AI Proxy

Function Description

The AI Proxy plugin implements AI proxy functionality based on the OpenAI API contract. It currently supports AI service providers such as OpenAI, Azure OpenAI, Moonshot, and Qwen.

Note:

When the request path suffix matches /v1/chat/completions, it corresponds to text-to-text scenarios. The request body will be parsed using OpenAI’s text-to-text protocol and then converted to the corresponding LLM vendor’s text-to-text protocol.

When the request path suffix matches /v1/embeddings, it corresponds to text vector scenarios. The request body will be parsed using OpenAI’s text vector protocol and then converted to the corresponding LLM vendor’s text vector protocol.

Configuration Fields

Basic Configuration

Name	Data Type	Requirement	Default	Description
`provider`	object	Required	-	Configures information for the target AI service provider

Details for the provider configuration fields:

Name	Data Type	Requirement	Default	Description
`type`	string	Required	-	Name of the AI service provider
`apiTokens`	array of string	Optional	-	Tokens used for authentication when accessing AI services. If multiple tokens are configured, the plugin randomly selects one for each request. Some service providers only support configuring a single token.
`timeout`	number	Optional	-	Timeout for accessing AI services, in milliseconds. The default value is 120000, which equals 2 minutes.
`modelMapping`	map of string	Optional	-	Mapping table for AI models, used to map model names in requests to names supported by the service provider. 1. Supports prefix matching. For example, “gpt-3-” matches all model names starting with “gpt-3-”; 2. Supports using "" as a key for a general fallback mapping; 3. If the mapped target name is an empty string "", the original model name is preserved.
`protocol`	string	Optional	-	API contract provided by the plugin. Currently supports the following values: openai (default, uses OpenAI’s interface contract), original (uses the raw interface contract of the target service provider)
`context`	object	Optional	-	Configuration for AI conversation context information
`customSettings`	array of customSetting	Optional	-	Specifies overrides or fills parameters for AI requests

Details for the context configuration fields:

Name	Data Type	Requirement	Default	Description
`fileUrl`	string	Required	-	File URL to save AI conversation context. Only supports file content of plain text type
`serviceName`	string	Required	-	Full name of the Higress backend service corresponding to the URL
`servicePort`	number	Required	-	Port for accessing the Higress backend service corresponding to the URL

Details for the customSettings configuration fields:

Name	Data Type	Requirement	Default	Description
`name`	string	Required	-	Name of the parameter to set, e.g., `max_tokens`
`value`	string/int/float/bool	Required	-	Value of the parameter to set, e.g., 0
`mode`	string	Optional	”auto”	Mode for setting the parameter, can be set to “auto” or “raw”; if “auto”, the parameter name will be automatically rewritten based on the protocol; if “raw”, no rewriting or restriction checks will be applied
`overwrite`	bool	Optional	true	If false, the parameter is only filled if the user has not set it; otherwise, it directly overrides the user’s existing parameter settings

The custom-setting adheres to the following table, replacing the corresponding field based on name and protocol. Users need to fill in values from the settingName column that exists in the table. For instance, if a user sets name to max_tokens, in the openai protocol, it replaces max_tokens; for gemini, it replaces maxOutputTokens. "none" indicates that the protocol does not support this parameter. If name is not in this table or the corresponding protocol does not support the parameter, and “raw” mode is not set, the configuration will not take effect.

settingName	openai	baidu	spark	qwen	gemini	hunyuan	claude	minimax
max_tokens	max_tokens	max_output_tokens	max_tokens	max_tokens	maxOutputTokens	none	max_tokens	tokens_to_generate
temperature	temperature	temperature	temperature	temperature	temperature	Temperature	temperature	temperature
top_p	top_p	top_p	none	top_p	topP	TopP	top_p	top_p
top_k	none	none	top_k	none	topK	none	top_k	none
seed	seed	none	none	seed	none	none	none	none

If raw mode is enabled, custom-setting will directly alter the JSON content using the input name and value, without any restrictions or modifications to the parameter names. For most protocols, custom-setting modifies or fills parameters at the root path of the JSON content. For the qwen protocol, ai-proxy configures under the parameters subpath. For the gemini protocol, it configures under the generation_config subpath.

Provider-Specific Configurations

OpenAI

For OpenAI, the corresponding type is openai. Its unique configuration fields include:

Name	Data Type	Requirement	Default	Description
`openaiCustomUrl`	string	Optional	-	Custom backend URL based on the OpenAI protocol, e.g., www.example.com/myai/v1/chat/completions
`responseJsonSchema`	object	Optional	-	Predefined Json Schema that OpenAI responses must adhere to; note that currently only a few specific models support this usage

Azure OpenAI

For Azure OpenAI, the corresponding type is azure. Its unique configuration field is:

Name	Data Type	Filling Requirements	Default Value	Description
`azureServiceUrl`	string	Required	-	The URL of the Azure OpenAI service, must include the `api-version` query parameter.

Note: Azure OpenAI only supports configuring one API Token.

Moonshot

For Moonshot, the corresponding type is moonshot. Its unique configuration field is:

Name	Data Type	Filling Requirements	Default Value	Description
`moonshotFileId`	string	Optional	-	The file ID uploaded via the file interface to Moonshot, whose content will be used as context for AI conversations. Cannot be configured with the `context` field.

Qwen (Tongyi Qwen)

For Qwen (Tongyi Qwen), the corresponding type is qwen. Its unique configuration fields are:

Name	Data Type	Filling Requirements	Default Value	Description
`qwenEnableSearch`	boolean	Optional	-	Whether to enable the built-in Internet search function provided by Qwen.
`qwenFileIds`	array of string	Optional	-	The file IDs uploaded via the Dashscope file interface, whose content will be used as context for AI conversations. Cannot be configured with the `context` field.

Baichuan AI

For Baichuan AI, the corresponding type is baichuan. It has no unique configuration fields.

Yi (Zero One Universe)

For Yi (Zero One Universe), the corresponding type is yi. It has no unique configuration fields.

Zhipu AI

For Zhipu AI, the corresponding type is zhipuai. It has no unique configuration fields.

DeepSeek

For DeepSeek, the corresponding type is deepseek. It has no unique configuration fields.

Groq

For Groq, the corresponding type is groq. It has no unique configuration fields.

ERNIE Bot

For ERNIE Bot, the corresponding type is baidu. It has no unique configuration fields.

360 Brain

For 360 Brain, the corresponding type is ai360. It has no unique configuration fields.

Mistral

For Mistral, the corresponding type is mistral. It has no unique configuration fields.

Minimax

For Minimax, the corresponding type is minimax. Its unique configuration field is:

Name	Data Type	Filling Requirements	Default Value	Description
`minimaxGroupId`	string	Required when using models `abab6.5-chat`, `abab6.5s-chat`, `abab5.5s-chat`, `abab5.5-chat`	-	When using models `abab6.5-chat`, `abab6.5s-chat`, `abab5.5s-chat`, `abab5.5-chat`, Minimax uses ChatCompletion Pro and requires setting the groupID.

Anthropic Claude

For Anthropic Claude, the corresponding type is claude. Its unique configuration field is:

Name	Data Type	Filling Requirements	Default Value	Description
`claudeVersion`	string	Optional	-	The version of the Claude service’s API, default is 2023-06-01.

Ollama

For Ollama, the corresponding type is ollama. Its unique configuration field is:

Name	Data Type	Filling Requirements	Default Value	Description
`ollamaServerHost`	string	Required	-	The host address of the Ollama server.
`ollamaServerPort`	number	Required	-	The port number of the Ollama server, defaults to 11434.

Hunyuan

For Hunyuan, the corresponding type is hunyuan. Its unique configuration fields are:

Name	Data Type	Filling Requirements	Default Value	Description
`hunyuanAuthId`	string	Required	-	Hunyuan authentication ID for version 3 authentication.
`hunyuanAuthKey`	string	Required	-	Hunyuan authentication key for version 3 authentication.

Stepfun

For Stepfun, the corresponding type is stepfun. It has no unique configuration fields.

Cloudflare Workers AI

For Cloudflare Workers AI, the corresponding type is cloudflare. Its unique configuration field is:

Name	Data Type	Filling Requirements	Default Value	Description
`cloudflareAccountId`	string	Required	-	Cloudflare Account ID.

Spark

For Spark, the corresponding type is spark. It has no unique configuration fields.

The apiTokens field value for Xunfei Spark (Xunfei Star) is APIKey:APISecret. That is, enter your own APIKey and APISecret, separated by :.

Gemini

For Gemini, the corresponding type is gemini. Its unique configuration field is:

Name	Data Type	Filling Requirements	Default Value	Description
`geminiSafetySetting`	map of string	Optional	-	Gemini AI content filtering and safety level settings. Refer to Safety settings.

DeepL

For DeepL, the corresponding type is deepl. Its unique configuration field is:

Name	Data Type	Requirement	Default	Description
`targetLang`	string	Required	-	The target language required by the DeepL translation service

Usage Examples

Using OpenAI Protocol Proxy for Azure OpenAI Service

Using the basic Azure OpenAI service without configuring any context.

Configuration Information

provider:
  type: azure
  apiTokens:
    - "YOUR_AZURE_OPENAI_API_TOKEN"
  azureServiceUrl: "https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/chat/completions?api-version=2024-02-15-preview",

Request Example

{
  "model": "gpt-3",
  "messages": [
    {
      "role": "user",
      "content": "Hello, who are you?"
    }
  ],
  "temperature": 0.3
}

Response Example

{
  "choices": [
    {
      "content_filter_results": {
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        },
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        }
      },
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "Hello! I am an AI assistant, here to answer your questions and provide assistance. Is there anything I can help you with?",
        "role": "assistant"
      }
    }
  ],
  "created": 1714807624,
  "id": "chatcmpl-abcdefg1234567890",
  "model": "gpt-35-turbo-16k",
  "object": "chat.completion",
  "prompt_filter_results": [
    {
      "prompt_index": 0,
      "content_filter_results": {
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        },
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        }
      }
    }
  ],
  "system_fingerprint": null,
  "usage": {
    "completion_tokens": 40,
    "prompt_tokens": 15,
    "total_tokens": 55
  }
}

Using OpenAI Protocol Proxy for Qwen Service

Using Qwen service and configuring the mapping relationship between OpenAI large models and Qwen models.

Configuration Information

provider:
  type: qwen
  apiTokens:
    - "YOUR_QWEN_API_TOKEN"
  modelMapping:
    'gpt-3': "qwen-turbo"
    'gpt-35-turbo': "qwen-plus"
    'gpt-4-turbo': "qwen-max"
    'gpt-4-*': "qwen-max"
    'gpt-4o': "qwen-vl-plus"
    'text-embedding-v1': 'text-embedding-v1'
    '*': "qwen-turbo"

AI Conversation Request Example

URL: http://your-domain/v1/chat/completions

Request Example:

{
  "model": "gpt-3",
  "messages": [
    {
      "role": "user",
      "content": "Hello, who are you?"
    }
  ],
  "temperature": 0.3
}

Response Example:

{
  "id": "c2518bd3-0f46-97d1-be34-bb5777cb3108",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I am Qwen, an AI assistant developed by Alibaba Cloud. I can answer various questions, provide information, and engage in conversations with users. How can I assist you?"
      },
      "finish_reason": "stop"
    }
  ],
  "created": 1715175072,
  "model": "qwen-turbo",
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 24,
    "completion_tokens": 33,
    "total_tokens": 57
  }
}

Multimodal Model API Request Example (Applicable to qwen-vl-plus and qwen-vl-max Models)

URL: http://your-domain/v1/chat/completions

Request Example:

{
    "model": "gpt-4o",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"
                    }
                },
                {
                    "type": "text",
                    "text": "Where is this picture from?"
                }
            ]
        }
    ],
    "temperature": 0.3
}

Response Example:

{
    "id": "17c5955d-af9c-9f28-bbde-293a9c9a3515",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": [
                    {
                        "text": "This photo depicts a woman and a dog on a beach. As I cannot access specific geographical information, I cannot pinpoint the exact location of this beach. However, visually, it appears to be a sandy coastline along a coastal area with waves breaking on the shore. Such scenes can be found in many beautiful seaside locations worldwide. If you need more precise information, please provide additional context or descriptive details."
                    }
                ]
            },
            "finish_reason": "stop"
        }
    ],
    "created": 1723949230,
    "model": "qwen-vl-plus",
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 1279,
        "completion_tokens": 78
    }
}

Text Embedding Request Example

URL: http://your-domain/v1/embeddings

Request Example:

{
  "model": "text-embedding-v1",
  "input": "Hello"
}

Response Example:

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -1.0437825918197632,
        5.208984375,
        3.0483806133270264,
        -1.7897135019302368,
        -2.0107421875,
        ...,
        0.8125,
        -1.1759847402572632,
        0.8174641728401184,
        1.0432943105697632,
        -0.5885213017463684
      ]
    }
  ],
  "model": "text-embedding-v1",
  "usage": {
    "prompt_tokens": 1,
    "total_tokens": 1
  }
}

Using Qwen Service with Pure Text Context Information

Using Qwen service while configuring pure text context information.

Configuration Information

provider:
  type: qwen
  apiTokens:
    - "YOUR_QWEN_API_TOKEN"
  modelMapping:
    "*": "qwen-turbo"
  context:
    - fileUrl: "http://file.default.svc.cluster.local/ai/context.txt",
      serviceName: "file.dns",
      servicePort: 80

Request Example

{
  "model": "gpt-3",
  "messages": [
    {
      "role": "user",
      "content": "Please summarize the content"
    }
  ],
  "temperature": 0.3
}

Response Example

{
  "id": "cmpl-77861a17681f4987ab8270dbf8001936",
  "object": "chat.completion",
  "created": 9756990,
  "model": "moonshot-v1-128k",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The content of this document is about..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 20181,
    "completion_tokens": 439,
    "total_tokens": 20620
  }
}

Using Qwen Service with Native File Context

Uploading files to Qwen in advance to use them as context when utilizing its AI service.

Configuration Information

provider:
  type: qwen
  apiTokens:
    - "YOUR_QWEN_API_TOKEN"
  modelMapping:
    "*": "qwen-long" # Qwen's file context can only be used in the qwen-long model
  qwenFileIds:
  - "file-fe-xxx"
  - "file-fe-yyy"

Request Example

{
  "model": "gpt-4-turbo",
  "messages": [
    {
      "role": "user",
      "content": "Please summarize the content"
    }
  ],
  "temperature": 0.3
}

Response Example

{
  "output": {
    "choices": [
      {
        "finish_reason": "stop",
        "message": {
          "role": "assistant",
          "content": "You uploaded two files, `context.txt` and `context_2.txt`, which seem to contain information about..."
        }
      }
    ]
  },
  "usage": {
    "total_tokens": 2023,
    "output_tokens": 530,
    "input_tokens": 1493
  },
  "request_id": "187e99ba-5b64-9ffe-8f69-01dafbaf6ed7"
}

Utilizing Moonshot with its Native File Context

Upload files to Moonshot in advance and use its AI services based on file content.

Configuration Information

provider:
  type: moonshot
  apiTokens:
    - "YOUR_MOONSHOT_API_TOKEN"
  moonshotFileId: "YOUR_MOONSHOT_FILE_ID",
  modelMapping:
    '*': "moonshot-v1-32k"

Example Request

{
  "model": "gpt-4-turbo",
  "messages": [
    {
      "role": "user",
      "content": "Please summarize the content"
    }
  ],
  "temperature": 0.3
}

Example Response

{
  "id": "cmpl-e5ca873642ca4f5d8b178c1742f9a8e8",
  "object": "chat.completion",
  "created": 1872961,
  "model": "moonshot-v1-128k",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The content of the text is about a payment platform named ‘xxxx’..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 11,
    "completion_tokens": 498,
    "total_tokens": 509
  }
}

Using OpenAI Protocol Proxy for Groq Service

Configuration Information

provider:
  type: groq
  apiTokens:
    - "YOUR_GROQ_API_TOKEN"

Example Request

{
  "model": "llama3-8b-8192",
  "messages": [
    {
      "role": "user",
      "content": "Hello, who are you?"
    }
  ]
}

Example Response

{
  "id": "chatcmpl-26733989-6c52-4056-b7a9-5da791bd7102",
  "object": "chat.completion",
  "created": 1715917967,
  "model": "llama3-8b-8192",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "😊 Ni Hao! (That's \"hello\" in Chinese!)\n\nI am LLaMA, an AI assistant developed by Meta AI that can understand and respond to human input in a conversational manner. I'm not a human, but a computer program designed to simulate conversations and answer questions to the best of my ability. I'm happy to chat with you in Chinese or help with any questions or topics you'd like to discuss! 😊"
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 16,
    "prompt_time": 0.005,
    "completion_tokens": 89,
    "completion_time": 0.104,
    "total_tokens": 105,
    "total_time": 0.109
  },
  "system_fingerprint": "fp_dadc9d6142",
  "x_groq": {
    "id": "req_01hy2awmcxfpwbq56qh6svm7qz"
  }
}

Using OpenAI Protocol Proxy for Claude Service

Configuration Information

provider:
  type: claude
  apiTokens:
    - "YOUR_CLAUDE_API_TOKEN"
  version: "2023-06-01"

Example Request

{
  "model": "claude-3-opus-20240229",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": "Hello, who are you?"
    }
  ]
}

Example Response

{
  "id": "msg_01Jt3GzyjuzymnxmZERJguLK",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello, I am a conversation system developed by Anthropic, a company specializing in artificial intelligence. My name is Claude, a friendly and knowledgeable chatbot. Nice to meet you! I can engage in discussions on various topics, answer questions, provide suggestions, and assist you. I'll do my best to give you helpful responses. I hope we have a pleasant exchange!"
      },
      "finish_reason": "stop"
    }
  ],
  "created": 1717385918,
  "model": "claude-3-opus-20240229",
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 16,
    "completion_tokens": 126,
    "total_tokens": 142
  }
}

Using OpenAI Protocol Proxy for Hunyuan Service

Configuration Information

provider:
  type: "hunyuan"
  hunyuanAuthKey: "<YOUR AUTH KEY>"
  apiTokens:
    - ""
  hunyuanAuthId: "<YOUR AUTH ID>"
  timeout: 1200000
  modelMapping:
    "*": "hunyuan-lite"

Example Request

Request script:

curl --location 'http://<your higress domain>/v1/chat/completions' \
--header 'Content-Type:  application/json' \
--data '{
  "model": "gpt-3",
  "messages": [
    {
      "role": "system",
      "content": "You are a professional developer!"
    },
    {
      "role": "user",
      "content": "Hello, who are you?"
    }
  ],
  "temperature": 0.3,
  "stream": false
}'

Example Response

{
    "id": "fd140c3e-0b69-4b19-849b-d354d32a6162",
    "choices": [
        {
            "index": 0,
            "delta": {
                "role": "assistant",
                "content": "Hello! I am a professional developer."
            },
            "finish_reason": "stop"
        }
    ],
    "created": 1717493117,
    "model": "hunyuan-lite",
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 15,
        "completion_tokens": 9,
        "total_tokens": 24
    }
}

Using OpenAI Protocol Proxy for ERNIE Bot Service

Configuration Information

provider:
  type: baidu
  apiTokens:
    - "YOUR_BAIDU_API_TOKEN"
  modelMapping:
    'gpt-3': "ERNIE-4.0"
    '*': "ERNIE-4.0"

Request Example

{
    "model": "gpt-4-turbo",
    "messages": [
        {
            "role": "user",
            "content": "Hello, who are you?"
        }
    ],
    "stream": false
}

Response Example

{
    "id": "as-e90yfg1pk1",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "Hello, I am ERNIE Bot. I can interact with people, answer questions, assist in creation, and efficiently provide information, knowledge, and inspiration."
            },
            "finish_reason": "stop"
        }
    ],
    "created": 1717251488,
    "model": "ERNIE-4.0",
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 4,
        "completion_tokens": 33,
        "total_tokens": 37
    }
}

Using OpenAI Protocol Proxy for MiniMax Service

Configuration Information

provider:
  type: minimax
  apiTokens:
    - "YOUR_MINIMAX_API_TOKEN"
  modelMapping:
    "gpt-3": "abab6.5g-chat"
    "gpt-4": "abab6.5-chat"
    "*": "abab6.5g-chat"
  minimaxGroupId: "YOUR_MINIMAX_GROUP_ID"

Request Example

{
    "model": "gpt-4-turbo",
    "messages": [
        {
            "role": "user",
            "content": "Hello, who are you?"
        }
    ],
    "stream": false
}

Response Example

{
    "id": "02b2251f8c6c09d68c1743f07c72afd7",
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "content": "Hello! I am MM Intelligent Assistant, a large language model developed by MiniMax. I can help answer questions, provide information, and engage in conversations. How can I assist you?",
                "role": "assistant"
            }
        }
    ],
    "created": 1717760544,
    "model": "abab6.5s-chat",
    "object": "chat.completion",
    "usage": {
        "total_tokens": 106
    },
    "input_sensitive": false,
    "output_sensitive": false,
    "input_sensitive_type": 0,
    "output_sensitive_type": 0,
    "base_resp": {
        "status_code": 0,
        "status_msg": ""
    }
}

Using OpenAI Protocol Proxy for 360 Brain Services

Configuration Information

provider:
  type: ai360
  apiTokens:
    - "YOUR_MINIMAX_API_TOKEN"
  modelMapping:
    "gpt-4o": "360gpt-turbo-responsibility-8k"
    "gpt-4": "360gpt2-pro"
    "gpt-3.5": "360gpt-turbo"
    "text-embedding-3-small": "embedding_s1_v1.2"
    "*": "360gpt-pro"

Request Example

{
  "model": "gpt-4o",
  "messages": [
    {
      "role": "system",
      "content": "You are a professional developer!"
    },
    {
      "role": "user",
      "content": "Hello, who are you?"
    }
  ]
}

Response Example

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "Hello, I am 360 Brain, a large language model. I can assist with answering various questions, providing information, engaging in conversations, and more. How can I assist you?"
      },
      "finish_reason": "",
      "index": 0
    }
  ],
  "created": 1724257207,
  "id": "5e5c94a2-d989-40b5-9965-5b971db941fe",
  "model": "360gpt-turbo",
  "object": "",
  "usage": {
    "completion_tokens": 33,
    "prompt_tokens": 24,
    "total_tokens": 57
  },
  "messages": [
    {
      "role": "system",
      "content": "You are a professional developer!"
    },
    {
      "role": "user",
      "content": "Hello, who are you?"
    }
  ],
  "context": null
}

Text Embedding Request Example

URL: http://your-domain/v1/embeddings

Request Example

{
  "input":["Hello"],
  "model":"text-embedding-3-small"
}

Response Example

{
  "data": [
    {
      "embedding": [
        -0.011237,
        -0.015433,
        ...,
        -0.028946,
        -0.052778,
        0.003768,
        -0.007917,
        -0.042201
      ],
      "index": 0,
      "object": ""
    }
  ],
  "model": "embedding_s1_v1.2",
  "object": "",
  "usage": {
    "prompt_tokens": 2,
    "total_tokens": 2
  }
}

Using OpenAI Protocol Proxy for Cloudflare Workers AI Service

Configuration Information

provider:
  type: cloudflare
  apiTokens:
    - "YOUR_WORKERS_AI_API_TOKEN"
  cloudflareAccountId: "YOUR_CLOUDFLARE_ACCOUNT_ID"
  modelMapping:
    "*": "@cf/meta/llama-3-8b-instruct"

Request Example

{
  "model": "gpt-3.5",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": "Who are you?"
    }
  ]
}

Response Example

{
  "id": "id-1720367803430",
  "object": "chat.completion",
  "created": 1720367803,
  "model": "@cf/meta/llama-3-8b-instruct",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I am LLaMA, an AI assistant developed by Meta AI that can understand and respond to human input in a conversational manner. I'm not a human, but a computer program designed to simulate conversation and answer questions to the best of my knowledge. I can be used to generate text on a wide range of topics, from science and history to entertainment and culture."
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ]
}

Using OpenAI Protocol Proxy for Spark Service

Configuration Information

provider:
  type: spark
  apiTokens:
    - "APIKey:APISecret"
  modelMapping:
    "gpt-4o": "generalv3.5"
    "gpt-4": "generalv3"
    "*": "general"

Request Example

{
    "model": "gpt-4o",
    "messages": [
        {
            "role": "system",
            "content": "You are a professional developer!"
        },
        {
            "role": "user",
            "content": "Hello, who are you?"
        }
    ],
    "stream": false
}

Response Example

{
    "id": "cha000c23c6@dx190ef0b4b96b8f2532",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "Hello! I am a professional developer skilled in programming and problem-solving. What can I assist you with?"
            }
        }
    ],
    "created": 1721997415,
    "model": "generalv3.5",
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 10,
        "completion_tokens": 19,
        "total_tokens": 29
    }
}

Utilizing OpenAI Protocol Proxy for Gemini Services

Configuration Information

provider:
  type: gemini
  apiTokens:
    - "YOUR_GEMINI_API_TOKEN"
  modelMapping:
    "*": "gemini-pro"
  geminiSafetySetting:
    "HARM_CATEGORY_SEXUALLY_EXPLICIT" :"BLOCK_NONE"
    "HARM_CATEGORY_HATE_SPEECH" :"BLOCK_NONE"
    "HARM_CATEGORY_HARASSMENT" :"BLOCK_NONE"
    "HARM_CATEGORY_DANGEROUS_CONTENT" :"BLOCK_NONE"

Request Example

{
    "model": "gpt-3.5",
    "messages": [
        {
            "role": "user",
            "content": "Who are you?"
        }
    ],
    "stream": false
}

Response Example

{
    "id": "chatcmpl-b010867c-0d3f-40ba-95fd-4e8030551aeb",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "I am a large multi-modal model, trained by Google. I am designed to provide information and answer questions to the best of my abilities."
            },
            "finish_reason": "stop"
        }
    ],
    "created": 1722756984,
    "model": "gemini-pro",
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 5,
        "completion_tokens": 29,
        "total_tokens": 34
    }
}

Utilizing OpenAI Protocol Proxy for DeepL Text Translation Service

Configuration Information

provider:
  type: deepl
  apiTokens:
    - "YOUR_DEEPL_API_TOKEN"
  targetLang: "ZH"

Request Example Here, model denotes the service tier of DeepL and can only be either Free or Pro. The content field contains the text to be translated; within role: system, content may include context that influences the translation but isn’t translated itself. For instance, when translating product names, including a product description as context could enhance translation quality.

{
  "model": "Free",
  "messages": [
    {
      "role": "system",
      "content": "money"
    },
    {
      "content": "sit by the bank"
    },
    {
      "content": "a bank in China"
    }
  ]
}

Response Example

{
  "choices": [
    {
      "index": 0,
      "message": { "name": "EN", "role": "assistant", "content": "operate a gambling establishment" }
    },
    {
      "index": 1,
      "message": { "name": "EN", "role": "assistant", "content": "Bank of China" }
    }
  ],
  "created": 1722747752,
  "model": "Free",
  "object": "chat.completion",
  "usage": {}
}

Full Configuration Example

Kubernetes Example

Here’s a full plugin configuration example using the OpenAI protocol proxy for Groq services.

apiVersion: extensions.higress.io/v1alpha1
kind: WasmPlugin
metadata:
  name: ai-proxy-groq
  namespace: higress-system
spec:
  matchRules:
  - config:
      provider:
        type: groq
        apiTokens:
          - "YOUR_API_TOKEN"
    ingress:
    - groq
  url: oci://higress-registry.cn-hangzhou.cr.aliyuncs.com/plugins/ai-proxy:1.0.0
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  annotations:
    higress.io/backend-protocol: HTTPS
    higress.io/destination: groq.dns
    higress.io/proxy-ssl-name: api.groq.com
    higress.io/proxy-ssl-server-name: "on"
  labels:
    higress.io/resource-definer: higress
  name: groq
  namespace: higress-system
spec:
  ingressClassName: higress
  rules:
  - host: <YOUR-DOMAIN>
    http:
      paths:
      - backend:
          resource:
            apiGroup: networking.higress.io
            kind: McpBridge
            name: default
        path: /
        pathType: Prefix
---
apiVersion: networking.higress.io/v1
kind: McpBridge
metadata:
  name: default
  namespace: higress-system
spec:
  registries:
  - domain: api.groq.com
    name: groq
    port: 443
    type: dns

Access Example:

curl "http://<YOUR-DOMAIN>/v1/chat/completions" -H "Content-Type: application/json" -d '{
  "model": "llama3-8b-8192",
  "messages": [
    {
      "role": "user",
      "content": "hello, who are you?"
    }
  ]
}'

Docker-Compose Example

docker-compose.yml configuration file:

version: '3.7'
services:
  envoy:
    image: higress-registry.cn-hangzhou.cr.aliyuncs.com/higress/envoy:1.20
    entrypoint: /usr/local/bin/envoy
    # Enables debug level logging for easier debugging
    command: -c /etc/envoy/envoy.yaml --component-log-level wasm:debug
    networks:
      - higress-net
    ports:
      - "10000:10000"
    volumes:
      - ./envoy.yaml:/etc/envoy/envoy.yaml
      - ./plugin.wasm:/etc/envoy/plugin.wasm
networks:
  higress-net: {}

envoy.yaml configuration file:

admin:
  address:
    socket_address:
      protocol: TCP
      address: 0.0.0.0
      port_value: 9901
static_resources:
  listeners:
    - name: listener_0
      address:
        socket_address:
          protocol: TCP
          address: 0.0.0.0
          port_value: 10000
      filter_chains:
        - filters:
            - name: envoy.filters.network.http_connection_manager
              typed_config:
                "@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager
                scheme_header_transformation:
                  scheme_to_overwrite: https
                stat_prefix: ingress_http
                # Outputs envoy logs to stdout
                access_log:
                  - name: envoy.access_loggers.stdout
                    typed_config:
                      "@type": type.googleapis.com/envoy.extensions.access_loggers.stream.v3.StdoutAccessLog
                # Modify as needed
                route_config:
                  name: local_route
                  virtual_hosts:
                    - name: local_service
                      domains: [ "*" ]
                      routes:
                        - match:
                            prefix: "/"
                          route:
                            cluster: claude
                            timeout: 300s
                http_filters:
                  - name: claude
                    typed_config:
                      "@type": type.googleapis.com/udpa.type.v1.TypedStruct
                      type_url: type.googleapis.com/envoy.extensions.filters.http.wasm.v3.Wasm
                      value:
                        config:
                          name: claude
                          vm_config:
                            runtime: envoy.wasm.runtime.v8
                            code:
                              local:
                                filename: /etc/envoy/plugin.wasm
                          configuration:
                            "@type": "type.googleapis.com/google.protobuf.StringValue"
                            value: | # Plugin configuration
                              {
                                "provider": {
                                  "type": "claude",
                                  "apiTokens": [
                                    "YOUR_API_TOKEN"
                                  ]
                                }
                              }
                  - name: envoy.filters.http.router
  clusters:
    - name: claude
      connect_timeout: 30s
      type: LOGICAL_DNS
      dns_lookup_family: V4_ONLY
      lb_policy: ROUND_ROBIN
      load_assignment:
        cluster_name: claude
        endpoints:
          - lb_endpoints:
              - endpoint:
                  address:
                    socket_address:
                      address: api.anthropic.com # Service address
                      port_value: 443
      transport_socket:
        name: envoy.transport_sockets.tls
        typed_config:
          "@type": type.googleapis.com/envoy.extensions.transport_sockets.tls.v3.UpstreamTlsContext
          "sni": "api.anthropic.com"

Access Example:

curl "http://localhost:10000/v1/chat/completions"  -H "Content-Type: application/json"  -d '{
  "model": "claude-3-opus-20240229",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": "hello, who are you?"
    }
  ]
}'