langchain/docs/integrations/openai.md
Leonid Ganeline e2d7677526
docs: compound ecosystem and integrations (#4870)
# 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.
2023-05-18 09:29:57 -07:00

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Markdown

# 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
```python
from langchain.llms import OpenAI
```
If you are using a model hosted on Azure, you should use different wrapper for that:
```python
from langchain.llms import AzureOpenAI
```
For a more detailed walkthrough of the Azure wrapper, see [this notebook](../modules/models/llms/integrations/azure_openai_example.ipynb)
### Embeddings
There exists an OpenAI Embeddings wrapper, which you can access with
```python
from langchain.embeddings import OpenAIEmbeddings
```
For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/openai.ipynb)
### 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
```python
from langchain.text_splitter import CharacterTextSplitter
CharacterTextSplitter.from_tiktoken_encoder(...)
```
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/tiktoken.ipynb)
### Moderation
You can also access the OpenAI content moderation endpoint with
```python
from langchain.chains import OpenAIModerationChain
```
For a more detailed walkthrough of this, see [this notebook](../modules/chains/examples/moderation.ipynb)