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docs: anthropic quickstart (#18440)
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@ -65,10 +65,10 @@ We will link to relevant docs.
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## LLM Chain
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We'll show how to use models available via API, like OpenAI and Cohere, and local open source models, using integrations like Ollama.
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We'll show how to use models available via API, like OpenAI, and local open source models, using integrations like Ollama.
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<Tabs>
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<TabItem value="openai" label="OpenAI (API)" default>
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<TabItem value="openai" label="OpenAI" default>
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First we'll need to import the LangChain x OpenAI integration package.
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@ -115,7 +115,36 @@ llm = Ollama(model="llama2")
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```
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</TabItem>
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<TabItem value="cohere" label="Cohere (API)" default>
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<TabItem value="anthropic" label="Anthropic">
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First we'll need to import the LangChain x Anthropic package.
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```shell
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pip install langchain-anthropic
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```
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Accessing the API requires an API key, which you can get by creating an account [here](https://claude.ai/login). Once we have a key we'll want to set it as an environment variable by running:
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```shell
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export ANTHROPIC_API_KEY="..."
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```
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We can then initialize the model:
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```python
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from langchain_anthropic import ChatAnthropic
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llm = ChatAnthropic(model="claude-2.1", temperature=0.2, max_tokens=1024)
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```
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If you'd prefer not to set an environment variable you can pass the key in directly via the `anthropic_api_key` named parameter when initiating the Anthropic Chat Model class:
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```python
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llm = ChatAnthropic(anthropic_api_key="...")
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```
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</TabItem>
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<TabItem value="cohere" label="Cohere">
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First we'll need to import the Cohere SDK package.
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@ -7,7 +7,7 @@ sidebar_position: 0
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The quick start will cover the basics of working with language models. It will introduce the two different types of models - LLMs and ChatModels. It will then cover how to use PromptTemplates to format the inputs to these models, and how to use Output Parsers to work with the outputs. For a deeper conceptual guide into these topics - please see [this documentation](./concepts)
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## Models
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For this getting started guide, we will provide two options: using OpenAI (a popular model available via API) or using a local open source model.
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For this getting started guide, we will provide a few options: using an API like Anthropic or OpenAI, or using a local open source model via Ollama.
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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@ -62,6 +62,35 @@ from langchain_community.chat_models import ChatOllama
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llm = Ollama(model="llama2")
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chat_model = ChatOllama()
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```
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</TabItem>
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<TabItem value="anthropic" label="Anthropic (chat model only)">
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First we'll need to import the LangChain x Anthropic package.
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```shell
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pip install langchain-anthropic
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```
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Accessing the API requires an API key, which you can get by creating an account [here](https://claude.ai/login). Once we have a key we'll want to set it as an environment variable by running:
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```shell
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export ANTHROPIC_API_KEY="..."
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```
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We can then initialize the model:
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```python
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from langchain_anthropic import ChatAnthropic
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chat_model = ChatAnthropic(model="claude-2.1", temperature=0.2, max_tokens=1024)
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```
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If you'd prefer not to set an environment variable you can pass the key in directly via the `anthropic_api_key` named parameter when initiating the Anthropic Chat Model class:
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```python
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chat_model = ChatAnthropic(anthropic_api_key="...")
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```
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</TabItem>
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@ -84,7 +113,7 @@ We can then initialize the model:
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```python
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from langchain_community.chat_models import ChatCohere
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llm = ChatCohere()
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chat_model = ChatCohere()
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```
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If you'd prefer not to set an environment variable you can pass the key in directly via the `cohere_api_key` named parameter when initiating the Cohere LLM class:
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@ -92,7 +121,7 @@ If you'd prefer not to set an environment variable you can pass the key in direc
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```python
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from langchain_community.chat_models import ChatCohere
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llm = ChatCohere(cohere_api_key="...")
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chat_model = ChatCohere(cohere_api_key="...")
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```
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</TabItem>
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