What data for this category consists of
This category of data primarily comes from customer interviews, bidding documents, vendor product materials, and historical project plans. The update frequency is per order or weekly. Daily produced documents include technical proposal documents, customer requirement research forms, quotation lists, and demo scripts. Most fields focus on technical routes, implementation boundaries, quotation details, and demo processes. There are no unified fixed units; values must be determined based on the business scenario of each individual document.
What constraints these characteristics impose on the model integration and configuration link
Data for this category is scattered and updated per order, which requires model integration configurations to support rapid adaptation and dynamic adjustment of multi-source documents. Daily documents have large structural differences, so configurations need flexible context processing capabilities to adapt to content in different formats such as proposal documents and research forms. Additionally, pre-sales plans have a high update frequency, so configurations must support lightweight modifications without complex rebuilding processes. Furthermore, data fields have no unified standards, so the model's input parsing needs strong generalization capabilities to avoid configuration failures caused by differences in field formats.
How to set the configurations
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of multi-document splicing for pre-sales solutions, avoiding overflow and loss of key technical route information |
maxTokens | 2000–3000 token | Meets the output length requirements for detailed technical proposal descriptions and test case breakdowns |
temperature | 0.3–0.5 | Maintains the rigor of proposal content, avoiding overly divergent technical route descriptions |
AIPROXY_API_ENDPOINT | AI Proxy service root address, such as http://192.168.1.100:8080 | Adapts to the pre-sales technical solution scenario of pure intranet deployment, ensuring data does not leave the intranet |
toolChoice | auto | Automatically selects whether to call tools to handle tasks related to test cases and defect analysis |
Three common mistakes
- Symptom: The service restarts continuously after intranet deployment, and logs prompt connection failures. Troubleshooting items: Incorrect configuration of
AIPROXY_API_ENDPOINTas the AI Proxy service root address, unconfigured non-emptyAIPROXY_API_TOKEN, or the configured endpoint is inaccessible to the current service. - Symptom: Calls to locally deployed large models have no response or throw errors. Troubleshooting items: Incorrect configuration of
AIPROXY_API_ENDPOINTas the AI Proxy service root address, unconfigured non-emptyAIPROXY_API_TOKEN, or incorrect configuration of upstream model channels. - Symptom: Inputting an English prompt and English knowledge base results in output still in Chinese. Cause: The
temperatureparameter is set improperly, or the model's default language configuration does not cover English scenarios, and the English output requirements for pre-sales solutions are not aligned.
How to confirm the configuration is complete
- Verify that the model access address is network-reachable with the deployment environment. This can be done using a local curl command to check the connection status.
- After adjusting the
maxContextandmaxTokensparameters, upload a single pre-sales solution document to confirm that context processing is not truncated. - Input an English test prompt, check that the output language matches the preset requirements, and confirm that the language configuration takes effect.
- Run a test case generation task, and check whether the output content complies with the technical boundary requirements of the pre-sales solution, with no out-of-scope descriptions.
The values given 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.