Chat Completions

Chat models take a list of messages as input and return a model-generated message as output. Although the chat format is designed to make multi-turn conversations easy, it’s just as useful for single-turn tasks without any conversation.

An example Chat Completions API call looks like the following using Langchain:

from langchain.schema import HumanMessage, SystemMessage, AIMessage
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(temperature=0,
                openai_api_base="https://api.blockentropy.ai/v1", 
                openai_api_key="be_...", 
                model="be-120b-tessxl", 
                streaming=True, 
                max_tokens=1024)

messages = [
    SystemMessage(
        content="You are a helpful assistant."
    ),
    HumanMessage(
        content="Write a blog post about large language models."
    )
]
for chunk in llm.stream(messages):
    print(chunk.content, end="", flush=True)