What This Category of Data Looks Like
State-owned capital investment companies’ work consultation data mainly comes from internal work guideline documents, state-owned assets supervision policy documents, historical consultation response records, and approval process archives. Data updates are synchronized with adjustments to state-owned assets policies and internal process optimizations. The core document update cycle is quarterly. Historical consultation records are retained in accordance with archiving requirements. A single document typically includes structured fields such as item number, handling subject, application material list, handling time limit, and frequently asked questions. The material list notes the required quantity. Handling time limits are measured in working days. Frequently asked questions are organized by consultation topic.
Constraints Imposed by These Characteristics on Model Access and Configuration
This category of work consultation data includes structured fields and classification labels. It requires the model to accurately extract key information such as item numbers and handling time limits. It must adapt to the context processing logic for structured data. The quarterly update rhythm means the knowledge base sync cycle does not need to be too short, but the connected model must be compatible with regularly updated document formats. The classification labels of historical consultation records require the model to recall relevant content by topic when called. It is necessary to set a reasonable limit on the number of documents recalled in a single call. Some documents include clear unit annotations such as working days and number of application materials. It is necessary to ensure the model does not confuse unit expressions when generating responses, and prevents content deviations that fail to meet state-owned assets supervision requirements.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context processing limit of FastGPT v4.17.0, and can accommodate multiple work guideline documents and historical consultation records |
temperature | 0.2–0.4 | Work consultation requires accurate process descriptions. Divergent content generation should be avoided to prevent expressions that do not meet supervision requirements |
quoteMaxToken | 2000–3000 characters | The length of a single work guideline document is typically 1000-2000 characters. Recalled document content must be complete and not exceed context limits |
toolChoice | auto | Work consultation may require calling knowledge base recall tools. Automatic tool selection improves response accuracy |
AIPROXY_API_ENDPOINT | Fill in according to the proxy address of the deployment environment | Enables compliant calls to large model interfaces, avoiding direct exposure of API keys |
AIPROXY_API_TOKEN | Fill in according to the actually applied proxy key | Supports secure calls to large model interfaces, preventing API key leakage |
Three Common Misconfigurations
- The symptom is a 500 status code or timeout returned during large model calls. The cause is incorrect configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, or fluctuations in the proxy network. - The symptom is unit confusion in generated responses, such as writing working days as calendar days. The cause is an excessively high
temperaturevalue, which causes the model to generate divergent content and fail to strictly match unit annotations in documents. - The symptom is a prompt that content length exceeds the limit during vector generation. The cause is failure to adjust document chunking logic based on the
quoteMaxTokenvalue, resulting in chunked content exceeding the length supported by the vector model.
How to Verify Proper Configuration
- Call the test interface, input typical work consultation questions, and verify that returned results include accurate structured information such as item numbers and handling time limits.
- Manually trigger a knowledge base update, and confirm that the update log shows successful document parsing with no format errors.
- Simulate a large model call failure scenario, and check for triggered preset retry logic or clear error prompts.
- Review the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTfields on the configuration page, and confirm they are correctly filled with no format errors.
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.