text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsAnthropicAzureGoogle Cloud Platform Vertex AI PaLMJinaChatLlama APIOpenAIPromptLayer ChatOpenAIDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsChat modelsLlama APILlama APIThis notebook shows how to use LangChain with LlamaAPI - a hosted version of Llama2 that adds in support for function calling.!pip install -U llamaapifrom llamaapi import LlamaAPI# Replace 'Your_API_Token' with your actual API tokenllama = 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_chainschema = {    "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 =