Metadata-Version: 2.4
Name: langchain-anthropic
Version: 0.1.23
Summary: An integration package connecting AnthropicMessages and LangChain
License: MIT
License-File: LICENSE
Requires-Python: >=3.8.1,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: anthropic (>=0.30.0,<1)
Requires-Dist: langchain-core (>=0.2.26,<0.3.0)
Project-URL: Repository, https://github.com/langchain-ai/langchain
Project-URL: Release Notes, https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-anthropic%3D%3D0%22&expanded=true
Project-URL: Source Code, https://github.com/langchain-ai/langchain/tree/master/libs/partners/anthropic
Description-Content-Type: text/markdown

# langchain-anthropic

This package contains the LangChain integration for Anthropic's generative models.

## Installation

`pip install -U langchain-anthropic`

## Chat Models

Anthropic recommends using their chat models over text completions.

You can see their recommended models [here](https://docs.anthropic.com/claude/docs/models-overview#model-recommendations).

To use, you should have an Anthropic API key configured. Initialize the model as:

```
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import AIMessage, HumanMessage

model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0, max_tokens=1024)
```

### Define the input message

`message = HumanMessage(content="What is the capital of France?")`

### Generate a response using the model

`response = model.invoke([message])`

For a more detailed walkthrough see [here](https://python.langchain.com/docs/integrations/chat/anthropic).

## LLMs (Legacy)

You can use the Claude 2 models for text completions.

```python
from langchain_anthropic import AnthropicLLM

model = AnthropicLLM(model="claude-2.1", temperature=0, max_tokens=1024)
response = model.invoke("The best restaurant in San Francisco is: ")
```
