What data for this category looks like
Data for this category primarily comes from client-provided business corpora, annotation results from in-house annotation platforms, industry-standard vocabularies, and quality inspection sampling results. Daily processed documents include annotation specification documents, annotation sample packages, quality inspection reports, and issue feedback forms. Data is updated daily or synchronized with each batch of annotation tasks. Most individual data entries are text fragments with semantic tags. Fields include original text content, annotation tags, quality inspection markers, and update times. No complex additional image fields are included.
What constraints these characteristics impose on model access and configuration
Client corpora come from scattered sources and have varied formats. Model access configurations must support adaptation to multiple types of text inputs. The text lengths of annotation specifications and issue feedback forms vary significantly. Configurations must support context processing capabilities for different text lengths. Quality inspection sampling results require the model to have consistent verification capabilities for annotation boundary judgments. Configurations must therefore support alignment verification of label accuracy for model outputs. Daily or batched updated data volumes fluctuate with tasks. Configurations must adapt to dynamic call frequencies and resource usage.
This document applies to FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to text inputs of varying lengths such as annotation specifications and sample packages, prevents semantic loss caused by context truncation |
maxTokens | 2000–3000 tokens | Matches the output length requirements of individual annotation samples, ensures complete output of tag explanations and quality inspection judgments |
temperature | 0.1–0.3 | Reduces output randomness, ensures consistency of annotation tags and quality inspection conclusions, and meets the strict requirements of compliance reviews |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000 | This environment variable is a required configuration for the App/Admin server. It specifies the AI Proxy service address, and must not include path suffixes such as /v1 |
AIPROXY_API_TOKEN | Non-empty administrator token matching the AI Proxy server configuration | This environment variable is a required configuration for the App/Admin server. Do not use application API keys, model vendor keys, or other types of tokens |
Three common mistakes
- Phenomenon: Model calls fail after Docker deployment, returning a connection timeout error. Troubleshooting: Check if the
AIPROXY_API_ENDPOINTenvironment variable is configured for the App/Admin server. This variable must be the root address of the AI Proxy service. Missing or incorrect configuration will cause startup verification failure or call timeout. - Phenomenon: After uploading an annotation sample package, some text fragments cannot be recognized by the model, returning empty results. Cause: The
maxTokenssetting is too low, truncating valid content of long text fragments and preventing the model from completing semantic analysis. - Phenomenon: When connecting a distilled DeepSeek model, authentication failure is returned after following the general access process. Troubleshooting: Check if the vendor credentials and endpoints in the model channel configuration are correct. The
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare server environment variables, and are unrelated to the authentication information of the model vendor.
How to confirm the configuration is properly set up
- Run a small-batch annotation sample test. Verify the match between model output tags and annotation specifications, then adjust configuration items until expected results are achieved.
- View model call logs to confirm normal interface connectivity, with no frequent timeouts or authentication errors.
- Simulate multi-batch data synchronization scenarios. Verify that call frequencies and resource usage meet task requirements.
- Test text samples of different lengths to confirm no context truncation or incomplete output occurs.
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 2026-10-19.