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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:

  • model_name: the name to pass TO litellm from the external client
  • litellm_params.model: the model string passed to the litellm.completion() function

E.g.:

  • 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 ### RECEIVED MODEL NAME ###
    litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
      model: azure/gpt-turbo-small-eu ### MODEL NAME sent to `litellm.completion()` ###
      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      # [OPTIONAL] 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: anthropic-claude
    litellm_params: 
      model: bedrock/anthropic.claude-instant-v1
      ### [OPTIONAL] SET AWS REGION ###
      aws_region_name: us-east-1
  - 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)
info

For more provider-specific info, go here

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, Keys, Temperature, Max Tokens, Organization, 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: openai-gpt-3.5
    litellm_params:
      model: openai/gpt-3.5-turbo
      extra_headers: {"AI-Resource Group": "ishaan-resource"}
      api_key: sk-123
      organization: org-ikDc4ex8NB
      temperature: 0.2
  - model_name: mistral-7b
    litellm_params:
      model: ollama/mistral
      api_base: your_ollama_api_base

Step 2: Start server with config

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

Load Balancing​

Use this to call multiple instances of the same model and configure things like routing strategy.

For optimal performance:

  • Set tpm/rpm per model deployment. Weighted picks are then based on the established tpm/rpm.
  • Select your optimal routing strategy in router_settings:routing_strategy.

LiteLLM supports

["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"`

When tpm/rpm is set + routing_strategy==simple-shuffle litellm will use a weighted pick based on set tpm/rpm. In our load tests setting tpm/rpm for all deployments + routing_strategy==simple-shuffle maximized throughput

  • When using multiple LiteLLM Servers / Kubernetes set redis settings router_settings:redis_host etc
model_list:
  - model_name: zephyr-beta
    litellm_params:
        model: huggingface/HuggingFaceH4/zephyr-7b-beta
        api_base: http://0.0.0.0:8001
        rpm: 60      # Optional[int]: When rpm/tpm set - litellm uses weighted pick for load balancing. rpm = Rate limit for this deployment: in requests per minute (rpm).
        tpm: 1000   # Optional[int]: tpm = Tokens Per Minute 
  - model_name: zephyr-beta
    litellm_params:
        model: huggingface/HuggingFaceH4/zephyr-7b-beta
        api_base: http://0.0.0.0:8002
        rpm: 600      
  - model_name: zephyr-beta
    litellm_params:
        model: huggingface/HuggingFaceH4/zephyr-7b-beta
        api_base: http://0.0.0.0:8003
        rpm: 60000      
  - model_name: gpt-3.5-turbo
    litellm_params:
        model: gpt-3.5-turbo
        api_key: <my-openai-key>
        rpm: 200      
  - model_name: gpt-3.5-turbo-16k
    litellm_params:
        model: gpt-3.5-turbo-16k
        api_key: <my-openai-key>
        rpm: 100      

litellm_settings:
  num_retries: 3 # retry call 3 times on each model_name (e.g. zephyr-beta)
  request_timeout: 10 # raise Timeout error if call takes longer than 10s. Sets litellm.request_timeout 
  fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo"]}] # fallback to gpt-3.5-turbo if call fails num_retries 
  context_window_fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}] # fallback to gpt-3.5-turbo-16k if context window error
  allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. 

router_settings: # router_settings are optional
  routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"
  model_group_alias: {"gpt-4": "gpt-3.5-turbo"} # all requests with `gpt-4` will be routed to models with `gpt-3.5-turbo`
  num_retries: 2
  timeout: 30                                  # 30 seconds
  redis_host: <your redis host>                # set this when using multiple litellm proxy deployments, load balancing state stored in redis
  redis_password: <your redis password>
  redis_port: 1992

Set Azure base_model for cost tracking​

Problem: Azure returns gpt-4 in the response when azure/gpt-4-1106-preview is used. This leads to inaccurate cost tracking

Solution ✅ : Set base_model on your config so litellm uses the correct model for calculating azure cost

Example config with base_model

model_list:
  - model_name: azure-gpt-3.5
    litellm_params:
      model: azure/chatgpt-v-2
      api_base: os.environ/AZURE_API_BASE
      api_key: os.environ/AZURE_API_KEY
      api_version: "2023-07-01-preview"
    model_info:
      base_model: azure/gpt-4-1106-preview

You can view your cost once you set up Virtual keys or custom_callbacks

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​

model_list:
  - model_name: bedrock-cohere
    litellm_params:
      model: "bedrock/cohere.command-text-v14"
      aws_region_name: "us-west-2"
  - model_name: bedrock-cohere
    litellm_params:
      model: "bedrock/cohere.command-text-v14"
      aws_region_name: "us-east-2"
  - model_name: bedrock-cohere
    litellm_params:
      model: "bedrock/cohere.command-text-v14"
      aws_region_name: "us-east-1"

Start Proxy​

litellm --config config.yaml

Make Request​

Sends Request to bedrock-cohere

curl --location 'http://0.0.0.0:8000/chat/completions' \
  --header 'Content-Type: application/json' \
  --data ' {
  "model": "bedrock-cohere",
  "messages": [
      {
      "role": "user",
      "content": "gm"
      }
  ]
}'

Configure DB Pool Limits + Connection Timeouts​

general_settings: 
  database_connection_pool_limit: 100 # sets connection pool for prisma client to postgres db at 100
  database_connection_timeout: 60 # sets a 60s timeout for any connection call to the db

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_connection_pool_limit": 0, # default 100
    "database_connection_timeout": 0, # default 60s
    "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
  }
}