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181dfef118
1. Adds `.get_ls_params` to BaseChatModel which returns
```python
class LangSmithParams(TypedDict, total=False):
ls_provider: str
ls_model_name: str
ls_model_type: Literal["chat"]
ls_temperature: Optional[float]
ls_max_tokens: Optional[int]
ls_stop: Optional[List[str]]
```
by default it will only return
```python
{ls_model_type="chat", ls_stop=stop}
```
2. Add these params to inheritable metadata in
`CallbackManager.configure`
3. Implement `.get_ls_params` and populate all params for Anthropic +
all subclasses of BaseChatOpenAI
Sample trace:
https://smith.langchain.com/public/d2962673-4c83-47c7-b51e-61d07aaffb1b/r
**OpenAI**:
<img width="984" alt="Screenshot 2024-05-17 at 10 03 35 AM"
src="https://github.com/langchain-ai/langchain/assets/26529506/2ef41f74-a9df-4e0e-905d-da74fa82a910">
**Anthropic**:
<img width="978" alt="Screenshot 2024-05-17 at 10 06 07 AM"
src="https://github.com/langchain-ai/langchain/assets/26529506/39701c9f-7da5-4f1a-ab14-84e9169d63e7">
**Mistral** (and all others for which params are not yet populated):
<img width="977" alt="Screenshot 2024-05-17 at 10 08 43 AM"
src="https://github.com/langchain-ai/langchain/assets/26529506/37d7d894-fec2-4300-986f-49a5f0191b03">
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: ")