LlamaIndex-LangChain-OpenAI.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-dnMwuJHmPy"
      },
      "source": [
        "# Retrieval-Augmented Generation (RAG) Notebook\n",
        "📒Notebook Created by ❤️ [@prasadmahamulkar](https://x.com/prsdm17).\n",
        "\n",
        "In this notebook, you will learn how to implement RAG (basic to advanced) using LangChain 🦜 and LlamaIndex 🦙.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tBCqCH3t9G7"
      },
      "source": [
        "# Basic RAG using LangChain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nw4rcbBS4gvm",
        "outputId": "51e3f011-8930-4935-e36c-c4a5faea4bee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Installing collected packages: langchain-openai\n",
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          ]
        }
      ],
      "source": [
        "! pip install sentence_transformers\n",
        "! pip install pypdf\n",
        "! pip install faiss-gpu\n",
        "! pip install langchain\n",
        "! pip install langchain-openai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FjsWxpWPL9hU"
      },
      "outputs": [],
      "source": [
        "# load pdf\n",
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "loader = PyPDFLoader(\"/content/qlora_paper.pdf\")\n",
        "documents = loader.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d9xWIBhDIfs5"
      },
      "outputs": [],
      "source": [
        "# split document content\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "text_splitter = RecursiveCharacterTextSplitter()\n",
        "text = text_splitter.split_documents(documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "outputId": "2aada974-fb08-4d75-d592-d611626cf507"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6bb2d89340b4a579e3c32d129f133b6",
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        {
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            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ]
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        {
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            "text/plain": [
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          },
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        {
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        {
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              "model_id": "f2d6ffe2eb514d2b8f178b27e5a3c1fd",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "424540c8e01b478e8076609235fd2a42",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c9ab93a2a232448caa296c249066f9b5",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1f4aad777da743e1815b0a3be3d39080",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "67d86e9f3535443f9458d6bdab1025c7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6b95855e97284fc7a8d56cb178dfac1b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# load embedding model\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"BAAI/bge-small-en-v1.5\", encode_kwargs = {\"normalize_embeddings\": True})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yl1H98v27Z_O"
      },
      "outputs": [],
      "source": [
        "# # check embeddings\n",
        "# check = embeddings.embed_query(\"what is Qlora?\")\n",
        "# check[0:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kMsEMWL-o4QL"
      },
      "outputs": [],
      "source": [
        "# create vectorstore using FAISS\n",
        "from langchain.vectorstores import FAISS\n",
        "vectorstore = FAISS.from_documents(text, embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "obF6HfOXPfeD"
      },
      "outputs": [],
      "source": [
        "# saving the vectorstore\n",
        "vectorstore.save_local(\"vectorstore.db\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BkUysdQbPttu"
      },
      "outputs": [],
      "source": [
        "# # load the vectorstore and check similarity\n",
        "# sub_docs = vectorstore.similarity_search(\"what is Qlora?\") # k=5\n",
        "# sub_docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aRFwzs70rrA3"
      },
      "outputs": [],
      "source": [
        "# create retriever\n",
        "retriever = vectorstore.as_retriever()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UuGsGEYtqt02"
      },
      "outputs": [],
      "source": [
        "# set api openai key\n",
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_OPENAI_API_KEY\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cvJv_637qwkZ"
      },
      "outputs": [],
      "source": [
        "# load llm\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bPQZzCrglC7I"
      },
      "outputs": [],
      "source": [
        "# create document chain\n",
        "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "\n",
        "template = \"\"\"\"\n",
        "You are an assistant for question-answering tasks.\n",
        "Use the provided context only to answer the following question:\n",
        "\n",
        "<context>\n",
        "{context}\n",
        "</context>\n",
        "\n",
        "Question: {input}\n",
        "\"\"\"\n",
        "prompt = ChatPromptTemplate.from_template(template)\n",
        "doc_chain = create_stuff_documents_chain(llm, prompt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c3ehqwEsl874"
      },
      "outputs": [],
      "source": [
        "# create retrieval chain\n",
        "from langchain.chains import create_retrieval_chain\n",
        "chain = create_retrieval_chain(retriever, doc_chain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fyp2de-3mF70"
      },
      "outputs": [],
      "source": [
        "response = chain.invoke({\"input\": \"what is Qlora?\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "cL5wYbQ0mLFY",
        "outputId": "171aa42f-ad4c-49c4-aea2-e4add92c3863"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'QLoRA is an efficient finetuning approach that allows for the finetuning of quantized language models without any performance degradation. It reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance.'"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "response['answer']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lUXUIKMwpWU"
      },
      "source": [
        "# Advanced RAG using LangChain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qeqox7AIUAts",
        "outputId": "21303441-e633-4163-aa3f-4b4430caacee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53723 sha256=14ee02796adfbafb70769a746d3a94a0088af984613f8bfdd2fe0d0d209888d5\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built pypika\n",
            "Installing collected packages: pypika, monotonic, mmh3, websockets, uvloop, uvicorn, python-dotenv, pulsar-client, overrides, opentelemetry-util-http, opentelemetry-semantic-conventions, opentelemetry-proto, importlib-metadata, humanfriendly, httptools, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, starlette, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, coloredlogs, opentelemetry-sdk, opentelemetry-instrumentation, onnxruntime, kubernetes, fastapi, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-grpc, opentelemetry-instrumentation-fastapi, chromadb\n",
            "  Attempting uninstall: importlib-metadata\n",
            "    Found existing installation: importlib-metadata 7.0.1\n",
            "    Uninstalling importlib-metadata-7.0.1:\n",
            "      Successfully uninstalled importlib-metadata-7.0.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed asgiref-3.7.2 backoff-2.2.1 bcrypt-4.1.2 chroma-hnswlib-0.7.3 chromadb-0.4.22 coloredlogs-15.0.1 deprecated-1.2.14 fastapi-0.109.2 httptools-0.6.1 humanfriendly-10.0 importlib-metadata-6.11.0 kubernetes-29.0.0 mmh3-4.1.0 monotonic-1.6 onnxruntime-1.17.0 opentelemetry-api-1.22.0 opentelemetry-exporter-otlp-proto-common-1.22.0 opentelemetry-exporter-otlp-proto-grpc-1.22.0 opentelemetry-instrumentation-0.43b0 opentelemetry-instrumentation-asgi-0.43b0 opentelemetry-instrumentation-fastapi-0.43b0 opentelemetry-proto-1.22.0 opentelemetry-sdk-1.22.0 opentelemetry-semantic-conventions-0.43b0 opentelemetry-util-http-0.43b0 overrides-7.7.0 posthog-3.4.0 pulsar-client-3.4.0 pypika-0.48.9 python-dotenv-1.0.1 starlette-0.36.3 uvicorn-0.27.0.post1 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0\n"
          ]
        }
      ],
      "source": [
        "! pip install chromadb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rhoYDtSePC-t"
      },
      "outputs": [],
      "source": [
        "# load pdf\n",
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "loader = PyPDFLoader(\"/content/qlora_paper.pdf\")\n",
        "documents = loader.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lWEwXmtIPqBy"
      },
      "outputs": [],
      "source": [
        "# split pages content\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "\n",
        "# create the parent documents - The big chunks\n",
        "parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)\n",
        "\n",
        "# create the child documents - The small chunks\n",
        "child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n",
        "\n",
        "# The storage layer for the parent chunks\n",
        "from langchain.storage import InMemoryStore\n",
        "store = InMemoryStore()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tj_UHL3qQewJ"
      },
      "outputs": [],
      "source": [
        "# load embedding model\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"BAAI/bge-small-en-v1.5\", encode_kwargs = {\"normalize_embeddings\": True})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8hxamHaFQnek"
      },
      "outputs": [],
      "source": [
        "# create vectorstore using Chromadb\n",
        "from langchain.vectorstores import Chroma\n",
        "vectorstore = Chroma(collection_name=\"split_parents\", embedding_function=embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p4b9mOALSYJ-"
      },
      "outputs": [],
      "source": [
        "# create retriever\n",
        "from langchain.retrievers import ParentDocumentRetriever\n",
        "retriever = ParentDocumentRetriever(\n",
        "    vectorstore=vectorstore,\n",
        "    docstore=store,\n",
        "    child_splitter=child_splitter,\n",
        "    parent_splitter=parent_splitter,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lHNhSKMmS-zs"
      },
      "outputs": [],
      "source": [
        "# add documents to vectorstore\n",
        "retriever.add_documents(documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0wsKPEaXYbmg"
      },
      "outputs": [],
      "source": [
        "# set api openai key\n",
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_OPENAI_API_KEY\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oG1m2SJ7YX9s"
      },
      "outputs": [],
      "source": [
        "# load llm\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wGAlt1jxXeyq"
      },
      "outputs": [],
      "source": [
        "# create document chain\n",
        "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "\n",
        "template = \"\"\"\"\n",
        "You are an assistant for question-answering tasks.\n",
        "Use the provided context only to answer the following question:\n",
        "\n",
        "<context>\n",
        "{context}\n",
        "</context>\n",
        "\n",
        "Question: {input}\n",
        "\"\"\"\n",
        "prompt = ChatPromptTemplate.from_template(template)\n",
        "doc_chain = create_stuff_documents_chain(llm, prompt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mnad_ya8YL0g"
      },
      "outputs": [],
      "source": [
        "# create retrieval chain\n",
        "from langchain.chains import create_retrieval_chain\n",
        "chain = create_retrieval_chain(retriever, doc_chain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "69T2v9X3YrAo"
      },
      "outputs": [],
      "source": [
        "response = chain.invoke({\"input\": \"what is Qlora?\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "Uywrf1h1Y6yL",
        "outputId": "99f8b493-de73-4276-9cb0-e0801ffd7d02"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'QLORA is a method that achieves high-fidelity 4-bit finetuning by using two techniques: 4-bit NormalFloat (NF4) quantization and Double Quantization. It also introduces Paged Optimizers to prevent memory spikes during gradient checkpointing. QLORA has one low-precision storage data type (usually 4-bit) and one computation data type (usually BFloat16). It dequantizes the low-precision tensor to BFloat16 and performs matrix multiplication in 16-bit.'"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "response['answer']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddA51DYXqUv8"
      },
      "source": [
        "# Basic RAG using LlamaIndex"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HH4H2YjSQN_U",
        "outputId": "74fb0524-c155-4e90-9e56-1fa4b4a95c04"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai>=1.1.0->llama_index) (1.2.0)\n",
            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json->llama_index) (23.2)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->llama_index) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->llama_index) (2.16.1)\n",
            "Installing collected packages: dirtyjson, retrying, pypdf, pyaml, mypy-extensions, marshmallow, html2text, h11, deprecated, typing-inspect, tiktoken, httpcore, httpx, dataclasses-json, openai, llama_index, llama_hub\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed dataclasses-json-0.6.4 deprecated-1.2.14 dirtyjson-1.0.8 h11-0.14.0 html2text-2020.1.16 httpcore-1.0.2 httpx-0.26.0 llama_hub-0.0.78 llama_index-0.9.45 marshmallow-3.20.2 mypy-extensions-1.0.0 openai-1.11.1 pyaml-23.12.0 pypdf-4.0.1 retrying-1.3.4 tiktoken-0.5.2 typing-inspect-0.9.0\n"
          ]
        }
      ],
      "source": [
        "! pip install -U llama_hub llama_index pypdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jidDuhyVVRxg"
      },
      "outputs": [],
      "source": [
        "# load pdf\n",
        "from llama_index import SimpleDirectoryReader\n",
        "documents = SimpleDirectoryReader(input_files=[\"/content/qlora_paper.pdf\"]).load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qT-QKvbocjyM"
      },
      "outputs": [],
      "source": [
        "# combine documents into one\n",
        "from llama_index import Document\n",
        "doc_text = \"\\n\\n\".join([d.get_content() for d in documents])\n",
        "text= [Document(text=doc_text)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PhklP7bdcCot"
      },
      "outputs": [],
      "source": [
        "# set up text chunk\n",
        "from llama_index.node_parser import SimpleNodeParser\n",
        "node_parser = SimpleNodeParser.from_defaults() # Default chunk size is 1024"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wVY7kep1cNqy"
      },
      "outputs": [],
      "source": [
        "# create chunks from text\n",
        "base_nodes = node_parser.get_nodes_from_documents(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3MyUE0JDeMMB"
      },
      "outputs": [],
      "source": [
        "# reset node ids\n",
        "from llama_index.schema import IndexNode\n",
        "for idx, node in enumerate(base_nodes):\n",
        "    node.id_ = f\"node-{idx}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333,
          "referenced_widgets": [
            "34c514173737469eb38110e4c68dd150",
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            "8322e145eb3f46218bf414405b2e90f5",
            "9782ddbd14a045f28af87b44a7a6167a",
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            "d300ffed077e48a280f3e45582839674",
            "4ec105beb55c488e83a3b9419fcf86ed",
            "9917fa350bc04cf28a78769abd487e86",
            "57b1aff52f5b4b1c84366d883e4f207a",
            "830ea0c814a94f38a89d2cdcf3fe653c",
            "8f0710901401467bbc1a1f5bf9d75522",
            "6beed6b270ca482e912feef3b3b8da86",
            "204c573258c74f4aa58027b99d4a870a",
            "081f27d394fe4ed7988fecd4c0c6000c",
            "58e80d4018df4782870ffa6766a101dd",
            "8a745892876544fca60c1a19caa8940d",
            "dce8b5eba94048a99c3c3771882823e4",
            "aea4c80017ac4017a63bb312131ffffb",
            "f750baefeed2496f912d944122da4201",
            "a56d83aa1967412fa863a799f1ad3a49",
            "f715e803709e47c1b95eae0c3e4600ec"
          ]
        },
        "id": "oIAEgPb7e4TR",
        "outputId": "0d2371c9-904a-46e0-94cc-e344f0c38c4c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "34c514173737469eb38110e4c68dd150",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/684 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0908fd8103524fdea7fa1d3be5fc966c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/133M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4c9f7343213f4731b384c411af5c0b77",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cff6f2b16a96468688aa6f3562ed6002",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "79cca14d594c4dad8bf260777261482f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f0710901401467bbc1a1f5bf9d75522",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# load embedding model\n",
        "from llama_index.embeddings import resolve_embed_model\n",
        "embed_model = resolve_embed_model(\"local:BAAI/bge-small-en\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wd5ouLXuXMzb"
      },
      "outputs": [],
      "source": [
        "# set api openai key\n",
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_OPENAI_API_KEY\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JAgVfj07X4yR"
      },
      "outputs": [],
      "source": [
        "# load llm\n",
        "from llama_index.llms import OpenAI\n",
        "llm = OpenAI(model=\"gpt-3.5-turbo\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jQiQ0KO5XAjp"
      },
      "outputs": [],
      "source": [
        "# set service context\n",
        "from llama_index import ServiceContext\n",
        "service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IFf687CDXct1"
      },
      "outputs": [],
      "source": [
        "# store in vectorstore index\n",
        "from llama_index import VectorStoreIndex\n",
        "index = VectorStoreIndex(base_nodes, service_context=service_context)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dg3S6OZWa5Ej"
      },
      "outputs": [],
      "source": [
        "# create retriever\n",
        "retriever = index.as_retriever() #similarity_top_k=2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6PtNYniYbaLi"
      },
      "outputs": [],
      "source": [
        "# set up query engine\n",
        "from llama_index.query_engine import RetrieverQueryEngine\n",
        "query_engine = RetrieverQueryEngine.from_args(retriever, service_context=service_context)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-xZ6-Lga8Pm",
        "outputId": "fe62fd23-6b0b-4423-b529-dbcd6cbc48df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "QLORA is an efficient finetuning approach that allows for the reduction of memory usage while preserving the performance of a pretrained language model. It achieves this by backpropagating gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters (LoRA). QLORA introduces several innovations to save memory, including the use of a new data type called 4-bit NormalFloat (NF4), Double Quantization to reduce memory footprint, and Paged Optimizers to manage memory spikes. QLORA has been used to finetune more than 1,000 models and has shown state-of-the-art results in chatbot performance.\n"
          ]
        }
      ],
      "source": [
        "# query\n",
        "response = query_engine.query(\"What is Qlora?\")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2iWxd3PfsDW"
      },
      "source": [
        "# Advanced RAG using LlamaIndex"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J8u3raAjiAv8"
      },
      "outputs": [],
      "source": [
        "# load pdf\n",
        "from llama_index import SimpleDirectoryReader\n",
        "documents = SimpleDirectoryReader(input_files=[\"/content/qlora_paper.pdf\"]).load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r-tX_lCJiDlZ"
      },
      "outputs": [],
      "source": [
        "# combine documents into one\n",
        "from llama_index import Document\n",
        "doc_text = \"\\n\\n\".join([d.get_content() for d in documents])\n",
        "text= [Document(text=doc_text)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HdXLkrESi60y"
      },
      "outputs": [],
      "source": [
        "# set up parent chunk\n",
        "from llama_index.node_parser import SimpleNodeParser\n",
        "node_parser = SimpleNodeParser.from_defaults()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hMGzUyY6jDnR"
      },
      "outputs": [],
      "source": [
        "from llama_index.schema import IndexNode\n",
        "base_nodes = node_parser.get_nodes_from_documents(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W6YMKWsdpsmW"
      },
      "outputs": [],
      "source": [
        "# reset node ids (optional)\n",
        "from llama_index.schema import IndexNode\n",
        "for idx, node in enumerate(base_nodes):\n",
        "    node.id_ = f\"node-{idx}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m1ZQUhndiXOe"
      },
      "outputs": [],
      "source": [
        "# set up child chunk\n",
        "sub_chunk_sizes = [128, 256, 512]\n",
        "sub_node_parsers = [\n",
        "    SimpleNodeParser.from_defaults(chunk_size=c,chunk_overlap=20) for c in sub_chunk_sizes\n",
        "]\n",
        "\n",
        "all_nodes = []\n",
        "for base_node in base_nodes:\n",
        "    for n in sub_node_parsers:\n",
        "        sub_nodes = n.get_nodes_from_documents([base_node])\n",
        "        sub_inodes = [\n",
        "            IndexNode.from_text_node(sn, base_node.node_id) for sn in sub_nodes\n",
        "        ]\n",
        "        all_nodes.extend(sub_inodes)\n",
        "\n",
        "    # also add original node to node\n",
        "    original_node = IndexNode.from_text_node(base_node, base_node.node_id)\n",
        "    all_nodes.append(original_node)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8wVNtxOon6TZ"
      },
      "outputs": [],
      "source": [
        "all_nodes_dict = {n.node_id: n for n in all_nodes}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f6s_vI7sjY-6"
      },
      "outputs": [],
      "source": [
        "# set api openai key\n",
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_OPENAI_API_KEY\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZsAw0cQ8jyE-"
      },
      "outputs": [],
      "source": [
        "# load llm\n",
        "from llama_index.llms import OpenAI\n",
        "llm = OpenAI(model=\"gpt-3.5-turbo\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "POv0tcOjj1qf"
      },
      "outputs": [],
      "source": [
        "# load embedding model\n",
        "from llama_index.embeddings import resolve_embed_model\n",
        "embed_model = resolve_embed_model(\"local:BAAI/bge-small-en\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2W6goM4Sj6mt"
      },
      "outputs": [],
      "source": [
        "# set service context\n",
        "from llama_index import ServiceContext\n",
        "service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kvQUB-1Dj9uM"
      },
      "outputs": [],
      "source": [
        "# create and store embedding in vectorstore\n",
        "from llama_index import VectorStoreIndex\n",
        "index = VectorStoreIndex(all_nodes, service_context=service_context)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m6vnDqw7nudG"
      },
      "outputs": [],
      "source": [
        "vector_retriever_chunk = index.as_retriever()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z0SrDxejnLsx"
      },
      "outputs": [],
      "source": [
        "# create retriever\n",
        "from llama_index.retrievers import RecursiveRetriever\n",
        "retriever_chunk = RecursiveRetriever(\n",
        "    \"vector\",\n",
        "    retriever_dict={\"vector\": vector_retriever_chunk},\n",
        "    node_dict=all_nodes_dict,\n",
        "    verbose=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DMJ_y3AmoL8v"
      },
      "outputs": [],
      "source": [
        "# create retriever query\n",
        "from llama_index.query_engine import RetrieverQueryEngine\n",
        "query_engine_chunk = RetrieverQueryEngine.from_args(retriever_chunk, service_context=service_context)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1dCVDci9omhj"
      },
      "outputs": [],
      "source": [
        "# # check source node\n",
        "# from llama_index.response.notebook_utils import display_source_node\n",
        "# nodes = retriever_chunk.retrieve( \"what is Qlora?\")\n",
        "# for node in nodes:\n",
        "#     display_source_node(node, source_length=2000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IW5uX2roUah",
        "outputId": "58e58415-8174-4dc9-d401-0362b57d3e47"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;34mRetrieving with query id None: What is Qlora?\n",
            "\u001b[0m\u001b[1;3;38;5;200mRetrieved node with id, entering: node-0\n",
            "\u001b[0m\u001b[1;3;34mRetrieving with query id node-0: What is Qlora?\n",
            "\u001b[0m\u001b[1;3;38;5;200mRetrieved node with id, entering: node-18\n",
            "\u001b[0m\u001b[1;3;34mRetrieving with query id node-18: What is Qlora?\n",
            "\u001b[0mQLORA is an efficient finetuning approach that allows for the finetuning of quantized language models without any performance degradation. It reduces memory usage and enables the finetuning of large models on a single GPU. QLORA uses a novel technique to quantize a pretrained model to 4-bit and incorporates Low-rank Adapters (LoRA) for backpropagating gradients through the quantized weights. It introduces several innovations to save memory without sacrificing performance, such as a new data type called 4-bit NormalFloat (NF4), Double Quantization to reduce memory footprint, and Paged Optimizers to manage memory spikes. QLORA has been used to finetune more than 1,000 models and has achieved state-of-the-art results on various benchmarks.\n"
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        }
      ],
      "source": [
        "# query\n",
        "response = query_engine_chunk.query(\"What is Qlora?\")\n",
        "print(str(response))"
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