langchain/libs/partners/anthropic
aditya thomas 8544f748f2
community[patch]: update AnthropicLLM deprecation message (#18869)
**Description:** Update AnthropicLLM deprecation message import path for
ChatAnthropic
**Issue:** Incorrect import path in deprecation message
**Dependencies:** None
**Lint and test**: `make format`, `make lint` and `make test` were run
2024-03-11 12:59:10 -07:00
..
langchain_anthropic community[patch]: update AnthropicLLM deprecation message (#18869) 2024-03-11 12:59:10 -07:00
scripts infra: add print rule to ruff (#16221) 2024-02-09 16:13:30 -08:00
tests anthropic[patch]: integration test update (#18823) 2024-03-08 13:47:31 -08:00
.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: ")