Data profile for this niche
Core data for this niche comes from ad placement platforms, creative asset libraries, user interaction data, and conversion data. Most updates run on a daily or weekly schedule. Daily produced documents include creative scripts, asset test sheets, placement performance reports, and iteration checklists. Document structures use structured fields primarily, covering creative asset types, variable combinations, placement performance metrics, test conclusions, and additional relevant details. Core data processing steps center on creative variable design, asset fatigue detection, and test conclusion summarization. This involves multi-dimensional asset combination comparisons and performance analysis.
Constraints on model integration and configuration
The multi-source nature of category data requires model calls to route through a unified proxy, to avoid exposing vendor credentials directly. Structured test documents and large volumes of creative samples require model context length to support multiple data sets without truncation. Requirements for visual asset analysis mean models must support visual processing capabilities. High-frequency iteration and summarization tasks require a balance between output consistency and flexibility. Additionally, server-side environment variable configuration must strictly follow FastGPT v4.17.0+ requirements. Application-layer configuration cannot replace these settings, or startup verification will fail or calls will encounter errors.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | http://aiproxy:3000 (or the deployed AI Proxy root address) | Complies with FastGPT v4.17.0+ environment variable requirements. Do not append /v1 or any path suffix |
AIPROXY_API_TOKEN | Administrator Token matching the AI Proxy server configuration | Must be non-empty. Must exactly match the server configuration. Do not use application API Keys or vendor secrets instead |
maxContext | 8000–12000 token | Supports multiple sample contexts for batch creative testing, to avoid truncating critical test data and variable information |
temperature | 0.3–0.6 | Balances consistency of test conclusions and flexibility of creative analysis, suitable for summarization tasks |
vision | Enabled | Supports fatigue and compliance analysis for image and video creative assets |
maxTokens | 1500–2000 token | Limits output length for individual test conclusions, to fit structured summarization for test reports |
Three common configuration errors
- Calls to OpenAI-compatible models or models deployed via Xinference return a 405 status code. Troubleshooting steps: Verify
AIPROXY_API_ENDPOINTis the AI Proxy root address, not the single model service address for VLLM/Ollama/Xinference or similar tools. - Using an application API Key or model vendor secret when filling in
AIPROXY_API_TOKEN. Troubleshooting steps: VerifyAIPROXY_API_TOKENis the administrator token matching the AI Proxy server configuration, and that it is non-empty. - Cannot detect fatigue characteristics or compliance risks for image or video creative assets. Troubleshooting steps: Verify the
visionparameter is enabled based on the asset type.
How to verify successful configuration
- Verify the FastGPT server environment variable configuration file or deployment parameters, to confirm
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare set correctly and are non-empty. - Navigate to the model channel configuration page, to verify that upstream model vendor credentials and addresses match the actual deployed services.
- Submit an analysis request for a creative test case, to confirm the returned results include expected content such as asset fatigue assessments and compliance prompts.
- Review the FastGPT service startup logs, to confirm there are no error messages related to AI Proxy connection failures or authentication failures.
The values provided here are common starting points. They should be evaluated against in-house test 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.