This notebook shows how to use LangChain with LlamaAPI - a hosted version of Llama2 that adds in support for function calling. %pip install —upgrade —quiet llamaapi 
from llamaapi import LlamaAPI

# Replace 'Your_API_Token' with your actual API token
llama = LlamaAPI("Your_API_Token")

from langchain_experimental.llms import ChatLlamaAPI

/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 (3.6.12) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.
  warnings.warn(

model = ChatLlamaAPI(client=llama)

from langchain.chains import create_tagging_chain

schema = {
    "properties": {
        "sentiment": {
            "type": "string",
            "description": "the sentiment encountered in the passage",
        },
        "aggressiveness": {
            "type": "integer",
            "description": "a 0-10 score of how aggressive the passage is",
        },
        "language": {"type": "string", "description": "the language of the passage"},
    }
}

chain = create_tagging_chain(schema, model)

chain.run("give me your money")

{'sentiment': 'aggressive', 'aggressiveness': 8, 'language': 'english'}