ai-proxy-multi

Description

The ai-proxy-multi Plugin simplifies access to LLM and embedding models by transforming Plugin configurations into the designated request format for OpenAI, DeepSeek, and other OpenAI-compatible APIs. It extends the capabilities of ai-proxy-multi with load balancing, retries, fallbacks, and health checks.

In addition, the Plugin also supports logging LLM request information in the access log, such as token usage, model, time to the first response, and more.

Request Format

Name	Type	Required	Description
messages	Array	True	An array of message objects.
messages.role	String	True	Role of the message (system, user, assistant).
messages.content	String	True	Content of the message.

Attributes

Name	Type	Required	Default	Valid Values	Description
fallback_strategy	string	False	instance_health_and_rate_limiting	instance_health_and_rate_limiting	Fallback strategy. When set, the Plugin will check whether the specified instance’s token has been exhausted when a request is forwarded. If so, forward the request to the next instance regardless of the instance priority. When not set, the Plugin will not forward the request to low priority instances when token of the high priority instance is exhausted.
balancer	object	False			Load balancing configurations.
balancer.algorithm	string	False	roundrobin	[roundrobin, chash]	Load balancing algorithm. When set to roundrobin, weighted round robin algorithm is used. When set to chash, consistent hashing algorithm is used.
balancer.hash_on	string	False		[vars, headers, cookie, consumer, vars_combinations]	Used when type is chash. Support hashing on NGINX variables, headers, cookie, consumer, or a combination of NGINX variables.
balancer.key	string	False			Used when type is chash. When hash_on is set to header or cookie, key is required. When hash_on is set to consumer, key is not required as the consumer name will be used as the key automatically.
instances	array[object]	True			LLM instance configurations.
instances.name	string	True			Name of the LLM service instance.
instances.provider	string	True		[openai, deepseek, openai-compatible]	LLM service provider. When set to openai, the Plugin will proxy the request to api.openai.com. When set to deepseek, the Plugin will proxy the request to api.deepseek.com. When set to openai-compatible, the Plugin will proxy the request to the custom endpoint configured in override.
instances.priority	integer	False	0		Priority of the LLM instance in load balancing. priority takes precedence over weight.
instances.weight	string	True	0	greater or equal to 0	Weight of the LLM instance in load balancing.
instances.auth	object	True			Authentication configurations.
instances.auth.header	object	False			Authentication headers. At least one of the header and query should be configured.
instances.auth.query	object	False			Authentication query parameters. At least one of the header and query should be configured.
instances.options	object	False			Model configurations. In addition to model, you can configure additional parameters and they will be forwarded to the upstream LLM service in the request body. For instance, if you are working with OpenAI or DeepSeek, you can configure additional parameters such as max_tokens, temperature, top_p, and stream. See your LLM provider's API documentation for more available options.
instances.options.model	string	False			Name of the LLM model, such as gpt-4 or gpt-3.5. See your LLM provider's API documentation for more available models.
logging	object	False			Logging configurations.
logging.summaries	boolean	False	false		If true, log request LLM model, duration, request, and response tokens.
logging.payloads	boolean	False	false		If true, log request and response payload.
logging.override	object	False			Override setting.
logging.override.endpoint	string	False			LLM provider endpoint to replace the default endpoint with. If not configured, the Plugin uses the default OpenAI endpoint https://api.openai.com/v1/chat/completions.
checks	object	False			Health check configurations. Note that at the moment, OpenAI and DeepSeek do not provide an official health check endpoint. Other LLM services that you can configure under openai-compatible provider may have available health check endpoints.
checks.active	object	True			Active health check configurations.
checks.active.type	string	False	http	[http, https, tcp]	Type of health check connection.
checks.active.timeout	number	False	1		Health check timeout in seconds.
checks.active.concurrency	integer	False	10		Number of upstream nodes to be checked at the same time.
checks.active.host	string	False			HTTP host.
checks.active.port	integer	False		between 1 and 65535 inclusive	HTTP port.
checks.active.http_path	string	False	/		Path for HTTP probing requests.
checks.active.https_verify_certificate	boolean	False	true		If true, verify the node's TLS certificate.
timeout	integer	False	30000	greater than or equal to 1	Request timeout in milliseconds when requesting the LLM service.
keepalive	boolean	False	true		If true, keep the connection alive when requesting the LLM service.
keepalive_timeout	integer	False	60000	greater than or equal to 1000	Request timeout in milliseconds when requesting the LLM service.
keepalive_pool	integer	False	30		Keepalive pool size for when connecting with the LLM service.
ssl_verify	boolean	False	true		If true, verify the LLM service's certificate.

Examples

The examples below demonstrate how you can configure ai-proxy-multi for different scenarios.

note

You can fetch the admin_key from config.yaml and save to an environment variable with the following command:

admin_key=$(yq '.deployment.admin.admin_key[0].key' conf/config.yaml | sed 's/"//g')

Load Balance between Instances

The following example demonstrates how you can configure two models for load balancing, forwarding 80% of the traffic to one instance and 20% to the other.

For demonstration and easier differentiation, you will be configuring one OpenAI instance and one DeepSeek instance as the upstream LLM services.

Create a Route as such and update with your LLM providers, models, API keys, and endpoints if applicable:

curl "http://127.0.0.1:9180/apisix/admin/routes" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "id": "ai-proxy-multi-route",
    "uri": "/anything",
    "methods": ["POST"],
    "plugins": {
      "ai-proxy-multi": {
        "instances": [
          {
            "name": "openai-instance",
            "provider": "openai",
            "weight": 8,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$OPENAI_API_KEY"'"
              }
            },
            "options": {
              "model": "gpt-4"
            }
          },
          {
            "name": "deepseek-instance",
            "provider": "deepseek",
            "weight": 2,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$DEEPSEEK_API_KEY"'"
              }
            },
            "options": {
              "model": "deepseek-chat"
            }
          }
        ]
      }
    }
  }'

Send 10 POST requests to the Route with a system prompt and a sample user question in the request body, to see the number of requests forwarded to OpenAI and DeepSeek:

openai_count=0
deepseek_count=0

for i in {1..10}; do
  model=$(curl -s "http://127.0.0.1:9080/anything" -X POST \
    -H "Content-Type: application/json" \
    -d '{
      "messages": [
        { "role": "system", "content": "You are a mathematician" },
        { "role": "user", "content": "What is 1+1?" }
      ]
    }' | jq -r '.model')

  if [[ "$model" == *"gpt-4"* ]]; then
    ((openai_count++))
  elif [[ "$model" == "deepseek-chat" ]]; then
    ((deepseek_count++))
  fi
done

echo "OpenAI responses: $openai_count"
echo "DeepSeek responses: $deepseek_count"

You should see a response similar to the following:

OpenAI responses: 8
DeepSeek responses: 2

Configure Instance Priority and Rate Limiting

The following example demonstrates how you can configure two models with different priorities and apply rate limiting on the instance with a higher priority. In the case where fallback_strategy is set to instance_health_and_rate_limiting, the Plugin should continue to forward requests to the low priority instance once the high priority instance's rate limiting quota is fully consumed.

Create a Route as such and update with your LLM providers, models, API keys, and endpoints if applicable:

curl "http://127.0.0.1:9180/apisix/admin/routes" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "id": "ai-proxy-multi-route",
    "uri": "/anything",
    "methods": ["POST"],
    "plugins": {
      "ai-proxy-multi": {
        "fallback_strategy: "instance_health_and_rate_limiting",
        "instances": [
          {
            "name": "openai-instance",
            "provider": "openai",
            "priority": 1,
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$OPENAI_API_KEY"'"
              }
            },
            "options": {
              "model": "gpt-4"
            }
          },
          {
            "name": "deepseek-instance",
            "provider": "deepseek",
            "priority": 0,
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$DEEPSEEK_API_KEY"'"
              }
            },
            "options": {
              "model": "deepseek-chat"
            }
          }
        ]
      },
      "ai-rate-limiting": {
        "instances": [
          {
            "name": "openai-instance",
            "limit": 10,
            "time_window": 60
          }
        ],
        "limit_strategy": "total_tokens"
      }
    }
  }'

Send a POST request to the Route with a system prompt and a sample user question in the request body:

curl "http://127.0.0.1:9080/anything" -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      { "role": "system", "content": "You are a mathematician" },
      { "role": "user", "content": "What is 1+1?" }
    ]
  }'

You should receive a response similar to the following:

{
  ...,
  "model": "gpt-4-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "1+1 equals 2.",
        "refusal": null
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 23,
    "completion_tokens": 8,
    "total_tokens": 31,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "audio_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0,
      "audio_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  },
  "service_tier": "default",
  "system_fingerprint": null
}

Since the total_tokens value exceeds the configured quota of 10, the next request within the 60-second window is expected to be forwarded to the other instance.

Within the same 60-second window, send another POST request to the route:

curl "http://127.0.0.1:9080/anything" -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      { "role": "system", "content": "You are a mathematician" },
      { "role": "user", "content": "Explain Newton law" }
    ]
  }'

You should see a response similar to the following:

{
  ...,
  "model": "deepseek-chat",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Certainly! Newton's laws of motion are three fundamental principles that describe the relationship between the motion of an object and the forces acting on it. They were formulated by Sir Isaac Newton in the late 17th century and are foundational to classical mechanics.\n\n---\n\n### **1. Newton's First Law (Law of Inertia):**\n- **Statement:** An object at rest will remain at rest, and an object in motion will continue moving at a constant velocity (in a straight line at a constant speed), unless acted upon by an external force.\n- **Key Idea:** This law introduces the concept of **inertia**, which is the tendency of an object to resist changes in its state of motion.\n- **Example:** If you slide a book across a table, it eventually stops because of the force of friction acting on it. Without friction, the book would keep moving indefinitely.\n\n---\n\n### **2. Newton's Second Law (Law of Acceleration):**\n- **Statement:** The acceleration of an object is directly proportional to the net force acting on it and inversely proportional to its mass. Mathematically, this is expressed as:\n  \\[\n  F = ma\n  \\]\n  where:\n  - \\( F \\) = net force applied (in Newtons),\n  -"
      },
      ...
    }
  ],
  ...
}

Load Balance and Rate Limit by Consumers

The following example demonstrates how you can configure two models for load balancing and apply rate limiting by consumer.

Create a Consumer johndoe and a rate limiting quota of 10 tokens in a 60-second window on openai-instance instance:

curl "http://127.0.0.1:9180/apisix/admin/consumers" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "username": "johndoe",
    "plugins": {
      "ai-rate-limiting": {
        "instances": [
          {
            "name": "openai-instance",
            "limit": 10,
            "time_window": 60
          }
        ],
        "rejected_code": 429,
        "limit_strategy": "total_tokens"
      }
    }
  }'

Configure key-auth credential for johndoe:

curl "http://127.0.0.1:9180/apisix/admin/consumers/johndoe/credentials" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "id": "cred-john-key-auth",
    "plugins": {
      "key-auth": {
        "key": "john-key"
      }
    }
  }'

Create another Consumer janedoe and a rate limiting quota of 10 tokens in a 60-second window on deepseek-instance instance:

curl "http://127.0.0.1:9180/apisix/admin/consumers" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "username": "johndoe",
    "plugins": {
      "ai-rate-limiting": {
        "instances": [
          {
            "name": "deepseek-instance",
            "limit": 10,
            "time_window": 60
          }
        ],
        "rejected_code": 429,
        "limit_strategy": "total_tokens"
      }
    }
  }'

Configure key-auth credential for janedoe:

curl "http://127.0.0.1:9180/apisix/admin/consumers/janedoe/credentials" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "id": "cred-jane-key-auth",
    "plugins": {
      "key-auth": {
        "key": "jane-key"
      }
    }
  }'

Create a Route as such and update with your LLM providers, models, API keys, and endpoints if applicable:

curl "http://127.0.0.1:9180/apisix/admin/routes" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "id": "ai-proxy-multi-route",
    "uri": "/anything",
    "methods": ["POST"],
    "plugins": {
      "key-auth": {},
      "ai-proxy-multi": {
        "fallback_strategy: "instance_health_and_rate_limiting",
        "instances": [
          {
            "name": "openai-instance",
            "provider": "openai",
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$OPENAI_API_KEY"'"
              }
            },
            "options": {
              "model": "gpt-4"
            }
          },
          {
            "name": "deepseek-instance",
            "provider": "deepseek",
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$DEEPSEEK_API_KEY"'"
              }
            },
            "options": {
              "model": "deepseek-chat"
            }
          }
        ]
      }
    }
  }'

Send a POST request to the Route without any consumer key:

curl -i "http://127.0.0.1:9080/anything" -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      { "role": "system", "content": "You are a mathematician" },
      { "role": "user", "content": "What is 1+1?" }
    ]
  }'

You should receive an HTTP/1.1 401 Unauthorized response.

Send a POST request to the Route with johndoe's key:

curl "http://127.0.0.1:9080/anything" -X POST \
  -H "Content-Type: application/json" \
  -H 'apikey: john-key' \
  -d '{
    "messages": [
      { "role": "system", "content": "You are a mathematician" },
      { "role": "user", "content": "What is 1+1?" }
    ]
  }'

You should receive a response similar to the following:

{
  ...,
  "model": "gpt-4-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "1+1 equals 2.",
        "refusal": null
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 23,
    "completion_tokens": 8,
    "total_tokens": 31,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "audio_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0,
      "audio_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  },
  "service_tier": "default",
  "system_fingerprint": null
}

Since the total_tokens value exceeds the configured quota of the openai instance for johndoe, the next request within the 60-second window from johndoe is expected to be forwarded to the deepseek instance.

Within the same 60-second window, send another POST request to the Route with johndoe's key:

curl "http://127.0.0.1:9080/anything" -X POST \
  -H "Content-Type: application/json" \
  -H 'apikey: john-key' \
  -d '{
    "messages": [
      { "role": "system", "content": "You are a mathematician" },
      { "role": "user", "content": "Explain Newtons laws to me" }
    ]
  }'

You should see a response similar to the following:

{
  ...,
  "model": "deepseek-chat",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Certainly! Newton's laws of motion are three fundamental principles that describe the relationship between the motion of an object and the forces acting on it. They were formulated by Sir Isaac Newton in the late 17th century and are foundational to classical mechanics.\n\n---\n\n### **1. Newton's First Law (Law of Inertia):**\n- **Statement:** An object at rest will remain at rest, and an object in motion will continue moving at a constant velocity (in a straight line at a constant speed), unless acted upon by an external force.\n- **Key Idea:** This law introduces the concept of **inertia**, which is the tendency of an object to resist changes in its state of motion.\n- **Example:** If you slide a book across a table, it eventually stops because of the force of friction acting on it. Without friction, the book would keep moving indefinitely.\n\n---\n\n### **2. Newton's Second Law (Law of Acceleration):**\n- **Statement:** The acceleration of an object is directly proportional to the net force acting on it and inversely proportional to its mass. Mathematically, this is expressed as:\n  \\[\n  F = ma\n  \\]\n  where:\n  - \\( F \\) = net force applied (in Newtons),\n  -"
      },
      ...
    }
  ],
  ...
}

Send a POST request to the Route with janedoe's key:

curl "http://127.0.0.1:9080/anything" -X POST \
  -H "Content-Type: application/json" \
  -H 'apikey: jane-key' \
  -d '{
    "messages": [
      { "role": "system", "content": "You are a mathematician" },
      { "role": "user", "content": "What is 1+1?" }
    ]
  }'

You should receive a response similar to the following:

{
  ...,
  "model": "deepseek-chat",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The sum of 1 and 1 is 2. This is a basic arithmetic operation where you combine two units to get a total of two units."
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 14,
    "completion_tokens": 31,
    "total_tokens": 45,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "prompt_cache_hit_tokens": 0,
    "prompt_cache_miss_tokens": 14
  },
  "system_fingerprint": "fp_3a5770e1b4_prod0225"
}

Since the total_tokens value exceeds the configured quota of the deepseek instance for janedoe, the next request within the 60-second window from janedoe is expected to be forwarded to the openai instance.

Within the same 60-second window, send another POST request to the Route with janedoe's key:

curl "http://127.0.0.1:9080/anything" -X POST \
  -H "Content-Type: application/json" \
  -H 'apikey: jane-key' \
  -d '{
    "messages": [
      { "role": "system", "content": "You are a mathematician" },
      { "role": "user", "content": "Explain Newtons laws to me" }
    ]
  }'

You should see a response similar to the following:

{
  ...,
  "model": "gpt-4-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Sure, here are Newton's three laws of motion:\n\n1) Newton's First Law, also known as the Law of Inertia, states that an object at rest will stay at rest, and an object in motion will stay in motion, unless acted on by an external force. In simple words, this law suggests that an object will keep doing whatever it is doing until something causes it to do otherwise. \n\n2) Newton's Second Law states that the force acting on an object is equal to the mass of that object times its acceleration (F=ma). This means that force is directly proportional to mass and acceleration. The heavier the object and the faster it accelerates, the greater the force.\n\n3) Newton's Third Law, also known as the law of action and reaction, states that for every action, there is an equal and opposite reaction. Essentially, any force exerted onto a body will create a force of equal magnitude but in the opposite direction on the object that exerted the first force.\n\nRemember, these laws become less accurate when considering speeds near the speed of light (where Einstein's theory of relativity becomes more appropriate) or objects very small or very large. However, for everyday situations, they provide a good model of how things move.",
        "refusal": null
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  ...
}

This shows ai-proxy-multi load balance the traffic with respect to the rate limiting rules in ai-rate-limiting by consumers.

Restrict Maximum Number of Completion Tokens

The following example demonstrates how you can restrict the number of completion_tokens used when generating the chat completion.

For demonstration and easier differentiation, you will be configuring one OpenAI instance and one DeepSeek instance as the upstream LLM services.

Create a Route as such and update with your LLM providers, models, API keys, and endpoints if applicable:

curl "http://127.0.0.1:9180/apisix/admin/routes" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "id": "ai-proxy-multi-route",
    "uri": "/anything",
    "methods": ["POST"],
    "plugins": {
      "ai-proxy-multi": {
        "instances": [
          {
            "name": "openai-instance",
            "provider": "openai",
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$OPENAI_API_KEY"'"
              }
            },
            "options": {
              "model": "gpt-4",
              "max_tokens": 50
            }
          },
          {
            "name": "deepseek-instance",
            "provider": "deepseek",
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$DEEPSEEK_API_KEY"'"
              }
            },
            "options": {
              "model": "deepseek-chat",
              "max_tokens": 100
            }
          }
        ]
      }
    }
  }'

Send a POST request to the Route with a system prompt and a sample user question in the request body:

curl "http://127.0.0.1:9080/anything" -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      { "role": "system", "content": "You are a mathematician" },
      { "role": "user", "content": "Explain Newtons law" }
    ]
  }'

If the request is proxied to OpenAI, you should see a response similar to the following, where the content is truncated per 50 max_tokens threshold:

{
  ...,
  "model": "gpt-4-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Newton's Laws of Motion are three physical laws that form the bedrock for classical mechanics. They describe the relationship between a body and the forces acting upon it, and the body's motion in response to those forces. \n\n1. Newton's First Law",
        "refusal": null
      },
      "logprobs": null,
      "finish_reason": "length"
    }
  ],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 50,
    "total_tokens": 70,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "audio_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0,
      "audio_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  },
  "service_tier": "default",
  "system_fingerprint": null
}

If the request is proxied to DeepSeek, you should see a response similar to the following, where the content is truncated per 100 max_tokens threshold:

{
  ...,
  "model": "deepseek-chat",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Newton's Laws of Motion are three fundamental principles that form the foundation of classical mechanics. They describe the relationship between a body and the forces acting upon it, and the body's motion in response to those forces. Here's a brief explanation of each law:\n\n1. **Newton's First Law (Law of Inertia):**\n   - **Statement:** An object will remain at rest or in uniform motion in a straight line unless acted upon by an external force.\n   - **Explanation:** This law"
      },
      "logprobs": null,
      "finish_reason": "length"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 100,
    "total_tokens": 110,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "prompt_cache_hit_tokens": 0,
    "prompt_cache_miss_tokens": 10
  },
  "system_fingerprint": "fp_3a5770e1b4_prod0225"
}

Proxy to Embedding Models

The following example demonstrates how you can configure the ai-proxy-multi Plugin to proxy requests and load balance between embedding models.

Create a Route as such and update with your LLM providers, embedding models, API keys, and endpoints:

curl "http://127.0.0.1:9180/apisix/admin/routes" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "id": "ai-proxy-multi-route",
    "uri": "/anything",
    "methods": ["POST"],
    "plugins": {
      "ai-proxy-multi": {
        "instances": [
          {
            "name": "openai-instance",
            "provider": "openai",
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$OPENAI_API_KEY"'"
              }
            },
            "options": {
              "model": "text-embedding-3-small"
            },
            "override": {
              "endpoint": "https://api.openai.com/v1/embeddings"
            }
          },
          {
            "name": "az-openai-instance",
            "provider": "openai-compatible",
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$AZ_OPENAI_API_KEY"'"
              }
            },
            "options": {
              "model": "text-embedding-3-small"
            },
            "override": {
              "endpoint": "https://ai-plugin-developer.openai.azure.com/openai/deployments/text-embedding-3-small/embeddings?api-version=2023-05-15"
            }
          }
        ]
      }
    }
  }'

Send a POST request to the Route with an input string:

curl "http://127.0.0.1:9080/embeddings" -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "input": "hello world"
  }'

You should receive a response similar to the following:

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -0.0067144386,
        -0.039197803,
        0.034177095,
        0.028763203,
        -0.024785956,
        -0.04201061,
        ...
      ],
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 2,
    "total_tokens": 2
  }
}

Enable Active Health Checks

The following example demonstrates how you can configure the ai-proxy-multi Plugin to proxy requests and load balance between models, and enable active health check to improve service availability. You can enable health check on one or multiple instances.

Create a Route as such and update the LLM providers, embedding models, API keys, and health check related configurations:

curl "http://127.0.0.1:9180/apisix/admin/routes" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
    "id": "ai-proxy-multi-route",
    "uri": "/anything",
    "methods": ["POST"],
    "plugins": {
      "ai-proxy-multi": {
        "instances": [
          {
            "name": "llm-instance-1",
            "provider": "openai-compatible",
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$YOUR_LLM_API_KEY"'"
              }
            },
            "options": {
              "model": "'"$YOUR_LLM_MODEL"'"
            }
          },
          {
            "name": "llm-instance-2",
            "provider": "openai-compatible",
            "weight": 0,
            "auth": {
              "header": {
                "Authorization": "Bearer '"$YOUR_LLM_API_KEY"'"
              }
            },
            "options": {
              "model": "'"$YOUR_LLM_MODEL"'"
            },
            "checks": {
              "active": {
                "type": "https",
                "host": "yourhost.com",
                "http_path": "/your/probe/path",
                "healthy": {
                  "interval": 2,
                  "successes": 1
                },
                "unhealthy": {
                  "interval": 1,
                  "http_failures": 3
                }
              }
            }
          }
        ]
      }
    }
  }'

For verification, the behaviours should be consistent with the verification in active health checks.

Include LLM Information in Access Log

The following example demonstrates how you can log LLM request related information in the gateway's access log to improve analytics and audit. The following variables are available:

• request_type: Type of request, where the value could be traditional_http, ai_chat, or ai_stream.
• llm_time_to_first_token: Duration from request sending to the first token received from the LLM service, in milliseconds.
• llm_model: LLM model.
• llm_prompt_tokens: Number of tokens in the prompt.
• llm_completion_tokens: Number of chat completion tokens in the prompt.

note

The usage in this example will become available in APISIX 3.13.0.

Update the access log format in your configuration file to include additional LLM related variables:

conf/config.yaml

nginx_config:
  http:
    access_log_format: "$remote_addr - $remote_user [$time_local] $http_host \"$request_line\" $status $body_bytes_sent $request_time \"$http_referer\" \"$http_user_agent\" $upstream_addr $upstream_status $upstream_response_time \"$upstream_scheme://$upstream_host$upstream_uri\" \"$apisix_request_id\" \"$request_type\" \"$llm_time_to_first_token\" \"$llm_model\" \"$llm_prompt_tokens\" \"$llm_completion_tokens\""

Reload APISIX for configuration changes to take effect.

Next, create a Route with the ai-proxy-multi Plugin and send a request. For instance, if the request is forwarded to OpenAI and you receive the following response:

{
  ...,
  "model": "gpt-4-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "1+1 equals 2.",
        "refusal": null,
        "annotations": []
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 23,
    "completion_tokens": 8,
    "total_tokens": 31,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "audio_tokens": 0
    },
    ...
  },
  "service_tier": "default",
  "system_fingerprint": null
}

In the gateway's access log, you should see a log entry similar to the following:

192.168.215.1 - - [21/Mar/2025:04:28:03 +0000] api.openai.com "POST /anything HTTP/1.1" 200 804 2.858 "-" "curl/8.6.0" - - - "http://api.openai.com" "5c5e0b95f8d303cb81e4dc456a4b12d9" "ai_chat" "2858" "gpt-4" "23" "8"

The access log entry shows the request type is ai_chat, time to first token is 2858 milliseconds, LLM model is gpt-4, prompt token usage is 23, and completion token usage is 8.