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+# Open AI Compatible
+
+Text Generation Inference (TGI) is compatible with OpenAI's API. This means you can use OpenAI's client libraries to interact with TGI. Here are some examples of how to do this.
+
+## Making a Request
+
+You can make a request to TGI using `curl`. Here's an example:
+
+```bash
+curl localhost:3000/v1/chat/completions \
+    -X POST \
+    -d '{
+  "model": "tgi",
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant."
+    },
+    {
+      "role": "user",
+      "content": "What is deep learning?"
+    }
+  ],
+  "stream": true,
+  "max_tokens": 20
+}' \
+    -H 'Content-Type: application/json'
+```
+
+## Streaming
+
+You can also use OpenAI's Python client library to make a streaming request. Here's how:
+
+```python
+from openai import OpenAI
+
+# init the client but point it to TGI
+client = OpenAI(
+    base_url="http://localhost:3000/v1",
+    api_key="-"
+)
+
+chat_completion = client.chat.completions.create(
+    model="tgi",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant." },
+        {"role": "user", "content": "What is deep learning?"}
+    ],
+    stream=True
+)
+
+# iterate and print stream
+for message in chat_completion:
+    print(message)
+```
+
+## Synchronous
+
+If you prefer to make a synchronous request, you can do so like this:
+
+```python
+from openai import OpenAI
+
+# init the client but point it to TGI
+client = OpenAI(
+    base_url="http://localhost:3000/v1",
+    api_key="-"
+)
+
+chat_completion = client.chat.completions.create(
+    model="tgi",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant." },
+        {"role": "user", "content": "What is deep learning?"}
+    ],
+    stream=False
+)
+
+print(chat_completion)
+```
+
+## Cloud Providers
+
+TGI can be deployed on various cloud providers for scalable and robust text generation. One such provider is Amazon SageMaker, which has recently added support for TGI. Here's how you can deploy TGI on Amazon SageMaker:
+
+## Amazon SageMaker
+
+Amazon SageMaker allows two routes: `/invocations` and `/ping` (or `/health`) for health checks. By default, we map `/generate` to `/invocations`. However, SageMaker does not allow requests to any other routes. 
+
+To provide the new feature of chat completion API, we have introduced an environment variable `OAI_ENABLED`. If `OAI_ENABLED=true`, the `chat_completions` method is used when `/invocations` is called, otherwise it defaults to `generate`. This allows users to opt in for the OAI format.
+
+Here's an example of running the router with `OAI_ENABLED` set to `true`:
+
+```bash
+OAI_ENABLED=true text-generation-launcher --model-id <MODEL-ID>
+```
+
+And here's an example request:
+
+```bash
+curl <SAGEMAKER-ENDPOINT>/invocations \
+    -X POST \
+    -d '{
+  "model": "tgi",
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant."
+    },
+    {
+      "role": "user",
+      "content": "What is deep learning?"
+    }
+  ],
+  "stream": true,
+  "max_tokens": 20
+}' \
+    -H 'Content-Type: application/json' | jq 
+```
+
+Please let us know if any naming changes are needed or if any other routes need similar functionality.