AdvancedRAG_CrossEncoder_Reranker_Zephyr7bAlpha_.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mickymultani/RAG-with-Cross-Encoder-Reranker/blob/main/AdvancedRAG_CrossEncoder_Reranker_Zephyr7bAlpha_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kiH95SuTwGc",
        "outputId": "a2692b89-7345-432e-8d1c-969542e92ee2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hCollecting importlib_metadata<7.0,>=6.0 (from cohere)\n",
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            "Installing collected packages: importlib_metadata, fastavro, backoff, cohere\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",
            "Successfully installed backoff-2.2.1 cohere-4.42 fastavro-1.9.3 importlib_metadata-6.11.0\n"
          ]
        }
      ],
      "source": [
        "!pip install -q pypdf\n",
        "!pip install -q python-dotenv\n",
        "!pip install -q llama-index\n",
        "!pip install -q gradio\n",
        "!pip install einops\n",
        "!pip install accelerate\n",
        "!pip install sentence-transformers\n",
        "!pip install cohere"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k5352GecirlX",
        "outputId": "0d2f9435-a9e4-4448-d94a-c65cad64f697"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting llama-cpp-python\n",
            "  Downloading llama_cpp_python-0.2.28.tar.gz (9.4 MB)\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: llama-cpp-python\n",
            "  Building wheel for llama-cpp-python (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for llama-cpp-python: filename=llama_cpp_python-0.2.28-cp310-cp310-manylinux_2_35_x86_64.whl size=8778940 sha256=fce7b1dc330a11b91027360285bfdd62ac5169d772f71f3af35a34fec8743163\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-f3pigg_g/wheels/93/6e/a9/478cce089dc2a082bdcffe468a1c65465c91b25d911b30da82\n",
            "Successfully built llama-cpp-python\n",
            "Installing collected packages: llama-cpp-python\n",
            "Successfully installed llama-cpp-python-0.2.28\n"
          ]
        }
      ],
      "source": [
        "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install  llama-cpp-python --no-cache-dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "vs122vqGTsBW"
      },
      "outputs": [],
      "source": [
        "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n",
        "from llama_index.llms import HuggingFaceLLM\n",
        "import torch\n",
        "\n",
        "documents = SimpleDirectoryReader(\"/content/data\").load_data()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReohpVquTmeE",
        "outputId": "3b0ad867-1b20-43f3-e00e-e855515100ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading url https://huggingface.co/TheBloke/zephyr-7B-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q5_K_M.gguf to path /tmp/llama_index/models/zephyr-7b-alpha.Q5_K_M.gguf\n",
            "total size (MB): 5131.41\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "4894it [01:50, 44.18it/s]                          \n",
            "AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | \n"
          ]
        }
      ],
      "source": [
        "#intialize our custom LLM\n",
        "import torch\n",
        "\n",
        "from llama_index.llms import LlamaCPP\n",
        "from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt\n",
        "llm = LlamaCPP(\n",
        "    # You can pass in the URL to a GGML model to download it automatically\n",
        "    model_url='https://huggingface.co/TheBloke/zephyr-7B-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q5_K_M.gguf',\n",
        "    # optionally, you can set the path to a pre-downloaded model instead of model_url\n",
        "    model_path=None,\n",
        "    temperature=0.1,\n",
        "    max_new_tokens=256,\n",
        "    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room\n",
        "    context_window=3900,\n",
        "    # kwargs to pass to __call__()\n",
        "    generate_kwargs={},\n",
        "    # kwargs to pass to __init__()\n",
        "    # set to at least 1 to use GPU\n",
        "    model_kwargs={\"n_gpu_layers\": -1},\n",
        "    # transform inputs into Llama2 format\n",
        "    messages_to_prompt=messages_to_prompt,\n",
        "    completion_to_prompt=completion_to_prompt,\n",
        "    verbose=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
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        },
        "id": "7x13lpwiTiXP",
        "outputId": "cc033c82-a4be-4d63-fd5e-84bbaa4c8597"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
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        },
        {
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              "model_id": "89fb637627fd4df1bb0e5bd46edf9fc7"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from llama_index.embeddings import HuggingFaceEmbedding\n",
        "\n",
        "# loads BAAI/bge-small-en\n",
        "# embed_model = HuggingFaceEmbedding()\n",
        "\n",
        "# loads BAAI/bge-small-en-v1.5\n",
        "# intilaize our custom embeddings\n",
        "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
        "\n",
        "service_context = ServiceContext.from_defaults(\n",
        "    chunk_size=512,\n",
        "    llm=llm,\n",
        "    embed_model=embed_model\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_08i2AbORTk"
      },
      "source": [
        "Advanced RAG with Cross Encoder Reranker . Referred from: https://wandb.ai/ayush-thakur/llama-index-report/reports/Building-Advanced-Query-Engine-and-Evaluation-with-LlamaIndex-and-W-B--Vmlldzo0OTIzMjMy"
      ]
    },
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      "cell_type": "code",
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        "outputId": "7450e7fd-897f-47f0-dc2c-193cddcf7ff4"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/791 [00:00<?, ?B/s]"
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        {
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          "metadata": {}
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        {
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        {
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              "model_id": "a283e041ebd34b1e91ced9a66573240b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from llama_index.indices.postprocessor import SentenceTransformerRerank\n",
        "# Initialize the reranker\n",
        "rerank = SentenceTransformerRerank(\n",
        "    model=\"cross-encoder/ms-marco-MiniLM-L-12-v2\", top_n=3 # Retrives top 3 chunks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "IlLCVBsbTdQ8"
      },
      "outputs": [],
      "source": [
        "#create query engine\n",
        "index = VectorStoreIndex.from_documents(documents, service_context=service_context)\n",
        "\n",
        "query_engine = index.as_query_engine() # Without reranker\n",
        "\n",
        "def predict(input, history):\n",
        "  response = query_engine.query(input)\n",
        "  return str(response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U-GWfRmxudCO"
      },
      "outputs": [],
      "source": [
        "#create query engine with cross encoder reranker\n",
        "index = VectorStoreIndex.from_documents(documents, service_context=service_context)\n",
        "\n",
        "query_engine = index.as_query_engine(similarity_top_k=10, node_postprocessors=[rerank]) # Note we are first selecting 10 chunks.\n",
        "\n",
        "def predict(input, history):\n",
        "  response = query_engine.query(input)\n",
        "  return str(response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "gZyvBS0STcQX",
        "outputId": "ce64b87b-b657-4f85-922d-9dfc5e58da19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://f67c9d4e76dfd3a736.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://f67c9d4e76dfd3a736.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "\n",
        "def predict(input, history):\n",
        "    start_time = time.time()  # Start the timer\n",
        "\n",
        "    response = query_engine.query(input)  # Process the query\n",
        "\n",
        "    end_time = time.time()  # Stop the timer\n",
        "    response_time = end_time - start_time  # Calculate the time taken\n",
        "\n",
        "    # Format the response to include the time taken\n",
        "    timed_response = f\"{response}\\n\\n(Response Time: {response_time:.2f} seconds)\"\n",
        "    return str(timed_response)\n",
        "\n",
        "# Launch gradio chat ui\n",
        "gr.ChatInterface(predict).launch(share=True, debug=True)"
      ]
    }
  ],
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    "accelerator": "GPU",
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