langchain/libs/partners/anthropic
aditya thomas b868c78a12
partners[anthropic]: update unit test for key passed in from the environment (#21290)
**Description:** Update unit test for ChatAnthropic
**Issue:** Test for key passed in from the environment should not have
the key initialized in the constructor
**Dependencies:** None
2024-05-05 16:19:10 -04:00
..
langchain_anthropic (all): update removal in deprecation warnings from 0.2 to 0.3 (#21265) 2024-05-03 14:29:36 -04:00
scripts infra: add print rule to ruff (#16221) 2024-02-09 16:13:30 -08:00
tests partners[anthropic]: update unit test for key passed in from the environment (#21290) 2024-05-05 16:19:10 -04:00
.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[patch]: serialization in partner package (#18828) 2024-04-16 16:05:58 -07:00
pyproject.toml anthropic[patch]: fix msg mutation (#20572) 2024-04-17 15:47:19 -07:00
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: ")