What the data for this category looks like
Core data for this category comes from advertising platforms, user profiles, budget spreadsheets, and attribution tools. Four types of documents are routinely produced: campaign plans, targeting settings, budget schedules, and conversion tracking configurations. Data updates occur daily or when triggered by individual campaigns. Documents include fields such as targeted audience tags, budget amounts, conversion event IDs, and more. Units include yuan, units, days, and others. Each document has a large number of fields, with inter-field relationships present.
What constraints these characteristics impose on the model integration and configuration workflow
Multi-source heterogeneous data sources require model configurations to adapt to the format specifications of different APIs to avoid data parsing errors. Frequently updated campaign data requires model context length to adapt to real-time changing configuration content, preventing truncation of critical configuration information. Document structures with inter-field relationships require models to have complex logic verification capabilities. Incorrect campaign configuration can lead to budget waste, failed campaign launches, and other negative outcomes. Therefore, model invocation stability requirements are high, and reasonable timeout and retry logic must be configured.
How to set the configurations
These configurations are based on the model integration logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length after splicing multiple documents such as campaign plans and targeting settings, avoiding truncation of critical configuration information |
maxTokens | 2000–3000 token | Meets the output length requirements for test case generation and defect analysis results, covering full configuration verification output |
temperature | `0.2–0.4 | Reduces random fluctuations, ensures consistency of test cases, and meets the rigor requirements for campaign configuration |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000 | Required environment variable configured for the FastGPT App/Admin server. Do not append /v1 or model API paths |
AIPROXY_API_TOKEN | Non-empty administrator token matching the AI Proxy server configuration | Required environment variable configured for the FastGPT App/Admin server. Do not use application API keys or model vendor keys as substitutes |
toolChoice | auto | Automatically selects calling tools to verify campaign configurations, adapting to complex verification logic for multiple fields |
Three common mistakes
- A
503 Service Unavailableerror appears, and logs show no text-embedding model is available in the default group. Verification and troubleshooting steps: Check model group configuration, confirm whether the text-embedding model is configured for the default group. - Generated test cases do not cover all targeting configuration fields. Verification and troubleshooting steps: Check the
maxContextvalue, confirm it adapts to the context length after splicing multiple documents. - Defect analysis results from the model have large deviations from actual configurations. Verification and troubleshooting steps: Check the
temperaturevalue, confirm it falls within the0.2–0.4range.
How to confirm configurations are correctly set
- Check the FastGPT server environment variables, confirm
AIPROXY_API_ENDPOINTis the AI Proxy service root address,AIPROXY_API_TOKENis the corresponding non-empty administrator token, and service startup verification passes. - Upload a complete campaign plan document, trigger the model to generate test cases, and verify that the test cases cover core fields of targeting settings, budget schedules, and conversion tracking configurations.
- Adjust the
temperatureparameter to the set range, trigger test case generation multiple times, and confirm output result consistency meets expectations. - Simulate a campaign plan with incorrect configurations, trigger defect analysis, and confirm the model can accurately identify abnormal items in the configuration.
The values provided are common starting points and 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.