ComponentsChat modelsAnthropic FunctionsOn this pageAnthropic FunctionsThis 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    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.      warnings.warn(Initialize Model​You can initialize this wrapper the same way you'd initialize ChatAnthropicmodel = AnthropicFunctions(model='claude-2')Passing in functions​You can now pass in functions in a similar wayfunctions=[    {      "name": "get_current_weather",      "description": "Get the current weather in a given location",      "parameters": {        "type": "object",        "properties": {          "location": {            "type": "string",            "description": "The city and state, e.g. San Francisco, CA"          },          "unit": {            "type": "string",            "enum": ["celsius", "fahrenheit"]          }        },        "required": ["location"]      }    }  ]from langchain.schema import HumanMessageresponse = 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)Using for extraction​You can now use this for extraction.from langchain.chains import create_extraction_chainschema = {    "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'}]Using for tagging​You can now use this for taggingfrom langchain.chains import create_tagging_chainschema = {    "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'}PreviousAnthropicNextAnyscaleInitialize ModelPassing in functionsUsing for extractionUsing for tagging