What this category of data looks like
Data for this category originates primarily from crowdsourcing platform backends, annotation quality statistical reports, staff training exam records, and financial settlement systems. Daily processed documents include personnel access rules, on-post training materials, monthly performance sheets, and quarterly settlement statements. Data update cadence is split into daily and monthly updates. Daily updates include completed daily annotation volume and quality inspection results. Monthly updates include staff performance rankings and monthly settlement details. Data fields include unique staff identifiers, admission review status, training assessment scores, daily task completion counts, settlement amounts, and more. No unified fixed unit exists; values vary based on document type.
Constraints These Characteristics Impose on Model Access and Configuration
Data sources for this category are scattered, involving crowdsourcing platforms, quality inspection systems, training records, and financial systems. This requires the model access link to support multi-source data docking, with configuration of call rules adapted to different interface formats. Differences exist in data update cadence between daily and monthly updates. This requires models to support dynamic loading of the latest data, while also adjusting context length parameters to adapt to document processing needs across different cycles. Daily processed document types are diverse, including rule texts, assessment answer sheets, performance reports, and settlement statements. This requires models to adapt to different text structures, while also limiting the number of context tokens to avoid processing overflow. This category involves sensitive personnel and financial information. Additional security verification parameters must be configured to prevent sensitive data leaks.
How to Set Configurations
This document is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapts to batch processing of multiple documents including personnel access rules and performance sheets, avoids context overflow |
maxTokens | 2000–3000 token | Used for generating personnel performance analysis and settlement verification results, balances output length and processing speed |
temperature | 0.2–0.4 | Ensures consistency of compliance review results, avoids unnecessary fluctuations in review standards |
toolChoice | auto | Automatically selects appropriate tools to handle multi-source data queries and format conversions, adapts to different document types |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000 (must not append /v1 or /chat/completions, not a single-model address) | Root address of the AI Proxy service configured for the App/Admin server, ensures interface connectivity |
AIPROXY_API_TOKEN | Admin token matching the AI Proxy server configuration, cannot be empty | Ensures security of AI Proxy interface calls, prevents unauthorized access. Must not be replaced with application API keys, model vendor keys, or similar |
Three Common Configuration Mistakes
- Interface calls return the "incorrect API Key" prompt. Troubleshooting steps: Check if the AI Proxy admin token was confused with application API keys, model vendor keys, or similar, and the correct AI Proxy admin token was not used to configure server environment variables.
- Model runtime display is abnormal: Online models only show first-token latency, while offline models show total latency. Cause: Unified configuration of model call time statistics rules was not applied, and statistical logic varies across deployment environments.
- Result truncation occurs during batch processing of settlement statements. Cause: The
maxTokensparameter was not set appropriately, causing output content exceeding the limit to be automatically truncated, preventing complete generation of settlement verification results.
How to Verify Proper Configuration
- Call the test interface with a single personnel access rule document, check if the returned result includes a compliance review conclusion, and verify the correlation between the returned content and the input document.
- View the App/Admin server startup logs or environment variable configurations, confirm that the values of
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTmatch the deployment environment, with no empty values or incorrect characters. This variable is a server-side environment variable, and must not be configured in workflow global variables or application API request headers. - Batch import documents with different update cycles (daily task data and monthly settlement statements), check if the model can normally load and process contexts of different lengths.
- Simulate test data containing personnel identity and financial amounts, check if the model triggers the security verification logic, with no risk of sensitive data leakage.
The values given 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 2026-10-19.