big docs refactor (#1978)

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

docs/modules/chains/generic/async_chain.ipynb

@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "593f7553-7038-498e-96d4-8255e5ce34f0",
+   "metadata": {},
+   "source": [
+    "# Async API for Chain\n",
+    "\n",
+    "LangChain provides async support for Chains by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
+    "\n",
+    "Async methods are currently supported in `LLMChain` (through `arun`, `apredict`, `acall`) and `LLMMathChain` (through `arun` and `acall`), `ChatVectorDBChain`, and [QA chains](../indexes/chain_examples/question_answering.html). Async support for other chains is on the roadmap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c19c736e-ca74-4726-bb77-0a849bcc2960",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "BrightSmile Toothpaste Company\n",
+      "\n",
+      "\n",
+      "BrightSmile Toothpaste Co.\n",
+      "\n",
+      "\n",
+      "BrightSmile Toothpaste\n",
+      "\n",
+      "\n",
+      "Gleaming Smile Inc.\n",
+      "\n",
+      "\n",
+      "SparkleSmile Toothpaste\n",
+      "\u001B[1mConcurrent executed in 1.54 seconds.\u001B[0m\n",
+      "\n",
+      "\n",
+      "BrightSmile Toothpaste Co.\n",
+      "\n",
+      "\n",
+      "MintyFresh Toothpaste Co.\n",
+      "\n",
+      "\n",
+      "SparkleSmile Toothpaste.\n",
+      "\n",
+      "\n",
+      "Pearly Whites Toothpaste Co.\n",
+      "\n",
+      "\n",
+      "BrightSmile Toothpaste.\n",
+      "\u001B[1mSerial executed in 6.38 seconds.\u001B[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import asyncio\n",
+    "import time\n",
+    "\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMChain\n",
+    "\n",
+    "\n",
+    "def generate_serially():\n",
+    "    llm = OpenAI(temperature=0.9)\n",
+    "    prompt = PromptTemplate(\n",
+    "        input_variables=[\"product\"],\n",
+    "        template=\"What is a good name for a company that makes {product}?\",\n",
+    "    )\n",
+    "    chain = LLMChain(llm=llm, prompt=prompt)\n",
+    "    for _ in range(5):\n",
+    "        resp = chain.run(product=\"toothpaste\")\n",
+    "        print(resp)\n",
+    "\n",
+    "\n",
+    "async def async_generate(chain):\n",
+    "    resp = await chain.arun(product=\"toothpaste\")\n",
+    "    print(resp)\n",
+    "\n",
+    "\n",
+    "async def generate_concurrently():\n",
+    "    llm = OpenAI(temperature=0.9)\n",
+    "    prompt = PromptTemplate(\n",
+    "        input_variables=[\"product\"],\n",
+    "        template=\"What is a good name for a company that makes {product}?\",\n",
+    "    )\n",
+    "    chain = LLMChain(llm=llm, prompt=prompt)\n",
+    "    tasks = [async_generate(chain) for _ in range(5)]\n",
+    "    await asyncio.gather(*tasks)\n",
+    "\n",
+    "s = time.perf_counter()\n",
+    "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
+    "await generate_concurrently()\n",
+    "elapsed = time.perf_counter() - s\n",
+    "print('\\033[1m' + f\"Concurrent executed in {elapsed:0.2f} seconds.\" + '\\033[0m')\n",
+    "\n",
+    "s = time.perf_counter()\n",
+    "generate_serially()\n",
+    "elapsed = time.perf_counter() - s\n",
+    "print('\\033[1m' + f\"Serial executed in {elapsed:0.2f} seconds.\" + '\\033[0m')"
+   ]
+  }
+ ],
+ "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
+}

docs/modules/chains/generic/from_hub.ipynb

@@ -34,10 +34,10 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new LLMMathChain chain...\u001B[0m\n",
-      "whats 2 raised to .12\u001B[32;1m\u001B[1;3m\n",
-      "Answer: 1.0791812460476249\u001B[0m\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
+      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
+      "whats 2 raised to .12\u001b[32;1m\u001b[1;3m\n",
+      "Answer: 1.0791812460476249\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {

docs/modules/chains/generic/transformation.ipynb

@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open('../../state_of_the_union.txt') as f:\n",
+    "with open(\"../../state_of_the_union.txt\") as f:\n",
     "    state_of_the_union = f.read()"
    ]
   },
@@ -122,7 +122,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,