{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c1ea03a-cc69-45b0-80d3-664e48ca6831",
   "metadata": {},
   "outputs": [],
   "source": [
    "## This demo app shows:\n",
    "* how to run Llama2 in the cloud hosted on Replicate;\n",
    "* how to use LangChain to ask Llama general questions and follow up questions;\n",
    "* how to use LangChain to load a recent PDF doc - the Llama2 paper pdf - and chat about it. This is the well known RAG (Retrieval Augmented Generation) method to let LLM such as Llama2 be able to answer questions about the data not publicly available when Llama2 was trained, or about your own data. RAG is one way to prevent LLM's hallucination. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c608df5",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install langchain replicate sentence-transformers chromadb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9c5546a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " ········\n"
     ]
    }
   ],
   "source": [
    "from getpass import getpass\n",
    "import os\n",
    "\n",
    "REPLICATE_API_TOKEN = getpass()\n",
    "os.environ[\"REPLICATE_API_TOKEN\"] = REPLICATE_API_TOKEN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad536adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import Replicate\n",
    "\n",
    "llama2_13b = \"meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d\"\n",
    "llm = Replicate(\n",
    "    model=llama2_13b,\n",
    "    model_kwargs={\"temperature\": 0.01, \"top_p\": 1, \"max_new_tokens\":500}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "493a7148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Hello! I'd be happy to help you with your question. The book \"The Innovator's Dilemma\" was written by Clayton Christensen, an American author and professor at Harvard Business School. It was first published in 1997 and has since become a widely influential work on innovation and business strategy. Do you have any other questions or would you like more information on this topic?\n"
     ]
    }
   ],
   "source": [
    "question = \"who wrote the book Innovator's dilemma?\"\n",
    "answer = llm(question)\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b5c8676",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Hello! I'm here to assist you with any questions or tasks you may have. I can provide information on a wide range of topics, from science and history to entertainment and culture. I can also help with practical tasks such as converting units of measurement or calculating dates and times. Is there something specific you would like to know or discuss?\n"
     ]
    }
   ],
   "source": [
    "# chat history not passed so Llama doesn't have the context and doesn't know this is more about the book\n",
    "followup = \"tell me more\"\n",
    "followup_answer = llm(followup)\n",
    "print(followup_answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5428ca27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using ConversationBufferMemory to pass memory (chat history) for follow up questions\n",
    "from langchain.chains import ConversationChain\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "memory = ConversationBufferMemory()\n",
    "conversation = ConversationChain(\n",
    "    llm=llm, \n",
    "    memory = memory,\n",
    "    verbose=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "baee2d22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Ah, you're asking about \"The Innovator's Dilemma,\" that classic book by Clayton Christensen! He's a renowned author and professor at Harvard Business School, known for his work on disruptive innovation and how established companies can struggle to adapt to new technologies and business models.\n",
      "\n",
      "In fact, I have access to a wealth of information on this topic, as well as other areas of expertise. Would you like me to share some interesting facts or insights about Clayton Christensen or his book? For example, did you know that he coined the term\n"
     ]
    }
   ],
   "source": [
    "# restart from the original question\n",
    "answer = conversation.predict(input=question)\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c7d67a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Sure thing! Here are some additional details about Clayton Christensen and his book \"The Innovator's Dilemma\":\n",
      "\n",
      "1. The book was first published in 1997 and has since become a seminal work in the field of innovation and entrepreneurship.\n",
      "2. Christensen's central argument is that successful companies often struggle to adopt new technologies and business models because they are too focused on sustaining their existing businesses. This can lead to a \"dilemma\" where these companies fail to innovate and eventually lose market share to newer, more ag\n"
     ]
    }
   ],
   "source": [
    "# pass context (previous question and answer) along with the follow up \"tell me more\" to Llama who now knows more of what\n",
    "memory.save_context({\"input\": question},\n",
    "                    {\"output\": answer})\n",
    "followup_answer = conversation.predict(input=followup)\n",
    "print(followup_answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5303d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's ask Llama2 about the Llama2 paper - without loading the paper and passing the paper content \n",
    "# to Llama2, it'd hallucinate - see HelloLlamaLocal for more info\n",
    "from langchain.document_loaders import PyPDFLoader\n",
    "loader = PyPDFLoader(\"https://arxiv.org/pdf/2307.09288.pdf\")\n",
    "docs = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "678c2b4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77 Llama 2 : Open Foundation and Fine-Tuned Chat Models\n",
      "Hugo Touvron∗Louis Martin†Kevin Stone†\n",
      "Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra\n",
      "Prajjwal Bhargava Shruti Bhosale Dan Bikel Lukas Blecher Cristian Canton Ferrer Moya Chen\n",
      "Guillem Cucurull David Esiobu Jude Fernande\n"
     ]
    }
   ],
   "source": [
    "# check docs length and content\n",
    "print(len(docs), docs[0].page_content[0:300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eecb6a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# there're more than 30 vector stores (DBs) supported by LangChain. Chroma is light-weight and in memory so it's easy to get started with\n",
    "# other vector stores can be used to store large amount of data - see https://python.langchain.com/docs/integrations/vectorstores\n",
    "from langchain.vectorstores import Chroma\n",
    "\n",
    "# embeddings are numerical representations of the question and answer text\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "\n",
    "# use a common text splitter to split text into chunks\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc65e161",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the loaded documents into chunks. \n",
    "# in genreral, use larger chuck sizes for highly structured text such as code and smaller size for \n",
    "# less structured text. you may need to experiment with different chunk sizes and overlap values to find out the best numbers.\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)\n",
    "all_splits = text_splitter.split_documents(docs)\n",
    "\n",
    "# create the vector db to store all the split chunks as embeddings\n",
    "embeddings = HuggingFaceEmbeddings()\n",
    "vectordb = Chroma.from_documents(\n",
    "    documents=all_splits,\n",
    "    embedding=embeddings,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00e3f72b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Based on the provided text, Llama2 appears to be a language model developed by Meta AI that is designed for dialogue use cases. It is a fine-tuned version of the original Llama model, with improved performance and safety features. The model has been trained on a large dataset of text and has undergone testing in English, but it may not cover all scenarios or produce accurate responses in certain instances. As such, developers are advised to perform safety testing and tuning before deploying any applications of Llama2. Additionally, the model is released under a responsible use guide and code\n"
     ]
    }
   ],
   "source": [
    "# use LangChain's RetrievalQA, to associate Llama with the loaded documents stored in the vector db\n",
    "from langchain.chains import RetrievalQA\n",
    "\n",
    "qa_chain = RetrievalQA.from_chain_type(\n",
    "    llm,\n",
    "    retriever=vectordb.as_retriever()\n",
    ")\n",
    "\n",
    "# for each question, LangChain performs a semantic similarity search of it in the vector db, then passes the search results as the context\n",
    "# the Llama to answer question about the data stored in the verctor db\n",
    "question = \"What is llama2?\"\n",
    "result = qa_chain({\"query\": question})\n",
    "print(result['result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "53f27473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Based on the context provided, I don't see any explicit mention of \"its\" use cases. However, I can provide some possible inferences based on the information given:\n",
      "\n",
      "The text mentions a partnerships team and product and technical organization support, which suggests that the tool or approach being referred to is likely related to product development or customer support.\n",
      "\n",
      "The emphasis on prioritizing harmlessness over informativeness and helpfulness suggests that the tool may be used for moderation or content review purposes, where the goal is to avoid causing harm or offense while still providing useful information.\n",
      "\n",
      "The\n"
     ]
    }
   ],
   "source": [
    "# no context passed so Llama2 doesn't have enough context to answer so it lets its imagination go wild\n",
    "result = qa_chain({\"query\": \"what are its use cases?\"})\n",
    "print(result['result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "743644a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use ConversationalRetrievalChain to pass chat history for follow up questions\n",
    "from langchain.chains import ConversationalRetrievalChain\n",
    "chat_chain = ConversationalRetrievalChain.from_llm(llm, vectordb.as_retriever(), return_source_documents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7c3d1142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Based on the provided text, Llama2 appears to be a language model developed by Meta AI that is designed for dialogue use cases. It is a fine-tuned version of the original Llama model, with improved performance and safety features. The model has been trained on a large dataset of text and has undergone testing in English, but it may not cover all scenarios or produce accurate responses in certain instances. As such, developers are advised to perform safety testing and tuning before deploying any applications of Llama2. Additionally, the model is released under a responsible use guide and code\n"
     ]
    }
   ],
   "source": [
    "# let's ask the original question \"What is llama2?\" again\n",
    "result = chat_chain({\"question\": question, \"chat_history\": []})\n",
    "print(result['answer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4b17f08f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Based on the provided context, here are some potential use cases for Llama2, a language model developed by Meta AI for dialogue use cases:\n",
      "\n",
      "1. Assistant-like chat: Tuned models of Llama2 can be used for assistant-like chat applications, such as customer service or personal assistants.\n",
      "2. Natural language generation tasks: Pretrained models of Llama2 can be adapted for various natural language generation tasks, such as text summarization, machine translation, and content creation.\n",
      "3. Research use cases: Llama2 can be used in research studies to\n"
     ]
    }
   ],
   "source": [
    "# this time we pass chat history along with the follow up so good things should happen\n",
    "chat_history = [(question, result[\"answer\"])]\n",
    "followup = \"what are its use cases?\"\n",
    "followup_answer = chat_chain({\"question\": followup, \"chat_history\": chat_history})\n",
    "print(followup_answer['answer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95d22347",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Based on the information provided, Llama2 can assist with various natural language generation tasks, particularly in English. The model has been fine-tuned for assistant-like chat and has shown proficiency in other languages as well, although its proficiency is limited due to the limited amount of pretraining data available in non-English languages.\n",
      "\n",
      "Specifically, Llama2 can be used for tasks such as:\n",
      "\n",
      "1. Dialogue systems: Llama2 can be fine-tuned for different dialogue systems, such as customer service chatbots, virtual assistants,\n"
     ]
    }
   ],
   "source": [
    "# further follow ups can be made possible by updating chat_history like this:\n",
    "chat_history.append((followup, followup_answer[\"answer\"]))\n",
    "more_followup = \"what tasks can it assist with?\"\n",
    "more_followup_answer = chat_chain({\"question\": more_followup, \"chat_history\": chat_history})\n",
    "print(more_followup_answer['answer'])\n",
    "\n",
    "# results get cut off - you may set \"max_new_tokens\" in the Replicate call above to a larger number (like 1000 below) to avoid the cut off\n",
    "#    model_kwargs={\"temperature\": 0.01, \"top_p\": 1, \"max_new_tokens\": 1000}"
   ]
  }
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