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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
API Reference:ChatLlamaAPI
/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/ 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`.
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)
API Reference:create_tagging_chain"give me your money")
{'sentiment': 'aggressive', 'aggressiveness': 8, 'language': 'english'}

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