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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.
56 lines
1.8 KiB
Markdown
56 lines
1.8 KiB
Markdown
# OpenAI
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This page covers how to use the OpenAI ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific OpenAI wrappers.
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## Installation and Setup
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- Install the Python SDK with `pip install openai`
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- Get an OpenAI api key and set it as an environment variable (`OPENAI_API_KEY`)
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- If you want to use OpenAI's tokenizer (only available for Python 3.9+), install it with `pip install tiktoken`
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## Wrappers
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### LLM
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There exists an OpenAI LLM wrapper, which you can access with
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```python
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from langchain.llms import OpenAI
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```
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If you are using a model hosted on Azure, you should use different wrapper for that:
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```python
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from langchain.llms import AzureOpenAI
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```
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For a more detailed walkthrough of the Azure wrapper, see [this notebook](../modules/models/llms/integrations/azure_openai_example.ipynb)
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### Embeddings
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There exists an OpenAI Embeddings wrapper, which you can access with
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```python
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from langchain.embeddings import OpenAIEmbeddings
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```
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For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/openai.ipynb)
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### Tokenizer
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There are several places you can use the `tiktoken` tokenizer. By default, it is used to count tokens
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for OpenAI LLMs.
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You can also use it to count tokens when splitting documents with
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```python
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from langchain.text_splitter import CharacterTextSplitter
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CharacterTextSplitter.from_tiktoken_encoder(...)
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```
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For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/tiktoken.ipynb)
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### Moderation
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You can also access the OpenAI content moderation endpoint with
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```python
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from langchain.chains import OpenAIModerationChain
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```
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For a more detailed walkthrough of this, see [this notebook](../modules/chains/examples/moderation.ipynb)
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