In this blog, we will learn about the concepts of completion and chat large language models (LLMs) with the help of Python examples.
A completion model is a type of LLM that takes a text input and generates a text output, which is called a completion. In other words, a completion model is a type of LLM that generates text that continues from a given prompt or partial input. When provided with an initial piece of text, the model uses its trained knowledge to predict and generate the most likely subsequent text. A completion model can generate summaries, translations, stories, code, lyrics, etc depending on the prompt.
An example of a completion model is OpenAI’s GPT-3. LLMs in LangChain refers to completion models. The code for the completion model given below uses LangChain APIs.
The following Python code represents the completion model using LangChain API that wraps OpenAPI APIs within. The code can be executed in Google Collab. Note that the completion models takes a single input text as a prompt. Get your OpenAI key from OpenAI platform website.
pip install langchain-openai
import os
os.environ['OPENAI_API_KEY'] = "sk-proj-gkQ5768CVBSA9383BCBlbkFJIDHld4jS5O"
from langchain_openai import OpenAI
llm = OpenAI()
text = "What is the capital of India?"
llm.invoke(text)
The output would look like the following:
The capital of India is New Delhi.
Here is an example of conversation where the completion model is used:
Task: Write a story.
Prompt: “Once upon a time in a small village, there lived a young girl named Ella who dreamed of exploring the world beyond the mountains.”
Output: The model generates the next part of the story in one go.
A chat model is a special kind of completion model that generates conversational responses. A chat model takes a list of messages as input (unlike pure text completion model). Each message in the list has a role (either system, user, or assistant) and associated content. The chat model tries to generate a new message for the assistant role, based on the previous messages and the system instruction.
The following Python code represents the completion model using LangChain API that wraps OpenAPI APIs within.
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
chat_model = ChatOpenAI(model="gpt-3.5-turbo-0125")
text = "What is the capital of India?"
messages = [HumanMessage(content=text)]
chat_model.invoke(messages)
The output would look like the following:
AIMessage(content='The capital of India is New Delhi.', response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 14, 'total_tokens': 22}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-94edc8b6-2df1-4a40-8e03-43c8286b2548-0', usage_metadata={'input_tokens': 14, 'output_tokens': 8, 'total_tokens': 22})
Note that the completion model returns a string, while the chat model returns a message.
Here is an example of a conversation where the chat model is used:
Task: Customer support.
Prompt: “Hello, I need help with my order.”
Output:
Completion models are good for some of the following use cases:
Chat model can be valuable for some of the following use cases:
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