What data for this category looks like
Core data for this category comes from ad delivery platforms, landing page servers, product qualification libraries, and advertising law compliance clause libraries. Daily processed documents include single ad creative files, landing page screenshots, sequentially generated review opinions and modification records. Data update frequency is daily or real-time, and changes sync with delivery plans, creative modifications, or compliance clause updates. Single pending review data includes creative text, visual elements, associated product information, and review history fields. Records are stored per single creative, single page screenshot, or single review opinion, with no unified fixed unit.
What constraints do these characteristics impose on model integration and configuration
The data characteristics of this category impose multiple constraints on model integration and configuration. First, data sources are scattered and support real-time updates. This requires integration configurations to support parallel pulling from multiple data sources, and real-time verification logic for incremental data. Second, pending review content includes two formats: text creatives and landing page screenshots. This requires configuring model parameters that support multimodal processing. The total content of a single pending review data set covers creative text, product qualification information, and historical review records. This requires limiting the model’s context and token consumption. Additionally, the review process needs to match compliance clauses one by one. This requires configuring stable call parameters to adapt to high-frequency verification scenarios, and avoid processing delays or result deviations caused by improper configuration.
How to set configurations
The following configurations are based on the parameter logic of FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total content of a single ad creative, landing page screenshot, and historical review records, and avoids context overflow |
maxTokens | 2000–3000 token | Matches the output length requirement for a single review result, and balances the detail level of compliance verification and processing efficiency |
vision | Enabled | Adapts to the visual element verification requirements for landing page screenshots, and identifies prohibited text and layout issues in images |
temperature | 0.1–0.3 | Reduces randomness in review results, and ensures consistency in compliance judgments |
AIPROXY_API_ENDPOINT (server environment variable) | Fill in the actual root address of the AI Proxy service. An example is http://aiproxy:3000. Do not append /v1 or /chat/completions paths, and do not use single-model addresses | Adapts to the real-time call requirements for ad creative review, and ensures model response timeliness |
AIPROXY_API_TOKEN (server environment variable) | Fill in the administrator token consistent with the AI Proxy server configuration. Do not leave it blank | Ensures authentication security for AI Proxy service calls, and prevents unauthorized access |
Three common configuration mistakes
- Symptom: After uploading a landing page screenshot, the model fails to parse it and returns the
invalid file formaterror. Troubleshooting step: Check whether thevisionconfiguration item is enabled. - Symptom: After enabling the reranking model, the final input to the large language model only contains a single matching entry with the highest similarity. Troubleshooting step: Check whether the
quoteMaxTokenparameter is configured correctly. - Symptom: Model calls time out and return the
504 Gateway Timeoutstatus code. Troubleshooting step: Check themaxContextparameter setting, or check the running status of the AI Proxy service.
How to confirm the configuration is correct
- Upload a single ad creative that includes text and images, and check whether the model can parse both the text content and prohibited elements in the image.
- Initiate multiple real-time review requests, and confirm that the response time of the interface call meets business requirements, with no frequent timeouts.
- After enabling the reranking model, check the number of context content passed to the large language model, and confirm that it matches the preset verification range.
- Verify the server environment variable configuration values for
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINT, and ensure they are consistent with the deployed AI Proxy service.
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.