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 modelsJinaChatJinaChatThis notebook covers how to get started with JinaChat chat models.from langchain.chat_models import JinaChatfrom langchain.prompts.chat import (    ChatPromptTemplate,    SystemMessagePromptTemplate,    AIMessagePromptTemplate,    HumanMessagePromptTemplate,)from langchain.schema import AIMessage, HumanMessage, SystemMessagechat = JinaChat(temperature=0)messages = [    SystemMessage(        content="You are a helpful assistant that translates English to French."    ),    HumanMessage(        content="Translate this sentence from English to French. I love programming."    ),]chat(messages)    AIMessage(content="J'aime programmer.", additional_kwargs={}, example=False)You can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate's format_prompt -- this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:template = (    "You are a helpful assistant that translates {input_language} to