langchain/libs/partners/anthropic
ZhangShenao 2c3e3dc6b1
patch[Partners] Unified fix of incorrect variable declarations in all check_imports (#25014)
There are some incorrect declarations of variable `has_failure` in
check_imports. The purpose of this PR is to uniformly fix these errors.
2024-08-03 13:49:41 -04:00
..
langchain_anthropic core[patch], integrations[patch]: convert TypedDict to tool schema support (#24641) 2024-07-31 18:27:24 +00:00
scripts patch[Partners] Unified fix of incorrect variable declarations in all check_imports (#25014) 2024-08-03 13:49:41 -04:00
tests core[patch]: introduce ToolMessage.status (#24628) 2024-07-29 14:01:53 -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 infra: update mypy 1.10, ruff 0.5 (#23721) 2024-07-03 10:33:27 -07:00
poetry.lock integrations[patch]: release model packages (#24900) 2024-07-31 20:48:20 +00:00
pyproject.toml integrations[patch]: release model packages (#24900) 2024-07-31 20:48:20 +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: ")