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Proxy Config.yaml

Set model list, api_base, api_key, temperature & proxy server settings (master-key) on the config.yaml.

Param NameDescription
model_listList of supported models on the server, with model-specific configs
router_settingslitellm Router settings, example routing_strategy="least-busy" see all
litellm_settingslitellm Module settings, example litellm.drop_params=True, litellm.set_verbose=True, litellm.api_base, litellm.cache see all
general_settingsServer settings, example setting master_key: sk-my_special_key
environment_variablesEnvironment Variables example, REDIS_HOST, REDIS_PORT

Complete List: Check the Swagger UI docs on <your-proxy-url>/#/config.yaml (e.g. http://0.0.0.0:8000/#/config.yaml), for everything you can pass in the config.yaml.

Quick Start​

Set a model alias for your deployments.

In the config.yaml the model_name parameter is the user-facing name to use for your deployment.

In the config below requests with:

  • model=vllm-models will route to openai/facebook/opt-125m.
  • model=gpt-3.5-turbo will load balance between azure/gpt-turbo-small-eu and azure/gpt-turbo-small-ca
model_list:
  - model_name: gpt-3.5-turbo # user-facing model alias
    litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
      model: azure/gpt-turbo-small-eu
      api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
      api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU")
      rpm: 6      # Rate limit for this deployment: in requests per minute (rpm)
  - model_name: bedrock-claude-v1 
    litellm_params:
      model: bedrock/anthropic.claude-instant-v1
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/gpt-turbo-small-ca
      api_base: https://my-endpoint-canada-berri992.openai.azure.com/
      api_key: "os.environ/AZURE_API_KEY_CA"
      rpm: 6
  - model_name: vllm-models
    litellm_params:
      model: openai/facebook/opt-125m # the `openai/` prefix tells litellm it's openai compatible
      api_base: http://0.0.0.0:8000
      rpm: 1440
    model_info: 
      version: 2

litellm_settings: # module level litellm settings - https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py
  drop_params: True
  set_verbose: True

general_settings: 
  master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234)

Step 2: Start Proxy with config​

$ litellm --config /path/to/config.yaml

Using Proxy - Curl Request, OpenAI Package, Langchain, Langchain JS​

Calling a model group

Sends request to model where model_name=gpt-3.5-turbo on config.yaml.

If multiple with model_name=gpt-3.5-turbo does Load Balancing

curl --location 'http://0.0.0.0:8000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "gpt-3.5-turbo",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
    }
'

Save Model-specific params (API Base, API Keys, Temperature, Max Tokens, Seed, Headers etc.)​

You can use the config to save model-specific information like api_base, api_key, temperature, max_tokens, etc.

All input params

Step 1: Create a config.yaml file

model_list:
  - model_name: gpt-4-team1
    litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body
      model: azure/chatgpt-v-2
      api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
      api_version: "2023-05-15"
      azure_ad_token: eyJ0eXAiOiJ
      seed: 12
      max_tokens: 20
  - model_name: gpt-4-team2
    litellm_params:
      model: azure/gpt-4
      api_key: sk-123
      api_base: https://openai-gpt-4-test-v-2.openai.azure.com/
      temperature: 0.2
  - model_name: mistral-7b
    litellm_params:
      model: ollama/mistral
      api_base: your_ollama_api_base
      headers: {
        "HTTP-Referer": "litellm.ai",  
        "X-Title": "LiteLLM Server"
      }

Step 2: Start server with config

$ litellm --config /path/to/config.yaml

Load API Keys​

Load API Keys from Environment​

If you have secrets saved in your environment, and don't want to expose them in the config.yaml, here's how to load model-specific keys from the environment. 

os.environ["AZURE_NORTH_AMERICA_API_KEY"] = "your-azure-api-key"
model_list:
  - model_name: gpt-4-team1
    litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body
      model: azure/chatgpt-v-2
      api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
      api_version: "2023-05-15"
      api_key: os.environ/AZURE_NORTH_AMERICA_API_KEY

See Code

s/o to @David Manouchehri for helping with this.

Load API Keys from Azure Vault​

  1. Install Proxy dependencies 
$ pip install 'litellm[proxy]' 'litellm[extra_proxy]'
  1. Save Azure details in your environment
export["AZURE_CLIENT_ID"]="your-azure-app-client-id"
export["AZURE_CLIENT_SECRET"]="your-azure-app-client-secret"
export["AZURE_TENANT_ID"]="your-azure-tenant-id"
export["AZURE_KEY_VAULT_URI"]="your-azure-key-vault-uri"
  1. Add to proxy config.yaml 
model_list: 
    - model_name: "my-azure-models" # model alias 
        litellm_params:
            model: "azure/<your-deployment-name>"
            api_key: "os.environ/AZURE-API-KEY" # reads from key vault - get_secret("AZURE_API_KEY")
            api_base: "os.environ/AZURE-API-BASE" # reads from key vault - get_secret("AZURE_API_BASE")

general_settings:
  use_azure_key_vault: True

You can now test this by starting your proxy: 

litellm --config /path/to/config.yaml

Set Custom Prompt Templates​

LiteLLM by default checks if a model has a prompt template and applies it (e.g. if a huggingface model has a saved chat template in it's tokenizer_config.json). However, you can also set a custom prompt template on your proxy in the config.yaml:

Step 1: Save your prompt template in a config.yaml

# Model-specific parameters
model_list:
  - model_name: mistral-7b # model alias
    litellm_params: # actual params for litellm.completion()
      model: "huggingface/mistralai/Mistral-7B-Instruct-v0.1" 
      api_base: "<your-api-base>"
      api_key: "<your-api-key>" # [OPTIONAL] for hf inference endpoints
      initial_prompt_value: "\n"
      roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}}
      final_prompt_value: "\n"
      bos_token: "<s>"
      eos_token: "</s>"
      max_tokens: 4096

Step 2: Start server with config

$ litellm --config /path/to/config.yaml

Setting Embedding Models​

See supported Embedding Providers & Models here

Use Sagemaker, Bedrock, Azure, OpenAI, XInference​

Create Config.yaml​

Here's how to route between GPT-J embedding (sagemaker endpoint), Amazon Titan embedding (Bedrock) and Azure OpenAI embedding on the proxy server: 

model_list:
  - model_name: sagemaker-embeddings
    litellm_params: 
      model: "sagemaker/berri-benchmarking-gpt-j-6b-fp16"
  - model_name: amazon-embeddings
    litellm_params:
      model: "bedrock/amazon.titan-embed-text-v1"
  - model_name: azure-embeddings
    litellm_params: 
      model: "azure/azure-embedding-model"
      api_base: "os.environ/AZURE_API_BASE" # os.getenv("AZURE_API_BASE")
      api_key: "os.environ/AZURE_API_KEY" # os.getenv("AZURE_API_KEY")
      api_version: "2023-07-01-preview"

general_settings:
  master_key: sk-1234 # [OPTIONAL] if set all calls to proxy will require either this key or a valid generated token

Start Proxy​

litellm --config config.yaml

Make Request​

Sends Request to deployed-codebert-base

curl --location 'http://0.0.0.0:8000/embeddings' \
  --header 'Content-Type: application/json' \
  --data ' {
  "model": "deployed-codebert-base",
  "input": ["write a litellm poem"]
  }'

Router Settings​

Use this to configure things like routing strategy. 

router_settings:
  routing_strategy: "least-busy"

model_list: # will route requests to the least busy ollama model
  - model_name: ollama-models
    litellm_params: 
      model: "ollama/mistral"
      api_base: "http://127.0.0.1:8001"
  - model_name: ollama-models
    litellm_params: 
      model: "ollama/codellama"
      api_base: "http://127.0.0.1:8002"
  - model_name: ollama-models
    litellm_params: 
      model: "ollama/llama2"
      api_base: "http://127.0.0.1:8003"

Max Parallel Requests​

To rate limit a user based on the number of parallel requests, e.g.: if user's parallel requests > x, send a 429 error if user's parallel requests <= x, let them use the API freely.

set the max parallel request limit on the config.yaml (note: this expects the user to be passing in an api key).

general_settings:
  max_parallel_requests: 100 # max parallel requests for a user = 100

All settings​

{
  "environment_variables": {},
  "model_list": [
    {
      "model_name": "string",
      "litellm_params": {},
      "model_info": {
        "id": "string",
        "mode": "embedding",
        "input_cost_per_token": 0,
        "output_cost_per_token": 0,
        "max_tokens": 2048,
        "base_model": "gpt-4-1106-preview",
        "additionalProp1": {}
      }
    }
  ],
  "litellm_settings": {}, # ALL (https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py)
  "general_settings": {
    "completion_model": "string",
    "key_management_system": "google_kms", # either google_kms or azure_kms
    "master_key": "string",
    "database_url": "string",
    "database_type": "dynamo_db",
    "database_args": {
      "billing_mode": "PROVISIONED_THROUGHPUT",
      "read_capacity_units": 0,
      "write_capacity_units": 0,
      "ssl_verify": true,
      "region_name": "string",
      "user_table_name": "LiteLLM_UserTable",
      "key_table_name": "LiteLLM_VerificationToken",
      "config_table_name": "LiteLLM_Config",
      "spend_table_name": "LiteLLM_SpendLogs"
    },
    "otel": true,
    "custom_auth": "string",
    "max_parallel_requests": 0,
    "infer_model_from_keys": true,
    "background_health_checks": true,
    "health_check_interval": 300,
    "alerting": [
      "string"
    ],
    "alerting_threshold": 0
  }
}