What Data for This Category Looks Like
Data for this category originates from advertising platforms, CRM systems, attribution SDKs, and form systems. Teams maintain daily documents including attribution rule configuration tables, lead distribution tables, channel effect reports, and exception checklists. Data updates occur on a daily or weekly basis. Individual datasets include fields such as channel identifiers, lead unique IDs, conversion timestamps, assigned sales accounts, and budget allocation nodes. Field units align with the native configuration of the associated business system, with no unified fixed standard.
Constraints Imposed by These Characteristics on Model Access and Configuration
The data characteristics of this category impose multiple constraints on the model access and configuration process. First, data sources are scattered, and fields lack unified standards. The configuration process must support multi-data source field mapping rules. This prevents model parsing failures caused by field format differences. Second, data updates follow a daily or weekly cadence. Model batch calls must adapt to non-real-time scheduling scenarios. Overly short timeout thresholds should be avoided. Daily maintained documents are mostly structured tables. Model input must prioritize structured data parsing. Reasonable context length parameters must be configured to accommodate complete attribution rules and lead lists. Dynamic data from temporary exception checklists must support fast loading. Model calls must allow temporary parameter adjustments to meet temporary verification needs. Additionally, the core time-consuming links of this category are channel parameter verification, duplicate lead deduplication, and sales assignment confirmation. Model access must support fast verification and batch correction. This avoids disrupting business pace due to overly long processing times. Consequences of errors include distorted channel ROI, sales assignment disputes, and budget mismatches. Model calls must ensure result accuracy and consistency.
How to Configure Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to batch input requirements for multiple structured attribution documents and lead lists, prevents context overflow |
maxTokens | 2000–3000 token | Meets output length requirements for channel attribution rule parsing and lead deduplication logic, prevents truncation of critical judgment basis |
temperature | 0.1–0.3 | Reduces randomness in attribution judgments, ensures consistent results for channel assignment and sales allocation |
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) | Root address of the AI Proxy service configured for the App/Admin server, ensures model requests are properly routed to the AI Proxy service |
AIPROXY_API_TOKEN | Admin token consistent with the AI Proxy server configuration, and cannot be empty | Authentication credential for the AI Proxy service. Must match the server configuration to complete verification, prevents unauthorized access |
Three Common Configuration Mistakes
- Phenomenon: Only some large models appear in the model selection dropdown of the channel attribution module. The GLM series models accessed via One API cannot be found. Cause: The adaptation switch for the corresponding channel was not enabled in FastGPT's model access configuration, or the model call scope of the attribution module was not limited to all accessed models.
- Phenomenon: A model call timeout error occurs when processing weekly lead data in batches, with a 504 status code returned. Cause: The
maxContextparameter was set too small, preventing complete loading of batch data, or the timeout threshold was set too short, failing to adapt to the parsing time of weekly datasets. - Phenomenon: Embedding models accessed outside of One API cannot be selected in the channel attribution module for lead field vectorization. Cause: Access information for the Embedding model was not configured separately. Only the call parameters for the chat model were configured, preventing the Embedding model from being recognized by the module.
How to Confirm Configuration is Complete
This verification process applies to FastGPT v4.17.0.
- Manually import a small attribution rule document and 10 test leads, trigger a model call, and check if the returned channel assignment results match the preset rules.
- View the model call logs, confirm that the server-configured
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave no spelling errors, and no 401 or 403 errors occur. - Adjust the
maxContextparameter to different ranges, import batch data, check for context overflow or truncation issues, and confirm that the value adapts to the current business data volume. - Enable batch task scheduling tests, verify that model calls can be automatically triggered on a daily or weekly cadence, with no duplicate execution or missed tasks.
The values provided 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.