docs(user_keys.md): docs on passing user keys to litellm proxy

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

@@ -0,0 +1,56 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Pass in User Keys
+
+Send user keys to the proxy
+
+
+Here's how to do it: 
+
+<Tabs>
+<TabItem value="openai-py" label="OpenAI Python">
+
+Pass in the litellm_params (E.g. api_key, api_base, etc.) via the `extra_body` parameter in the OpenAI client. 
+
+```python
+import openai
+client = openai.OpenAI(
+    api_key="sk-1234",
+    base_url="http://0.0.0.0:8000"
+)
+
+# request sent to model set on litellm proxy, `litellm --model`
+response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
+    {
+        "role": "user",
+        "content": "this is a test request, write a short poem"
+    }
+], 
+    extra_body={"api_key": "my-bad-key"}) # 👈 User Key
+
+print(response)
+```
+</TabItem>
+<TabItem value="openai-js" label="OpenAI JS">
+
+```javascript
+const { OpenAI } = require('openai');
+
+const openai = new OpenAI({
+  apiKey: "sk-1234", // This is the default and can be omitted
+  baseURL: "http://0.0.0.0:8000"
+});
+
+async function main() {
+  const chatCompletion = await openai.chat.completions.create({
+    messages: [{ role: 'user', content: 'Say this is a test' }],
+    model: 'gpt-3.5-turbo',
+    api_key: "my-bad-key" // 👈 User Key
+  });
+}
+
+main();
+```
+</TabItem>
+</Tabs>

docs/my-website/sidebars.js

@@ -102,6 +102,7 @@ const sidebars = {
         "proxy/embedding", 
         "proxy/load_balancing", 
         "proxy/virtual_keys",
+        "proxy/user_keys",
         "proxy/model_management",
         "proxy/reliability",
         "proxy/health",

litellm/proxy/proxy_server.py

@@ -1126,7 +1126,10 @@ async def completion(
             if llm_model_list is not None
             else []
         )
-        if (
+        # skip router if user passed their key
+        if "api_key" in data:
+            response = await litellm.atext_completion(**data)
+        elif (
             llm_router is not None and data["model"] in router_model_names
         ):  # model in router model list
             response = await llm_router.atext_completion(**data)
@@ -1259,7 +1262,10 @@ async def chat_completion(
             if llm_model_list is not None
             else []
         )
-        if (
+        # skip router if user passed their key
+        if "api_key" in data:
+            response = await litellm.acompletion(**data)
+        elif (
             llm_router is not None and data["model"] in router_model_names
         ):  # model in router model list
             response = await llm_router.acompletion(**data)
@@ -1414,7 +1420,10 @@ async def embeddings(
             user_api_key_dict=user_api_key_dict, data=data, call_type="embeddings"
         )
         ## ROUTE TO CORRECT ENDPOINT ##
-        if (
+        # skip router if user passed their key
+        if "api_key" in data:
+            response = await litellm.aembedding(**data)
+        elif (
             llm_router is not None and data["model"] in router_model_names
         ):  # model in router model list
             response = await llm_router.aembedding(**data)
@@ -1515,7 +1524,10 @@ async def image_generation(
             user_api_key_dict=user_api_key_dict, data=data, call_type="embeddings"
         )
         ## ROUTE TO CORRECT ENDPOINT ##
-        if (
+        # skip router if user passed their key
+        if "api_key" in data:
+            response = await litellm.aimage_generation(**data)
+        elif (
             llm_router is not None and data["model"] in router_model_names
         ):  # model in router model list
             response = await llm_router.aimage_generation(**data)

litellm/router.py

@@ -1060,12 +1060,12 @@ class Router:
                 "custom_llm_provider", None
             )  # i.e. azure
             metadata = kwargs.get("litellm_params", {}).get("metadata", None)
-            deployment_id = (
-                kwargs.get("litellm_params", {}).get("model_info", {}).get("id", None)
-            )
-            self._set_cooldown_deployments(
-                deployment_id
-            )  # setting deployment_id in cooldown deployments
+            _model_info = kwargs.get("litellm_params", {}).get("model_info", {})
+            if isinstance(_model_info, dict):
+                deployment_id = _model_info.get("id", None)
+                self._set_cooldown_deployments(
+                    deployment_id
+                )  # setting deployment_id in cooldown deployments
             if metadata:
                 deployment = metadata.get("deployment", None)
                 deployment_exceptions = self.model_exception_map.get(deployment, [])