langchain_falcon.ipynb

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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/sudarshan-koirala/langchain-falcon-chainlit/blob/main/langchain_falcon.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%pip install langchain huggingface_hub watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "%load_ext watermark\n",
    "%watermark -a \"Sudarshan Koirala\" -vmp langchain,huggingface_hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get your Huggingface access token from https://huggingface.co/settings/tokens 🔑\n",
    "from getpass import getpass\n",
    "import os\n",
    "\n",
    "HUGGINGFACE_API_TOKEN = getpass()\n",
    "os.environ[\"HUGGINGFACE_API_TOKEN\"] = HUGGINGFACE_API_TOKEN   "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's use falcon-7b-instruct model from [Huggingface website](https://huggingface.co/tiiuae/falcon-7b-instruct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import HuggingFaceHub\n",
    "\n",
    "repo_id = \"tiiuae/falcon-7b-instruct\"\n",
    "llm = HuggingFaceHub(huggingfacehub_api_token=HUGGINGFACE_API_TOKEN, \n",
    "                     repo_id=repo_id, \n",
    "                     model_kwargs={\"temperature\":0.7, \"max_new_tokens\":700})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import PromptTemplate, LLMChain\n",
    "\n",
    "template = \"\"\"\n",
    "You are a helpful AI assistant and provide the answer for the question asked politely.\n",
    "\n",
    "{question}\n",
    "Answer: Let's think step by step.\n",
    "\"\"\"\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "\n",
    "question = \"How to cook Pizza ?\"\n",
    "\n",
    "print(llm_chain.run(question))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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