What the data for this category looks like
The source data for advertising compliance logging compliance materials comes from advertising platform backends, customer qualification documents, contract systems, and publicly available regulatory rules. Data update frequency changes with advertising campaign progress or monthly cycles, with no unified bulk update cycle. Daily processed documents include structured advertising review forms, qualification attachments in scanned document format, time-series delivery records, and notification documents with rectification requirements. Data fields include unique advertising identifiers, delivery time periods, qualification numbers, compliance status, rectification deadlines, etc. There is no unified fixed format template, so adaptation to multiple document structures is required.
Constraints imposed on model integration and configuration by these characteristics
Decentralized data sources require model integration to support targeted pulling from multiple data sources, and adaptation to different access methods such as advertising platform open APIs and customer file storage paths. Update frequency changes with campaigns, so configuration must support on-demand data pulling to adapt to temporarily added advertising campaign data. Diverse document structures require the model to have the ability to parse structured tables, image attachments, and plain text to avoid missing key fields. The compliance attributes of fields require that output results strictly match data field definitions, and core information must not be arbitrarily altered or omitted.
How to configure
This page's configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Compliance materials usually include multiple documents, so sufficient context must be retained to fully load all review data |
maxTokens | 2000–3000 token | Compliance review results must cover content such as qualification verification and delivery compliance judgment, so sufficient output length is required |
quoteMaxToken | 1000–1500 token | Limit the length of referenced compliance material content to avoid exceeding the model's processing limit |
temperature | 0.1–0.3 | Stable, compliance-aligned output results are required, so excessive randomness must be avoided |
toolChoice | auto | Automatically call corresponding tools to pull decentralized compliance data sources, no manual tool specification required |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions, and do not fill in single-model service addresses | This variable is an App/Admin server environment variable used to configure the communication endpoint with the AI Proxy service |
AIPROXY_API_TOKEN | Administrator token consistent with the AI Proxy server configuration, cannot be empty, and other credentials such as application API Key or model vendor key must not be used | This variable is an App/Admin server environment variable used for identity authentication with the AI Proxy service |
Three common configuration mistakes
- Phenomenon: Generated compliance review results include
<think></think>tags, and in some scenarios there is no valid content inside the tags. Cause: The connected model outputs thinking process content by default, and no corresponding filtering rules are configured. - Phenomenon: Delivery records or qualification attachments cannot be pulled during testing, and the backend displays an interface error. Cause: Incorrect
AIPROXY_API_ENDPOINTconfiguration, incorrect or unconfiguredAIPROXY_API_TOKENconfiguration, unreachable AI Proxy service, or incorrect data source interface configuration. - Phenomenon: Compliance review results have missing fields or incomplete information. Cause: Insufficient
maxContextvalue, unable to fully load all compliance material content.
How to confirm the configuration is correct
- Initiate a single advertising campaign compliance material synchronization request, and check whether the model can correctly extract core fields such as advertising ID and delivery time period.
- View interface call logs to confirm that
AIPROXY_API_TOKENauthentication is passed, and no abnormal error returns are present. - Generate a compliance review result, and check that no
<think></think>tag content is included. - Adjust the
temperaturevalue range to verify that the stability of the output result meets expectations.
The values provided here 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.