notebooks/llamaindex_vector_summary_retriever.ipynb

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      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ewhm8XYufoNu",
        "outputId": "e5d97285-9128-4c09-d135-46f7f6b22710"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/132.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.8/132.8 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install -q llama-index==0.9.19 chromadb loguru pypdf openai sentence_transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "R_mLTsrVj6sc"
      },
      "outputs": [],
      "source": [
        "import openai\n",
        "\n",
        "from google.colab import userdata\n",
        "openai.api_key = userdata.get('OPENAI_API_KEY')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OuYUzg4ZjHa7",
        "outputId": "fdac4788-4f3b-4b36-bfd4-7ce15d58c9dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-02-02 22:03:09--  https://dl.dropbox.com/scl/fi/boxb3mvc0mv7d7tndmy0j/BAIDU-INC.-CODE-OF-BUSINESS-CONDUCT-AND-ETHICS.pdf?rlkey=gs378jtvi6gwzy0ncn0jx0876\n",
            "Resolving dl.dropbox.com (dl.dropbox.com)... 162.125.64.15, 2620:100:6022:15::a27d:420f\n",
            "Connecting to dl.dropbox.com (dl.dropbox.com)|162.125.64.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://dl.dropboxusercontent.com/scl/fi/boxb3mvc0mv7d7tndmy0j/BAIDU-INC.-CODE-OF-BUSINESS-CONDUCT-AND-ETHICS.pdf?rlkey=gs378jtvi6gwzy0ncn0jx0876 [following]\n",
            "--2024-02-02 22:03:09--  https://dl.dropboxusercontent.com/scl/fi/boxb3mvc0mv7d7tndmy0j/BAIDU-INC.-CODE-OF-BUSINESS-CONDUCT-AND-ETHICS.pdf?rlkey=gs378jtvi6gwzy0ncn0jx0876\n",
            "Resolving dl.dropboxusercontent.com (dl.dropboxusercontent.com)... 162.125.71.15, 2620:100:6021:15::a27d:410f\n",
            "Connecting to dl.dropboxusercontent.com (dl.dropboxusercontent.com)|162.125.71.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 221928 (217K) [application/pdf]\n",
            "Saving to: ‘Baidu-Inc-Code-of-Business-Conduct-and-Ethics.pdf’\n",
            "\n",
            "Baidu-Inc-Code-of-B 100%[===================>] 216.73K   360KB/s    in 0.6s    \n",
            "\n",
            "2024-02-02 22:03:10 (360 KB/s) - ‘Baidu-Inc-Code-of-Business-Conduct-and-Ethics.pdf’ saved [221928/221928]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O Baidu-Inc-Code-of-Business-Conduct-and-Ethics.pdf https://dl.dropbox.com/scl/fi/boxb3mvc0mv7d7tndmy0j/BAIDU-INC.-CODE-OF-BUSINESS-CONDUCT-AND-ETHICS.pdf?rlkey=gs378jtvi6gwzy0ncn0jx0876"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "FlfxNqQkncE9"
      },
      "outputs": [],
      "source": [
        "FILE_NAME = \"Baidu-Inc-Code-of-Business-Conduct-and-Ethics.pdf\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "YhDbu8hbgPug"
      },
      "outputs": [],
      "source": [
        "from typing import List\n",
        "import os\n",
        "import chromadb\n",
        "from chromadb.api.models.Collection import Collection\n",
        "from llama_index import (\n",
        "    QueryBundle,\n",
        "    ServiceContext,\n",
        "    SimpleDirectoryReader,\n",
        "    StorageContext,\n",
        "    VectorStoreIndex,\n",
        "    get_response_synthesizer,\n",
        ")\n",
        "\n",
        "from llama_index.response_synthesizers.type import ResponseMode\n",
        "from llama_index.core.base_query_engine import BaseQueryEngine\n",
        "from llama_index.core.base_retriever import BaseRetriever\n",
        "from llama_index.embeddings import OpenAIEmbedding\n",
        "from llama_index.embeddings.openai import OpenAIEmbeddingModelType\n",
        "from llama_index.embeddings.utils import EmbedType\n",
        "from llama_index.indices.base import BaseIndex\n",
        "from llama_index.indices.document_summary import DocumentSummaryIndex\n",
        "from llama_index.llms import OpenAI\n",
        "from llama_index.llms.utils import LLMType\n",
        "from llama_index.query_engine import RetrieverQueryEngine\n",
        "from llama_index.response.schema import RESPONSE_TYPE\n",
        "from llama_index.response_synthesizers.base import BaseSynthesizer\n",
        "from llama_index.schema import NodeWithScore\n",
        "from llama_index.vector_stores import ChromaVectorStore\n",
        "from loguru import logger\n",
        "from llama_index.indices.document_summary import (\n",
        "    DocumentSummaryIndexEmbeddingRetriever,\n",
        "    DocumentSummaryIndexLLMRetriever,\n",
        ")\n",
        "from llama_index.node_parser import SentenceWindowNodeParser\n",
        "from llama_index.indices.postprocessor import MetadataReplacementPostProcessor\n",
        "from llama_index.indices.postprocessor import SentenceTransformerRerank\n",
        "from llama_index.postprocessor.types import BaseNodePostprocessor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "HKibE5P2gGeZ"
      },
      "outputs": [],
      "source": [
        "MODE = \"OR\"\n",
        "TEMPERATURE = 0.0\n",
        "SIM_TOP_K = 3\n",
        "RERANK_TOP_K = 3\n",
        "CHUNK_OVERLAP = 100\n",
        "CHUNK_SIZE = 5000\n",
        "WIN_SZ = 3\n",
        "DOC_DIR = \"./\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Z5ElLwRGgMtU"
      },
      "outputs": [],
      "source": [
        "def create_vectors(path: str, collection_name=\"tmp_collection\") -> Collection:\n",
        "    chroma_client = chromadb.PersistentClient(path)\n",
        "    # https://github.com/run-llama/llama_index/issues/6528\n",
        "    return chroma_client.get_or_create_collection(collection_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "50e_OApfhbnV"
      },
      "outputs": [],
      "source": [
        "class MultiVectorSummaryRetriever(BaseRetriever):\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        summary_retriever: BaseRetriever,\n",
        "        vector_retriever: BaseRetriever,\n",
        "        mode: str = \"OR\",\n",
        "    ) -> None:\n",
        "        \"\"\"Init params.\"\"\"\n",
        "\n",
        "        self._summary_retriever = summary_retriever\n",
        "        self._vector_retriever = vector_retriever\n",
        "        if mode not in (\"AND\", \"OR\"):\n",
        "            raise ValueError(\"Invalid mode.\")\n",
        "        self._mode = mode\n",
        "        super().__init__()\n",
        "\n",
        "    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:\n",
        "        \"\"\"Retrieve nodes given query.\"\"\"\n",
        "\n",
        "        summary_nodes = self._summary_retriever.retrieve(query_bundle)\n",
        "        vector_nodes = self._vector_retriever.retrieve(query_bundle)\n",
        "\n",
        "        summary_ids = {n.node.node_id for n in summary_nodes}\n",
        "        vector_ids = {n.node.node_id for n in vector_nodes}\n",
        "\n",
        "        combined_dict = {n.node.node_id: n for n in vector_nodes}\n",
        "        combined_dict.update({n.node.node_id: n for n in summary_nodes})\n",
        "\n",
        "        if self._mode == \"AND\":\n",
        "            retrieve_ids = vector_ids.intersection(summary_ids)\n",
        "        else:\n",
        "            retrieve_ids = vector_ids.union(summary_ids)\n",
        "\n",
        "        retrieve_nodes = [combined_dict[rid] for rid in retrieve_ids]\n",
        "        return retrieve_nodes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "VsRswqK8hpGE"
      },
      "outputs": [],
      "source": [
        "def create_service_context(llm: LLMType, embs: EmbedType) -> ServiceContext:\n",
        "    node_parser = SentenceWindowNodeParser.from_defaults(\n",
        "        window_size=WIN_SZ,\n",
        "        window_metadata_key=\"window\",\n",
        "        original_text_metadata_key=\"original_text\",\n",
        "    )\n",
        "\n",
        "    return ServiceContext.from_defaults(\n",
        "        #chunk_overlap=CHUNK_OVERLAP,\n",
        "        #chunk_size=CHUNK_SIZE,\n",
        "        node_parser=node_parser,\n",
        "        llm=llm,\n",
        "        embed_model=embs,\n",
        "    )\n",
        "\n",
        "\n",
        "def create_storage_context() -> StorageContext:\n",
        "    path: str = \"./db/LlamaIndexMultiVectorSummary\"\n",
        "    return StorageContext.from_defaults(\n",
        "        vector_store=ChromaVectorStore(create_vectors(path=path))\n",
        "    )"
      ]
    },
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          "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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        },
        {
          "name": "stdout",
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          "text": [
            "current doc id: 8fd8b1d0-55d2-4198-8a2d-1dcfd42c8817\n",
            "current doc id: c87c09bf-204b-4d67-9fe5-6a9c2edf25a5\n",
            "current doc id: 7c19cb1d-6f44-4d67-8fd1-20fe82cb83c2\n",
            "current doc id: 7ab088a3-f36b-43cb-ba8e-665773788e93\n",
            "current doc id: e269692a-5538-4218-a6e0-ed1f8047ce0f\n",
            "current doc id: 3071c51b-b3ae-43ee-8bd7-2194d4a8ed71\n",
            "current doc id: 1e94837f-90db-4bd3-abf0-2d63a11901b3\n",
            "current doc id: 5fdd883e-f9af-44a2-a13c-3695dc2f71f3\n",
            "current doc id: 7750fbe7-5e31-4ed4-9f19-672638f4a235\n",
            "current doc id: 890efcaa-7f06-4551-9bb4-6be3413e3d5c\n"
          ]
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            "text/plain": [
              "Generating embeddings:   0%|          | 0/10 [00:00<?, ?it/s]"
            ]
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              "Generating embeddings:   0%|          | 0/160 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[32m2024-02-02 22:06:12.536\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<cell line: 35>\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mLoading index from storage\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "llm = OpenAI(model=\"gpt-4-1106-preview\", temperature=TEMPERATURE)\n",
        "embs = \"local:BAAI/bge-small-en-v1.5\"#OpenAIEmbedding(model=OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002)\n",
        "\n",
        "service_context: ServiceContext = (\n",
        "    create_service_context(llm, embs)\n",
        ")\n",
        "\n",
        "storage_context: StorageContext = (\n",
        "    create_storage_context()\n",
        ")\n",
        "\n",
        "required_exts: List[str] = [\".pdf\"]\n",
        "# Create a directory and put some files in it for querying.\n",
        "input_files: List[str] = [FILE_NAME]\n",
        "docs: SimpleDirectoryReader = SimpleDirectoryReader(\n",
        "    DOC_DIR,\n",
        "    required_exts=required_exts,\n",
        "    input_files=input_files,\n",
        ").load_data()\n",
        "\n",
        "summary_index: BaseIndex = DocumentSummaryIndex.from_documents(\n",
        "    docs,\n",
        "    storage_context=storage_context,\n",
        "    service_context=service_context,\n",
        "    show_progress=True,\n",
        ")\n",
        "\n",
        "vector_index: BaseIndex = VectorStoreIndex.from_documents(\n",
        "    docs,\n",
        "    service_context=service_context,\n",
        "    storage_context=storage_context,\n",
        "    show_progress=True,\n",
        ")\n",
        "\n",
        "logger.info(\"Loading index from storage\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "wXEuJljPk1zU"
      },
      "outputs": [],
      "source": [
        "# DocumentSummaryIndexEmbeddingRetriever, for embedding retriever mode\n",
        "# DocumentSummaryIndexLLMRetriever, for LLM based retriever mode\n",
        "summary_retriever: BaseRetriever = DocumentSummaryIndexLLMRetriever(\n",
        "    summary_index,\n",
        "    similarity_top_k=SIM_TOP_K,\n",
        ")\n",
        "vector_retriever: BaseRetriever = vector_index.as_retriever(\n",
        "    similarity_top_k=SIM_TOP_K,\n",
        ")\n",
        "\n",
        "multi_vec_sum_retriever: BaseRetriever = MultiVectorSummaryRetriever(\n",
        "    summary_retriever=summary_retriever,\n",
        "    vector_retriever=vector_retriever,\n",
        "    mode=MODE,\n",
        ")\n",
        "response_synthesizer: BaseSynthesizer = get_response_synthesizer(\n",
        "    service_context=service_context,\n",
        "    response_mode=ResponseMode.REFINE,\n",
        ")\n",
        "\n",
        "postproc: BaseNodePostprocessor = MetadataReplacementPostProcessor(target_metadata_key=\"window\")\n",
        "rerank :BaseNodePostprocessor = SentenceTransformerRerank(\n",
        "    top_n=RERANK_TOP_K, model=\"BAAI/bge-reranker-base\"\n",
        ")\n",
        "\n",
        "query_engine: BaseQueryEngine = RetrieverQueryEngine(\n",
        "    retriever=multi_vec_sum_retriever,\n",
        "    response_synthesizer=response_synthesizer,\n",
        "    node_postprocessors=[postproc, rerank]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlnlh-fYkQCD"
      },
      "source": [
        "# Query"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LiovAzeG6a7B"
      },
      "source": [
        "The settings of\n",
        "\n",
        "SIM_TOP_K = 3\n",
        "\n",
        "RERANK_TOP_K = 3\n",
        "\n",
        "can affect the query duration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "Edccsd43l9XA"
      },
      "outputs": [],
      "source": [
        "prompt = \"\"\"What is the topic of content?\n",
        "    The response should be presented as a list of key points, after creating the title of the content,\n",
        "    formatted in HTML with appropriate markup for clarity and organization.\n",
        "    \"\"\"\n",
        "result: RESPONSE_TYPE = query_engine.query(prompt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "8QN3z9XZmKeC",
        "outputId": "92cf20a2-3717-47aa-d150-2df019e5e2d9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "```html\n",
              "<!DOCTYPE html>\n",
              "<html>\n",
              "<head>\n",
              "<title>Code of Business Conduct and Ethics at Baidu Inc.</title>\n",
              "</head>\n",
              "<body>\n",
              "\n",
              "<h1>Key Topics Covered in Baidu Inc.'s Code of Business Conduct and Ethics</h1>\n",
              "\n",
              "<ul>\n",
              "  <li>Authorized disclosure of company information to securities market professionals and shareholders.</li>\n",
              "  <li>Procedures for employees when approached for company information by securities market professionals or shareholders.</li>\n",
              "  <li>Guidelines for employee communication with the media.</li>\n",
              "  <li>Protocols for handling potential public relations emergencies.</li>\n",
              "  <li>Criteria for the selection of suppliers and fair procurement practices.</li>\n",
              "</ul>\n",
              "\n",
              "</body>\n",
              "</html>\n",
              "```"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from IPython.display import display, HTML\n",
        "\n",
        "display(HTML(result.response))"
      ]
    }
  ],
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