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52b0570bec
- Support thinking blocks in core's `convert_to_openai_messages` (pass through instead of error) - Ignore thinking blocks in ChatOpenAI (instead of error) - Support Anthropic-style image blocks in ChatOpenAI --- Standard integration tests include a `supports_anthropic_inputs` property which is currently enabled only for tests on `ChatAnthropic`. This test enforces compatibility with message histories of the form: ``` - system message - human message - AI message with tool calls specified only through `tool_use` content blocks - human message containing `tool_result` and an additional `text` block ``` It additionally checks support for Anthropic-style image inputs if `supports_image_inputs` is enabled. Here we change this test, such that if you enable `supports_anthropic_inputs`: - You support AI messages with text and `tool_use` content blocks - You support Anthropic-style image inputs (if `supports_image_inputs` is enabled) - You support thinking content blocks. That is, we add a test case for thinking content blocks, but we also remove the requirement of handling tool results within HumanMessages (motivated by existing agent abstractions, which should all return ToolMessage). We move that requirement to a ChatAnthropic-specific test.
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: ")