What This Category of Data Looks Like
This category of data primarily comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. The update frequency is annual. Commonly processed documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Most documents contain structured indicator fields, fund usage details, qualitative evaluation content, and supporting attachment links. Fields mostly correspond one-to-one with fiscal budget codes and project performance indicators.
What Constraints These Characteristics Impose on Model Access and Configuration
Annual batch-updated data requires that model access supports periodic incremental synchronization configuration. Mixed input scenarios of multiple document types (structured tables, long-text reports, attachments) require configurations to be compatible with parsing and chunking rules for different formats. The strong correlation between fund expenditure and performance targets requires retrieved context to accurately match project codes and indicator fields, to avoid interference from irrelevant content. The verification requirement for supporting materials requires the model to reference original document fragments, while also controlling the total context length to avoid exceeding the model's capacity limits.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context retrieval needs of long documents such as performance evaluation reports and self-assessment forms, while avoiding exceeding the context capacity limits of mainstream large models |
maxTokens | 2000–3000 token | Meets the content output length requirements for performance evaluation report summarization and rectification suggestion writing, balancing generation quality and response speed |
temperature | 0.2–0.4 | Reduces randomness in generated content, ensuring rigorous and accurate expression of performance scores and indicator correspondence |
AIPROXY_API_ENDPOINT | http://127.0.0.1:8000 | Adapts to locally deployed document parsing services, supporting structured extraction of performance-related PDFs and attachments |
AIPROXY_API_TOKEN | Exclusive token generated during service deployment | Ensures secure access permissions for the document parsing interface, preventing unauthorized calls |
Three Common Misconfigurations
- Symptom: Unable to select a configured and enabled model in workflow nodes. The interface displays blank or no matching options. Cause: The API endpoint of the corresponding model is not bound in the system settings' model channels, or the enabled status of the model channel has not been synchronized to workflow nodes.
- Symptom: Empty content or format corruption is returned when parsing performance-related PDF documents. Cause:
AIPROXY_API_ENDPOINTis not configured to point to the correct parsing service address, or the parsing service port has not been opened for normal access permissions. - Symptom: The generated performance evaluation content has low matching degree with original data, with incorrect indicator correspondence. Cause: The
temperaturevalue is set too high, causing generated content to deviate from the rigorous expression of original performance indicators, ormaxContextis set too small, leading to insufficient retrieved associated data.
How to Verify Successful Configuration
- Run a model channel connectivity test to confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations can normally return call results. - Upload a single performance target form document to verify that the chunked and retrieved content includes core fields such as project codes and performance indicators.
- Start a batch indexing task to confirm that annual batch data can be chunked and uploaded according to the configured
maxContextandmaxTokensparameters. - Add a model call node in the workflow to verify that the configured model appears normally in the optional list.
The values provided are common starting points and should be evaluated against respective deployment 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.