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Logging GCS, s3 Buckets

LiteLLM Supports Logging to the following Cloud Buckets

Logging Proxy Input/Output to Google Cloud Storage Buckets​

Log LLM Logs to Google Cloud Storage Buckets

info

✨ This is an Enterprise only feature Get Started with Enterprise here

Usage​

  1. Add gcs_bucket to LiteLLM Config.yaml
model_list:
- litellm_params:
    api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
    api_key: my-fake-key
    model: openai/my-fake-model
  model_name: fake-openai-endpoint

litellm_settings:
  callbacks: ["gcs_bucket"] # 👈 KEY CHANGE # 👈 KEY CHANGE
  1. Set required env variables
GCS_BUCKET_NAME="<your-gcs-bucket-name>"
GCS_PATH_SERVICE_ACCOUNT="/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json
  1. Start Proxy
litellm --config /path/to/config.yaml
  1. Test it! 
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "fake-openai-endpoint",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
    }
'

Expected Logs on GCS Buckets​

Fields Logged on GCS Buckets​

Example payload of a /chat/completion request logged on GCS

{
  "request_kwargs": {
    "model": "gpt-3.5-turbo",
    "messages": [
      {
        "role": "user",
        "content": "This is a test"
      }
    ],
    "optional_params": {
      "temperature": 0.7,
      "max_tokens": 10,
      "user": "ishaan-2",
      "extra_body": {}
    }
  },
  "response_obj": {
    "id": "chatcmpl-bd836a8c-89bc-4abd-bee5-e3f1ebfdb541",
    "choices": [
      {
        "finish_reason": "stop",
        "index": 0,
        "message": {
          "content": "Hi!",
          "role": "assistant",
          "tool_calls": null,
          "function_call": null
        }
      }
    ],
    "created": 1722868456,
    "model": "gpt-3.5-turbo",
    "object": "chat.completion",
    "system_fingerprint": null,
    "usage": {
      "prompt_tokens": 10,
      "completion_tokens": 20,
      "total_tokens": 30
    }
  },
  "start_time": "2024-08-05 07:34:16",
  "end_time": "2024-08-05 07:34:16"
}

Getting service_account.json from Google Cloud Console​

  1. Go to Google Cloud Console
  2. Search for IAM & Admin
  3. Click on Service Accounts
  4. Select a Service Account
  5. Click on 'Keys' -> Add Key -> Create New Key -> JSON
  6. Save the JSON file and add the path to GCS_PATH_SERVICE_ACCOUNT

Logging Proxy Input/Output - s3 Buckets​

We will use the --config to set

  • litellm.success_callback = ["s3"]

This will log all successfull LLM calls to s3 Bucket

Step 1 Set AWS Credentials in .env

AWS_ACCESS_KEY_ID = ""
AWS_SECRET_ACCESS_KEY = ""
AWS_REGION_NAME = ""

Step 2: Create a config.yaml file and set litellm_settings: success_callback

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  success_callback: ["s3"]
  s3_callback_params:
    s3_bucket_name: logs-bucket-litellm   # AWS Bucket Name for S3
    s3_region_name: us-west-2              # AWS Region Name for S3
    s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID  # us os.environ/<variable name> to pass environment variables. This is AWS Access Key ID for S3
    s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY  # AWS Secret Access Key for S3
    s3_path: my-test-path # [OPTIONAL] set path in bucket you want to write logs to
    s3_endpoint_url: https://s3.amazonaws.com  # [OPTIONAL] S3 endpoint URL, if you want to use Backblaze/cloudflare s3 buckets

Step 3: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data ' {
    "model": "Azure OpenAI GPT-4 East",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
    }'

Your logs should be available on the specified s3 Bucket