What data for this category looks like
Data for this category typically comes from advertising delivery platforms, attribution systems, CRM lead databases, and landing page data. Most updates run on a daily or real-time sync schedule. Daily produced documents include delivery daily and weekly reports, daily price adjustment records, material A/B test sheets, and optimization review reports. Core data fields often include metrics like impressions, clicks, conversion cost, budget balance, and material click-through rate. There is no unified fixed single-document structure. Multi-dimensional heterogeneous data is often integrated in table form.
Constraints imposed on model integration and configuration
The business characteristics of delivery effect optimization impose multiple constraints on model integration and configuration. First, real-time or near-real-time data updates require model call latency to stay within a reasonable range. This avoids affecting the timeliness of price adjustments and material adjustments. Second, the need to integrate multi-source heterogeneous data requires the model integration link to support structured and unstructured data in different formats. Third, frequent abnormal indicator positioning and budget allocation work requires the model to quickly process limited-length context information. It must also maintain consistency in output suggestions. Finally, errors may lead to increased customer acquisition costs or early budget exhaustion. This requires the configuration link to include complete failure handling and retry mechanisms.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must include price adjustment records, material test data, and core delivery metrics from the last 7 days. This ensures the model can access sufficient historical optimization basis |
maxTokens | 2000–3000 token | Delivery optimization suggestions do not require overly long output. This controls token consumption while ensuring the completeness of suggestions |
temperature | 0.3–0.5 | Maintains consistency in optimization suggestions. This avoids unstable adjustment strategies caused by random fluctuations |
toolChoice | auto | Automatically selects whether to call tools. This adapts to structured data query and adjustment suggestion generation requirements across different scenarios |
AIPROXY_API_ENDPOINT | Enter the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions. Do not use single-model addresses such as VLLM/Ollama/Xinference | Required environment variable for the App/Admin server. Used to specify the AI Proxy service address |
AIPROXY_API_TOKEN | Enter the administrator token matching the AI Proxy server configuration. This value cannot be empty. Store it as an environment variable for the App/Admin server | Do not use application API keys, model vendor keys, etc. Follow security configuration specifications to avoid leakage |
Three common mistakes
- A
tls: failed to verify certificate: x509: certificate signed by unknown authorityerror is returned when calling the model. Verification and troubleshooting steps: Check the AI Proxy server certificate trust chain configuration, or confirm that self-signed certificates have been imported into the system trust list. - The interface only supports selecting a single vector model, but the business needs to connect vector data from multiple different sources. Cause: The multi-vector database configuration function is not enabled, or the used FastGPT v4.17.0 version does not support this configuration item.
- Vector data that works normally during local testing cannot be recalled after being uploaded to the server. Cause: The vector model dimensions used locally and on the server are inconsistent, or the data splitting parameters are not synchronized to the server environment.
How to confirm successful configuration
- Verify that the model integration configuration complies with rules. Confirm that AI Proxy environment variables are only configured in the App/Admin server environment variables. They are not incorrectly written into workflow global variables, application API request headers, or the general model management page.
- View the model call log. Confirm that the actual values of configuration parameters such as
maxContextandmaxTokensmatch the preset values. - Simulate an abnormal indicator scenario, such as a sudden increase in daily conversion cost. Trigger a model call. Check whether the system can correctly locate the abnormal cause and provide targeted adjustment suggestions.
- Check whether the App/Admin server startup verification passes. Confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, and there are no connection or permission-related errors.
The values provided here are common starting points. They should be evaluated 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.