What this category of data looks like
This category of data is generated across the full lifecycle of data collection tasks. Sources include clear client collection requirements, publicly available compliant data sources, sample libraries accumulated from self-developed collection tools, and raw data collected on-site. Daily deliverable documents include collection plans, sample specification descriptions, authorization compliance documents, and task scheduling tables. Update frequency follows individual project timelines or weekly iterations. Common data fields include sample scope, collection fields, format requirements, authorization period, and more. There are no unified mandatory units; configurations must match client needs and the output format requirements of collection tools.
What constraints these characteristics impose on the model integration and configuration link
This category of data has characteristics of scattered sources, dynamically adjusted fields based on client needs, and clear compliance requirements, which impose multiple constraints on model integration and configuration. First, dynamically changing sample fields require the model to support flexible parameter adaptation to avoid collection deviations caused by fixed templates. Second, authorization information related to compliance needs to be accurately identified and verified by the model, requiring sufficient context length to carry compliance document content. Finally, scattered data sources require the model to adapt to the call rules of different interfaces, requiring clear configuration of interface endpoints and verification parameters to prevent collection task interruptions caused by data source incompatibility. This document is written based on FastGPT v4.17.0.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Carries context content from multiple documents such as collection plans and sample specifications, avoiding loss of key compliance and field information due to context overflow |
maxTokens | 2000 token | Adapts to the single result output length of collection tasks, ensuring complete transmission of field descriptions and compliance instructions |
temperature | 0.3–0.5 | Reduces generation randomness, ensuring output consistency of collection fields and format requirements, and avoiding collection deviations caused by parameter fluctuations |
AIPROXY_API_ENDPOINT | http://127.0.0.1:8080 | Root address of the AI Proxy service, a required environment variable for the FastGPT App/Admin server. Do not append /v1 or model interface paths |
AIPROXY_API_TOKEN | Non-empty administrator token consistent with the AI Proxy server configuration | A required environment variable for the FastGPT App/Admin server, used for identity verification with the AI Proxy service |
vision | Disabled | This category of data mainly consists of text-based collection plans and field descriptions, no visual model capabilities are required, reducing unnecessary resource consumption |
Three common configuration mistakes
- Phenomenon: When configuring OneAPI to connect to a local Ollama model, interface tests return timeout errors, while host port access and container service operation are normal. Troubleshooting: Misconfiguring
AIPROXY_API_ENDPOINTas a single model service address such as Ollama (including the /v1 path), or confusing model channel configuration and AI Proxy service environment variables. - Phenomenon: When using text-embedding-3-large as the indexing model, the data collection task continues to stall without response during execution. Cause: No reasonable value is configured for
maxContext, or the configured value exceeds the context upper limit supported by the model, leading to resource exhaustion when the model loads index data. - Phenomenon: After passing a valid String-type result output from a code block to the large model, the system returns a Question is empty error, while the result itself contains valid content. Cause: The input format requirements for tool calls are not adapted in the model integration configuration, causing the model to fail to correctly parse the incoming non-standard text content and incorrectly judge the request as empty.
How to confirm the configuration is correct
- Run FastGPT service startup verification, confirm that the configurations of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENcan normally connect to the AI Proxy service, with no permission or routing errors. - Adjust the values of
maxContextandmaxTokens, simulate the maximum context requirements of collection tasks, and verify that the model can normally load and process relevant document content. - Configure a test collection task, pass preset sample fields and compliance instructions, and confirm that the collection rules output by the model are consistent with preset requirements, with no format deviations.
- Check the value of
temperature, verify the consistency of generation results through multiple tests, and confirm that it meets the format requirements of the collection task.
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.