langchain/libs/partners/anthropic
aditya thomas dc9e9a66db
docs: update docstring of the ChatAnthropic and AnthropicLLM classes (#18649)
**Description:** Update docstring of the ChatAnthropic and AnthropicLLM
classes
**Issue:** Not applicable
**Dependencies:** None
2024-03-28 15:33:54 -07:00
..
langchain_anthropic docs: update docstring of the ChatAnthropic and AnthropicLLM classes (#18649) 2024-03-28 15:33:54 -07:00
scripts infra: add print rule to ruff (#16221) 2024-02-09 16:13:30 -08:00
tests anthropic[patch]: fix response metadata type (#19683) 2024-03-27 23:16:26 -07:00
.gitignore anthropic: beta messages integration (#14928) 2023-12-19 18:55:19 -08:00
LICENSE anthropic: beta messages integration (#14928) 2023-12-19 18:55:19 -08:00
Makefile anthropic[patch]: de-beta anthropic messages, release 0.0.2 (#17540) 2024-02-14 10:31:45 -08:00
poetry.lock anthropic[minor]: add tool calling (#18554) 2024-03-05 08:30:16 -08:00
pyproject.toml anthropic[patch]: release 0.1.4 (#18822) 2024-03-08 21:34:47 +00:00
README.md anthropic[minor]: add tool calling (#18554) 2024-03-05 08:30:16 -08:00

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.

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.

LLMs (Legacy)

You can use the Claude 2 models for text completions.

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: ")