doc Tag Based Routing

docs/my-website/docs/proxy/tag_routing.md

@@ -1,7 +1,11 @@
 # Tag Based Routing
 
 Route requests based on tags. 
-This is useful for implementing free / paid tiers for users
+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
+
+## Quick Start
 
 ### 1. Define tags on config.yaml 
 
@@ -131,3 +135,154 @@ Response
  }
 }
 ```
+
+## ✨ 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**
+
+
+:::info
+
+This is an enterprise feature, [Contact us here to get a free trial](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
+
+:::
+
+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:
+
+   ```yaml
+   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:
+
+   ```shell
+   # 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"]}'
+   ```
+
+   ```shell
+   # 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:
+
+   ```shell
+   # 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"}'
+   ```
+
+   ```shell
+   # 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. **Make requests with team-specific keys:**
+
+   When making requests to the `/chat/completions` endpoint, use the team-specific keys. The proxy will automatically route the request to the appropriate model based on the team's tags:
+
+   ```shell
+   # Request using Team A's key
+   curl -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!"}
+       ]
+     }'
+   ```
+
+   ```shell
+   # Request using Team B's key
+   curl -X POST http://0.0.0.0:4000/chat/completions \
+     -H "Authorization: Bearer team_b_key_here" \
+     -H "Content-Type: application/json" \
+     -d '{
+       "model": "fake-openai-endpoint",
+       "messages": [
+         {"role": "user", "content": "Hello!"}
+       ]
+     }'
+   ```
+
+   Replace `team_a_key_here` and `team_b_key_here` with the actual keys generated in step 3.
+
+5. **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)
+   ```shell
+   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: teama
+   ```
+
+   Similarly, when using Team B's key, you should see:
+   ```
+   x-litellm-model-id: teamb
+   ```
+
+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.
+
+
+