OpenAI API Clients#

Introduction#

Beside Tensorflow Serving API and KServe API frontends, the model server has now option to delegate the REST input deserialization and output serialization to a MediaPipe graph. A custom calculator can implement any form of REST API including streaming based on Server-sent events.

That way we are introducing a preview of OpenAI compatible endpoint chat/completions. More endpoints are planned for the implementation.

Python Client#

When creating a Python-based client application, you can use OpenAI client library - openai.

Alternatively, it is possible to use just a curl command or requests python library.

Install the Package#

pip3 install openai
pip3 install requests

Request chat completions with unary calls#

python [OpenAI]

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v3", api_key="unused")
response = client.chat.completions.create(
    model="meta-llama/Llama-2-7b-chat-hf",
    messages=[{"role": "user", "content": "Say this is a test"}],
    stream=False,
)

print(response.choices[0].message)

python [requests]

import requests
payload = {"model": "meta-llama/Llama-2-7b-chat-hf", "messages": [ {"role": "user","content": "Say this is a test" }]}
headers = {"Content-Type": "application/json", "Authorization": "not used"}
response = requests.post("http://localhost:8000/v3/chat/completions", json=payload, headers=headers)
print(response.text)

curl

curl http://localhost:8000/v3/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Llama-2-7b-chat-hf", "messages": [ {"role": "user","content": "Say this is a test" }]}'

Request completions with unary calls#

python [OpenAI]

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v3", api_key="unused")
response = client.completions.create(
    model="meta-llama/Llama-2-7b",
    prompt="Say this is a test",
    stream=False,
)

print(response.choices[0].text)

python [requests]

import requests
payload = {"model": "meta-llama/Llama-2-7b", "prompt": "Say this is a test"}
headers = {"Content-Type": "application/json", "Authorization": "not used"}
response = requests.post("http://localhost:8000/v3/completions", json=payload, headers=headers)
print(response.text)

curl

curl http://localhost:8000/v3/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Llama-2-7b", "prompt": "Say this is a test"}'

Request chat completions with streaming#

python [OpenAI]

from openai import OpenAI
client = OpenAI(
  base_url="http://localhost:8000/v3",
  api_key="unused"
)

stream = client.chat.completions.create(
    model="meta-llama/Llama-2-7b-chat-hf",
    messages=[{"role": "user", "content": "Say this is a test"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Request completions with streaming#

python [OpenAI]

from openai import OpenAI
client = OpenAI(
  base_url="http://localhost:8000/v3",
  api_key="unused"
)

stream = client.completions.create(
    model="meta-llama/Llama-2-7b",
    prompt="Say this is a test",
    stream=True,
)
for chunk in stream:
    if chunk.choices[0].text is not None:
        print(chunk.choices[0].text, end="")