add llm sdk integration example (kserve#395)

add sdk integration example

Signed-off-by: Lize Cai <[email protected]>

docs/modelserving/v1beta1/llm/huggingface/README.md

@@ -3,7 +3,7 @@ The Hugging Face serving runtime implements two backends namely `Hugging Face` a
 The preprocess and post-process handlers are already implemented based on different ML tasks, for example text classification,
 token-classification, text-generation, text2text-generation, fill-mask.
 
-KServe Hugging Face runtime by default uses [`vLLM`](https://github.com/vllm-project/vllm) backend to serve `text generation` and `text2text generation` LLM models for faster time-to-first-token (TTFT) and higher token generation throughput than the Hugging Face API. 
+KServe Hugging Face runtime by default uses [`vLLM`](https://github.com/vllm-project/vllm) backend to serve `text generation` and `text2text generation` LLM models for faster time-to-first-token (TTFT) and higher token generation throughput than the Hugging Face API.
 vLLM is implemented with common inference optimization techniques, such as [PagedAttention](https://vllm.ai), [continuous batching](https://www.anyscale.com/blog/continuous-batching-llm-inference) and an optimized CUDA kernel.
 If the model is not supported by the vLLM engine, KServe falls back to the Hugging Face backend as a failsafe.
 
@@ -22,7 +22,7 @@ For information on the models supported by the vLLM backend, please visit [vLLM'
 ## API Endpoints
 Both of the backends support serving generative models (text generation and text2text generation) using [OpenAI's Completion](https://platform.openai.com/docs/api-reference/completions) and [Chat Completion](https://platform.openai.com/docs/api-reference/chat) API.
 
-The other types of tasks like token classification, sequence classification, and fill mask are served using KServe's [Open Inference Protocol](../../../data_plane/v2_protocol.md) or [V1 API](../../../data_plane/v1_protocol.md). 
+The other types of tasks like token classification, sequence classification, and fill mask are served using KServe's [Open Inference Protocol](../../../data_plane/v2_protocol.md) or [V1 API](../../../data_plane/v1_protocol.md).
 
 ## Examples
 The following examples demonstrate how to deploy and perform inference using the Hugging Face runtime with different ML tasks:
@@ -32,6 +32,7 @@ The following examples demonstrate how to deploy and perform inference using the
 - [Token Classification using BERT](token_classification/README.md)
 - [Sequence Classification (Text Classification) using distilBERT](text_classification/README.md)
 - [Fill Mask using BERT](fill_mask/README.md)
+- [SDK Integration](sdk_integration/README.md)
 
 !!! note
     The Hugging Face runtime image has the following environment variables set by default:

docs/modelserving/v1beta1/llm/huggingface/sdk_integration/README.md

@@ -0,0 +1,147 @@
+# Integrate KServe LLM Deployment with LLM SDKs
+
+This document provides the example of how to integrate KServe LLM Inference Service with the popular LLM SDKs.
+
+## Deploy a KServe LLM Inference Service
+
+Please follow this example: [Text Generation using LLama3](../text_generation/README.md) to deploy a KServe LLM Inference Service.
+
+Get the `SERVICE_HOSTNAME` by running the following command:
+
+```bash
+SERVICE_HOSTNAME=$(kubectl get inferenceservice huggingface-llama3 -o jsonpath='{.status.url}' | cut -d "/" -f 3)
+```
+
+The model name for the above example is `llama3`.
+
+## How to integrate with OpenAI SDK
+
+Install the [OpenAI SDK](https://github.com/openai/openai-python):
+
+```bash
+pip3 install openai
+```
+
+Create a Python script to interact with the KServe LLM Inference Service and save it as `sample_openai.py`:
+
+=== "python"
+```python
+from openai import OpenAI
+
+Deployment_url = "<SERVICE_HOSTNAME>"
+client = OpenAI(
+    base_url=f"{Deployment_url}/openai/v1",
+    api_key="empty",
+)
+
+# typial chat completion response
+print("Typical chat completion response:")
+response = client.chat.completions.create(
+    model="llama3",
+    messages=[
+        {'role': 'user', 'content': "What's 1+1? Answer in one word."}
+    ],
+    temperature=0,
+    max_tokens=256
+)
+
+reply = response.choices[0].message
+print(f"Extracted reply: \n{reply.content}\n")
+
+# streaming chat completion response
+print("Streaming chat completion response:")
+stream = client.chat.completions.create(
+    model='llama3',
+    messages=[
+        {'role': 'user', 'content': 'Count to 100, with a comma between each number and no newlines. E.g., 1, 2, 3, ...'}
+    ],
+    temperature=0,
+    max_tokens=300,
+    stream=True  # this time, we set stream=True
+)
+
+for chunk in stream:
+    print(chunk.choices[0].delta.content or "", end="", flush=True)
+```
+
+Run the python script:
+
+```bash
+python3 sample_openai.py
+```
+
+!!! success "Expected Output"
+
+    ```{ .bash .no-copy }
+    Typical chat completion response:
+    Extracted reply: 
+    Two.
+
+    Streaming chat completion response:
+    1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100
+    ```
+
+## How to integrate with Langchain SDK
+
+Install the [Langchain SDK](https://python.langchain.com/v0.2/docs/how_to/installation/#integration-packages):
+
+```bash
+pip3 install langchain-openai
+```
+
+Create a Python script to interact with the KServe LLM Inference Service and save it as `sample_langchain.py`:
+
+=== "python"
+```python
+from langchain_openai import ChatOpenAI
+
+Deployment_url = "<SERVICE_HOSTNAME>"
+
+llm = ChatOpenAI(
+    model_name="llama3",
+    base_url=f"{Deployment_url}/openai/v1",
+    openai_api_key="empty",
+    temperature=0,
+    max_tokens=256,
+)
+
+# typial chat completion response
+print("Typical chat completion response:")
+
+messages = [
+    (
+        "system",
+        "You are a helpful assistant that translates English to French. Translate the user sentence.",
+    ),
+    ("human", "I love programming."),
+]
+reply = llm.invoke(messages)
+print(f"Extracted reply: \n{reply.content}\n")
+
+# streaming chat completion response
+print("Streaming chat completion response:")
+for chunk in llm.stream("Write me a 1 verse song about goldfish on the moon"):
+    print(chunk.content, end="", flush=True)
+```
+
+Run the python script:
+
+```bash
+python3 sample_langchain.py
+```
+
+!!! success "Expected Output"
+
+    ```{ .bash .no-copy }
+        Typical chat completion response:
+        Extracted reply: 
+        Je adore le programmation.
+
+        Streaming chat completion response:
+        Here is a 1-verse song about goldfish on the moon:
+
+        "In the lunar lake, where the craters shine
+        A school of goldfish swim, in a celestial shrine
+        Their scales glimmer bright, like stars in the night
+        As they dart and play, in the moon's gentle light"
+    ```

mkdocs.yml

@@ -49,6 +49,7 @@ nav:
                   - Token Classification: modelserving/v1beta1/llm/huggingface/token_classification/README.md
                   - Text Classification: modelserving/v1beta1/llm/huggingface/text_classification/README.md
                   - Fill Mask: modelserving/v1beta1/llm/huggingface/fill_mask/README.md
+                  - SDK Integration: modelserving/v1beta1/llm/huggingface/sdk_integration/README.md
                 - TorchServe LLM: modelserving/v1beta1/llm/torchserve/accelerate/README.md
               - How to write a custom predictor: modelserving/v1beta1/custom/custom_model/README.md
           - Multi Model Serving: