985496f4be
Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
1.8 KiB
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