What the data for this category looks like
Business process data for state-owned capital operation companies comes mainly from business ledgers submitted via the state-owned assets supervision platform, process specification documents and handling records from internal business departments. Data updates trigger synchronously with adjustments to business processes, with regular bulk updates completed each quarter. Each structured data entry includes item ID, item name, handling link serial number, link name, handling subject, required material list, approval requirements, and handling time limit (unit: working days). Unstructured service guide PDF documents are also provided, containing process steps, frequently asked questions, and other content.
What constraints do these characteristics impose on the model access and configuration link
The characteristics of the business data for state-owned capital operation companies impose three core constraints on model access and configuration. First, the dual data source structure that mixes structured business ledgers and unstructured service guides requires configuration adaptations for both precise matching of structured fields and chunk parsing logic for unstructured documents. Second, data fields include handling time limits with working days as the unit, so the unit mapping rule must be locked in the model configuration to avoid unit confusion when generating guidance. Third, data updates occur quarterly, so the vector database synchronization cycle must match this rhythm to ensure recalled process data stays consistent with the latest business requirements. In addition, the uniqueness of business item IDs requires grouping during the recall stage by item ID to avoid confusion of process information across items.
How to set the configuration
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Business process documents contain multiple link steps and material lists, requiring sufficient context to carry complete process information |
temperature | 0.1–0.3 | State-owned capital operation business requires strict accuracy, and overly divergent process guidance content should not be generated |
quoteMaxToken | 2000–3000 token | The single chunk content length of service guide documents is moderate, requiring sufficient original reference details to be retained |
toolChoice | auto | Business process guidance requires automatic selection of appropriate tools to call process query and material matching functions |
AIPROXY_API_ENDPOINT | Locally deployed large model service address | Local deployment of large models is required to ensure the security of state-owned capital operation data |
AIPROXY_API_TOKEN | Key generated according to local deployment configuration | Used to verify access permissions for model services and ensure the security of interface calls |
Three Common Mistakes
- A 401 Unauthorized error is returned when calling a locally deployed model. The cause is incorrect configuration of the
AIPROXY_API_TOKENparameter, or a mismatch between the key and the verification key of the local model service. - The handling time limit unit in the process guidance generated by the model is incorrect. The cause is failure to lock the unit mapping rule for the field, leading the model to confuse working days and calendar days.
- Recalled process data does not match the latest business requirements. The cause is that the vector database synchronization cycle does not match the quarterly update rhythm, resulting in recalled expired data.
How to Confirm the Configuration is Complete
- Check the configuration values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENto confirm they match the local model service’s address and key. - Initiate a test query to verify whether the returned process guidance contains the correct handling time limit unit.
- Trigger a vector database synchronization task to confirm the number of data entries after synchronization matches the latest business ledger.
- Adjust the model generation parameters and run multiple tests to confirm the generated content’s rigor meets business requirements.
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.