What the data for this category looks like
This page is written based on FastGPT v4.17.0. Documents associated with this category mainly include advertising delivery daily and weekly reports, daily price adjustment records, material effect test sheets, and delivery optimization review reports. Data sources include advertising platform backend data, user attribution systems, CRM customer lead databases, and landing page access data. Data is mostly updated via daily synchronization or real-time pushes. Fields in individual documents include delivery time period, consumption amount, lead volume, material click-through rate, and other relevant fields. No unified fixed format exists, and some fields have minor changes corresponding to adjustments in advertising platform APIs.
Constraints on model access and configuration
Decentralized data sources and inconsistent formats require model access to support parsing logic for multi-source heterogeneous data, to prevent recognition failures caused by field differences. Real-time or daily updated data requires model configuration to support high-frequency calls. Single request context length must be limited to avoid timeout risks. Field differences across different documents require presetting multiple sets of field mapping rules during configuration, to adapt to material review requirements for scenarios such as advertising delivery and lead attribution. Compliance review links delivery data and material content. Model access must support both text parsing and structured data matching, so relevant parameters for multimodal or structured data processing must be configured in advance.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context length of multi-source delivery documents, balances completeness of compliance review and request response speed |
maxTokens | 2000–3000 token | Limits the length of compliance judgment results output in a single review, avoids exceeding conventional reading and processing thresholds |
quoteMaxToken | 1500 token | Defines the maximum length of original document fragments referenced, ensures review basis is clearly traceable |
temperature | 0.1–0.3 | Reduces output randomness, ensures consistency of compliance review results, adapts to standardized material verification requirements |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions paths | Serves as an environment variable for the App/Admin server, configures access routing for the AI Proxy service, avoids latency or blocking issues from cross-region access |
AIPROXY_API_TOKEN | Admin token consistent with AI Proxy server configuration, cannot be empty | Serves as an environment variable for the App/Admin server, completes identity verification with the AI Proxy service, ensures request legitimacy and security |
Three common configuration errors
- The text understanding model list is empty, and available models cannot be selected. The cause is that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables for the App/Admin server are not correctly configured, or the AI Proxy service is unavailable, preventing the platform from pulling the model list. - Review tasks frequently trigger timeout errors, returning status code 504. The cause is that
maxContextis set too large, which, combined with high-frequency call requirements for real-time data, exceeds the response time limit of the model interface. - Compliance review results omit some non-compliant fields in material test sheets. The cause is that no field mapping rules are preset for material test sheets, so the model cannot recognize non-standardized material attribute fields.
How to verify successful configuration
- Check the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables configured on the App/Admin server, initiate a basic connectivity test, and confirm that a normal model list is returned. - Import a typical delivery daily report document, trigger a compliance review task, and check whether the length of context reference fragments in the returned result matches the
quoteMaxTokenconfiguration value. - Adjust the
temperatureparameter to 0.2, initiate two identical review tasks consecutively, verify consistency between the two results, and confirm the parameter takes effect. - Import a price adjustment record document containing structured data, verify that the model can correctly extract fields such as consumption amount and delivery time period, and confirm that multi-source data adaptation configuration takes effect.
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.