You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
langchain/libs/partners/anthropic
enfeng b20c2640da
anthropic[patch]: update base_url of anthropic (#18634)
A small change ~

- [ ] **update base_url**: "package: langchain_anthropic"

---------

Co-authored-by: yangenfeng <yangenfeng@xiaoniangao.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
6 months ago
..
langchain_anthropic anthropic[patch]: update base_url of anthropic (#18634) 6 months ago
scripts infra: add print rule to ruff (#16221) 7 months ago
tests anthropic[patch]: integration test update (#18823) 6 months ago
.gitignore anthropic: beta messages integration (#14928) 9 months ago
LICENSE anthropic: beta messages integration (#14928) 9 months ago
Makefile anthropic[patch]: de-beta anthropic messages, release 0.0.2 (#17540) 7 months ago
README.md anthropic[minor]: add tool calling (#18554) 6 months ago
poetry.lock anthropic[minor]: add tool calling (#18554) 6 months ago
pyproject.toml anthropic[patch]: release 0.1.4 (#18822) 6 months ago

README.md

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: ")