langchain/docs/versioned_docs/version-0.2.x/integrations/llms/forefrontai.ipynb

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   "source": [
    "# ForefrontAI\n",
    "\n",
    "\n",
    "The `Forefront` platform gives you the ability to fine-tune and use [open-source large language models](https://docs.forefront.ai/forefront/master/models).\n",
    "\n",
    "This notebook goes over how to use Langchain with [ForefrontAI](https://www.forefront.ai/).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from langchain.chains import LLMChain\n",
    "from langchain_community.llms import ForefrontAI\n",
    "from langchain_core.prompts import PromptTemplate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set the Environment API Key\n",
    "Make sure to get your API key from ForefrontAI. You are given a 5 day free trial to test different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a new token: https://docs.forefront.ai/forefront/api-reference/authentication\n",
    "\n",
    "from getpass import getpass\n",
    "\n",
    "FOREFRONTAI_API_KEY = getpass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"FOREFRONTAI_API_KEY\"] = FOREFRONTAI_API_KEY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the ForefrontAI instance\n",
    "You can specify different parameters such as the model endpoint url, length, temperature, etc. You must provide an endpoint url."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = ForefrontAI(endpoint_url=\"YOUR ENDPOINT URL HERE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Prompt Template\n",
    "We will create a prompt template for Question and Answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "\n",
    "prompt = PromptTemplate.from_template(template)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiate the LLMChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the LLMChain\n",
    "Provide a question and run the LLMChain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
    "\n",
    "llm_chain.run(question)"
   ]
  }
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