What this category of data looks like
This category of data primarily comes from user behavior logs, push systems, experiment platforms, and CRM systems, with update frequencies mostly daily or weekly. Daily produced documents include growth experiment plans, user segmentation tables, outreach copy, and retention analysis reports. Data fields include user segmentation tags, outreach copy content, experiment grouping parameters, retention rate metrics, channel placement data, and more. The number of fields per document varies by analysis dimension, with no fixed single data length.
What constraints do these characteristics impose on the "model integration and configuration" link
The data sources for this category are scattered and have diverse formats, including structured user segmentation tables and experiment data, as well as unstructured outreach copy and analysis reports. This requires model integration configuration to support multiple input types. Update frequencies are mostly daily or weekly, so model response speed must match business iteration rhythms to avoid delays affecting experiment schedules. Additionally, compliance review of outreach copy has strict requirements. The configuration must ensure accurate identification of sensitive content, and must adapt to multi-dimensional user segmentation data fields to avoid truncation of key analysis information.
How to set the configuration
This document is written for FastGPT v4.17.0. The configuration items and recommendations are as follows:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-dimensional user segmentation data and field combinations in experiment reports, avoiding truncation of key analysis information |
maxTokens | 2000–3000 token | Covers the output needs of outreach copy compliance review and experiment attribution analysis, matching the content length of business analysis |
quoteMaxToken | 1500 token | Limits the length of referenced user behavior logs and CRM data, avoiding interference from irrelevant information with review results |
temperature | 0.1–0.3 | Ensures consistency of compliance review results, reducing random deviations in sensitive content identification |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions paths, do not fill in single-model addresses such as VLLM/Ollama/Xinference | This variable is a required environment variable for FastGPT App/Admin server, used to specify the root address of the AI Proxy service |
AIPROXY_API_TOKEN | Non-empty administrator token matching the AI Proxy server configuration | This variable is a required environment variable for FastGPT App/Admin server, used for AI Proxy service identity authentication |
Three common configuration errors
- 422 error returned when calling the model: Check if
AIPROXY_API_TOKENis a valid administrator token matching the AI Proxy server configuration; check ifmaxContextexceeds the maximum context length supported by the model. - Only the default model list is displayed on the interface after configuring the external model API: Check if the FastGPT App/Admin server has correctly configured
AIPROXY_API_ENDPOINT(AI Proxy service root address) andAIPROXY_API_TOKEN(AI Proxy administrator token); check ifAIPROXY_API_ENDPOINThas no extra paths appended and no single-model address filled in. - Timeout occurs when calling a specified model in a workflow: Check if
maxTokensexceeds the maximum output token limit supported by the model; check if the AI Proxy service has normal network connectivity.
How to confirm the configuration is complete
- Confirm that the FastGPT App/Admin server starts successfully with no errors indicating missing
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKEN. - Call the test interface, input a compliant outreach copy, and check if the return result normally returns a compliance review conclusion with no error messages.
- Upload a user segmentation table and experiment report, verify that the model can fully read and identify sensitive content within them with no information truncation.
- View the API call logs to confirm that the AI Proxy service call request has been successfully sent and returned a valid response.
All provided values 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.