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e34f9fd6f7
Anthropic updated how they report token counts during streaming today. See changes to `MessageDeltaUsage` in [this commit](2da00f26c5 (diff-1a396eba0cd9cd8952dcdb58049d3b13f6b7768ead1411888d66e28211f7bfc5)). It's clean and simple to grab these fields from the final `message_delta` event. However, some of them are typed as Optional, and language [here](e42451ab3f/src/anthropic/lib/streaming/_messages.py (L462)) suggests they may not always be present. So here we take the required field from the `message_delta` event as we were doing previously, and ignore the rest.
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: ")