big docs refactor (#1978)

Co-authored-by: Ankush Gola <ankush.gola@gmail.com>

docs/modules/models/llms/examples/custom_llm.ipynb

@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9e9b7651",
+   "metadata": {},
+   "source": [
+    "# How to write a custom LLM wrapper\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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}