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AI Audit Log Reference

Kong AI Gateway provides a standardized logging format for AI plugins, enabling the emission of analytics events and facilitating the aggregation of AI usage analytics across various providers.

Log Formats

Each AI plugin returns a set of tokens.

All log entries include the following attributes:

"ai": {
    "payload": { "request": "[$optional_payload_request_]" },
    "[$plugin_name_1]": {
      "payload": { "response": "[$optional_payload_response]" },
      "usage": {
        "prompt_token": 28,
        "total_tokens": 48,
        "completion_token": 20,
        "cost": 0.0038,
        "time_per_token": 133
      },
      "meta": {
        "request_model": "command",
        "provider_name": "cohere",
        "response_model": "command",
        "plugin_id": "546c3856-24b3-469a-bd6c-f6083babd2cd",
        "llm_latency": 2670
      }
    },
    "[$plugin_name_2]": {
      "payload": { "response": "[$optional_payload_response]" },
      "usage": {
        "prompt_token": 89,
        "total_tokens": 145,
        "completion_token": 56,
        "cost": 0.0012,
        "time_per_token": 87
      },
      "meta": {
        "request_model": "gpt-35-turbo",
        "provider_name": "azure",
        "response_model": "gpt-35-turbo",
        "plugin_id": "5df193be-47a3-4f1b-8c37-37e31af0568b",
        "llm_latency": 4927
      }
    }
  }

Log Details

Each log entry includes the following details:

Property	Description
`ai.payload.request`	The request payload.
`ai.[$plugin_name].payload.response`	The response payload.
`ai.[$plugin_name].usage.prompt_token`	Number of tokens used for prompting.
`ai.[$plugin_name].usage.completion_token`	Number of tokens used for completion.
`ai.[$plugin_name].usage.total_tokens`	Total number of tokens used.
`ai.[$plugin_name].usage.cost`	The total cost of the request (input and output cost).
`ai.[$plugin_name].usage.time_per_token`	The average time to generate an output token, in milliseconds.
`ai.[$plugin_name].meta.request_model`	Model used for the AI request.
`ai.[$plugin_name].meta.provider_name`	Name of the AI service provider.
`ai.[$plugin_name].meta.response_model`	Model used for the AI response.
`ai.[$plugin_name].meta.plugin_id`	Unique identifier of the plugin.
`ai.[$plugin_name].meta.llm_latency`	The time, in milliseconds, it took the LLM provider to generate the full response.
`ai.[$plugin_name].cache.cache_status`	The cache status. This can be Hit, Miss, Bypass or Refresh.
`ai.[$plugin_name].cache.fetch_latency`	The time, in milliseconds, it took to return a cache response.
`ai.[$plugin_name].cache.embeddings_provider`	For semantic caching, the provider used to generate the embeddings.
`ai.[$plugin_name].cache.embeddings_model`	For semantic caching, the model used to generate the embeddings.
`ai.[$plugin_name].cache.embeddings_latency`	For semantic caching, the time taken to generate the embeddings.

Caches logging

If you’re using the AI Semantic Cache plugin, logging will include some additional details about caching:

"ai": {
    "payload": { "request": "[$optional_payload_request_]" },
    "[$plugin_name_1]": {
      "payload": { "response": "[$optional_payload_response]" },
      "usage": {
        "prompt_token": 28,
        "total_tokens": 48,
        "completion_token": 20,
        "cost": 0.0038,
        "time_per_token": 133
      },
      "meta": {
        "request_model": "command",
        "provider_name": "cohere",
        "response_model": "command",
        "plugin_id": "546c3856-24b3-469a-bd6c-f6083babd2cd",
        "llm_latency": 2670
      },
      "cache": {
        "cache_status": "Hit",
        "fetch_latency": 21
      }
    },
    "[$plugin_name_2]": {
      "payload": { "response": "[$optional_payload_response]" },
      "usage": {
        "prompt_token": 89,
        "total_tokens": 145,
        "completion_token": 56,
        "cost": 0.0012,
      },
      "meta": {
        "request_model": "gpt-35-turbo",
        "provider_name": "azure",
        "response_model": "gpt-35-turbo",
        "plugin_id": "5df193be-47a3-4f1b-8c37-37e31af0568b",
      },
      "cache": {
        "cache_status": "Hit",
        "fetch_latency": 444,
        "embeddings_provider": "openai",
        "embeddings_model": "text-embedding-3-small",
        "embeddings_latency": 424
      }
    }
  }

Note: When returning a cache response, time_per_token and llm_latency are omitted. The cache response can be returned either as a semantic cache or an exact cache. If it’s returned as a semantic cache, it will include additional details such as the embeddings provider, embeddings model, and embeddings latency.