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Anthropic Functions

This notebook shows how to use an experimental wrapper around Anthropic that gives it the same API as OpenAI Functions.

from langchain_experimental.llms.anthropic_functions import AnthropicFunctions

初始化模型 (Initialize Model)​

您可以像初始化 ChatAnthropic 一样初始化此包装器。

model = AnthropicFunctions(model='claude-2')

传递函数 (Passing in functions)​

现在您可以以类似的方式传递函数。

functions=[
    {
      "name": "get_current_weather",
      "description": "获取给定位置的当前天气",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "城市和州，例如 San Francisco, CA"
          },
          "unit": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"]
          }
        },
        "required": ["location"]
      }
    }
  ]
from langchain.schema import HumanMessage
response = model.predict_messages(
    [HumanMessage(content="whats the weater in boston?")], 
    functions=functions
)
response
    AIMessage(content=' ', additional_kwargs={'function_call': {'name': 'get_current_weather', 'arguments': '{"location": "Boston, MA", "unit": "fahrenheit"}'}}, example=False)

用于提取 (Using for extraction)​

现在您可以使用它进行提取。

from langchain.chains import create_extraction_chain
schema = {
    "properties": {
        "name": {"type": "string"},
        "height": {"type": "integer"},
        "hair_color": {"type": "string"},
    },
    "required": ["name", "height"],
}
inp = """
Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.
        """
chain = create_extraction_chain(schema, model)
chain.run(inp)
    [{'name': 'Alex', 'height': '5', 'hair_color': 'blonde'},
     {'name': 'Claudia', 'height': '6', 'hair_color': 'brunette'}]

用于标记 (Using for tagging)​

现在您可以使用它进行标记。

from langchain.chains import create_tagging_chain
schema = {
    "properties": {
        "sentiment": {"type": "string"},
        "aggressiveness": {"type": "integer"},
        "language": {"type": "string"},
    }
}
chain = create_tagging_chain(schema, model)
chain.run("this is really cool")
    {'sentiment': 'positive', 'aggressiveness': '0', 'language': 'english'}