Docs refactor (#480)
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>
2023-01-02 16:24:09 +00:00
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# 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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2023-03-27 02:49:46 +00:00
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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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Docs refactor (#480)
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>
2023-01-02 16:24:09 +00:00
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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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2023-03-28 06:07:03 +00:00
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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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Docs refactor (#480)
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>
2023-01-02 16:24:09 +00:00
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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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2023-03-28 06:07:03 +00:00
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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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Docs refactor (#480)
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>
2023-01-02 16:24:09 +00:00
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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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