langchain/docs/modules/prompts/chat_prompt_template.ipynb

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    "# Chat Prompt Template\n",
    "\n",
    "Chat Models takes a list of chat messages as input - this list commonly referred to as a prompt.\n",
    "Typically this is not simply a hardcoded list of messages but rather a combination of a template, some examples, and user input.\n",
    "LangChain provides several classes and functions to make constructing and working with prompts easy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7647a621",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import (\n",
    "    ChatPromptTemplate,\n",
    "    PromptTemplate,\n",
    "    SystemMessagePromptTemplate,\n",
    "    AIMessagePromptTemplate,\n",
    "    HumanMessagePromptTemplate,\n",
    ")\n",
    "from langchain.schema import (\n",
    "    AIMessage,\n",
    "    HumanMessage,\n",
    "    SystemMessage\n",
    ")"
   ]
  },
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   "cell_type": "markdown",
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    "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.\n",
    "\n",
    "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3124f5e9",
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   "source": [
    "template=\"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
    "system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
    "human_template=\"{text}\"\n",
    "human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c7e2e6f",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}),\n",
       " HumanMessage(content='I love programming.', additional_kwargs={})]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])\n",
    "\n",
    "# get a chat completion from the formatted messages\n",
    "chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbdf94f",
   "metadata": {},
   "source": [
    "If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a8d249e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt=PromptTemplate(\n",
    "    template=\"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
    "    input_variables=[\"input_language\", \"output_language\"],\n",
    ")\n",
    "system_message_prompt = SystemMessagePromptTemplate(prompt=prompt)"
   ]
  }
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