What data for this category looks like
Data for this category primarily comes from landing page access logs, heatmaps, form submission data, and advertising source parameters. Daily processed documents include landing page plans, event tracking requirement documents, AB test reports, and form field lists. Data update frequency is weekly or within a single campaign cycle. Fields include page path, dwell time, form submission fields, advertising channel identifiers, and other items. These fields vary by specific analysis dimensions and have no unified fixed units.
Constraints on model integration and configuration
Data for this category is scattered across multiple types of logs and documents. The core time-consuming steps are page path analysis, form blockage troubleshooting, and experimental result judgment. A single compliance review requires associating multi-dimensional information such as page paths, form fields, and advertising channels. This places requirements on model context carrying capacity and call efficiency. Data update frequency fluctuates with campaign cycles. The system must support dynamic adjustment of model call context scope. Security and compliance reviews must ensure that landing page materials, form fields, and other content processed by the model do not involve prohibited content. Compliance verification logic must be configured during model integration. Multi-dimensional data association analysis requires the model to accurately match retrieved compliance materials with business data. This avoids situations where content is retrieved but cannot be associated.
Configuration settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-dimensional business data association analysis needs for this category, preventing failure to match compliance materials and business data due to insufficient context |
quoteMaxToken | 1500–2000 token | Controls the reference length of a single compliance material, meeting content review accuracy requirements in landing page conversion optimization scenarios |
temperature | 0.2–0.4 | Reduces randomness of generated results, ensures consistency of compliance review results, and adapts to the rigor requirements of business analysis |
AIPROXY_API_ENDPOINT | Enter the root address of the AI Proxy service, for example http://aiproxy:3000. Do not add path suffixes such as /v1 or /chat/completions | Required environment variable for the App/Admin server, ensuring normal model call link operation |
AIPROXY_API_TOKEN | Enter the administrator token consistent with the AI Proxy server configuration. Cannot be empty | Required environment variable for the App/Admin server, ensuring identity legitimacy of model calls |
Three common configuration errors
- Symptom: Model call timeout, slow query speed. Cause: Failure to limit the value of
maxContextorquoteMaxToken, resulting in too many tokens passed per call, exceeding the model's carrying limit. - Symptom: Semantic retrieval returns multiple results, but the model prompts that no relevant answers are found. Cause:
temperatureis set too high, causing the model's generated results to deviate from retrieved content, orquoteMaxTokenis set too small, truncating key compliance material content. - Symptom: Unable to integrate the specified model, or identity verification failure error occurs. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or upstream model channel configuration has errors.
How to verify successful configuration
- View model call logs to confirm that the values of
maxContextandquoteMaxTokenpassed in each call are within the preset range. - Simulate submission of a compliance material review task to verify whether the model can correctly associate retrieved content and generate compliance judgment results.
- Check the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables deployed on the App/Admin server to ensure they meet requirements and can normally initiate model call requests. - Adjust the
temperaturevalue, compare review result consistency across different settings, and confirm alignment with business needs.
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.