HTTP API Reference

Text Generation Inference custom API
OpenAI Messages API

The HTTP API is a RESTful API that allows you to interact with the text-generation-inference component. Two endpoints are available:

Text Generation Inference custom API
OpenAI’s Messages API

Text Generation Inference custom API

Check the API documentation for more information on how to interact with the Text Generation Inference API.

OpenAI Messages API

Text Generation Inference (TGI) now supports the Messages API, which is fully compatible with the OpenAI Chat Completion API. This feature is available starting from version 1.4.0. You can use OpenAI’s client libraries or third-party libraries expecting OpenAI schema to interact with TGI’s Messages API. Below are some examples of how to utilize this compatibility.

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.

Making a Request

You can make a request to TGI’s Messages API using curl. Here’s an example:

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:

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:

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)

Hugging Face Inference Endpoints

The Messages API is integrated with Inference Endpoints. Every endpoint that uses “Text Generation Inference” with an LLM, which has a chat template can now be used. Below is 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.

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:

Amazon SageMaker

Amazon Sagemaker natively supports the message API:

import json
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri

try:
 role = sagemaker.get_execution_role()
except ValueError:
 iam = boto3.client('iam')
 role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']

# Hub Model configuration. https://huggingface.co/models
hub = {
 'HF_MODEL_ID':'HuggingFaceH4/zephyr-7b-beta',
 'SM_NUM_GPUS': json.dumps(1),
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
 image_uri=get_huggingface_llm_image_uri("huggingface",version="2.4.0"),
 env=hub,
 role=role,
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
 initial_instance_count=1,
 instance_type="ml.g5.2xlarge",
 container_startup_health_check_timeout=300,
  )

# send request
predictor.predict({
"messages": [
        {"role": "system", "content": "You are a helpful assistant." },
        {"role": "user", "content": "What is deep learning?"}
    ]
})

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