Getting Started with Configuration

Request Address

Whether it is a model provided by a cloud service provider or a privately deployed model, an HTTP service address must be provided, which can be either an IP address or a domain name.

Cloud Service Providers

The model endpoints provided by cloud service providers usually end with completions, for example:

• DeepSeek: https://platform.deepseek.com/chat/completions
• OpenAI: https://api.openai.com/v1/chat/completions

The model endpoint for Microsoft Azure depends on your own deployment. Refer to this step to obtain the request URL.

Privately Deployed Models

The address of a privately deployed model depends on the specific implementation. If the service is provided via Ollama within an intranet, the address is typically http://localhost:8000/api/generate.

Key

Models provided by cloud service providers usually require an API Key.

For privately deployed models, it depends on the specific implementation. If the service is provided via Ollama on an intranet, the key can be left empty.

Note

The API Key from the model provider must be applied for by the user and kept secure.

Other Providers

On the page, you can also see that we support the following model providers, but we currently cannot guarantee the performance of these models:

• DeepSeek
• OpenAI
• 01AI
• Baichuan AI
• Bedrock
• Groq Cloud
• MiniMax
• MistralAI
• MoonShot AI
• Nvidia
• TogetherAI
• Tongyi Qianwen

Note

The performance of large models is influenced by the model providers, and HENGSHI SENSE cannot guarantee the effectiveness of all models. If you encounter unsatisfactory results, please contact support@hengshi.com or the model provider promptly.

OpenAI-API-Compatible

If you need to use models other than those listed above, please select the OpenAI-API-Compatible option. Any model compatible with the OpenAI API format can be used.

Take Doubao AI as an example, you can configure it as follows:

OpenAI API Format

If you need to deploy a private model, ensure that the HTTP service's request address and response format are consistent with the OpenAI API. The input and output formats are as follows:

Request

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "developer",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }'

Response

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "gpt-4o-mini",
  "system_fingerprint": "fp_44709d6fcb",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "\n\nHello there, how may I assist you today?",
    },
    "logprobs": null,
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 9,
    "completion_tokens": 12,
    "total_tokens": 21,
    "completion_tokens_details": {
      "reasoning_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  }
}

Test Model Connection

After configuring the model's API Key, click the Test Model Connection button to check if the model connection is functioning properly. As shown in the image below, if the connection is successful, the returned content from the model interface will be displayed.

Response Speed

The output speed of a large model is the result of multiple factors, including hardware, model complexity, input and output length, optimization techniques, and system environment. Generally speaking, privately deployed small models tend to have slower response speeds and poorer performance compared to models provided by cloud service providers.