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Tag Based Routing

Route requests based on tags. This is useful for

  • Implementing free / paid tiers for users
  • Controlling model access per team, example Team A can access gpt-4 deployment A, Team B can access gpt-4 deployment B (LLM Access Control For Teams )

Quick Start​

1. Define tags on config.yaml​

  • A request with tags=["free"] will get routed to openai/fake
  • A request with tags=["paid"] will get routed to openai/gpt-4o
model_list:
  - model_name: gpt-4
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/
      tags: ["free"] # 👈 Key Change
  - model_name: gpt-4
    litellm_params:
      model: openai/gpt-4o
      api_key: os.environ/OPENAI_API_KEY
      tags: ["paid"] # 👈 Key Change

router_settings:
  enable_tag_filtering: True # 👈 Key Change
general_settings: 
  master_key: sk-1234

2. Make Request with tags=["free"]​

This request includes "tags": ["free"], which routes it to openai/fake

curl -i http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "gpt-4",
    "messages": [
      {"role": "user", "content": "Hello, Claude gm!"}
    ],
    "tags": ["free"]
  }'

Expected Response

Expect to see the following response header when this works

x-litellm-model-api-base: https://exampleopenaiendpoint-production.up.railway.app/

Response

{
 "id": "chatcmpl-33c534e3d70148218e2d62496b81270b",
 "choices": [
   {
     "finish_reason": "stop",
     "index": 0,
     "message": {
       "content": "\n\nHello there, how may I assist you today?",
       "role": "assistant",
       "tool_calls": null,
       "function_call": null
     }
   }
 ],
 "created": 1677652288,
 "model": "gpt-3.5-turbo-0125",
 "object": "chat.completion",
 "system_fingerprint": "fp_44709d6fcb",
 "usage": {
   "completion_tokens": 12,
   "prompt_tokens": 9,
   "total_tokens": 21
 }
}

3. Make Request with tags=["paid"]​

This request includes "tags": ["paid"], which routes it to openai/gpt-4

curl -i http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "gpt-4",
    "messages": [
      {"role": "user", "content": "Hello, Claude gm!"}
    ],
    "tags": ["paid"]
  }'

Expected Response

Expect to see the following response header when this works

x-litellm-model-api-base: https://api.openai.com

Response

{
 "id": "chatcmpl-9maCcqQYTqdJrtvfakIawMOIUbEZx",
 "choices": [
   {
     "finish_reason": "stop",
     "index": 0,
     "message": {
       "content": "Good morning! How can I assist you today?",
       "role": "assistant",
       "tool_calls": null,
       "function_call": null
     }
   }
 ],
 "created": 1721365934,
 "model": "gpt-4o-2024-05-13",
 "object": "chat.completion",
 "system_fingerprint": "fp_c4e5b6fa31",
 "usage": {
   "completion_tokens": 10,
   "prompt_tokens": 12,
   "total_tokens": 22
 }
}

✨ Team based tag routing (Enterprise)​

LiteLLM Proxy supports team-based tag routing, allowing you to associate specific tags with teams and route requests accordingly. Example Team A can access gpt-4 deployment A, Team B can access gpt-4 deployment B (LLM Access Control For Teams)

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This is an enterprise feature, Contact us here to get a free trial

Here's how to set up and use team-based tag routing using curl commands:

  1. Enable tag filtering in your proxy configuration:

    In your proxy_config.yaml, ensure you have the following setting:

    model_list:
     - model_name: fake-openai-endpoint
       litellm_params:
         model: openai/fake
         api_key: fake-key
         api_base: https://exampleopenaiendpoint-production.up.railway.app/
         tags: ["teamA"] # 👈 Key Change
       model_info:
         id: "team-a-model" # used for identifying model in response headers
     - model_name: fake-openai-endpoint
       litellm_params:
         model: openai/fake
         api_key: fake-key
         api_base: https://exampleopenaiendpoint-production.up.railway.app/
         tags: ["teamB"] # 👈 Key Change
       model_info:
         id: "team-b-model" # used for identifying model in response headers

    router_settings:
     enable_tag_filtering: True # 👈 Key Change

    general_settings: 
     master_key: sk-1234

  2. Create teams with tags:

    Use the /team/new endpoint to create teams with specific tags:

    # Create Team A
    curl -X POST http://0.0.0.0:4000/team/new \
      -H "Authorization: Bearer sk-1234" \
      -H "Content-Type: application/json" \
      -d '{"tags": ["teamA"]}'
    # Create Team B
    curl -X POST http://0.0.0.0:4000/team/new \
      -H "Authorization: Bearer sk-1234" \
      -H "Content-Type: application/json" \
      -d '{"tags": ["teamB"]}'

    These commands will return JSON responses containing the team_id for each team.

  3. Generate keys for team members:

    Use the /key/generate endpoint to create keys associated with specific teams:

    # Generate key for Team A
    curl -X POST http://0.0.0.0:4000/key/generate \
      -H "Authorization: Bearer sk-1234" \
      -H "Content-Type: application/json" \
      -d '{"team_id": "team_a_id_here"}'
    # Generate key for Team B
    curl -X POST http://0.0.0.0:4000/key/generate \
      -H "Authorization: Bearer sk-1234" \
      -H "Content-Type: application/json" \
      -d '{"team_id": "team_b_id_here"}'

    Replace team_a_id_here and team_b_id_here with the actual team IDs received from step 2.

  4. Verify routing:

    Check the x-litellm-model-id header in the response to confirm that the request was routed to the correct model based on the team's tags. You can use the -i flag with curl to include the response headers:

    Request with Team A's key (including headers)

    curl -i -X POST http://0.0.0.0:4000/chat/completions \
      -H "Authorization: Bearer team_a_key_here" \
      -H "Content-Type: application/json" \
      -d '{
        "model": "fake-openai-endpoint",
        "messages": [
          {"role": "user", "content": "Hello!"}
        ]
      }'

    In the response headers, you should see:

    x-litellm-model-id: team-a-model

    Similarly, when using Team B's key, you should see:

    x-litellm-model-id: team-b-model

By following these steps and using these curl commands, you can implement and test team-based tag routing in your LiteLLM Proxy setup, ensuring that different teams are routed to the appropriate models or deployments based on their assigned tags.

Other Tag Based Features​