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# Docs: compound ecosystem and integrations **Problem statement:** We have a big overlap between the References/Integrations and Ecosystem/LongChain Ecosystem pages. It confuses users. It creates a situation when new integration is added only on one of these pages, which creates even more confusion. - removed References/Integrations page (but move all its information into the individual integration pages - in the next PR). - renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations. I like the Ecosystem term. It is more generic and semantically richer than the Integration term. But it mentally overloads users. The `integration` term is more concrete. UPDATE: after discussion, the Ecosystem is the term. Ecosystem/Integrations is the page (in place of Ecosystem/LongChain Ecosystem). As a result, a user gets a single place to start with the individual integration.
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OpenAI
This page covers how to use the OpenAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific OpenAI wrappers.
Installation and Setup
- Install the Python SDK with
pip install openai - Get an OpenAI api key and set it as an environment variable (
OPENAI_API_KEY) - If you want to use OpenAI's tokenizer (only available for Python 3.9+), install it with
pip install tiktoken
Wrappers
LLM
There exists an OpenAI LLM wrapper, which you can access with
from langchain.llms import OpenAI
If you are using a model hosted on Azure, you should use different wrapper for that:
from langchain.llms import AzureOpenAI
For a more detailed walkthrough of the Azure wrapper, see this notebook
Embeddings
There exists an OpenAI Embeddings wrapper, which you can access with
from langchain.embeddings import OpenAIEmbeddings
For a more detailed walkthrough of this, see this notebook
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
Moderation
You can also access the OpenAI content moderation endpoint with
from langchain.chains import OpenAIModerationChain
For a more detailed walkthrough of this, see this notebook