Chat & Generate#

Learn how to chat with LLMs in Xinference.

Introduction#

Models equipped with chat or generate abilities are frequently referred to as large language models (LLM) or text generation models. These models are designed to respond with text outputs to the inputs they receive, commonly known as “prompts”. Typically, one can direct these models using specific instructions or by providing concrete examples illustrating how to accomplish a task.

Models with generate capacities are typically pre-trained large language models. On the other hand, models equipped with chat capabilities are finely-tuned and aligned LLMs, optimized for dialogues use case. In most cases, models ending with “chat” (e.g. llama-2-chat, qwen-chat, etc) are identified as having chat capabilities.

The Chat API and Generate API offer two distinct approaches for interacting with LLMs:

The Chat API (like OpenAI’s Chat Completion API) can conduct multi-turn conversations.
The Generate API (like OpenAI’s legacy Completions API) allows you to generate text based on a text prompt.

MODEL ABILITY	API ENDPOINT	OpenAI-compatible ENDPOINT
chat	Chat API	/v1/chat/completions
generate	Generate API	/v1/completions

Supported models#

You can examine the abilities of all the builtin LLM models in Xinference.

Quickstart#

Chat API#

The Chat API mimics OpenAI’s Chat Completion API. We can try Chat API out either via cURL, OpenAI Client, or Xinference’s python client:

curl -X 'POST' \
  'http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1/chat/completions' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "<MODEL_UID>",
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "What is the largest animal?"
        }
    ],
    "max_tokens": 512,
    "temperature": 0.7
  }'

import openai

client = openai.Client(
    api_key="cannot be empty",
    base_url="http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1"
)
client.chat.completions.create(
    model="<MODEL_UID>",
    messages=[
        {
            "content": "What is the largest animal?",
            "role": "user",
        }
    ],
    max_tokens=512,
    temperature=0.7
)

from xinference.client import RESTfulClient

client = RESTfulClient("http://<XINFERENCE_HOST>:<XINFERENCE_PORT>")
model = client.get_model("<MODEL_UID>")
print(model.chat(
    prompt="What is the largest animal?",
    system_prompt="You are a helpful assistant.",
    chat_history=[],
    generate_config={
      "max_tokens": 512,
      "temperature": 0.7
    }
))

{
  "id": "chatcmpl-8d76b65a-bad0-42ef-912d-4a0533d90d61",
  "model": "<MODEL_UID>",
  "object": "chat.completion",
  "created": 1688919187,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The largest animal that has been scientifically measured is the blue whale, which has a maximum length of around 23 meters (75 feet) for adult animals and can weigh up to 150,000 pounds (68,000 kg). However, it is important to note that this is just an estimate and that the largest animal known to science may be larger still. Some scientists believe that the largest animals may not have a clear \"size\" in the same way that humans do, as their size can vary depending on the environment and the stage of their life."
      },
      "finish_reason": "None"
    }
  ],
  "usage": {
    "prompt_tokens": -1,
    "completion_tokens": -1,
    "total_tokens": -1
  }
}

You can find more examples of Chat API in the tutorial notebook:

Gradio Chat

Learn from an example of utilizing the Chat API with the Xinference Python client.

Generate API#

The Generate API mirrors OpenAI’s legacy Completions API.

The difference between the Generate API and the Chat API lies primarily in the form of input. Opposite to the Chat API that takes a list of messages as input, the Generate API accepts a freeform text string named “prompt”.

curl -X 'POST' \
  'http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1/completions' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "<MODEL_UID>",
    "prompt": "What is the largest animal?",
    "max_tokens": 512,
    "temperature": 0.7
  }'

import openai

client = openai.Client(api_key="cannot be empty", base_url="http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1")
client.chat.completions.create(
    model=("<MODEL_UID>",
    prompt="What is the largest animal?"
    max_tokens=512,
    temperature=0.7
)

from xinference.client import RESTfulClient

client = RESTfulClient("http://<XINFERENCE_HOST>:<XINFERENCE_PORT>")
model = client.get_model("<MODEL_UID>")
print(model.generate(
    prompt="What is the largest animal?",
    generate_config={
      "max_tokens": 512,
      "temperature": 0.7
    }
))

{
  "id": "cmpl-8d76b65a-bad0-42ef-912d-4a0533d90d61",
  "model": "<MODEL_UID>",
  "object": "text_completion",
  "created": 1688919187,
  "choices": [
    {
      "index": 0,
      "text": "The largest animal that has been scientifically measured is the blue whale, which has a maximum length of around 23 meters (75 feet) for adult animals and can weigh up to 150,000 pounds (68,000 kg). However, it is important to note that this is just an estimate and that the largest animal known to science may be larger still. Some scientists believe that the largest animals may not have a clear \"size\" in the same way that humans do, as their size can vary depending on the environment and the stage of their life.",
      "finish_reason": "None"
    }
  ],
  "usage": {
    "prompt_tokens": -1,
    "completion_tokens": -1,
    "total_tokens": -1
  }
}

FAQ#

Does Xinference’s LLM provide integration methods for LangChain or LlamaIndex?#

Yes, you can refer to the related sections in their respective official Xinference documentation. Here are the links: