rag_pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UaHcYzEKlwI_"
      },
      "source": [
        "This is a notebook implements the qdrant databases, as well as performing RAG experiments with the databases.\n",
        "\n",
        "In this notebook, we use 4-bit quantization to run Llama-7B Chat model. This code uses only 10 GB of VRAM. It can run on a free instance of Google Colab or on a local GPU (e.g., RTX 3060 12GB).\n",
        "[More details here.](https://open.substack.com/pub/kaitchup/p/run-llama-2-chat-models-on-your-computer?r=2kp66c&utm_campaign=post&utm_medium=web)\n",
        "\n",
        "\n",
        "We only need the following libraries:\n",
        "\n",
        "\n",
        "*   transformers\n",
        "*   accelerate (for device_map)\n",
        "*   bitsandbytes (for 4-bit quantization)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgGyWYanlvr_",
        "outputId": "4864fcca-7ae6-4d54-cbc6-a011c9ec6109"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        },
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            "\n",
            "[notice] A new release of pip is available: 23.1.2 -> 24.0\n",
            "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
          ]
        },
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            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING: Ignoring invalid distribution -orch (c:\\users\\dng09\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n",
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            "\n",
            "[notice] A new release of pip is available: 23.1.2 -> 24.0\n",
            "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
          ]
        }
      ],
      "source": [
        "%pip install qdrant-client sentence-transformers accelerate\n",
        "%pip install langchain-groq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KCdND_LSkVPg"
      },
      "source": [
        "Note that to run the following code, you must have got access to Llama 2's weights and have an access token from Hugging Face. You can find instructions on the model cards on the hugging face hub: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "tZXRq-37B9h7"
      },
      "outputs": [],
      "source": [
        "import uuid\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import qdrant_client as qc\n",
        "import qdrant_client.http.models as qmodels\n",
        "from torch import cuda\n",
        "from qdrant_client.http.models import *\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "from langchain_groq import ChatGroq\n",
        "from langchain.vectorstores import Qdrant\n",
        "from langchain.schema import SystemMessage, HumanMessage, AIMessage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [],
      "source": [
        "# embedding model\n",
        "\n",
        "embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'\n",
        "\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "embed_model = HuggingFaceEmbeddings(\n",
        "    model_name=embed_model_id,\n",
        "    model_kwargs={'device': device},\n",
        "    encode_kwargs={'device': device}\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
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        },
        "id": "oaxHRFHFDEUG",
        "outputId": "30c8df72-46f6-4914-b42b-f863e2199614"
      },
      "outputs": [],
      "source": [
        "initialized = True\n",
        "\n",
        "client = qc.QdrantClient(host=\"localhost\", port=6333)\n",
        "collection_name = 'Taylor_Song_DataBase'\n",
        "\n",
        "if not initialized:\n",
        "    collection = client.recreate_collection(\n",
        "        collection_name=collection_name,\n",
        "        vectors_config=VectorParams(size=384, distance=Distance.COSINE, on_disk=True),\n",
        "        on_disk_payload=True\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>song_name</th>\n",
              "      <th>album</th>\n",
              "      <th>happy_sad</th>\n",
              "      <th>relationship</th>\n",
              "      <th>feelings_of_self</th>\n",
              "      <th>glass_half_full</th>\n",
              "      <th>stages</th>\n",
              "      <th>tempo</th>\n",
              "      <th>seriousness</th>\n",
              "      <th>future_prospects</th>\n",
              "      <th>feelings_of_male</th>\n",
              "      <th>togetherness</th>\n",
              "      <th>lyrics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>cold as you</td>\n",
              "      <td>Taylor Swift</td>\n",
              "      <td>-10</td>\n",
              "      <td>-8</td>\n",
              "      <td>-1</td>\n",
              "      <td>-3</td>\n",
              "      <td>-3</td>\n",
              "      <td>-3</td>\n",
              "      <td>-3</td>\n",
              "      <td>-3</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>{ Of a mess of a dreamer with the nerve to ado...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>i'm only me when i'm with you</td>\n",
              "      <td>Taylor Swift</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>{ And I don't try to hide my tears,  In a fiel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>invisible</td>\n",
              "      <td>Taylor Swift</td>\n",
              "      <td>-1</td>\n",
              "      <td>-4</td>\n",
              "      <td>0</td>\n",
              "      <td>-2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-3</td>\n",
              "      <td>{ Whenever she walks by,  I just wanna open yo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mary's song</td>\n",
              "      <td>Taylor Swift</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>{ And all I need is you next to me,  Take me b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>our song</td>\n",
              "      <td>Taylor Swift</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>{ When we're on the phone and you talk real sl...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       song_name         album  happy_sad  relationship  \\\n",
              "0                    cold as you  Taylor Swift        -10            -8   \n",
              "1  i'm only me when i'm with you  Taylor Swift          9            10   \n",
              "2                      invisible  Taylor Swift         -1            -4   \n",
              "3                    mary's song  Taylor Swift          5            12   \n",
              "4                       our song  Taylor Swift          5             6   \n",
              "\n",
              "   feelings_of_self  glass_half_full  stages  tempo  seriousness  \\\n",
              "0                -1               -3      -3     -3           -3   \n",
              "1                 3                3       1      2            2   \n",
              "2                 0               -2       1      0            0   \n",
              "3                 0                2       1      2            3   \n",
              "4                 2                2       1      0            1   \n",
              "\n",
              "   future_prospects  feelings_of_male  togetherness  \\\n",
              "0                -3                -1            -1   \n",
              "1                 2                 3             3   \n",
              "2                 0                -1            -3   \n",
              "3                 3                 3             3   \n",
              "4                 1                 3             1   \n",
              "\n",
              "                                              lyrics  \n",
              "0  { Of a mess of a dreamer with the nerve to ado...  \n",
              "1  { And I don't try to hide my tears,  In a fiel...  \n",
              "2  { Whenever she walks by,  I just wanna open yo...  \n",
              "3  { And all I need is you next to me,  Take me b...  \n",
              "4  { When we're on the phone and you talk real sl...  "
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ts_lyrics = pd.read_csv('data/cleaned_data/rag_dataset.csv')\n",
        "ts_lyrics['lyrics'] = ts_lyrics['lyrics'].apply(lambda x: set([verse for verse in x.split('\\n') if verse != '' and verse != ' ' and verse != '   ']))\n",
        "ts_lyrics.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The number of chunks is 6504\n"
          ]
        }
      ],
      "source": [
        "n_chunks = sum([ len(chunk) for chunk in ts_lyrics['lyrics'] ])\n",
        "print(f'The number of chunks is {n_chunks}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [],
      "source": [
        "if not initialized:\n",
        "    for i in range(0, len(ts_lyrics)):\n",
        "        if((i+1) % 30 == 0):\n",
        "            print(f'Processing song number {i+1}')\n",
        "            print(f'The number of verses is {len(ts_lyrics.iloc[i][\"lyrics\"])}')\n",
        "        song = ts_lyrics.iloc[i]\n",
        "        vectors = embed_model.embed_documents(song['lyrics'])\n",
        "        payload = []\n",
        "        ids = []\n",
        "\n",
        "        for verse in song['lyrics']:\n",
        "            ids.append(str(uuid.uuid4()))\n",
        "\n",
        "            payload.append({\n",
        "                'page_content': verse, \n",
        "                'metadata':{\n",
        "                    'song_name': f\"{song['song_name']}\",\n",
        "                    'album': song['album'],\n",
        "                    'happy_sad': song['happy_sad'].item(),\n",
        "                    'relationship': song['relationship'].item(),\n",
        "                    'feeling_of_self': song['feelings_of_self'].item(),\n",
        "                    'glass_half_full': song['glass_half_full'].item(),\n",
        "                    'stages': song['stages'].item(),\n",
        "                    'tempo': song['tempo'].item(),\n",
        "                    'seriousness': song['seriousness'].item(),\n",
        "                    'future_prospect': song['future_prospects'].item(),\n",
        "                    'feelings_of_male': song['feelings_of_male'].item(),\n",
        "                    'togetherness': song['togetherness'].item(),}\n",
        "            })\n",
        "\n",
        "        client.upsert(\n",
        "            collection_name=collection_name,\n",
        "            points=Batch(\n",
        "                ids=ids,\n",
        "                vectors=vectors,\n",
        "                payloads=payload\n",
        "            )\n",
        "        )\n",
        "\n",
        "qdrant = Qdrant(client=client, collection_name=collection_name, embeddings=embed_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Experiment\n",
        "\n",
        "In the following experiment, we use a short query and try to retrieve the relevant songs from the database"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [],
      "source": [
        "chat = ChatGroq(temperature=0, model_name='mixtral-8x7b-32768', api_key='groq_api_key')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Design a preprompt that will be prepended to the users query that specify how the bot should behave\n",
        "\n",
        "def custom_prompt(query: str):\n",
        "    results = qdrant.similarity_search(query, k=3)\n",
        "    source_knowledge = \"\\n\".join([x.page_content for x in results])\n",
        "    augment_prompt = f\"\"\"Detect the emotion from the query and then give the user a song from Taylor Swift that matches the emtion. The song should have a similar lyrics to the context below:\n",
        "\n",
        "    Contexts:\n",
        "    {source_knowledge}\n",
        "\n",
        "    Query: {query}\"\"\"\n",
        "    return augment_prompt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('the outside - 17', \" I've been a lot of lonely places\"),\n",
              " ('lavender haze - 11', ' I feel (I feel)'),\n",
              " ('22 - 38', \" We're happy, free, confused, and lonely at the same time\"),\n",
              " ('enchanted - 12', ' Same old tired, lonely place'),\n",
              " ('breathe - 16', \" It's 2 AM, feelin' like I just lost a friend\")]"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Make a test query and perform experiment\n",
        "\n",
        "query = 'I feel sad and lonely'\n",
        "\n",
        "qdrant.similarity_search(query, k=5)\n",
        "\n",
        "# prompt = HumanMessage(\n",
        "#     content=custom_prompt(query)\n",
        "# )\n",
        "\n",
        "# messages = []\n",
        "# messages.append(prompt)\n",
        "# res = chat.invoke(messages)\n",
        "# print(res.content)\n",
        "[(doc.metadata['song_name'], doc.page_content) for doc in qdrant.similarity_search(query, k=5)]"
      ]
    }
  ],
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