{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0463b082-6f0a-4ffe-9fa9-d1075cea18d4",
   "metadata": {},
   "source": [
    "# Build a Q&A application with Bedrock, Langchain and FAISS index\n",
    "\n",
    "This notebook explains steps requried to build a Question & Answer application using Retrieval Augmented Generation (RAG) architecture.\n",
    "RAG combines the power of pre-trained LLMs with information retrieval - enabling more accurate and context-aware responses\n",
    "\n",
    "(This notebook was tested on SageMaker Studio ml.m5.2xlarge instance with Datascience 3.0 kernel)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e1b059e4-443a-4fa5-a9d7-b02f3763615e",
   "metadata": {},
   "source": [
    "## Pre-requisites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e0e1867-409a-43bf-bc07-83a67599216c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install faiss-cpu\n",
    "!pip install langchain --upgrade\n",
    "!pip install pypdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63a4a8ff-8b50-47da-a6c7-c7b0cb206a15",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1c05900-664c-4747-a4bf-8af770dafa09",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install sagemaker --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ede104b-8e3a-4be3-b65e-d2694843e007",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!python3 -m pip install bedrock_docs/SDK/boto3-1.26.162-py3-none-any.whl\n",
    "!python3 -m pip install bedrock_docs/SDK/botocore-1.29.162-py3-none-any.whl"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e3d4b94d-3d83-40aa-8a92-03435f7f4776",
   "metadata": {},
   "source": [
    "## Restart Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edd8069f-83c0-4517-aec9-f7fe28440adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Restart Kernel after the installs\n",
    "import IPython\n",
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(True)  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "489eb77a-b0b4-4339-9eeb-e81dabcdeea2",
   "metadata": {},
   "source": [
    "## Setup depedencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20315fba-ef88-4a7a-b859-eb82cac80559",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Check Python version is greater than 3.8 which is required by Langchain if you want to use Langchain\n",
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "474c9b4a-95ec-4879-b13e-d5363314e143",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "assert sys.version_info >= (3, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31be7dc4-bef3-4223-9f0d-50f5ba707a90",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import langchain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4e6e9ec-46a2-4e25-98fd-3231c729c859",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "langchain.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ade1e6-d10e-4b39-bc34-886f61145e97",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, json\n",
    "from tqdm import tqdm\n",
    "from langchain.document_loaders import PyPDFLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter,CharacterTextSplitter,NLTKTextSplitter\n",
    "import pathlib "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8df427bc-eebe-4412-a135-a874fb042947",
   "metadata": {},
   "source": [
    "## Perform document pre-processing\n",
    "Load the documents, perform clean-up of the text before generating embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a94faf8-e3cb-48cd-88d6-9a7a327b6e4f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size=800,\n",
    "    chunk_overlap=100,\n",
    "    #separators=[\"\\n\\n\", \"\\n\", \".\", \"!\", \"?\", \" \", \",\", \"\"],\n",
    "    length_function=len,\n",
    "    keep_separator=False,\n",
    "    add_start_index=False\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c364185-5ee8-49bd-8390-4c349e6e3422",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Put your directory containing PDFs here\n",
    "index_name = 'firetv'\n",
    "directory = f'pdfs/{index_name}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e11839f-a854-4fc5-9259-cb8abfe7b81e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pdf_documents = [os.path.join(directory, filename) for filename in os.listdir(directory)]\n",
    "pdf_documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7a157ee-8c76-49ef-8203-2b7ef03f6ef7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "langchain_documents = []\n",
    "for document in pdf_documents:\n",
    "    loader = PyPDFLoader(document)\n",
    "    data = loader.load()\n",
    "    langchain_documents.extend(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c6bea2e-ca2b-4150-ad27-da5ea62e71d5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(\"loaded document pages: \", len(langchain_documents))\n",
    "print(\"Splitting all documents\")\n",
    "split_docs = text_splitter.split_documents(langchain_documents)\n",
    "print(\"Num split pages: \", len(split_docs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d68869a-d8ea-42bd-b696-69fdbb8794ba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "split_docs[0].page_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2003c5b7-2f8d-4ce4-9e30-89c873b4f515",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import regex as re\n",
    "for d in split_docs:\n",
    "    text = d.page_content\n",
    "    text = re.sub(r\"(\\w+)-\\n(\\w+)\", r\"\\1\\2\", text)\n",
    "    text = re.sub(r\"(?<!\\n\\s)\\n(?!\\s\\n)\", \" \", text.strip()) # Remove newlines \n",
    "    text = re.sub(r\"\\n\\s*\\n\", \"\\n\\n\", text)\n",
    "    text = re.sub(r'[/X]', \"\", text)     #Remove hexadecimal chars\n",
    "    text = re.sub(r\"(\\\\u[0-9A-Fa-f]+)\",\" \",text) #Remove other speciail characters\n",
    "    d.page_content = text"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7ae50b01-0f7f-41fa-b3fa-3ecc630d5747",
   "metadata": {},
   "source": [
    "## Generate Embeddings\n",
    "Use an embeddings model to generate embeddings of the cleaned-up doc"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3ea739ef-f75b-4aa5-9bbb-cb4ae137639a",
   "metadata": {},
   "source": [
    "### Option 1- Bedrock Titan Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb7b13d0-347e-4819-80c3-5e0f3a6bf752",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "session = boto3.Session()\n",
    "sagemaker_session = sagemaker.Session()\n",
    "studio_region = sagemaker_session.boto_region_name \n",
    "bedrock = session.client(\"bedrock\", region_name=studio_region)\n",
    "\n",
    "from langchain.embeddings import BedrockEmbeddings\n",
    "emb = BedrockEmbeddings(region_name =\"us-east-1\",model_id = \"amazon.titan-e1t-medium\")\n",
    "emb.model_kwargs = {}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7afbaff8-568a-429c-b19f-fb9092484466",
   "metadata": {},
   "source": [
    "### Option 2- Huggingface Embeddings - Requires sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01159685-f3e0-4d87-a8bb-ac942a0d95a9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "emb = HuggingFaceEmbeddings()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "35d6a4c9-cd67-45a1-9d3c-1ace18e26cf9",
   "metadata": {},
   "source": [
    "## Setup local Vector store - FAISS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dc43db5-2a05-44fc-a629-c0609011d1d2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.vectorstores import FAISS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eea489f2-7133-4037-b81b-8a36b36da3e1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(\"Embed and create vector index\")\n",
    "db = FAISS.from_documents(split_docs, embedding=emb)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e6cbdb61-8740-422f-adff-443dcfaa1f90",
   "metadata": {},
   "source": [
    "### Save the indices locally as a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "100af08f-89cb-43f6-a0a3-a3a69d64864a",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_path = 'faiss_indices'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e82059e-6097-4da6-b0c1-1e75ba8113f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Save the index created locally')\n",
    "pathlib.Path(index_path).mkdir(parents=True, exist_ok=True)\n",
    "db.save_local(folder_path=index_path, index_name= index_name)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3ac2aded-5d4c-4ba6-ba57-90055270edbe",
   "metadata": {},
   "source": [
    "### Load from local file cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c15d13a5-395b-4b54-ac9d-ea23b8ec5f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "#Check if load local works properly\n",
    "db_local = FAISS.load_local(folder_path=index_path, embeddings=emb, index_name=index_name)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3f500810-861e-4cca-b0cf-ea87fc878118",
   "metadata": {},
   "source": [
    "### Perform a similarity search and get top 3 matching docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "343462fe-f118-4f2c-97a5-fa7d8f81360d",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"How to setup Parental controls?\"\n",
    "docs = db_local.similarity_search(query, k=3)\n",
    "docs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e6884175-f9d0-4ed5-8cf2-1f4c19508eec",
   "metadata": {},
   "source": [
    "## Access LLM with the context from vecor store"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f964b28c-0a25-40e9-9610-daeeab30d295",
   "metadata": {},
   "source": [
    "### Method 1- Simple query with  Vector store wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a861591-c5f9-409d-a143-82baf445848f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms.bedrock import Bedrock\n",
    "\n",
    "#Creating Anthropic Claude\n",
    "model_args= {'max_tokens_to_sample':200,'temperature':0}\n",
    "llm = Bedrock(model_id=\"anthropic.claude-v1\", client=bedrock, model_kwargs=model_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea8818c2-6d8f-4835-90b8-c70a5d2325a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.indexes.vectorstore import VectorStoreIndexWrapper\n",
    "wrapper_store = VectorStoreIndexWrapper(vectorstore=db_local)\n",
    "\n",
    "response = wrapper_store.query(question=query, llm=llm)\n",
    "print(response)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "78ef8bec-abf8-4e9f-b9e5-15312fbea28b",
   "metadata": {},
   "source": [
    "### Method 2- Query with chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca929068-513e-481b-a083-0dda1a91b441",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chains.question_answering import load_qa_chain\n",
    "chain = load_qa_chain(llm, chain_type=\"stuff\")\n",
    "documents = db_local.similarity_search(query=query, k=5)\n",
    "print(chain.run(input_documents=documents, question=query))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7b8109fa-9901-44de-8c2c-c2369f3ef77f",
   "metadata": {},
   "source": [
    "### Method 3- Query with Prompt template (Provides prompt customization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26733b78-347a-4d91-acf6-3131c679e010",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chains import RetrievalQA\n",
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "prompt_template = \"\"\"Human: Use the following pieces of context to provide a concise answer to the question at the end. \n",
    "\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "Assistant:\"\"\"\n",
    "PROMPT = PromptTemplate(\n",
    "    template=prompt_template, input_variables=[\"context\", \"question\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "487c1db0-05e0-4e6c-95c6-abb22a5b4b1f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "qa = RetrievalQA.from_chain_type(\n",
    "    llm=llm,\n",
    "    chain_type=\"stuff\",\n",
    "    retriever=db_local.as_retriever(\n",
    "        search_type=\"similarity\", search_kwargs={\"k\": 3}\n",
    "    ),\n",
    "    return_source_documents=True,\n",
    "    chain_type_kwargs={\"prompt\": PROMPT}\n",
    ")\n",
    "\n",
    "response = qa({'query':query})\n",
    "print(response['result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "686e0267-f2b4-45f8-b99a-5b848e1e3143",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "response['source_documents']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a552bb0a-79c3-4a3e-89aa-12dde3b392c7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Implement RAG architecture with Kendra Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f8ef8cc-b0a2-48a2-ae4a-92785b082916",
   "metadata": {},
   "outputs": [],
   "source": [
    "kendra_index = \"\" #Provide Kendra index here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aee8f42-7d3f-4842-983b-42cf69d24dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema.document import Document\n",
    "\n",
    "kendra = boto3.client('kendra')\n",
    "response = kendra.retrieve(IndexId=kendra_index,QueryText=query)\n",
    "docs = [Document(page_content = r['Content']) for r in response['ResultItems']]\n",
    "docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07489ecb-35a1-46c1-a082-1b3f6a7f4123",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = load_qa_chain(llm, chain_type=\"stuff\")\n",
    "print(chain.run(input_documents=docs, question=query))"
   ]
  }
 ],
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