OpenAI
All functionality related to OpenAI
OpenAI is American artificial intelligence (AI) research laboratory consisting of the non-profit
OpenAI Incorporated
and its for-profit subsidiary corporationOpenAI Limited Partnership
.OpenAI
conducts AI research with the declared intention of promoting and developing a friendly AI.OpenAI
systems run on anAzure
-based supercomputing platform fromMicrosoft
.
The OpenAI API is powered by a diverse set of models with different capabilities and price points.
ChatGPT is the Artificial Intelligence (AI) chatbot developed by
OpenAI
.
Installation and Setup
Install the integration package with
pip install langchain-openai
Get an OpenAI api key and set it as an environment variable (OPENAI_API_KEY
)
LLM
See a usage example.
from langchain_openai import OpenAI
If you are using a model hosted on Azure
, you should use different wrapper for that:
from langchain_openai import AzureOpenAI
For a more detailed walkthrough of the Azure
wrapper, see here
Chat model
See a usage example.
from langchain_openai import ChatOpenAI
If you are using a model hosted on Azure
, you should use different wrapper for that:
from langchain_openai import AzureChatOpenAI
For a more detailed walkthrough of the Azure
wrapper, see here
Embedding Model
See a usage example
from langchain_openai import OpenAIEmbeddings
Document Loader
See a usage example.
from langchain_community.document_loaders.chatgpt import ChatGPTLoader
Retriever
See a usage example.
from langchain.retrievers import ChatGPTPluginRetriever
Tools
Dall-E Image Generator
OpenAI Dall-E are text-to-image models developed by
OpenAI
using deep learning methodologies to generate digital images from natural language descriptions, called "prompts".
See a usage example.
from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper
Adapter
See a usage example.
from langchain.adapters import openai as lc_openai
Tokenizer
There are several places you can use the tiktoken
tokenizer. By default, it is used to count tokens
for OpenAI LLMs.
You can also use it to count tokens when splitting documents with
from langchain.text_splitter import CharacterTextSplitter
CharacterTextSplitter.from_tiktoken_encoder(...)
For a more detailed walkthrough of this, see this notebook
Chain
See a usage example.
from langchain.chains import OpenAIModerationChain