From 02912ad27311f468fc74fa673ea3744d2aa8a506 Mon Sep 17 00:00:00 2001
From: drbh <david.richard.holtz@gmail.com>
Date: Thu, 1 Feb 2024 13:36:24 -0500
Subject: [PATCH] feat: add ie update to message docs

---
 docs/source/messages_api.md | 44 +++++++++++++++++++++++++++++++++++--
 1 file changed, 42 insertions(+), 2 deletions(-)

diff --git a/docs/source/messages_api.md b/docs/source/messages_api.md
index 1e342686..beb141b6 100644
--- a/docs/source/messages_api.md
+++ b/docs/source/messages_api.md
@@ -4,6 +4,15 @@ Text Generation Inference (TGI) now supports the Messages API, which is fully co
 
 > **Note:** The Messages API is supported from TGI version 1.4.0 and above. Ensure you are using a compatible version to access this feature.
 
+#### Table of Contents
+
+- [Making a Request](#making-a-request)
+- [Streaming](#streaming)
+- [Synchronous](#synchronous)
+- [Hugging Face Inference Endpoints](#hugging-face-inference-endpoints)
+- [Cloud Providers](#cloud-providers)
+  - [Amazon SageMaker](#amazon-sagemaker)
+
 ## Making a Request
 
 You can make a request to TGI's Messages API using `curl`. Here's an example:
@@ -81,6 +90,37 @@ chat_completion = client.chat.completions.create(
 print(chat_completion)
 ```
 
+## Hugging Face Inference Endpoints
+
+TGI is now integrated with [Inference Endpoints](https://huggingface.co/inference-endpoints/dedicated) and can be easily accessed with only a few lines of code. Here's an example of how to use IE with TGI using OpenAI's Python client library:
+
+> **Note:** Make sure to replace `base_url` with your endpoint URL and to include `v1/` at the end of the URL. The `api_key` should be replaced with your Hugging Face API key.
+
+```python
+from openai import OpenAI
+
+# init the client but point it to TGI
+client = OpenAI(
+    # replace with your endpoint url, make sure to include "v1/" at the end
+    base_url="https://vlzz10eq3fol3429.us-east-1.aws.endpoints.huggingface.cloud/v1/",
+    # replace with your API key
+    api_key="hf_XXX"
+)
+
+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.choices[0].delta.content, end="")
+```
+
 ## 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:
@@ -114,7 +154,7 @@ hub = {
 huggingface_model = HuggingFaceModel(
  image_uri=get_huggingface_llm_image_uri("huggingface",version="1.4.0"),
  env=hub,
- role=role, 
+ role=role,
 )
 
 # deploy model to SageMaker Inference
@@ -123,7 +163,7 @@ predictor = huggingface_model.deploy(
  instance_type="ml.g5.2xlarge",
  container_startup_health_check_timeout=300,
   )
-  
+
 # send request
 predictor.predict({
 "messages": [