What the data for this category looks like
Data for this category typically comes from image samples on annotation platforms, preset category dictionaries, spot check results, and daily outputs from annotation teams. Daily associated documents include annotation specifications, bounding box examples, quality inspection reports, and rework lists. Updates occur daily or per batch of tasks. Core data centers on the position and category information of visual targets. Unlike text annotation projects, this category handles visual feature fields such as bounding boxes and occlusion status, with no additional quantitative statistical values.
Constraints on model access and configuration
Data sources for this category are scattered across annotation platforms, local image sample libraries, and spot check results. This requires model access to adapt to multi-source input reading logic. Data updates occur daily or per batch of tasks, so support is needed for dynamically loaded category dictionaries and latest samples. Visual data processing requirements mean connected models must support visual modalities. Context and token limits adapted for visual tasks must also be configured. Daily associated structured documents such as quality inspection reports and rework lists require models to handle reading and verification of structured fields. This further constrains the model’s context window and tool call configuration.
Configuration Settings
The following configuration items apply to the FastGPT v4.17.0 model access workflow:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled (set to true) | This category requires processing visual features of image samples, so a model supporting visual modalities must be called |
maxContext | 8000–12000 token | Must carry context from multiple documents including annotation specifications, bounding box examples, and quality inspection reports, to avoid truncation of critical rules |
maxTokens | 2000–3000 token | Used to generate annotation verification results or rework suggestions, to meet long-text output requirements |
quoteMaxToken | 1500 token | Limits the length of referenced annotation document fragments, to avoid interference from redundant information on model judgment |
temperature | 0.1–0.3 | Must maintain consistency of annotation rules, reducing the probability of random outputs |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions. Do not fill in single-model service addresses | This variable is a required environment variable for the App/Admin server, and must match the deployment configuration of the AI Proxy server |
Three common configuration mistakes
- Symptom: Identity verification failure or request parameter mismatch error occurs after deployment. Cause: The server environment variable
AIPROXY_API_TOKENis not configured correctly. Its value must match the administrator token configured on the AI Proxy server and cannot be empty. Or the upstream model channel configuration does not enable visual model call permissions. - Symptom: The model returns no visual recognition results after submitting image samples. Cause: The
visionconfiguration item is not enabled, or the connected model does not declare support for visual modalities. - Symptom: Critical rules are truncated during annotation result verification. Cause: The
maxContextvalue is too small, causing context documents such as annotation specifications and category dictionaries to fail to load completely.
How to confirm correct configuration
- A single image with annotation examples is uploaded, a compliance verification request is initiated, and whether a judgment on annotation compliance based on visual features is returned is verified.
- A complete annotation specification document is imported, and the model call log is checked to confirm that the document content is fully loaded with no truncation prompts.
- After configuring the
temperatureparameter, the same verification request is initiated multiple times, and the consistency of the output results is confirmed to meet expectations. - The proxy service call log is checked to confirm that model requests have been initiated normally with no identity verification failure records.
The values provided here are common starting points, and should be validated against the user’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.