliant policy citations and clear service guidance.
- Check system logs, confirm that the request parameters for model calls match the configured items, with no abnormal errors.
The values listed above 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.