What This Type of Data Looks Like
Data is sourced from internal business handling systems of power generation groups, offline window work order archives, and official process manuals. It is categorized and archived by business line. Updates are triggered by adjustments to internal group processes. They are synchronized when new processes launch or existing processes are optimized. Each business document includes fields such as business number, list of handling nodes, required materials details, handling time limit, and responsible department. Field units use standard government service units like working days, copies, and items.
Constraints Imposed by These Characteristics on Model Access and Configuration
Power generation group business handling data is categorized by business line. Process nodes differ significantly across businesses. When accessing the model, limit context to match the corresponding business domain. This avoids confusion across cross-business processes. Data updates have no fixed cycle. Configure appropriate synchronization rules to ensure model-called process information matches the group’s latest requirements. Each document includes multiple fields with unified units. The model must accurately identify fields and their units. Adjust the context length parameter to prevent key field information from being truncated.
How to Configure Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the multi-field structure of power generation group business documents, preventing key handling nodes and material requirements from being truncated |
maxTokens | 1500–2000 token | Controls the length of single-round responses to ensure complete output of business handling steps |
quoteMaxToken | 3000 token | Limits the length of referenced knowledge base content, matching the scale of single-node information in business documents |
temperature | 0.1–0.3 | Reduces response randomness to ensure accuracy of business process guidance |
toolChoice | auto | Allows the model to independently choose whether to call the knowledge base, adapting to query requirements of different business scenarios |
AIPROXY_API_TOKEN | Enter the exclusive token assigned by the group | Verifies call permissions and complies with data security and compliance requirements for state-owned enterprises and public sector entities |
Three Common Misconfigurations
- Phenomenon: Model call times out, and the interface returns a 504 status code. Cause: The
maxContextandquoteMaxTokenparameters are not set appropriately, causing the model to process overly long context and referenced content, which slows down response speed. - Phenomenon: The model passes tests on a third-party transit platform, but returns 401 or 403 errors when tested after creating a new application in FastGPT. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the token permissions do not cover the FastGPT call domain. - Phenomenon: Query results include outdated business process steps, such as canceled approval nodes. Cause: Appropriate knowledge base synchronization rules are not configured, and business handling process data adjusted by the group is not synchronized in a timely manner.
How to Verify Correct Configuration
- Run a query test for a single business process, and check whether the returned content includes the latest handling nodes and material requirements currently in effect for the group.
- Adjust the
temperatureparameter to different ranges, compare the consistency of query results, and confirm that response randomness meets business requirements. - Trigger a knowledge base synchronization operation, wait for synchronization to complete, then query content related to old processes, and confirm that outdated information has been replaced.
- View the application monitoring panel of FastGPT v4.17.0, and confirm that the response time of model calls is within a reasonable range.
Note: The configuration values listed above are common starting points. They should be evaluated against the specific samples and requirements of the relevant deployment.
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.