{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/towardsai/ai-tutor-rag-system/blob/main/notebooks/04-RAG_with_VectorStore.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BGJ3fxhOk2V"
      },
      "source": [
        "# Install Packages and Setup Variables\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "QPJzr-I9XQ7l"
      },
      "outputs": [],
      "source": [
        "!pip install -q llama-index==0.10.57 llama-index-vector-stores-chroma llama-index-llms-gemini==0.1.11 langchain_google_genai google-generativeai==0.5.4 langchain==0.1.17 langchain-chroma langchain_openai==0.1.5 openai==1.37.0 chromadb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "riuXwpSPcvWC"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "# Set the following API Keys in the Python environment. Will be used later.\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_API_KEY>\"\n",
        "os.environ[\"GOOGLE_API_KEY\"] = \"<YOUR_API_KEY>\"\n",
        "\n",
        "# from google.colab import userdata\n",
        "# os.environ[\"OPENAI_API_KEY\"] = userdata.get('openai_api_key')\n",
        "# os.environ[\"GOOGLE_API_KEY\"] = userdata.get('Google_api_key')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I9JbAzFcjkpn"
      },
      "source": [
        "# Load the Dataset (CSV)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Tif8-JoRH68"
      },
      "source": [
        "## Download\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4fQaa1LN1mXL"
      },
      "source": [
        "The dataset includes several articles from the TowardsAI blog, which provide an in-depth explanation of the LLaMA2 model. Read the dataset as a long string.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QTUkdfJjY4N",
        "outputId": "34becd46-808a-42ee-e620-3e6b18f79e1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  169k  100  169k    0     0   609k      0 --:--:-- --:--:-- --:--:--  612k\n"
          ]
        }
      ],
      "source": [
        "!curl -o ./mini-dataset.csv https://raw.githubusercontent.com/AlaFalaki/tutorial_notebooks/main/data/mini-llama-articles.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zk-4alIxROo8"
      },
      "source": [
        "## Read File\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7CYwRT6R0o0I",
        "outputId": "394603bd-6d33-40aa-8e06-6ef802879234"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "171044\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "\n",
        "text = \"\"\n",
        "\n",
        "# Load the file as a JSON\n",
        "with open(\"./mini-dataset.csv\", mode=\"r\", encoding=\"utf-8\") as file:\n",
        "    csv_reader = csv.reader(file)\n",
        "\n",
        "    for idx, row in enumerate(csv_reader):\n",
        "        if idx == 0:\n",
        "            continue\n",
        "        text += row[1]\n",
        "\n",
        "# The number of characters in the dataset.\n",
        "print(len(text))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S17g2RYOjmf2"
      },
      "source": [
        "# Chunking\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STACTMUR1z9N",
        "outputId": "d5360ce2-2c1e-459b-a3b3-e9899fe762b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "335\n"
          ]
        }
      ],
      "source": [
        "chunk_size = 512\n",
        "chunks = []\n",
        "\n",
        "# Split the long text into smaller manageable chunks of 512 characters.\n",
        "for i in range(0, len(text), chunk_size):\n",
        "    chunks.append(text[i : i + chunk_size])\n",
        "\n",
        "print(len(chunks))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fOomeMGqu10"
      },
      "source": [
        "#Interface of Chroma with LlamaIndex\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CtdsIUQ81_hT"
      },
      "outputs": [],
      "source": [
        "from llama_index.core import Document\n",
        "\n",
        "# Convert the chunks to Document objects so the LlamaIndex framework can process them.\n",
        "documents = [Document(text=t) for t in chunks]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWaT6rL7ksp8"
      },
      "source": [
        "Save on Chroma\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mXi56KTXk2sp"
      },
      "outputs": [],
      "source": [
        "import chromadb\n",
        "\n",
        "# create client and a new collection\n",
        "# chromadb.EphemeralClient saves data in-memory.\n",
        "chroma_client = chromadb.PersistentClient(path=\"./mini-chunked-dataset\")\n",
        "chroma_collection = chroma_client.create_collection(\"mini-chunked-dataset\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jKXURvLtkuTS"
      },
      "outputs": [],
      "source": [
        "from llama_index.vector_stores.chroma import ChromaVectorStore\n",
        "from llama_index.core import StorageContext\n",
        "\n",
        "# Define a storage context object using the created vector database.\n",
        "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
        "storage_context = StorageContext.from_defaults(vector_store=vector_store)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "1a0185d42be8489c87874049e5d78424",
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            "3ba2fa0f0c8449c3a22ff3593d2b6629",
            "eb7ce1338f6a4c9a8fb9a92e8de78821",
            "d9daeb567dcc4387a7ba406a559aa422",
            "2a99f51fa5c24ce09f629d9d5322879d",
            "edfd151ed26646d8a6d293a3cf1ecce6",
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            "28d35749a3fa4c6f80f626221f95cde1",
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            "dfa74d96a1174e7ead6554b5c984f526"
          ]
        },
        "id": "WsD52wtrlESi",
        "outputId": "2d522a85-cdea-477b-a693-e26b404c8ed9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing nodes:   0%|          | 0/335 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a0185d42be8489c87874049e5d78424"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/335 [00:00<?, ?it/s]"
            ],
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              "model_id": "a0991085ef794c8cbf509370b67df911"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from llama_index.core import VectorStoreIndex\n",
        "from llama_index.core.node_parser import SentenceSplitter\n",
        "from llama_index.embeddings.openai import OpenAIEmbedding\n",
        "\n",
        "# Build index / generate embeddings using OpenAI embedding model\n",
        "index = VectorStoreIndex.from_documents(\n",
        "    documents,\n",
        "    embed_model=OpenAIEmbedding(model=\"text-embedding-3-small\"),\n",
        "    storage_context=storage_context,\n",
        "    show_progress=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JPD8yAinVSq"
      },
      "source": [
        "Query Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mzS13x1ZlZ5X"
      },
      "outputs": [],
      "source": [
        "# Define a query engine that is responsible for retrieving related pieces of text,\n",
        "# and using a LLM to formulate the final answer.\n",
        "\n",
        "from llama_index.llms.gemini import Gemini\n",
        "\n",
        "llm = Gemini(model=\"models/gemini-1.5-flash\", temperature=1, max_tokens=512)\n",
        "\n",
        "query_engine = index.as_query_engine(llm=llm, similarity_top_k=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AYsQ4uLN_Oxg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "f56876cc-f7bc-4e63-bb7b-9515e9b404cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The LLaMA 2 model has four different sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. \n",
            "\n"
          ]
        }
      ],
      "source": [
        "response = query_engine.query(\"How many parameters LLaMA2 model has?\")\n",
        "print(response)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWK571VNg-qR"
      },
      "source": [
        "# Interface of Chroma with LangChain\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SMPAniL2e4NP"
      },
      "outputs": [],
      "source": [
        "from langchain.schema.document import Document\n",
        "\n",
        "# Convert the chunks to Document objects so the LangChain framework can process them.\n",
        "documents = [Document(page_content=t) for t in chunks]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBt8qGxArUPD"
      },
      "source": [
        "Save on Chroma\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2xas7HkuhJ8A"
      },
      "outputs": [],
      "source": [
        "from langchain_chroma import Chroma\n",
        "from langchain_openai import OpenAIEmbeddings\n",
        "\n",
        "# Add the documents to chroma DB and create Index / embeddings\n",
        "\n",
        "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
        "chroma_db = Chroma.from_documents(\n",
        "    documents=documents,\n",
        "    embedding=embeddings,\n",
        "    persist_directory=\"./mini-chunked-dataset\",\n",
        "    collection_name=\"mini-chunked-dataset\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8AXJJyBrZWF"
      },
      "source": [
        "Query Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-H64YLxshM2b"
      },
      "outputs": [],
      "source": [
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "\n",
        "# Initializing the LLM model\n",
        "#llm = ChatOpenAI(temperature=0, model=\"gpt-4o-mini\", max_tokens=512)\n",
        "\n",
        "llm = ChatGoogleGenerativeAI(\n",
        "    model=\"gemini-1.5-flash\",\n",
        "    temperature=0,\n",
        "    max_tokens=512,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AxBqPNtthPaa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "607b082a-66c2-49a2-ca59-b3cb85bf6067"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The LLaMA 2 model comes in four different sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. \n",
            "\n"
          ]
        }
      ],
      "source": [
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "query = \"How many parameters LLaMA 2 model has?\"\n",
        "retriever = chroma_db.as_retriever(search_kwargs={\"k\": 4})\n",
        "# Define a RetrievalQA chain that is responsible for retrieving related pieces of text,\n",
        "# and using a LLM to formulate the final answer.\n",
        "chain = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=retriever)\n",
        "\n",
        "response = chain.invoke(query)\n",
        "print(response[\"result\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AKr16L_kwyYX"
      },
      "outputs": [],
      "source": []
    }
  ],
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