higress/plugins/wasm-go/extensions/ai-proxy

title, keywords, description

title	keywords	description
AI Proxy	AI Gateway AI Proxy	Reference for configuring the AI Proxy plugin

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.

🚀 Auto Protocol Compatibility

The plugin now supports automatic protocol detection, allowing seamless compatibility with both OpenAI and Claude protocol formats without configuration:

• OpenAI Protocol: Request path /v1/chat/completions, using standard OpenAI Messages API format
• Claude Protocol: Request path /v1/messages, using Anthropic Claude Messages API format
• Intelligent Conversion: Automatically detects request protocol and performs conversion if the target provider doesn't natively support it
• Zero Configuration: No need to set protocol field, the plugin handles everything automatically

Protocol Support:

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/messages, it corresponds to Claude text-to-text scenarios. The plugin automatically detects provider capabilities: if native Claude protocol is supported, requests are forwarded directly; otherwise, they are converted to OpenAI protocol first.

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.

When the request path suffix matches /v1/images/generations, it corresponds to text-to-image scenarios. The request body will be parsed using OpenAI's image generation protocol and then converted to the corresponding LLM vendor's image generation protocol.

Execution Properties

Plugin execution phase: Default Phase Plugin execution priority: 100

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. Only used when retrieving context data. Won't affect the request forwarded to the LLM upstream.
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). Note: Auto protocol detection is now supported, no need to configure this field to support both OpenAI and Claude protocols
context	object	Optional	-	Configuration for AI conversation context information
customSettings	array of customSetting	Optional	-	Specifies overrides or fills parameters for AI requests
subPath	string	Optional	-	If subPath is configured, the prefix will be removed from the request path before further processing.
contextCleanupCommands	array of string	Optional	-	List of context cleanup commands. When a user message in the request exactly matches any of the configured commands, that message and all non-system messages before it will be removed, keeping only system messages and messages after the command. This enables users to actively clear conversation history.

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:

1. Azure OpenAI only supports configuring one API Token.
2. azureServiceUrl accepts three formats：
   1. Full URL. e.g. https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/chat/completions?api-version=2024-02-15-preview
      • Request will be forwarded to the given URL, no matter what original path the request uses.
   2. Resource name + deployment name，e.g. https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME?api-version=2024-02-15-preview
      • The path will be updated based on the actual request path, leaving the deployment name unchanged. APIs with no deployment name in the path are also support.
   3. Resource name only.e.g.https://YOUR_RESOURCE_NAME.openai.azure.com?api-version=2024-02-15-preview
      • The path will be updated based on the actual request path. The deployment name will be filled based on the model name in the request and the configured model mapping rule. APIs with no deployment name in the path are also support.

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.
qwenEnableCompatible	boolean	Optional	false	Enable Qwen compatibility mode. When Qwen compatibility mode is enabled, the compatible mode interface of Qwen will be called, and the request/response will not be modified.

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.

Grok

For Grok, the corresponding type is grok. It has no unique configuration fields.

OpenRouter

For OpenRouter, the corresponding type is openrouter. It has no unique configuration fields.

Fireworks AI

For Fireworks AI, the corresponding type is fireworks. 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 fields are:

Name	Data Type	Filling Requirements	Default Value	Description
claudeVersion	string	Optional	-	The version of the Claude service's API, default is 2023-06-01.
claudeCodeMode	boolean	Optional	false	Enable Claude Code mode for OAuth token authentication. When enabled, requests will be formatted as Claude Code client requests.

Claude Code Mode

When claudeCodeMode: true is enabled, the plugin will:

• Use Bearer Token authentication instead of x-api-key (compatible with Claude Code OAuth tokens)
• Set Claude Code-specific request headers (user-agent, x-app, anthropic-beta)
• Add ?beta=true query parameter to request URLs
• Automatically inject Claude Code system prompt if not provided

This enables direct use of Claude Code OAuth tokens for authentication in Higress.

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.

Generic

For a vendor-agnostic passthrough, set the provider type to generic. Requests are forwarded without path remapping, while still benefiting from the shared header/basePath utilities.

Name	Data Type	Requirement	Default	Description
genericHost	string	Optional	-	Overrides the upstream Host header. Use it to route traffic to a specific backend domain for generic proxying.

• When apiTokens are configured, the Generic provider injects Authorization: Bearer <token> automatically.
• firstByteTimeout applies to any request whose body sets stream: true, ensuring consistent streaming behavior even without capability definitions.
• basePath and basePathHandling remain available to strip or prepend prefixes before forwarding.

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.
apiVersion	string	非必填	v1beta	To specify the version of the API, you can choose either 'v1' or 'v1beta'. Version differences refer to https://ai.google.dev/gemini-api/docs/api-versions
geminiThinkingBudget	number	非必填	-	The parameters of the gemini2.5 series: 0 indicates no thinking mode, -1 represents dynamic adjustment. For specific parameter references, please refer to the official website

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

Google Vertex AI

For Vertex, the corresponding type is vertex. It supports two authentication modes:

Standard Mode (using Service Account):

Name	Data Type	Requirement	Default	Description
vertexAuthKey	string	Required	-	Google Service Account JSON Key used for authentication. The format should be PEM encoded PKCS#8 private key along with client_email and other information
vertexRegion	string	Required	-	Google Cloud region (e.g., us-central1, europe-west4) used to build the Vertex API address
vertexProjectId	string	Required	-	Google Cloud Project ID, used to identify the target GCP project
vertexAuthServiceName	string	Required	-	Service name for OAuth2 authentication, used to access oauth2.googleapis.com
vertexGeminiSafetySetting	map of string	Optional	-	Gemini model content safety filtering settings.
vertexTokenRefreshAhead	number	Optional	-	Vertex access token refresh ahead time in seconds

Express Mode (using API Key, simplified configuration):

Express Mode is a simplified access mode introduced by Vertex AI. You can quickly get started with just an API Key, without configuring a Service Account. See Vertex AI Express Mode documentation.

Name	Data Type	Requirement	Default	Description
apiTokens	array of string	Required	-	API Key for Express Mode, obtained from Google Cloud Console under API & Services > Credentials
vertexGeminiSafetySetting	map of string	Optional	-	Gemini model content safety filtering settings.

OpenAI Compatible Mode (using Vertex AI Chat Completions API):

Vertex AI provides an OpenAI-compatible Chat Completions API endpoint, allowing you to use OpenAI format requests and responses directly without protocol conversion. See Vertex AI OpenAI Compatibility documentation.

Name	Data Type	Requirement	Default	Description
vertexOpenAICompatible	boolean	Optional	false	Enable OpenAI compatible mode. When enabled, uses Vertex AI's OpenAI-compatible Chat Completions API
vertexAuthKey	string	Required	-	Google Service Account JSON Key for authentication
vertexRegion	string	Required	-	Google Cloud region (e.g., us-central1, europe-west4)
vertexProjectId	string	Required	-	Google Cloud Project ID
vertexAuthServiceName	string	Required	-	Service name for OAuth2 authentication

Note: OpenAI Compatible Mode and Express Mode are mutually exclusive. You cannot configure both apiTokens and vertexOpenAICompatible at the same time.

AWS Bedrock

For AWS Bedrock, the corresponding type is bedrock. It supports two authentication methods:

1. AWS Signature V4 Authentication: Uses awsAccessKey and awsSecretKey for standard AWS signature authentication
2. Bearer Token Authentication: Uses apiTokens to configure AWS Bearer Token (suitable for IAM Identity Center and similar scenarios)

Note: Choose one of the two authentication methods. If apiTokens is configured, Bearer Token authentication will be used preferentially.

Its unique configuration fields are:

Name	Data Type	Requirement	Default	Description
apiTokens	array of string	Either this or ak/sk	-	AWS Bearer Token for Bearer Token authentication
awsAccessKey	string	Either this or apiTokens	-	AWS Access Key for AWS Signature V4 authentication
awsSecretKey	string	Either this or apiTokens	-	AWS Secret Access Key for AWS Signature V4 authentication
awsRegion	string	Required	-	AWS region, e.g., us-east-1
bedrockAdditionalFields	map	Optional	-	Additional inference parameters that the model supports

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"
}

Forwards requests to AliCloud Bailian with the "original" protocol

Configuration Information

activeProviderId: my-qwen
providers:
  - id: my-qwen
    type: qwen
    apiTokens:
      - "YOUR_DASHSCOPE_API_TOKEN"
    protocol: original

Example Request

{
  "input": {
    "prompt": "What is Dubbo?"
  },
  "parameters": {},
  "debug": {}
}

Example Response

{
  "output": {
    "finish_reason": "stop",
    "session_id": "677e7e8fbb874e1b84792b65042e1599",
    "text": "Apache Dubbo is a..."
  },
  "usage": {
    "models": [
      {
        "output_tokens": 449,
        "model_id": "qwen-max",
        "input_tokens": 282
      }
    ]
  },
  "request_id": "b59e45e3-5af4-91df-b7c6-9d746fd3297c"
}

Using OpenAI Protocol Proxy for Doubao Service

activeProviderId: my-doubao
providers:
- id: my-doubao
  type: doubao
  apiTokens:
    - YOUR_DOUBAO_API_KEY
  modelMapping:
    '*': YOUR_DOUBAO_ENDPOINT
  timeout: 1200000

Using original Protocol Proxy for Coze applications

provider:
  type: coze
  apiTokens:
    - YOUR_COZE_API_KEY
  protocol: original

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 Grok Service

Configuration Information

provider:
  type: grok
  apiTokens:
    - "YOUR_GROK_API_TOKEN"

Example Request

{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant that can answer questions and help with tasks."
    },
    {
      "role": "user",
      "content": "What is 101*3?"
    }
  ],
  "model": "grok-4"
}

Example Response

{
  "id": "a3d1008e-4544-40d4-d075-11527e794e4a",
  "object": "chat.completion",
  "created": 1752854522,
  "model": "grok-4",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "101 multiplied by 3 is 303.",
        "refusal": null
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 32,
    "completion_tokens": 9,
    "total_tokens": 135,
    "prompt_tokens_details": {
      "text_tokens": 32,
      "audio_tokens": 0,
      "image_tokens": 0,
      "cached_tokens": 6
    },
    "completion_tokens_details": {
      "reasoning_tokens": 94,
      "audio_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    },
    "num_sources_used": 0
  },
  "system_fingerprint": "fp_3a7881249c"
}

Using OpenAI Protocol Proxy for OpenRouter Service

Configuration Information

provider:
  type: openrouter
  apiTokens:
    - 'YOUR_OPENROUTER_API_TOKEN'
  modelMapping:
    'gpt-4': 'openai/gpt-4-turbo-preview'
    'gpt-3.5-turbo': 'openai/gpt-3.5-turbo'
    'claude-3': 'anthropic/claude-3-opus'
    '*': 'openai/gpt-3.5-turbo'

Example Request

{
  "model": "gpt-4",
  "messages": [
    {
      "role": "user",
      "content": "Hello, who are you?"
    }
  ],
  "temperature": 0.7
}

Example Response

{
  "id": "gen-1234567890abcdef",
  "object": "chat.completion",
  "created": 1699123456,
  "model": "openai/gpt-4-turbo-preview",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I am an AI assistant powered by OpenRouter. I can help answer questions, assist with creative tasks, engage in conversations, and more. How can I assist you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 35,
    "total_tokens": 47
  }
}

Using OpenAI Protocol Proxy for Fireworks AI Service

Configuration Information

provider:
  type: fireworks
  apiTokens:
    - "YOUR_FIREWORKS_API_TOKEN"
  modelMapping:
    "gpt-4": "accounts/fireworks/models/llama-v3p1-70b-instruct"
    "gpt-3.5-turbo": "accounts/fireworks/models/llama-v3p1-8b-instruct"
    "*": "accounts/fireworks/models/llama-v3p1-8b-instruct"

Request Example

{
  "model": "gpt-4",
  "messages": [
    {
      "role": "user",
      "content": "Hello, who are you?"
    }
  ],
  "temperature": 0.7,
  "max_tokens": 100
}

Response Example

{
  "id": "fw-123456789",
  "object": "chat.completion",
  "created": 1699123456,
  "model": "accounts/fireworks/models/llama-v3p1-70b-instruct",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I am an AI assistant powered by Fireworks AI, based on the Llama 3.1 model. I can help answer questions, engage in conversations, and provide various information. How can I assist you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 15,
    "completion_tokens": 38,
    "total_tokens": 53
  }
}

Using Auto Protocol Compatibility

The plugin now supports automatic protocol detection, capable of handling both OpenAI and Claude protocol format requests simultaneously.

Configuration Information

provider:
  type: claude  # Provider with native Claude protocol support
  apiTokens:
    - "YOUR_CLAUDE_API_TOKEN"
  version: "2023-06-01"

OpenAI Protocol Request Example

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

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

Claude Protocol Request Example

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

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

Example Response

Both protocol formats will return responses in their respective formats:

{
  "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 Claude Code Mode

Claude Code is Anthropic's official CLI tool. By enabling claudeCodeMode, you can authenticate using Claude Code OAuth tokens:

Configuration Information

provider:
  type: claude
  apiTokens:
    - "sk-ant-oat01-xxxxx"  # Claude Code OAuth Token
  claudeCodeMode: true  # Enable Claude Code mode

Once this mode is enabled, the plugin will automatically:

• Use Bearer Token authentication (instead of x-api-key)
• Set Claude Code-specific request headers and query parameters
• Inject Claude Code system prompt if not provided

Request Example

{
  "model": "claude-sonnet-4-5-20250929",
  "max_tokens": 8192,
  "messages": [
    {
      "role": "user",
      "content": "List files in current directory"
    }
  ]
}

The plugin will automatically transform the request into Claude Code format, including:

• Adding system prompt: "You are Claude Code, Anthropic's official CLI for Claude."
• Setting appropriate authentication and request headers

Using Intelligent Protocol Conversion

When the target provider doesn't natively support Claude protocol, the plugin automatically performs protocol conversion:

Configuration Information

provider:
  type: qwen  # Doesn't natively support Claude protocol, auto-conversion applied
  apiTokens:
    - "YOUR_QWEN_API_TOKEN"
  modelMapping:
    'claude-3-opus-20240229': 'qwen-max'
    '*': 'qwen-turbo'

Claude Protocol Request

URL: http://your-domain/v1/messages (automatically converted to OpenAI protocol for provider)

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

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_AI360_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": {}
}

Utilizing OpenAI Protocol Proxy for Together-AI Services

Configuration Information

provider:
  type: together-ai
  apiTokens:
    - "YOUR_TOGETHER_AI_API_TOKEN"
  modelMapping:
    "*": "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"

Request Example

{
    "model": "Qwen/Qwen2.5-72B-Instruct-Turbo",
    "messages": [
        {
            "role": "user",
            "content": "Who are you?"
        }
    ]
}

Response Example

{
  "id": "8f5809d54b73efac",
  "object": "chat.completion",
  "created": 1734785851,
  "model": "Qwen/Qwen2.5-72B-Instruct-Turbo",
  "prompt": [],
  "choices": [
    {
      "finish_reason": "eos",
      "seed": 12830868308626506000,
      "logprobs": null,
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I am Qwen, a large language model created by Alibaba Cloud. I am designed to assist users in generating various types of text, such as articles, stories, poems, and more, as well as answering questions and providing information on a wide range of topics. How can I assist you today?",
        "tool_calls": []
      }
    }
  ],
  "usage": {
    "prompt_tokens": 33,
    "completion_tokens": 61,
    "total_tokens": 94
  }
}

Utilizing OpenAI Protocol Proxy for Google Vertex Services (Standard Mode)

Configuration Information

provider:
  type: vertex
  vertexAuthKey: |
    {
      "type": "service_account",
      "project_id": "your-project-id",
      "private_key_id": "your-private-key-id",
      "private_key": "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n",
      "client_email": "your-service-account@your-project.iam.gserviceaccount.com",
      "token_uri": "https://oauth2.googleapis.com/token"
    }
  vertexRegion: us-central1
  vertexProjectId: your-project-id
  vertexAuthServiceName: your-auth-service-name

Request Example

{
  "model": "gemini-2.0-flash-001",
  "messages": [
    {
      "role": "user",
      "content": "Who are you?"
    }
  ],
  "stream": false
}

Response Example

{
  "id": "chatcmpl-0000000000000",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I am the Gemini model provided by Vertex AI, developed by Google. I can answer questions, provide information, and assist in completing various tasks. How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "created": 1729986750,
  "model": "gemini-2.0-flash-001",
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 15,
    "completion_tokens": 43,
    "total_tokens": 58
  }
}

Utilizing OpenAI Protocol Proxy for Google Vertex Services (Express Mode)

Express Mode is a simplified access mode for Vertex AI. You only need an API Key to get started quickly.

Configuration Information

provider:
  type: vertex
  apiTokens:
    - "YOUR_API_KEY"

Request Example

{
  "model": "gemini-2.5-flash",
  "messages": [
    {
      "role": "user",
      "content": "Who are you?"
    }
  ],
  "stream": false
}

Response Example

{
  "id": "chatcmpl-0000000000000",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I am Gemini, an AI assistant developed by Google. How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "created": 1729986750,
  "model": "gemini-2.5-flash",
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 25,
    "total_tokens": 35
  }
}

Utilizing OpenAI Protocol Proxy for Google Vertex Services (OpenAI Compatible Mode)

OpenAI Compatible Mode uses Vertex AI's OpenAI-compatible Chat Completions API. Both requests and responses use OpenAI format, requiring no protocol conversion.

Configuration Information

provider:
  type: vertex
  vertexOpenAICompatible: true
  vertexAuthKey: |
    {
      "type": "service_account",
      "project_id": "your-project-id",
      "private_key_id": "your-private-key-id",
      "private_key": "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n",
      "client_email": "your-service-account@your-project.iam.gserviceaccount.com",
      "token_uri": "https://oauth2.googleapis.com/token"
    }
  vertexRegion: us-central1
  vertexProjectId: your-project-id
  vertexAuthServiceName: your-auth-service-name
  modelMapping:
    "gpt-4": "gemini-2.0-flash"
    "*": "gemini-1.5-flash"

Request Example

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

Response Example

{
  "id": "chatcmpl-abc123",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I am Gemini, an AI model developed by Google. I can help answer questions, provide information, and engage in conversations. How can I assist you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "created": 1729986750,
  "model": "gemini-2.0-flash",
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 35,
    "total_tokens": 47
  }
}

Utilizing OpenAI Protocol Proxy for Google Vertex Image Generation

Vertex AI supports image generation using Gemini models. Through the ai-proxy plugin, you can use OpenAI's /v1/images/generations API to call Vertex AI's image generation capabilities.

Configuration Information

provider:
  type: vertex
  apiTokens:
    - "YOUR_API_KEY"
  modelMapping:
    "dall-e-3": "gemini-2.0-flash-exp"
  geminiSafetySetting:
    HARM_CATEGORY_HARASSMENT: "OFF"
    HARM_CATEGORY_HATE_SPEECH: "OFF"
    HARM_CATEGORY_SEXUALLY_EXPLICIT: "OFF"
    HARM_CATEGORY_DANGEROUS_CONTENT: "OFF"

Using curl

curl -X POST "http://your-gateway-address/v1/images/generations" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.0-flash-exp",
    "prompt": "A cute orange cat napping in the sunshine",
    "size": "1024x1024"
  }'

Using OpenAI Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="any-value",  # Can be any value, authentication is handled by the gateway
    base_url="http://your-gateway-address/v1"
)

response = client.images.generate(
    model="gemini-2.0-flash-exp",
    prompt="A cute orange cat napping in the sunshine",
    size="1024x1024",
    n=1
)

# Get the generated image (base64 encoded)
image_data = response.data[0].b64_json
print(f"Generated image (base64): {image_data[:100]}...")

Response Example

{
  "created": 1729986750,
  "data": [
    {
      "b64_json": "iVBORw0KGgoAAAANSUhEUgAABAAAAAQACAIAAADwf7zUAAAA..."
    }
  ],
  "usage": {
    "total_tokens": 1356,
    "input_tokens": 13,
    "output_tokens": 1120
  }
}

Supported Size Parameters

Vertex AI supported aspect ratios: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9

Vertex AI supported resolutions (imageSize): 1k, 2k, 4k

OpenAI size parameter	Vertex AI aspectRatio	Vertex AI imageSize
256x256	1:1	1k
512x512	1:1	1k
1024x1024	1:1	1k
1792x1024	16:9	2k
1024x1792	9:16	2k
2048x2048	1:1	2k
4096x4096	1:1	4k
1536x1024	3:2	2k
1024x1536	2:3	2k
1024x768	4:3	1k
768x1024	3:4	1k
1280x1024	5:4	1k
1024x1280	4:5	1k
2560x1080	21:9	2k

Notes

• Image generation uses Gemini models (e.g., gemini-2.0-flash-exp, gemini-3-pro-image-preview). Model availability may vary by region
• The returned image data is in base64 encoded format (b64_json)
• Content safety filtering levels can be configured via geminiSafetySetting
• If you need model mapping (e.g., mapping dall-e-3 to a Gemini model), configure modelMapping

Utilizing OpenAI Protocol Proxy for AWS Bedrock Services

AWS Bedrock supports two authentication methods:

Method 1: Using AWS Access Key/Secret Key Authentication (AWS Signature V4)

Configuration Information

provider:
  type: bedrock
  awsAccessKey: "YOUR_AWS_ACCESS_KEY_ID"
  awsSecretKey: "YOUR_AWS_SECRET_ACCESS_KEY"
  awsRegion: "us-east-1"
  bedrockAdditionalFields:
    top_k: 200

Method 2: Using Bearer Token Authentication (suitable for IAM Identity Center and similar scenarios)

Configuration Information

provider:
  type: bedrock
  apiTokens:
    - "YOUR_AWS_BEARER_TOKEN"
  awsRegion: "us-east-1"
  bedrockAdditionalFields:
    top_k: 200

Request Example

{
  "model": "us.anthropic.claude-3-5-haiku-20241022-v1:0",
  "messages": [
    {
      "role": "user",
      "content": "who are you"
    }
  ],
  "stream": false
}

Response Example

{
  "id": "d52da49d-daf3-49d9-a105-0b527481fe14",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I'm Claude, an AI created by Anthropic. I aim to be helpful, honest, and harmless. I won't pretend to be human, and I'll always try to be direct and truthful about what I am and what I can do."
      },
      "finish_reason": "stop"
    }
  ],
  "created": 1749659050,
  "model": "arn:aws:bedrock:us-west-2::foundation-model/anthropic.claude-3-5-haiku-20241022-v1:0",
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 57,
    "total_tokens": 67
  }
}

Utilizing OpenAI Protocol Proxy for NVIDIA Triton Interference Server Services

Configuration Information

providers:
  - type: triton
    tritonDomain: <LOCAL_TRITON_DOMAIN>
    tritonModelVersion: <MODEL_VERSION>
    apiTokens:
      - "****"
    modelMapping:
      "*": gpt2

Request Example

{
  "model": "gpt2",
  "messages": [
    {
      "role": "user",
      "content": "Hi, who are you？"
    }
  ],
  "stream": false
}

Response Example

{
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "I am a lagguage model."
            },
            "finish_reason": "stop",
        }
    ],
    "model": "gpt2",
}

Using Context Cleanup Commands

After configuring context cleanup commands, users can actively clear conversation history by sending specific messages, achieving a "start over" effect.

Configuration

provider:
  type: qwen
  apiTokens:
    - "YOUR_QWEN_API_TOKEN"
  modelMapping:
    "*": "qwen-turbo"
  contextCleanupCommands:
    - "clear context"
    - "/clear"
    - "start over"
    - "new conversation"

Request Example

When a user sends a request containing a cleanup command:

{
  "model": "gpt-3",
  "messages": [
    {
      "role": "system",
      "content": "You are an assistant"
    },
    {
      "role": "user",
      "content": "Hello"
    },
    {
      "role": "assistant",
      "content": "Hello! How can I help you?"
    },
    {
      "role": "user",
      "content": "What's the weather like today"
    },
    {
      "role": "assistant",
      "content": "Sorry, I cannot get real-time weather information."
    },
    {
      "role": "user",
      "content": "clear context"
    },
    {
      "role": "user",
      "content": "Let's start a new topic, introduce yourself"
    }
  ]
}

Actual Request Sent to AI Service

The plugin automatically removes the cleanup command and all non-system messages before it:

{
  "model": "qwen-turbo",
  "messages": [
    {
      "role": "system",
      "content": "You are an assistant"
    },
    {
      "role": "user",
      "content": "Let's start a new topic, introduce yourself"
    }
  ]
}

Notes

• The cleanup command must exactly match the configured string; partial matches will not trigger cleanup
• When multiple cleanup commands exist in messages, only the last matching command is processed
• Cleanup preserves all system messages and removes user, assistant, and tool messages before the command
• All messages after the cleanup command are preserved

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?"
    }
  ]
}'