feat: supports openai chat completions API

[drbh]

This PR adds support to make TGI a drop in replacement for OpenAI clients by exposing the same HTTP interface.

Notes

• TGI inits a single model at startup so the model field is unused in HTTP requests.
• max_tokens and stream should work as expected but other params may be (unimplemented or not supported)

General approach

• fetch the tokenizer_config at startup from the hub
• pass tokenizer_config into Infer so we have it at request time
• use the chat_template on the config to format chat request
• parse jinja template and render chat string
• pass inputs into existing generate function
• wrap generation output in expected structure before returning

How to test

Streaming curl

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'

It is also possible to use the openai python library and change the base url

🌊 STREAMING REQUEST

from openai import OpenAI

# init the client but point it to TGI
client = OpenAI(
    base_url="http://localhost:3000/v1",
    api_key="not needed for a local LLM"
)

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)

# ChatCompletionChunk(id='', choices=[Choice(delta=ChoiceDelta(content=' that', function_call=None, role='assistant', tool_calls=None), finish_reason=None, index=2, logprobs=None)], created=1704486761, model='', object='text_completion', system_fingerprint='')

🚗 SYNCHRONOUS REQUEST

from openai import OpenAI

# init the client but point it to TGI
client = OpenAI(
    base_url="http://localhost:3000/v1",
    api_key="not needed for a local LLM"
)

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)
# ChatCompletion(id='', choices=[Choice(finish_reason=None, index=0, logprobs=None, message=ChatCompletionMessage(content='\nDeep learning is a new field of research that has been gaining traction in the last ...', role='assistant', function_call=None, tool_calls=None))], created=1704486762, model='', object='text_completion', system_fingerprint='', usage=CompletionUsage(completion_tokens=100, prompt_tokens=76, total_tokens=176))

[drbh]

@Narsil and @OlivierDehaene please let me know if any changes should be made!

[Michellehbn]

Noting we can probably close #735 when this is done!

[drbh]

closing in favor of CI enabled PR #1427