{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4spPB1okEaLy"
      },
      "source": [
        "#  🔍 Improve retrieval by embedding meaningful metadata 🏷️\n",
        "\n",
        "\n",
        "*Notebook by [Stefano Fiorucci](https://github.com/anakin87)*\n",
        "\n",
        "In this notebook, I do some experiments on embedding meaningful metadata to improve Document retrieval."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R_Abekd7m0Ps"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "! pip install wikipedia haystack-ai sentence_transformers rich"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oZxQ0sY2I8fL"
      },
      "outputs": [],
      "source": [
        "import rich"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FN3CR1uGtTxH"
      },
      "source": [
        "## Load data from Wikipedia\n",
        "\n",
        "We are going to download the Wikipedia pages related to some bands, using the python library `wikipedia`.\n",
        "\n",
        "These pages are converted into Haystack Documents."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M1JMji_Vm289"
      },
      "outputs": [],
      "source": [
        "some_bands=\"\"\"The Beatles\n",
        "Rolling stones\n",
        "Dire Straits\n",
        "The Cure\n",
        "The Smiths\"\"\".split(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hsWE_glDoqVh"
      },
      "outputs": [],
      "source": [
        "import wikipedia\n",
        "from haystack.dataclasses import Document\n",
        "\n",
        "raw_docs=[]\n",
        "\n",
        "for title in some_bands:\n",
        "    page = wikipedia.page(title=title, auto_suggest=False)\n",
        "    doc = Document(content=page.content, meta={\"title\": page.title, \"url\":page.url})\n",
        "    raw_docs.append(doc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAYcX3qU3HVn"
      },
      "source": [
        "## 🔧 Setup the experiment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JVo2FlEtbTZ"
      },
      "source": [
        "### Utility functions to create Pipelines\n",
        "\n",
        "The **indexing Pipeline** transforms the Documents and stores them (with vectors) in a Document Store. The **retrieval Pipeline** takes a query as input and perform the vector search.\n",
        "\n",
        "\n",
        "I build some utility functions to create different indexing and retrieval Pipelines.\n",
        "\n",
        "In fact, I am interested in comparing the standard approach (where we only embed text) with the embedding metadata strategy (we embed text + meaningful metadata).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kLZ8HO4kpREU"
      },
      "outputs": [],
      "source": [
        "from haystack import Pipeline\n",
        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
        "from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter\n",
        "from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder\n",
        "from haystack.components.writers import DocumentWriter\n",
        "from haystack.document_stores.types import DuplicatePolicy\n",
        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
        "from haystack.utils import ComponentDevice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "htqK8XSTwOzP"
      },
      "outputs": [],
      "source": [
        "def create_indexing_pipeline(document_store, metadata_fields_to_embed):\n",
        "\n",
        "  indexing = Pipeline()\n",
        "  indexing.add_component(\"cleaner\", DocumentCleaner())\n",
        "  indexing.add_component(\"splitter\", DocumentSplitter(split_by='sentence', split_length=2))\n",
        "\n",
        "  # in the following componente, we can specify the parameter `metadata_fields_to_embed`, with the metadata to embed\n",
        "  indexing.add_component(\"doc_embedder\", SentenceTransformersDocumentEmbedder(model=\"thenlper/gte-large\",\n",
        "                                                                              device=ComponentDevice.from_str(\"cuda:0\"),\n",
        "                                                                              meta_fields_to_embed=metadata_fields_to_embed)\n",
        "  )\n",
        "  indexing.add_component(\"writer\", DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE))\n",
        "\n",
        "  indexing.connect(\"cleaner\", \"splitter\")\n",
        "  indexing.connect(\"splitter\", \"doc_embedder\")\n",
        "  indexing.connect(\"doc_embedder\", \"writer\")\n",
        "\n",
        "  return indexing\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TOzjzUuaCbFI"
      },
      "outputs": [],
      "source": [
        "def create_retrieval_pipeline(document_store):\n",
        "\n",
        "  retrieval = Pipeline()\n",
        "  retrieval.add_component(\"text_embedder\", SentenceTransformersTextEmbedder(model=\"thenlper/gte-large\",\n",
        "                                                                            device=ComponentDevice.from_str(\"cuda:0\")))\n",
        "  retrieval.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store, scale_score=False, top_k=3))\n",
        "\n",
        "  retrieval.connect(\"text_embedder\", \"retriever\")\n",
        "\n",
        "  return retrieval"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bu8xMz-A1axx"
      },
      "source": [
        "###  Create the Pipelines"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnGGIIQIGUqZ"
      },
      "source": [
        "Let's define 2 Document Stores, to compare the different approaches."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7vgQ3G3GwQ83"
      },
      "outputs": [],
      "source": [
        "document_store = InMemoryDocumentStore(embedding_similarity_function=\"cosine\")\n",
        "document_store_w_embedded_metadata = InMemoryDocumentStore(embedding_similarity_function=\"cosine\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTIo-x0c18n3"
      },
      "source": [
        "Now, I create the 2 indexing pipelines and run them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FP2yffvZ2G-d"
      },
      "outputs": [],
      "source": [
        "indexing_pipe_std = create_indexing_pipeline(document_store=document_store, metadata_fields_to_embed=[])\n",
        "\n",
        "# here we specify the fields to embed\n",
        "# we select the field `title`, containing the name of the band\n",
        "indexing_pipe_w_embedded_metadata = create_indexing_pipeline(document_store=document_store_w_embedded_metadata, metadata_fields_to_embed=[\"title\"])"
      ]
    },
    {
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        },
        "id": "qZGQOSt12g8l",
        "outputId": "3403009a-0bb2-4f43-ae6e-80471814053c"
      },
      "outputs": [],
      "source": [
        "indexing_pipe_std.run({\"cleaner\":{\"documents\":raw_docs}})\n",
        "indexing_pipe_w_embedded_metadata.run({\"cleaner\":{\"documents\":raw_docs}})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Ha3rjwt4n42",
        "outputId": "635478cc-99e1-4a57-e70b-079b683fe5bf"
      },
      "outputs": [],
      "source": [
        "print(len(document_store.filter_documents()))\n",
        "print(len(document_store_w_embedded_metadata.filter_documents()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njSjs9x42vON"
      },
      "source": [
        "Create the 2 retrieval pipelines."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fT7hfC6t2z81"
      },
      "outputs": [],
      "source": [
        "retrieval_pipe_std = create_retrieval_pipeline(document_store=document_store)\n",
        "\n",
        "retrieval_pipe_w_embedded_metadata = create_retrieval_pipeline(document_store=document_store_w_embedded_metadata)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GfJ6O1Hf3aan"
      },
      "source": [
        "## 🧪 Run the experiment!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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            "d4a511821e47400eb013ae818471160a"
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        "id": "cYWX7sjtDqaL",
        "outputId": "c9fb2ead-51cf-4f60-e5f0-b800253437cd"
      },
      "outputs": [],
      "source": [
        "# standard approach (no metadata embedding)\n",
        "\n",
        "res=retrieval_pipe_std.run({\"text_embedder\":{\"text\":\"have the beatles ever been to bangor?\"}})\n",
        "for doc in res['retriever']['documents']:\n",
        "  rich.print(doc)\n",
        "  rich.print(doc.content+\"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AkxD6XiX4-Vl"
      },
      "source": [
        "❌ the retrieved Documents seem irrelevant"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417,
          "referenced_widgets": [
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        "outputId": "3483764e-e591-40a2-fec6-ad26882b5906"
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      "outputs": [],
      "source": [
        "# embedding meaningful metadata\n",
        "\n",
        "res=retrieval_pipe_w_embedded_metadata.run({\"text_embedder\":{\"text\":\"have the beatles ever been to bangor?\"}})\n",
        "for doc in res['retriever']['documents']:\n",
        "  rich.print(doc)\n",
        "  rich.print(doc.content+\"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9lCMR9T5f26"
      },
      "source": [
        "✅ the first Document is relevant"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      "outputs": [],
      "source": [
        "# standard approach (no metadata embedding)\n",
        "\n",
        "res=retrieval_pipe_std.run({\"text_embedder\":{\"text\":\"What announcements did the band The Cure make in 2022?\"}})\n",
        "for doc in res['retriever']['documents']:\n",
        "  rich.print(doc)\n",
        "  rich.print(doc.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHE7HzXg57ms"
      },
      "source": [
        "❌ the retrieved Documents seem irrelevant"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      "outputs": [],
      "source": [
        "# embedding meaningful metadata\n",
        "\n",
        "res=retrieval_pipe_w_embedded_metadata.run({\"text_embedder\":{\"text\":\"What announcements did the band The Cure make in 2022?\"}})\n",
        "for doc in res['retriever']['documents']:\n",
        "  rich.print(doc)\n",
        "  rich.print(doc.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-kTFEIKR6JOU"
      },
      "source": [
        "✅ some Documents are relevant"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvUb-deT6a05"
      },
      "source": [
        "## ⚠️ Notes of caution\n",
        "\n",
        "- This technique is not a silver bullet\n",
        "- It works well when the embedded metadata are meaningful and distinctive\n",
        "- I would say that the embedded metadata should be meaningful from the perspective of the embedding model. For example, I don't expect embedding numbers to work well."
      ]
    }
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