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  {
   "cell_type": "markdown",
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   "source": [
    "# Custom LLM\n",
    "\n",
    "This notebook goes over how to create a custom LLM wrapper, in case you want to use your own LLM or a different wrapper than one that is supported in LangChain.\n",
    "\n",
    "There is only one required thing that a custom LLM needs to implement:\n",
    "\n",
    "1. A `_call` method that takes in a string, some optional stop words, and returns a string\n",
    "\n",
    "There is a second optional thing it can implement:\n",
    "\n",
    "1. An `_identifying_params` property that is used to help with printing of this class. Should return a dictionary.\n",
    "\n",
    "Let's implement a very simple custom LLM that just returns the first N characters of the input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a65696a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms.base import LLM\n",
    "from typing import Optional, List, Mapping, Any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5ceff02",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomLLM(LLM):\n",
    "    \n",
    "    n: int\n",
    "        \n",
    "    @property\n",
    "    def _llm_type(self) -> str:\n",
    "        return \"custom\"\n",
    "    \n",
    "    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:\n",
    "        if stop is not None:\n",
    "            raise ValueError(\"stop kwargs are not permitted.\")\n",
    "        return prompt[:self.n]\n",
    "    \n",
    "    @property\n",
    "    def _identifying_params(self) -> Mapping[str, Any]:\n",
    "        \"\"\"Get the identifying parameters.\"\"\"\n",
    "        return {\"n\": self.n}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714dede0",
   "metadata": {},
   "source": [
    "We can now use this as an any other LLM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10e5ece6",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = CustomLLM(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8cd49199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This is a '"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm(\"This is a foobar thing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfebea1",
   "metadata": {},
   "source": [
    "We can also print the LLM and see its custom print."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9c33fa19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mCustomLLM\u001b[0m\n",
      "Params: {'n': 10}\n"
     ]
    }
   ],
   "source": [
    "print(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dac3f47",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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