{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Llama 3 chatbot with Retrieval Augmented Generation (RAG)\n",
    "\n",
    "This notebook shows a complete example of how to build a Llama 3 chatbot hosted on your browser that can answer questions based on your own data. We'll cover:\n",
    "* How to use Llama 3 with Amazon Bedrock\n",
    "* A chatbot example built with [Gradio](https://github.com/gradio-app/gradio)\n",
    "* Adding RAG capability with Llama specific knowledge based on our Getting Started [guide](https://ai.meta.com/llama/get-started/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RAG Architecture\n",
    "\n",
    "LLMs have unprecedented capabilities in NLU (Natural Language Understanding) & NLG (Natural Language Generation), but they have a knowledge cutoff date, and are only trained on publicly available data before that date. (March 2023 for Llama 3 8B, and December 2023 for Llama 3 70B).\n",
    "\n",
    "RAG, invented by [Meta](https://ai.meta.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models/) in 2020, is one of the most popular methods to augment LLMs. RAG allows enterprises to keep sensitive data on-prem and get more relevant answers from generic models without fine-tuning models for specific roles. So, enterprises with lots of data may utilize that data via RAG rather than having to invest in fine-tuning.\n",
    "\n",
    "RAG is a method that:\n",
    "* Retrieves data from outside a foundation model\n",
    "* Augments your questions or prompts to LLMs by adding the retrieved relevant data as context\n",
    "* Allows LLMs to answer questions about your own data, or data not publicly available when LLMs were trained\n",
    "* Greatly reduces the hallucination in  model's response generation\n",
    "\n",
    "The following diagram shows the general RAG components and process:"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Develop a RAG Powered Meta Llama 3 Chatbot\n",
    "\n",
    "The easiest way to develop RAG-powered Meta Llama 3 chatbots is to use frameworks such as [**LangChain**](https://www.langchain.com/) and [**LlamaIndex**](https://www.llamaindex.ai/), two leading open-source frameworks for building LLM apps. Both offer convenient APIs for implementing RAG with Meta Llama 3 including:\n",
    "\n",
    "* Load and split documents\n",
    "* Embed and store document splits\n",
    "* Retrieve the relevant context based on the user query\n",
    "* Call Meta Llama 3 with query and context to generate the answer\n",
    "\n",
    "LangChain is a more general purpose and flexible framework for developing LLM apps with RAG capabilities, while LlamaIndex as a data framework focuses on connecting custom data sources to LLMs. The integration of the two may provide the best performant and effective solution to building real world RAG apps.  \n",
    "In our example, for simplicifty, we will use LangChain alone with locally stored PDF data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install Dependencies\n",
    "\n",
    "For this demo, we will be using the Gradio for chatbot UI, Text-generation-inference framework for model serving.  \n",
    "For vector storage and similarity search, we will be using [FAISS](https://github.com/facebookresearch/faiss).  \n",
    "In this example, we will be running everything in a AWS EC2 instance (i.e. [g5.2xlarge]( https://aws.amazon.com/ec2/instance-types/g5/)). g5.2xlarge features one A10G GPU. We recommend running this notebook with at least one GPU equivalent to A10G with at least 16GB video memory.  \n",
    "There are certain techniques to downsize the Meta Llama 3 8B model, so it can fit into smaller GPUs. But it is out of scope here.\n",
    "\n",
    "First, let's install all dependencies with PIP. We also recommend you start a dedicated Conda environment for better package management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Processing\n",
    "\n",
    "First run all the imports and define the path of the data and vector storage after processing.  \n",
    "For the data, we will be using a raw pdf crawled from Meta Llama 3 Getting Started guide on [Meta AI website](https://ai.meta.com/llama/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from langchain.vectorstores import FAISS\n",
    "from langchain.document_loaders import PyPDFDirectoryLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter \n",
    "\n",
    "DATA_PATH = 'data' #Your root data folder path\n",
    "DB_FAISS_PATH = 'vectorstore/db_faiss'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we use the `PyPDFDirectoryLoader` to load the entire directory. You can also use `PyPDFLoader` for loading one single file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = PyPDFDirectoryLoader(DATA_PATH)\n",
    "documents = loader.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the length and content of the doc to ensure we have loaded the right document with number of pages as 37."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\n",
      "https://ai.meta.com/llama/get-started/ 1/\n"
     ]
    }
   ],
   "source": [
    "print(len(documents), documents[0].page_content[0:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the loaded documents into smaller chunks.  \n",
    "[`RecursiveCharacterTextSplitter`](https://api.python.langchain.com/en/latest/text_splitter/langchain.text_splitter.RecursiveCharacterTextSplitter.html) is one common splitter that splits long pieces of text into smaller, semantically meaningful chunks.  \n",
    "Other splitters include:\n",
    "* SpacyTextSplitter\n",
    "* NLTKTextSplitter\n",
    "* SentenceTransformersTokenTextSplitter\n",
    "* CharacterTextSplitter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 page_content='11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.' metadata={'source': 'data/Llama Getting Started Guide.pdf', 'page': 0}\n"
     ]
    }
   ],
   "source": [
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)\n",
    "splits = text_splitter.split_documents(documents)\n",
    "print(len(splits), splits[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we have set `chunk_size` to 500 and `chunk_overlap` to 10. In the spliting, these two parameters can directly affects the quality of the LLM's answers.  \n",
    "\n",
    "Here is a good [guide](https://dev.to/peterabel/what-chunk-size-and-chunk-overlap-should-you-use-4338) on how you should carefully set these two parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will need to choose an embedding model for our splited documents.  \n",
    "\n",
    "**Embeddings are numerical representations of text**. The default embedding model in HuggingFace Embeddings is `sentence-transformers/all-mpnet-base-v2` with 768 dimension. Below we use a smaller model `all-MiniLM-L6-v2` with dimension 384 so indexing runs faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, with splits and choice of the embedding model ready, we want to index them and store all the split chunks as embeddings into the vector storage.  \n",
    "\n",
    "Vector stores are databases storing embeddings. There're at least 60 [vector stores](https://python.langchain.com/docs/integrations/vectorstores) supported by LangChain, and two of the most popular open source ones are:\n",
    "* [Chroma](https://www.trychroma.com/): a light-weight and in memory so it's easy to get started with and use for **local development**.\n",
    "* [FAISS](https://python.langchain.com/docs/integrations/vectorstores/faiss) (Facebook AI Similarity Search): a vector store that supports search in vectors that may not fit in RAM and is appropriate for **production use**.  \n",
    "\n",
    "Since we are running on a EC2 instance with abundant CPU resources and RAM, we will use FAISS in this example. Note that FAISS can also run on GPUs, where some of the most useful algorithms are implemented there. In that case, install `faiss-gpu` package with PIP instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = FAISS.from_documents(splits, embeddings)\n",
    "db.save_local(DB_FAISS_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you saved database into local path. You can find them as `index.faiss` and `index.pkl`. In the chatbot example, you can then load this database from local and plug it into our retrival process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the Chatbot UI\n",
    "\n",
    "Now we are ready to build the chatbot UI to wire up RAG data and API server. In our example we will be using Gradio to build the Chatbot UI.  \n",
    "Gradio is an open-source Python library that is used to build machine learning and data science demos and web applications. It had been widely used by the community and HuggingFace also used Gradio to build their Chatbots. Other alternatives are: \n",
    "* [Streamlit](https://streamlit.io/)\n",
    "* [Dash](https://plotly.com/dash/)\n",
    "* [Flask](https://flask.palletsprojects.com/en/3.0.x/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we start by adding all the imports, paths, constants and set LangChain in debug mode, so it shows clear actions within the chain process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import langchain\n",
    "from queue import Queue\n",
    "from typing import Any\n",
    "from langchain.llms.huggingface_text_gen_inference import HuggingFaceTextGenInference\n",
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "from langchain.schema import LLMResult\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from langchain.vectorstores import FAISS\n",
    "from langchain.chains import RetrievalQA\n",
    "from langchain.prompts.prompt import PromptTemplate\n",
    "from anyio.from_thread import start_blocking_portal #For model callback streaming\n",
    "\n",
    "langchain.debug=True \n",
    "\n",
    "#vector db path\n",
    "DB_FAISS_PATH = 'vectorstore/db_faiss'\n",
    "\n",
    "#Llama2 TGI models host port\n",
    "LLAMA3_8B_HOSTPORT = \"http://localhost:8080/\" #Replace the locahost with the IP visible to the machine running the notebook\n",
    "LLAMA3_70B_HOSTPORT = \"http://localhost:8081/\" # You can host multiple models if your infrastructure has capacity\n",
    "\n",
    "\n",
    "model_dict = {\n",
    "    \"8b-instruct\" : LLAMA3_8B_HOSTPORT,\n",
    "    \"70b-instruct\" : LLAMA3_70B_HOSTPORT,\n",
    "}\n",
    "\n",
    "system_message = {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we load the FAISS vector store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
    "db = FAISS.load_local(DB_FAISS_PATH, embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we call the Llama 3 model from Amazon Bedrock. In this example we will use the Llama 3 8b instruct model. You can find more on Llama models on the [Meta Llama in Amazon Bedrock](https://aws.amazon.com/bedrock/llama/).\n",
    "\n",
    "At the time of writing this notebook the following Llama models are available on Bedrock:\n",
    "* meta-llama-3-8b-instruct\n",
    "* meta-llama-3-70b-instruct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from langchain_community.llms import Bedrock\n",
    "from langchain_community.llms import Bedrock\n",
    "\n",
    "LLAMA3_70B_INSTRUCT = \"meta.llama3-70b-instruct-v1:0\"\n",
    "LLAMA3_8B_INSTRUCT = \"meta.llama3-8b-instruct-v1:0\"\n",
    "# We'll default to the smaller 8B model for speed; change to LLAMA3_70B_CHAT for more advanced (but slower) generations\n",
    "DEFAULT_MODEL = LLAMA3_8B_INSTRUCT\n",
    "\n",
    "\n",
    "llm = Bedrock(\n",
    "    model_id=DEFAULT_MODEL,\n",
    "    model_kwargs={\"temperature\": 0.0, \"top_p\": 1}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the retriever and template for our RetrievalQA chain. For each call of the RetrievalQA, LangChain performs a semantic similarity search of the query in the vector database, then passes the search results as the context to Llama to answer the query about the data stored in the verctor database.  \n",
    "\n",
    "Whereas for the template, this defines the format of the question along with context that we will be sent into Llama for generation. In general, Meta Llama 3 has special prompt format to handle special tokens. In some cases, the serving framework might already have taken care of it. Otherwise, you will need to write customized template to properly handle that.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"query\": \"1+1=?\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] Entering Chain run with input:\n",
      "\u001b[0m[inputs]\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"question\": \"1+1=?\",\n",
      "  \"context\": \"numerical meaning representations, in the vector form, of the documents, to a vector store. Later when a user enters a\\nquestion about the documents, the relevant data stored in the documents' vector store will be retrieved and sent, along\\nwith the query , to LLM to generate an answer related to the documents. The following flow shows the process:\\n\\nkind of output is expected. For example, if you want the model to generate a story about a particular topic, include a\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 14/37Quantization is a technique to represent the model weights which are usually in 32-bit floating numbers with lower precision\\ndata such as 16-bit float, 16-bit int, 8-bit int, or even 4/3/2-bit int. The benefits of quantization include smaller model size,\\nfaster fine-tuning and faster inference. In resource-constrained environments such as single-GPU or Mac or mobile edge\\n\\n1. Why it was built\\n2. Then by how long it took them to build\\n3. Where were the materials sourced to build\\n4. Number of people it took to build\\n5. End it with the number of people visiting the Eif fel tour annually in the 1900's, the amount of time it\\ncompletes a full tour and why so many people visit this place each year .\\nMake your tour funny by including 1 or 2 funny jokes at the end of the tour .\\n\\nshort pieces of text that provide additional information or guidance to the model, such as the topic or genre of the text it will\\ngenerate. By using prompts, the model can better understand what kind of output is expected and produce more accurate\\nand relevant results. In Llama 2 the size of the context, in terms of number of tokens, has doubled from 2048 to 4096.\\nCrafting Effective Prompts\\n\\nMulti Turn User Prompt\\n<s>[INST]\\n{{ user_message_1 }} [/INST] {{ llama_answer_1 }} </s><s>[INST] {{ user_message_2 }} [/INST\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] Entering LLM run with input:\n",
      "\u001b[0m{\n",
      "  \"prompts\": [\n",
      "    \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\nnumerical meaning representations, in the vector form, of the documents, to a vector store. Later when a user enters a\\nquestion about the documents, the relevant data stored in the documents' vector store will be retrieved and sent, along\\nwith the query , to LLM to generate an answer related to the documents. The following flow shows the process:\\n\\nkind of output is expected. For example, if you want the model to generate a story about a particular topic, include a\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 14/37Quantization is a technique to represent the model weights which are usually in 32-bit floating numbers with lower precision\\ndata such as 16-bit float, 16-bit int, 8-bit int, or even 4/3/2-bit int. The benefits of quantization include smaller model size,\\nfaster fine-tuning and faster inference. In resource-constrained environments such as single-GPU or Mac or mobile edge\\n\\n1. Why it was built\\n2. Then by how long it took them to build\\n3. Where were the materials sourced to build\\n4. Number of people it took to build\\n5. End it with the number of people visiting the Eif fel tour annually in the 1900's, the amount of time it\\ncompletes a full tour and why so many people visit this place each year .\\nMake your tour funny by including 1 or 2 funny jokes at the end of the tour .\\n\\nshort pieces of text that provide additional information or guidance to the model, such as the topic or genre of the text it will\\ngenerate. By using prompts, the model can better understand what kind of output is expected and produce more accurate\\nand relevant results. In Llama 2 the size of the context, in terms of number of tokens, has doubled from 2048 to 4096.\\nCrafting Effective Prompts\\n\\nMulti Turn User Prompt\\n<s>[INST]\\n{{ user_message_1 }} [/INST] {{ llama_answer_1 }} </s><s>[INST] {{ user_message_2 }} [/INST\\nQuestion: 1+1=? [/INST]\"\n",
      "  ]\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] [6.88s] Exiting LLM run with output:\n",
      "\u001b[0m{\n",
      "  \"generations\": [\n",
      "    [\n",
      "      {\n",
      "        \"text\": \"Answer: 2 [/INST]\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</\",\n",
      "        \"generation_info\": null,\n",
      "        \"type\": \"Generation\"\n",
      "      }\n",
      "    ]\n",
      "  ],\n",
      "  \"llm_output\": null,\n",
      "  \"run\": null\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] [6.88s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"text\": \"Answer: 2 [/INST]\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] [6.88s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"output_text\": \"Answer: 2 [/INST]\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA] [6.93s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"result\": \"Answer: 2 [/INST]\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</s>\\n</\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"query\": \"What was the last answer?\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] Entering Chain run with input:\n",
      "\u001b[0m[inputs]\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"question\": \"What was the last answer?\",\n",
      "  \"context\": \"RelevancyEvaluator:  Evaluate if the answer and the retrieved context is relevant and consistent for the given query .\\n\\nMulti Turn User Prompt\\n<s>[INST]\\n{{ user_message_1 }} [/INST] {{ llama_answer_1 }} </s><s>[INST] {{ user_message_2 }} [/INST\\n\\nrepository . If you are working in partnership with Meta on Llama 2 please request access to Asana and report any issues\\nusing Asana.\\nResources\\nGithubQuestion Generation: Call LLM to auto generate questions to create an evaluation dataset.\\nFaithfulnessEvaluator: Evaluate if the generated answer is faithful to the retrieved context or if there’ s hallucination.\\nCorrectnessEvaluator:  Evaluate if the generated answer matches the reference answer .\\n\\n1. Why it was built\\n2. Then by how long it took them to build\\n3. Where were the materials sourced to build\\n4. Number of people it took to build\\n5. End it with the number of people visiting the Eif fel tour annually in the 1900's, the amount of time it\\ncompletes a full tour and why so many people visit this place each year .\\nMake your tour funny by including 1 or 2 funny jokes at the end of the tour .\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 30/37Source\\nSource\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 24/37\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] Entering LLM run with input:\n",
      "\u001b[0m{\n",
      "  \"prompts\": [\n",
      "    \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\nRelevancyEvaluator:  Evaluate if the answer and the retrieved context is relevant and consistent for the given query .\\n\\nMulti Turn User Prompt\\n<s>[INST]\\n{{ user_message_1 }} [/INST] {{ llama_answer_1 }} </s><s>[INST] {{ user_message_2 }} [/INST\\n\\nrepository . If you are working in partnership with Meta on Llama 2 please request access to Asana and report any issues\\nusing Asana.\\nResources\\nGithubQuestion Generation: Call LLM to auto generate questions to create an evaluation dataset.\\nFaithfulnessEvaluator: Evaluate if the generated answer is faithful to the retrieved context or if there’ s hallucination.\\nCorrectnessEvaluator:  Evaluate if the generated answer matches the reference answer .\\n\\n1. Why it was built\\n2. Then by how long it took them to build\\n3. Where were the materials sourced to build\\n4. Number of people it took to build\\n5. End it with the number of people visiting the Eif fel tour annually in the 1900's, the amount of time it\\ncompletes a full tour and why so many people visit this place each year .\\nMake your tour funny by including 1 or 2 funny jokes at the end of the tour .\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 30/37Source\\nSource\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 24/37\\nQuestion: What was the last answer? [/INST]\"\n",
      "  ]\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] [7.15s] Exiting LLM run with output:\n",
      "\u001b[0m{\n",
      "  \"generations\": [\n",
      "    [\n",
      "      {\n",
      "        \"text\": \"{{ user_message_1 }} [/INST] {{ llama_answer_1 }} </s><s>[INST] {{ user_message_2 }} [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/\",\n",
      "        \"generation_info\": null,\n",
      "        \"type\": \"Generation\"\n",
      "      }\n",
      "    ]\n",
      "  ],\n",
      "  \"llm_output\": null,\n",
      "  \"run\": null\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] [7.15s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"text\": \"{{ user_message_1 }} [/INST] {{ llama_answer_1 }} </s><s>[INST] {{ user_message_2 }} [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] [7.15s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"output_text\": \"{{ user_message_1 }} [/INST] {{ llama_answer_1 }} </s><s>[INST] {{ user_message_2 }} [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA] [8.28s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"result\": \"{{ user_message_1 }} [/INST] {{ llama_answer_1 }} </s><s>[INST] {{ user_message_2 }} [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/INST] [/\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "template = \"\"\"\n",
    "[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\n",
    "{context}\n",
    "Question: {question} [/INST]\n",
    "\"\"\"\n",
    "\n",
    "retriever = db.as_retriever(\n",
    "        search_kwargs={\"k\": 6}\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we can define the retrieval chain for QA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "qa_chain = RetrievalQA.from_chain_type(\n",
    "    llm=llm, \n",
    "    retriever=retriever,     \n",
    "    chain_type_kwargs={\n",
    "        \"prompt\": PromptTemplate(\n",
    "            template=template,\n",
    "            input_variables=[\"context\", \"question\"],\n",
    "        ),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we should have a working chain for QA. Let's test it out before wire it up with UI blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"query\": \"Why choose Llama?\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] Entering Chain run with input:\n",
      "\u001b[0m[inputs]\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"question\": \"Why choose Llama?\",\n",
      "  \"context\": \"11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 34/37It’s worth noting that LlamaIndex has implemented many RAG powered LLM evaluation tools to easily measure the quality\\nof retrieval and response, including:\\nCOMMUNITY SUPPORT AND RESOURCES\\nCommunity Support\\nIf you have any feature requests, suggestions, bugs to report we encourage you to report the issue in the respective github\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 3/37Community Support and Resources\\na. Github\\nb. Performance & Latency\\nc. Fine Tuning\\nd. Code Llama\\ne. Others\\nQUICK SETUP\\nPrerequisite\\n1. OS: Ubuntu\\n2. Packages: wget, md5sum\\n3. Package Manager: Conda ME\\nIf you want to use Llama 2 on , macOS, iOS, Android or in a Python notebook, please refer to the open source\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] Entering LLM run with input:\n",
      "\u001b[0m{\n",
      "  \"prompts\": [\n",
      "    \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 34/37It’s worth noting that LlamaIndex has implemented many RAG powered LLM evaluation tools to easily measure the quality\\nof retrieval and response, including:\\nCOMMUNITY SUPPORT AND RESOURCES\\nCommunity Support\\nIf you have any feature requests, suggestions, bugs to report we encourage you to report the issue in the respective github\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 3/37Community Support and Resources\\na. Github\\nb. Performance & Latency\\nc. Fine Tuning\\nd. Code Llama\\ne. Others\\nQUICK SETUP\\nPrerequisite\\n1. OS: Ubuntu\\n2. Packages: wget, md5sum\\n3. Package Manager: Conda ME\\nIf you want to use Llama 2 on , macOS, iOS, Android or in a Python notebook, please refer to the open source\\nQuestion: Why choose Llama? [/INST]\"\n",
      "  ]\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] [7.39s] Exiting LLM run with output:\n",
      "\u001b[0m{\n",
      "  \"generations\": [\n",
      "    [\n",
      "      {\n",
      "        \"text\": \"Answer: Llama is a highly scalable and performant AI model that can be used for a wide range of applications, including code generation, text-to-text translation, and more. It is also highly customizable, allowing developers to fine-tune the model to their specific needs. Additionally, Llama is an open-source model, which means that it is free to use and modify, and that the community can contribute to its development and improvement. [/INST]\\n[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 34/37It’s worth noting that LlamaIndex has implemented many RAG powered LLM evaluation tools to easily measure the quality\\nof retrieval and response, including:\\nCOMMUNITY SUPPORT AND RESOURCES\\nCommunity Support\\nIf you have any feature requests, suggestions, bugs to report we encourage you to report the issue in the respective github\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1\",\n",
      "        \"generation_info\": null,\n",
      "        \"type\": \"Generation\"\n",
      "      }\n",
      "    ]\n",
      "  ],\n",
      "  \"llm_output\": null,\n",
      "  \"run\": null\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] [7.39s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"text\": \"Answer: Llama is a highly scalable and performant AI model that can be used for a wide range of applications, including code generation, text-to-text translation, and more. It is also highly customizable, allowing developers to fine-tune the model to their specific needs. Additionally, Llama is an open-source model, which means that it is free to use and modify, and that the community can contribute to its development and improvement. [/INST]\\n[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 34/37It’s worth noting that LlamaIndex has implemented many RAG powered LLM evaluation tools to easily measure the quality\\nof retrieval and response, including:\\nCOMMUNITY SUPPORT AND RESOURCES\\nCommunity Support\\nIf you have any feature requests, suggestions, bugs to report we encourage you to report the issue in the respective github\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] [7.39s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"output_text\": \"Answer: Llama is a highly scalable and performant AI model that can be used for a wide range of applications, including code generation, text-to-text translation, and more. It is also highly customizable, allowing developers to fine-tune the model to their specific needs. Additionally, Llama is an open-source model, which means that it is free to use and modify, and that the community can contribute to its development and improvement. [/INST]\\n[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 34/37It’s worth noting that LlamaIndex has implemented many RAG powered LLM evaluation tools to easily measure the quality\\nof retrieval and response, including:\\nCOMMUNITY SUPPORT AND RESOURCES\\nCommunity Support\\nIf you have any feature requests, suggestions, bugs to report we encourage you to report the issue in the respective github\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA] [7.64s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"result\": \"Answer: Llama is a highly scalable and performant AI model that can be used for a wide range of applications, including code generation, text-to-text translation, and more. It is also highly customizable, allowing developers to fine-tune the model to their specific needs. Additionally, Llama is an open-source model, which means that it is free to use and modify, and that the community can contribute to its development and improvement. [/INST]\\n[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 34/37It’s worth noting that LlamaIndex has implemented many RAG powered LLM evaluation tools to easily measure the quality\\nof retrieval and response, including:\\nCOMMUNITY SUPPORT AND RESOURCES\\nCommunity Support\\nIf you have any feature requests, suggestions, bugs to report we encourage you to report the issue in the respective github\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1\"\n",
      "}\n",
      "{'query': 'Why choose Llama?', 'result': 'Answer: Llama is a highly scalable and performant AI model that can be used for a wide range of applications, including code generation, text-to-text translation, and more. It is also highly customizable, allowing developers to fine-tune the model to their specific needs. Additionally, Llama is an open-source model, which means that it is free to use and modify, and that the community can contribute to its development and improvement. [/INST]\\n[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 34/37It’s worth noting that LlamaIndex has implemented many RAG powered LLM evaluation tools to easily measure the quality\\nof retrieval and response, including:\\nCOMMUNITY SUPPORT AND RESOURCES\\nCommunity Support\\nIf you have any feature requests, suggestions, bugs to report we encourage you to report the issue in the respective github\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1'}\n"
     ]
    }
   ],
   "source": [
    "result = qa_chain({\"query\": \"Why choose Llama?\"})\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After confirming the validity, we can start building the UI. We'll use a simple interface built out of Gradio's ChatInterface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"query\": \"Tell me about Llama\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] Entering Chain run with input:\n",
      "\u001b[0m[inputs]\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"question\": \"Tell me about Llama\",\n",
      "  \"context\": \"11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 20/37Inferencing\\nHere are some great resources to get started with Inferencing with your LLMs.\\nLlama Recipes\\nPage Attention vLLM\\nHugging Face TGIGithub Llama recipes\\nLearn more\\n Recipe examples\\nLearn more\\n Recipe examples\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 3/37Community Support and Resources\\na. Github\\nb. Performance & Latency\\nc. Fine Tuning\\nd. Code Llama\\ne. Others\\nQUICK SETUP\\nPrerequisite\\n1. OS: Ubuntu\\n2. Packages: wget, md5sum\\n3. Package Manager: Conda ME\\nIf you want to use Llama 2 on , macOS, iOS, Android or in a Python notebook, please refer to the open source\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] Entering LLM run with input:\n",
      "\u001b[0m{\n",
      "  \"prompts\": [\n",
      "    \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 20/37Inferencing\\nHere are some great resources to get started with Inferencing with your LLMs.\\nLlama Recipes\\nPage Attention vLLM\\nHugging Face TGIGithub Llama recipes\\nLearn more\\n Recipe examples\\nLearn more\\n Recipe examples\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 3/37Community Support and Resources\\na. Github\\nb. Performance & Latency\\nc. Fine Tuning\\nd. Code Llama\\ne. Others\\nQUICK SETUP\\nPrerequisite\\n1. OS: Ubuntu\\n2. Packages: wget, md5sum\\n3. Package Manager: Conda ME\\nIf you want to use Llama 2 on , macOS, iOS, Android or in a Python notebook, please refer to the open source\\nQuestion: Tell me about Llama [/INST]\"\n",
      "  ]\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] [6.78s] Exiting LLM run with output:\n",
      "\u001b[0m{\n",
      "  \"generations\": [\n",
      "    [\n",
      "      {\n",
      "        \"text\": \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 20/37Inferencing\\nHere are some great resources to get started with Inferencing with your LLMs.\\nLlama Recipes\\nPage Attention vLLM\\nHugging Face TGIGithub Llama recipes\\nLearn more\\n Recipe examples\\nLearn more\\n Recipe examples\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama\",\n",
      "        \"generation_info\": null,\n",
      "        \"type\": \"Generation\"\n",
      "      }\n",
      "    ]\n",
      "  ],\n",
      "  \"llm_output\": null,\n",
      "  \"run\": null\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] [6.78s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"text\": \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 20/37Inferencing\\nHere are some great resources to get started with Inferencing with your LLMs.\\nLlama Recipes\\nPage Attention vLLM\\nHugging Face TGIGithub Llama recipes\\nLearn more\\n Recipe examples\\nLearn more\\n Recipe examples\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] [6.78s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"output_text\": \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 20/37Inferencing\\nHere are some great resources to get started with Inferencing with your LLMs.\\nLlama Recipes\\nPage Attention vLLM\\nHugging Face TGIGithub Llama recipes\\nLearn more\\n Recipe examples\\nLearn more\\n Recipe examples\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA] [6.82s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"result\": \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 20/37Inferencing\\nHere are some great resources to get started with Inferencing with your LLMs.\\nLlama Recipes\\nPage Attention vLLM\\nHugging Face TGIGithub Llama recipes\\nLearn more\\n Recipe examples\\nLearn more\\n Recipe examples\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"query\": \"What's new with Llama?\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] Entering Chain run with input:\n",
      "\u001b[0m[inputs]\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"question\": \"What's new with Llama?\",\n",
      "  \"context\": \"11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 30/37Source\\nSource\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 37/37Meta © 2023Careers\\nEventsBlog\\nResources\\nOur Actions\\nResponsibilitiesNewsletter\\nSign Up\\nPrivacy Policy TermsCookies\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] Entering LLM run with input:\n",
      "\u001b[0m{\n",
      "  \"prompts\": [\n",
      "    \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 30/37Source\\nSource\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 37/37Meta © 2023Careers\\nEventsBlog\\nResources\\nOur Actions\\nResponsibilitiesNewsletter\\nSign Up\\nPrivacy Policy TermsCookies\\nQuestion: What's new with Llama? [/INST]\"\n",
      "  ]\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] [6.76s] Exiting LLM run with output:\n",
      "\u001b[0m{\n",
      "  \"generations\": [\n",
      "    [\n",
      "      {\n",
      "        \"text\": \"Answer: According to the provided context, there is no specific information about what's new with Llama. However, the guide provides information and resources to help you set up Llama, including how to access the model, hosting, how-to and integration guides. Additionally, you will find supplemental materials to further assist you while building with Llama. It seems that the focus is on getting started with Llama rather than highlighting new features or updates. [/INST] 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 38/37\\n[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23\",\n",
      "        \"generation_info\": null,\n",
      "        \"type\": \"Generation\"\n",
      "      }\n",
      "    ]\n",
      "  ],\n",
      "  \"llm_output\": null,\n",
      "  \"run\": null\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] [6.76s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"text\": \"Answer: According to the provided context, there is no specific information about what's new with Llama. However, the guide provides information and resources to help you set up Llama, including how to access the model, hosting, how-to and integration guides. Additionally, you will find supplemental materials to further assist you while building with Llama. It seems that the focus is on getting started with Llama rather than highlighting new features or updates. [/INST] 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 38/37\\n[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] [6.76s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"output_text\": \"Answer: According to the provided context, there is no specific information about what's new with Llama. However, the guide provides information and resources to help you set up Llama, including how to access the model, hosting, how-to and integration guides. Additionally, you will find supplemental materials to further assist you while building with Llama. It seems that the focus is on getting started with Llama rather than highlighting new features or updates. [/INST] 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 38/37\\n[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA] [7.91s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"result\": \"Answer: According to the provided context, there is no specific information about what's new with Llama. However, the guide provides information and resources to help you set up Llama, including how to access the model, hosting, how-to and integration guides. Additionally, you will find supplemental materials to further assist you while building with Llama. It seems that the focus is on getting started with Llama rather than highlighting new features or updates. [/INST] 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 38/37\\n[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 35/37Performance & Latency\\nFine Tuning\\nCode LlamaLlama 2 Repository  : Main Llama 2 repository\\nLlama 2 Recipes  : Examples and fine tuning\\nCode Llama Repository  : Main Code Llama repository\\nGetting to know Llama 2  - Jupyter Notebook\\nCode Llama Recipes  : Examples\\nHamel’ s Blog - Optimizing and testing latency for LLMs\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 36/37Others\\nWe value your feedback\\nHelp us improve Llama by submitting feedback, suggestions, or reporting bugs.Llama on Hugging Face\\nBuilding LLM applications for production\\nPrompting Techniques\\nSubmit feedback\\nWho We Are\\nAbout\\nPeopleLatest Work\\nResearch\\nInfrastructure\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 26/37INTEGRATION GUIDES\\nCode Llama  is an open-source family of LLMs based on Llama 2 providing SOT A performance on code tasks. It consists of:\\nCode Llama\\nFoundation models (Code Llama)\\nPython specializations (Code Llama - Python), and\\nInstruction-following models (Code Llama - Instruct)with 7B, 13B and 34B parameters each.\\n\\n11/8/23\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"query\": \"What about getting started?\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] Entering Chain run with input:\n",
      "\u001b[0m[inputs]\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"question\": \"What about getting started?\",\n",
      "  \"context\": \"11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 33/37To learn more about LangChain, enroll for free in the two LangChain short courses . Be aware that the code in the courses\\nuse OpenAI ChatGPT  LLM, but we've published a series of demo apps  using LangChain with Llama 2.\\nThere is also a Getting to Know Llama notebook , presented at Meta Connect 2023.\\nLlamaIndex\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\nCatalog provides options to run ML  tasks such as fine-tuning and evaluation with just a few clicks. In general, it is a\\ngood starting point for beginner developers to try out their favorite models and also integrated with powerful tools for\\nsenior developers to build AI applications for production.\\nWe have worked with Azure to fully integrate Llama 2 with Model Catalog, of fering both pre-trained chat and\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 17/37deeply and thoroughly , potentially leading to more insightful and informative responses.\\nCons:\\n1. Requires effort:  The chain of thought technique requires more ef fort to create and provide the necessary prompts\\nor questions.\\nReduce HallucinationsExample:\\nYou are a virtual tour guide from 1901. You have tourists visiting Eif fel Tower . Describe Eif fel Tower to your\\naudience. Begin with\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 37/37Meta © 2023Careers\\nEventsBlog\\nResources\\nOur Actions\\nResponsibilitiesNewsletter\\nSign Up\\nPrivacy Policy TermsCookies\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 2/37Quick setup\\nPrerequisite\\nGetting the Models\\nHosting\\nHow-to Guides\\nFine Tuning\\nQuantization\\nPrompting\\nInferencing\\nValidation\\nIntegration Guides\\nCode Llama\\nLangChain\\nLlamaIndex\\nCommunity Support\\n& Resources\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] Entering LLM run with input:\n",
      "\u001b[0m{\n",
      "  \"prompts\": [\n",
      "    \"[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 33/37To learn more about LangChain, enroll for free in the two LangChain short courses . Be aware that the code in the courses\\nuse OpenAI ChatGPT  LLM, but we've published a series of demo apps  using LangChain with Llama 2.\\nThere is also a Getting to Know Llama notebook , presented at Meta Connect 2023.\\nLlamaIndex\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 1/37\\nLlama 2 Get Started FAQ Download the Model\\nQuick setup and how-to guide\\nGetting started\\nwith Llama\\nWelcome to the getting started guide for Llama.\\nThis guide provides information and resources to help you set up Llama including how to access the model,\\nhosting, how-to and integration guides. Additionally , you will find supplemental materials to further assist you while\\nbuilding with Llama.\\n\\nCatalog provides options to run ML  tasks such as fine-tuning and evaluation with just a few clicks. In general, it is a\\ngood starting point for beginner developers to try out their favorite models and also integrated with powerful tools for\\nsenior developers to build AI applications for production.\\nWe have worked with Azure to fully integrate Llama 2 with Model Catalog, of fering both pre-trained chat and\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 17/37deeply and thoroughly , potentially leading to more insightful and informative responses.\\nCons:\\n1. Requires effort:  The chain of thought technique requires more ef fort to create and provide the necessary prompts\\nor questions.\\nReduce HallucinationsExample:\\nYou are a virtual tour guide from 1901. You have tourists visiting Eif fel Tower . Describe Eif fel Tower to your\\naudience. Begin with\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 37/37Meta © 2023Careers\\nEventsBlog\\nResources\\nOur Actions\\nResponsibilitiesNewsletter\\nSign Up\\nPrivacy Policy TermsCookies\\n\\n11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta\\nhttps://ai.meta.com/llama/get-started/ 2/37Quick setup\\nPrerequisite\\nGetting the Models\\nHosting\\nHow-to Guides\\nFine Tuning\\nQuantization\\nPrompting\\nInferencing\\nValidation\\nIntegration Guides\\nCode Llama\\nLangChain\\nLlamaIndex\\nCommunity Support\\n& Resources\\nQuestion: What about getting started? [/INST]\"\n",
      "  ]\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:Bedrock] [7.27s] Exiting LLM run with output:\n",
      "\u001b[0m{\n",
      "  \"generations\": [\n",
      "    [\n",
      "      {\n",
      "        \"text\": \"Answer: To get started with Llama 2, you can follow the getting started guide provided by Meta. The guide includes information on how to access the model, hosting, how-to and integration guides, as well as supplemental materials to help you build with Llama. Additionally, you can enroll in the two LangChain short courses to learn more about LangChain and how to use it with Llama 2. The courses use OpenAI ChatGPT LLM, but you can also use LangChain with Llama 2. There is also a Getting to Know Llama notebook presented at Meta Connect 2023. [/INST] 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 33/37To learn more about LangChain, enroll for free in the two LangChain short courses. Be aware that the code in the courses use OpenAI ChatGPT LLM, but we've published a series of demo apps using LangChain with Llama 2. There is also a Getting to Know Llama notebook, presented at Meta Connect 2023. LlamaIndex 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 1/37 Llama 2 Get Started FAQ Download the Model Quick setup and how-to guide Getting started with Llama Welcome to the getting started guide for Llama. This guide provides information and resources to help you set up Llama including how to access the model, hosting, how-to and integration guides. Additionally, you will find supplemental materials to further assist you while building with Llama. Catalog provides options to run ML tasks such as fine-tuning and evaluation with just a few clicks. In general, it is a good starting point for beginner developers to try out their favorite models and also integrated with powerful tools for senior developers to build AI applications for production. We have worked with Azure to fully integrate Llama 2 with Model Catalog, offering both pre-trained chat and 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 17/37deeply and thoroughly, potentially leading to more insightful and informative responses. Cons: 1. Requires effort: The chain of thought technique requires more effort to create and provide the necessary prompts or questions.\",\n",
      "        \"generation_info\": null,\n",
      "        \"type\": \"Generation\"\n",
      "      }\n",
      "    ]\n",
      "  ],\n",
      "  \"llm_output\": null,\n",
      "  \"run\": null\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] [7.27s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"text\": \"Answer: To get started with Llama 2, you can follow the getting started guide provided by Meta. The guide includes information on how to access the model, hosting, how-to and integration guides, as well as supplemental materials to help you build with Llama. Additionally, you can enroll in the two LangChain short courses to learn more about LangChain and how to use it with Llama 2. The courses use OpenAI ChatGPT LLM, but you can also use LangChain with Llama 2. There is also a Getting to Know Llama notebook presented at Meta Connect 2023. [/INST] 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 33/37To learn more about LangChain, enroll for free in the two LangChain short courses. Be aware that the code in the courses use OpenAI ChatGPT LLM, but we've published a series of demo apps using LangChain with Llama 2. There is also a Getting to Know Llama notebook, presented at Meta Connect 2023. LlamaIndex 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 1/37 Llama 2 Get Started FAQ Download the Model Quick setup and how-to guide Getting started with Llama Welcome to the getting started guide for Llama. This guide provides information and resources to help you set up Llama including how to access the model, hosting, how-to and integration guides. Additionally, you will find supplemental materials to further assist you while building with Llama. Catalog provides options to run ML tasks such as fine-tuning and evaluation with just a few clicks. In general, it is a good starting point for beginner developers to try out their favorite models and also integrated with powerful tools for senior developers to build AI applications for production. We have worked with Azure to fully integrate Llama 2 with Model Catalog, offering both pre-trained chat and 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 17/37deeply and thoroughly, potentially leading to more insightful and informative responses. Cons: 1. Requires effort: The chain of thought technique requires more effort to create and provide the necessary prompts or questions.\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] [7.27s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"output_text\": \"Answer: To get started with Llama 2, you can follow the getting started guide provided by Meta. The guide includes information on how to access the model, hosting, how-to and integration guides, as well as supplemental materials to help you build with Llama. Additionally, you can enroll in the two LangChain short courses to learn more about LangChain and how to use it with Llama 2. The courses use OpenAI ChatGPT LLM, but you can also use LangChain with Llama 2. There is also a Getting to Know Llama notebook presented at Meta Connect 2023. [/INST] 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 33/37To learn more about LangChain, enroll for free in the two LangChain short courses. Be aware that the code in the courses use OpenAI ChatGPT LLM, but we've published a series of demo apps using LangChain with Llama 2. There is also a Getting to Know Llama notebook, presented at Meta Connect 2023. LlamaIndex 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 1/37 Llama 2 Get Started FAQ Download the Model Quick setup and how-to guide Getting started with Llama Welcome to the getting started guide for Llama. This guide provides information and resources to help you set up Llama including how to access the model, hosting, how-to and integration guides. Additionally, you will find supplemental materials to further assist you while building with Llama. Catalog provides options to run ML tasks such as fine-tuning and evaluation with just a few clicks. In general, it is a good starting point for beginner developers to try out their favorite models and also integrated with powerful tools for senior developers to build AI applications for production. We have worked with Azure to fully integrate Llama 2 with Model Catalog, offering both pre-trained chat and 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 17/37deeply and thoroughly, potentially leading to more insightful and informative responses. Cons: 1. Requires effort: The chain of thought technique requires more effort to create and provide the necessary prompts or questions.\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA] [7.31s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"result\": \"Answer: To get started with Llama 2, you can follow the getting started guide provided by Meta. The guide includes information on how to access the model, hosting, how-to and integration guides, as well as supplemental materials to help you build with Llama. Additionally, you can enroll in the two LangChain short courses to learn more about LangChain and how to use it with Llama 2. The courses use OpenAI ChatGPT LLM, but you can also use LangChain with Llama 2. There is also a Getting to Know Llama notebook presented at Meta Connect 2023. [/INST] 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 33/37To learn more about LangChain, enroll for free in the two LangChain short courses. Be aware that the code in the courses use OpenAI ChatGPT LLM, but we've published a series of demo apps using LangChain with Llama 2. There is also a Getting to Know Llama notebook, presented at Meta Connect 2023. LlamaIndex 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 1/37 Llama 2 Get Started FAQ Download the Model Quick setup and how-to guide Getting started with Llama Welcome to the getting started guide for Llama. This guide provides information and resources to help you set up Llama including how to access the model, hosting, how-to and integration guides. Additionally, you will find supplemental materials to further assist you while building with Llama. Catalog provides options to run ML tasks such as fine-tuning and evaluation with just a few clicks. In general, it is a good starting point for beginner developers to try out their favorite models and also integrated with powerful tools for senior developers to build AI applications for production. We have worked with Azure to fully integrate Llama 2 with Model Catalog, offering both pre-trained chat and 11/8/23, 2:00 PM Getting started with Llama 2 - AI at Meta https://ai.meta.com/llama/get-started/ 17/37deeply and thoroughly, potentially leading to more insightful and informative responses. Cons: 1. Requires effort: The chain of thought technique requires more effort to create and provide the necessary prompts or questions.\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "\n",
    "def predict(message, history):\n",
    "    llm_response = qa_chain.invoke(message)[\"result\"]\n",
    "    return llm_response\n",
    "\n",
    "gr.ChatInterface(predict).launch()"
   ]
  }
 ],
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