RAG.ipynb

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      "metadata": {
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        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/DigitalResultsPros/30-Days-Of-React/blob/master/RAG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "source": [
        "! pip install haystack-ai transformers \"huggingface_hub>=0.22.0\" sentence-transformers accelerate bitsandbytes"
      ],
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      },
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        {
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          "text": [
            "Collecting haystack-ai\n",
            "  Downloading haystack_ai-2.2.4-py3-none-any.whl (346 kB)\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.1/314.1 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting bitsandbytes\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "  Downloading lazy_imports-0.3.1-py3-none-any.whl (12 kB)\n",
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            "Successfully installed accelerate-0.32.1 backoff-2.2.1 bitsandbytes-0.43.1 h11-0.14.0 haystack-ai-2.2.4 httpcore-1.0.5 httpx-0.27.0 lazy-imports-0.3.1 monotonic-1.6 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 openai-1.35.10 posthog-3.5.0 sentence-transformers-3.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass, os\n",
        "\n",
        "\n",
        "os.environ[\"HF_API_TOKEN\"] = getpass.getpass(\"Your Hugging Face token\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1L0iEi0gGHSJ",
        "outputId": "0e62eb63-6612-42d5-9225-753dda00637c"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Your Hugging Face token··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack import Pipeline, Document\n",
        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
        "from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder\n",
        "from haystack.components.writers import DocumentWriter\n",
        "from haystack.components.preprocessors import DocumentSplitter\n",
        "from haystack.components.retrievers import InMemoryEmbeddingRetriever\n",
        "from haystack.components.generators import HuggingFaceLocalGenerator\n",
        "from haystack.components.builders import PromptBuilder\n",
        "from haystack.components.joiners import DocumentJoiner\n",
        "from haystack import component\n",
        "from typing import List, Dict, Any\n",
        "\n",
        "@component\n",
        "class DocumentFormatter:\n",
        "    @component.output_types(formatted_documents=str)\n",
        "    def run(self, documents: List[Document]):\n",
        "        formatted = \"\\n\".join([doc.content for doc in documents])\n",
        "        print(f\"Formatted documents: {formatted}\")  # Debug print\n",
        "        return {\"formatted_documents\": formatted}\n",
        "\n",
        "@component\n",
        "class QueryAdder:\n",
        "    @component.output_types(template_variables=Dict[str, Any])\n",
        "    def run(self, formatted_documents: str, query: str):\n",
        "        template_vars = {\"documents\": formatted_documents, \"query\": query}\n",
        "        print(f\"Template variables: {template_vars}\")  # Debug print\n",
        "        return {\"template_variables\": template_vars}\n",
        "\n",
        "class AdvancedRAGSystem:\n",
        "    def __init__(self, embedding_model=\"sentence-transformers/all-mpnet-base-v2\", llm_model=\"google/flan-t5-small\"):\n",
        "        self.main_document_store = InMemoryDocumentStore()\n",
        "        self.memory_document_store = InMemoryDocumentStore()\n",
        "        self.lessons_document_store = InMemoryDocumentStore()\n",
        "\n",
        "        self.embedding_model = embedding_model\n",
        "        self.llm_model = llm_model\n",
        "\n",
        "        self.indexing_pipeline = self._create_indexing_pipeline()\n",
        "        self.query_pipeline = self._create_query_pipeline()\n",
        "\n",
        "    def _create_indexing_pipeline(self):\n",
        "        indexing_pipeline = Pipeline()\n",
        "        indexing_pipeline.add_component(\"splitter\", DocumentSplitter(split_by=\"word\", split_length=200))\n",
        "        indexing_pipeline.add_component(\n",
        "            \"embedder\",\n",
        "            SentenceTransformersDocumentEmbedder(model=self.embedding_model)\n",
        "        )\n",
        "        indexing_pipeline.add_component(\"writer\", DocumentWriter(document_store=self.main_document_store))\n",
        "        indexing_pipeline.connect(\"splitter\", \"embedder\")\n",
        "        indexing_pipeline.connect(\"embedder\", \"writer\")\n",
        "        return indexing_pipeline\n",
        "\n",
        "    def _create_query_pipeline(self):\n",
        "        query_pipeline = Pipeline()\n",
        "        query_pipeline.add_component(\n",
        "            \"text_embedder\",\n",
        "            SentenceTransformersTextEmbedder(model=self.embedding_model)\n",
        "        )\n",
        "        query_pipeline.add_component(\"main_retriever\", InMemoryEmbeddingRetriever(document_store=self.main_document_store, top_k=3))\n",
        "        query_pipeline.add_component(\"memory_retriever\", InMemoryEmbeddingRetriever(document_store=self.memory_document_store, top_k=2))\n",
        "        query_pipeline.add_component(\"lessons_retriever\", InMemoryEmbeddingRetriever(document_store=self.lessons_document_store, top_k=1))\n",
        "\n",
        "        query_pipeline.add_component(\"joiner\", DocumentJoiner(join_mode=\"concatenate\"))\n",
        "        query_pipeline.add_component(\"formatter\", DocumentFormatter())\n",
        "        query_pipeline.add_component(\"query_adder\", QueryAdder())\n",
        "\n",
        "        prompt_template = \"\"\"\n",
        "        You are an AI assistant tasked with answering questions based on the given context. Your goal is to provide accurate and helpful responses.\n",
        "\n",
        "        Context:\n",
        "        {documents}\n",
        "\n",
        "        Human: {query}\n",
        "\n",
        "        Assistant: Let's approach this step-by-step:\n",
        "\n",
        "        1) First, I'll review the context provided.\n",
        "        2) Then, I'll identify the relevant information to answer the question.\n",
        "        3) Finally, I'll formulate a clear and concise answer.\n",
        "\n",
        "        Based on the context and the question, here's my response:\n",
        "\n",
        "        \"\"\"\n",
        "        query_pipeline.add_component(\"prompt_builder\", PromptBuilder(template=prompt_template))\n",
        "\n",
        "        query_pipeline.add_component(\"generator\", HuggingFaceLocalGenerator(model=self.llm_model))\n",
        "\n",
        "        query_pipeline.connect(\"text_embedder.embedding\", \"main_retriever.query_embedding\")\n",
        "        query_pipeline.connect(\"text_embedder.embedding\", \"memory_retriever.query_embedding\")\n",
        "        query_pipeline.connect(\"text_embedder.embedding\", \"lessons_retriever.query_embedding\")\n",
        "        query_pipeline.connect(\"main_retriever\", \"joiner\")\n",
        "        query_pipeline.connect(\"memory_retriever\", \"joiner\")\n",
        "        query_pipeline.connect(\"lessons_retriever\", \"joiner\")\n",
        "        query_pipeline.connect(\"joiner.documents\", \"formatter.documents\")\n",
        "        query_pipeline.connect(\"formatter.formatted_documents\", \"query_adder.formatted_documents\")\n",
        "        query_pipeline.connect(\"query_adder.template_variables\", \"prompt_builder.template_variables\")\n",
        "        query_pipeline.connect(\"prompt_builder\", \"generator\")\n",
        "\n",
        "        return query_pipeline\n",
        "\n",
        "    def add_documents(self, documents):\n",
        "        result = self.indexing_pipeline.run({\"splitter\": {\"documents\": documents}})\n",
        "        print(f\"Indexing result: {result}\")  # Debug print\n",
        "        print(f\"Document store contents: {self.main_document_store.filter_documents()}\")  # Debug print\n",
        "\n",
        "    def add_lesson(self, lesson):\n",
        "        lesson_doc = Document(content=lesson)\n",
        "        self.lessons_document_store.write_documents([lesson_doc])\n",
        "\n",
        "    def query(self, query):\n",
        "        print(f\"Documents in store before query: {self.main_document_store.filter_documents()}\")  # Debug print\n",
        "\n",
        "        # Debug: Check if documents have embeddings\n",
        "        for doc in self.main_document_store.filter_documents():\n",
        "            print(f\"Document ID: {doc.id}, Has Embedding: {doc.embedding is not None}, Embedding size: {len(doc.embedding) if doc.embedding is not None else 'N/A'}\")\n",
        "\n",
        "        result = self.query_pipeline.run({\n",
        "            \"text_embedder\": {\"text\": query},\n",
        "            \"query_adder\": {\"query\": query}\n",
        "        })\n",
        "\n",
        "        # Debug: Print retrieved documents\n",
        "        if \"main_retriever\" in result:\n",
        "            print(f\"Retrieved documents: {result['main_retriever']}\")\n",
        "\n",
        "        print(f\"Query result: {result}\")  # Debug print\n",
        "        return result\n",
        "\n",
        "    def save_state(self, filename):\n",
        "        # Implement saving of document stores\n",
        "        pass\n",
        "\n",
        "    def load_state(self, filename):\n",
        "        # Implement loading of document stores\n",
        "        pass\n",
        "\n",
        "# Usage example\n",
        "if __name__ == \"__main__\":\n",
        "    rag_system = AdvancedRAGSystem()\n",
        "\n",
        "    # Add documents\n",
        "    raw_docs = [Document(content=\"The capital of France is Paris.\")]\n",
        "    rag_system.add_documents(raw_docs)\n",
        "\n",
        "    # Add a lesson\n",
        "    rag_system.add_lesson(\"Always provide context when discussing geographical facts.\")\n",
        "\n",
        "    # Query the system\n",
        "    result = rag_system.query(\"What is the capital of France?\")\n",
        "    if \"generator\" in result and result[\"generator\"] and \"replies\" in result[\"generator\"]:\n",
        "        print(result[\"generator\"][\"replies\"][0])\n",
        "    else:\n",
        "        print(\"No response generated.\")\n",
        "\n",
        "    # The memory is automatically updated with each query"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "id": "tSvax3GvOsAp",
        "outputId": "cc3d12c2-b39c-4380-a706-57c25073d94a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/SentenceTransformer.py:174: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v3 of SentenceTransformers.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
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        {
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          "text": [
            "Indexing result: {'writer': {'documents_written': 1}}\n",
            "Document store contents: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 768)]\n",
            "Documents in store before query: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 768)]\n",
            "Document ID: 48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, Has Embedding: True, Embedding size: 768\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "model_id": "deabceeed9d14aaab6534d6bd6486d1b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:haystack.document_stores.in_memory.document_store:No Documents found with embeddings. Returning empty list. To generate embeddings, use a DocumentEmbedder.\n",
            "WARNING:haystack.document_stores.in_memory.document_store:No Documents found with embeddings. Returning empty list. To generate embeddings, use a DocumentEmbedder.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Formatted documents: The capital of France is Paris.\n",
            "Template variables: {'documents': 'The capital of France is Paris.', 'query': 'What is the capital of France?'}\n",
            "Query result: {'generator': {'replies': [\"I'll review the context.\"]}}\n",
            "I'll review the context.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Usage example\n",
        "if __name__ == \"__main__\":\n",
        "\n",
        "    rag_system.add_documents(raw_docs)\n",
        "    rag_system = AdvancedRAGSystem()\n",
        "    print(raw_docs)\n",
        "\n",
        "    # Add documents\n",
        "    raw_docs = [Document(content=\"The capital of France is Paris.\")]\n",
        "    rag_system.add_documents(raw_docs)\n",
        "    rag_system = AdvancedRAGSystem()\n",
        "    # Add a lesson\n",
        "    rag_system.add_lesson(\"Always provide context when discussing geographical facts.\")\n",
        "\n",
        "    # Query the system\n",
        "    result = rag_system.query(\"What is the capital of France?\")\n",
        "    if \"generator\" in result and result[\"generator\"] and \"replies\" in result[\"generator\"]:\n",
        "        print(result[\"generator\"][\"replies\"][0])\n",
        "    else:\n",
        "        print(\"No response generated.\")\n",
        "\n",
        "    # The memory is automatically updated with each query"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359,
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        "outputId": "3f16b41b-0fa6-4236-c1a3-73a98d26c27d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "dc7f8d631a9e436fb10e2bd0feab02b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Indexing result: {'writer': {'documents_written': 1}}\n",
            "Document store contents: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 384)]\n",
            "[Document(id=4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b, content: 'The capital of France is Paris.')]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "model_id": "8ea02f8e0629476dbfe48c38445acb5c"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Indexing result: {'writer': {'documents_written': 1}}\n",
            "Document store contents: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 384)]\n",
            "Documents in store before query: []\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6b731c2e08648f5802153358680e317"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:haystack.document_stores.in_memory.document_store:No Documents found with embeddings. Returning empty list. To generate embeddings, use a DocumentEmbedder.\n",
            "WARNING:haystack.document_stores.in_memory.document_store:No Documents found with embeddings. Returning empty list. To generate embeddings, use a DocumentEmbedder.\n",
            "WARNING:haystack.document_stores.in_memory.document_store:No Documents found with embeddings. Returning empty list. To generate embeddings, use a DocumentEmbedder.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Formatted documents: \n",
            "Template variables: {'documents': '', 'query': 'What is the capital of France?'}\n",
            "Query result: {'generator': {'replies': ['can be used to search for documents']}}\n",
            "can be used to search for documents\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "import torch\n",
        "from haystack import Pipeline, Document\n",
        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
        "from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder\n",
        "from haystack.components.writers import DocumentWriter\n",
        "from haystack.components.preprocessors import DocumentSplitter\n",
        "from haystack.components.retrievers import InMemoryEmbeddingRetriever\n",
        "from haystack.components.generators import HuggingFaceLocalGenerator\n",
        "from haystack.components.builders import PromptBuilder\n",
        "from haystack import component\n",
        "from typing import List, Dict, Any\n",
        "\n",
        "@component\n",
        "class DocumentFormatter:\n",
        "    @component.output_types(formatted_documents=str)\n",
        "    def run(self, documents: List[Document]):\n",
        "        formatted = \"\\n\".join([f\"- {doc.content}\" for doc in documents])\n",
        "        print(f\"Formatted documents: {formatted}\")  # Debug print\n",
        "        return {\"formatted_documents\": formatted}\n",
        "\n",
        "@component\n",
        "class QueryAdder:\n",
        "    @component.output_types(template_variables=Dict[str, Any])\n",
        "    def run(self, formatted_documents: str, query: str):\n",
        "        template_vars = {\"documents\": formatted_documents, \"query\": query}\n",
        "        print(f\"Template variables: {template_vars}\")  # Debug print\n",
        "        return {\"template_variables\": template_vars}\n",
        "\n",
        "class AdvancedRAGSystem:\n",
        "    def __init__(self, embedding_model=\"Snowflake/snowflake-arctic-embed-l\", llm_model=\"meta-llama/Meta-Llama-3-8B-Instruct\"):\n",
        "        self.document_store = InMemoryDocumentStore()\n",
        "        self.embedding_model = embedding_model\n",
        "        self.llm_model = llm_model\n",
        "\n",
        "        self.indexing_pipeline = self._create_indexing_pipeline()\n",
        "        self.query_pipeline = self._create_query_pipeline()\n",
        "\n",
        "    def _create_indexing_pipeline(self):\n",
        "        indexing_pipeline = Pipeline()\n",
        "        indexing_pipeline.add_component(\"splitter\", DocumentSplitter(split_by=\"word\", split_length=100))\n",
        "        indexing_pipeline.add_component(\n",
        "            \"embedder\",\n",
        "            SentenceTransformersDocumentEmbedder(model=self.embedding_model)\n",
        "        )\n",
        "        indexing_pipeline.add_component(\"writer\", DocumentWriter(document_store=self.document_store))\n",
        "        indexing_pipeline.connect(\"splitter\", \"embedder\")\n",
        "        indexing_pipeline.connect(\"embedder\", \"writer\")\n",
        "        return indexing_pipeline\n",
        "\n",
        "    def _create_query_pipeline(self):\n",
        "        query_pipeline = Pipeline()\n",
        "        query_pipeline.add_component(\n",
        "            \"text_embedder\",\n",
        "            SentenceTransformersTextEmbedder(model=self.embedding_model)\n",
        "        )\n",
        "        query_pipeline.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=self.document_store, top_k=3))\n",
        "        query_pipeline.add_component(\"formatter\", DocumentFormatter())\n",
        "        query_pipeline.add_component(\"query_adder\", QueryAdder())\n",
        "\n",
        "        prompt_template = \"\"\"\n",
        "        Answer the question based on the given context. If you can't answer the question based on the context, say \"I don't know\".\n",
        "\n",
        "        Context:\n",
        "        {documents}\n",
        "\n",
        "        Question: {query}\n",
        "\n",
        "        Answer:\n",
        "        \"\"\"\n",
        "        query_pipeline.add_component(\"prompt_builder\", PromptBuilder(template=prompt_template))\n",
        "\n",
        "        query_pipeline.add_component(\"generator\", HuggingFaceLocalGenerator(model=self.llm_model))\n",
        "\n",
        "        query_pipeline.connect(\"text_embedder.embedding\", \"retriever.query_embedding\")\n",
        "        query_pipeline.connect(\"retriever\", \"formatter\")\n",
        "        query_pipeline.connect(\"formatter.formatted_documents\", \"query_adder.formatted_documents\")\n",
        "        query_pipeline.connect(\"query_adder.template_variables\", \"prompt_builder.template_variables\")\n",
        "        query_pipeline.connect(\"prompt_builder\", \"generator\")\n",
        "\n",
        "        return query_pipeline\n",
        "\n",
        "    def add_documents(self, documents):\n",
        "        result = self.indexing_pipeline.run({\"splitter\": {\"documents\": documents}})\n",
        "        print(f\"Indexing result: {result}\")  # Debug print\n",
        "        print(f\"Document store contents: {self.document_store.filter_documents()}\")  # Debug print\n",
        "        gc.collect()\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "    def query(self, query):\n",
        "        print(f\"Documents in store before query: {self.document_store.filter_documents()}\")  # Debug print\n",
        "        result = self.query_pipeline.run({\n",
        "            \"text_embedder\": {\"text\": query},\n",
        "            \"query_adder\": {\"query\": query}\n",
        "        })\n",
        "        print(f\"Query result: {result}\")  # Debug print\n",
        "        gc.collect()\n",
        "        torch.cuda.empty_cache()\n",
        "        return result\n",
        "\n",
        "    def clear_memory(self):\n",
        "        all_docs = self.document_store.filter_documents()\n",
        "        if all_docs:\n",
        "            doc_ids = [doc.id for doc in all_docs]\n",
        "            self.document_store.delete_documents(doc_ids)\n",
        "        gc.collect()\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "# Usage example\n",
        "if __name__ == \"__main__\":\n",
        "    rag_system = AdvancedRAGSystem()\n",
        "\n",
        "    # Add documents\n",
        "    raw_docs = [Document(content=\"The capital of France is Paris.\")]\n",
        "    rag_system.add_documents(raw_docs)\n",
        "\n",
        "    # Query the system\n",
        "    result = rag_system.query(\"What is the capital of France?\")\n",
        "    if \"generator\" in result and result[\"generator\"] and \"replies\" in result[\"generator\"]:\n",
        "        print(result[\"generator\"][\"replies\"][0])\n",
        "    else:\n",
        "        print(\"No response generated.\")\n",
        "\n",
        "    # Clear memory after usage\n",
        "    rag_system.clear_memory()"
      ],
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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          "text": [
            "Indexing result: {'writer': {'documents_written': 1}}\n",
            "Document store contents: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 384)]\n",
            "Documents in store before query: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 384)]\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "import torch\n",
        "from haystack import Pipeline, Document\n",
        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
        "from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder\n",
        "from haystack.components.writers import DocumentWriter\n",
        "from haystack.components.preprocessors import DocumentSplitter\n",
        "from haystack.components.retrievers import InMemoryEmbeddingRetriever\n",
        "from haystack.components.generators import HuggingFaceLocalGenerator\n",
        "from haystack.components.builders import PromptBuilder\n",
        "from haystack import component\n",
        "from typing import List, Dict, Any\n",
        "\n",
        "@component\n",
        "class DocumentFormatter:\n",
        "    @component.output_types(formatted_documents=str)\n",
        "    def run(self, documents: List[Document]):\n",
        "        formatted = \"\\n\".join([f\"- {doc.content}\" for doc in documents])\n",
        "        print(f\"Formatted documents: {formatted}\")  # Debug print\n",
        "        return {\"formatted_documents\": formatted}\n",
        "\n",
        "@component\n",
        "class QueryAdder:\n",
        "    @component.output_types(template_variables=Dict[str, Any])\n",
        "    def run(self, formatted_documents: str, query: str):\n",
        "        template_vars = {\"documents\": formatted_documents, \"query\": query}\n",
        "        print(f\"Template variables: {template_vars}\")  # Debug print\n",
        "        return {\"template_variables\": template_vars}\n",
        "\n",
        "class AdvancedRAGSystem:\n",
        "    def __init__(self, embedding_model=\"Snowflake/snowflake-arctic-embed-l\", llm_model=\"meta-llama/Meta-Llama-3-8B-Instruct\"):\n",
        "        self.document_store = InMemoryDocumentStore()\n",
        "        self.embedding_model = embedding_model\n",
        "        self.llm_model = llm_model\n",
        "\n",
        "        self.indexing_pipeline = self._create_indexing_pipeline()\n",
        "        self.query_pipeline = self._create_query_pipeline()\n",
        "\n",
        "    def _create_indexing_pipeline(self):\n",
        "        indexing_pipeline = Pipeline()\n",
        "        indexing_pipeline.add_component(\"splitter\", DocumentSplitter(split_by=\"word\", split_length=100))\n",
        "        indexing_pipeline.add_component(\n",
        "            \"embedder\",\n",
        "            SentenceTransformersDocumentEmbedder(model=self.embedding_model)\n",
        "        )\n",
        "        indexing_pipeline.add_component(\"writer\", DocumentWriter(document_store=self.document_store))\n",
        "        indexing_pipeline.connect(\"splitter\", \"embedder\")\n",
        "        indexing_pipeline.connect(\"embedder\", \"writer\")\n",
        "        return indexing_pipeline\n",
        "\n",
        "    def _create_query_pipeline(self):\n",
        "        query_pipeline = Pipeline()\n",
        "        query_pipeline.add_component(\n",
        "            \"text_embedder\",\n",
        "            SentenceTransformersTextEmbedder(model=self.embedding_model)\n",
        "        )\n",
        "        query_pipeline.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=self.document_store, top_k=3))\n",
        "        query_pipeline.add_component(\"formatter\", DocumentFormatter())\n",
        "        query_pipeline.add_component(\"query_adder\", QueryAdder())\n",
        "\n",
        "        prompt_template = \"\"\"\n",
        "        Answer the question based on the given context. Be direct and use the information provided.\n",
        "\n",
        "        Context:\n",
        "        {documents}\n",
        "\n",
        "        Question: {query}\n",
        "\n",
        "        Answer: The answer to the question \"{query}\" based on the given context is\n",
        "        \"\"\"\n",
        "        query_pipeline.add_component(\"prompt_builder\", PromptBuilder(template=prompt_template))\n",
        "\n",
        "        query_pipeline.add_component(\"generator\", HuggingFaceLocalGenerator(\n",
        "            model=self.llm_model,\n",
        "            huggingface_pipeline_kwargs={\n",
        "                \"device_map\": \"auto\",\n",
        "                \"model_kwargs\": {\n",
        "                    \"load_in_4bit\": True,\n",
        "                    \"bnb_4bit_use_double_quant\": True,\n",
        "                    \"bnb_4bit_quant_type\": \"nf4\",\n",
        "                    \"bnb_4bit_compute_dtype\": torch.bfloat16\n",
        "                }\n",
        "            },\n",
        "            generation_kwargs={\"max_new_tokens\": 500}\n",
        "        ))\n",
        "\n",
        "        query_pipeline.connect(\"text_embedder.embedding\", \"retriever.query_embedding\")\n",
        "        query_pipeline.connect(\"retriever\", \"formatter\")\n",
        "        query_pipeline.connect(\"formatter.formatted_documents\", \"query_adder.formatted_documents\")\n",
        "        query_pipeline.connect(\"query_adder.template_variables\", \"prompt_builder.template_variables\")\n",
        "        query_pipeline.connect(\"prompt_builder\", \"generator\")\n",
        "\n",
        "        return query_pipeline\n",
        "\n",
        "    def add_documents(self, documents):\n",
        "        result = self.indexing_pipeline.run({\"splitter\": {\"documents\": documents}})\n",
        "        print(f\"Indexing result: {result}\")  # Debug print\n",
        "        print(f\"Document store contents: {self.document_store.filter_documents()}\")  # Debug print\n",
        "        gc.collect()\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "    def query(self, query):\n",
        "        print(f\"Documents in store before query: {self.document_store.filter_documents()}\")  # Debug print\n",
        "        result = self.query_pipeline.run({\n",
        "            \"text_embedder\": {\"text\": query},\n",
        "            \"query_adder\": {\"query\": query}\n",
        "        })\n",
        "        print(f\"Query result: {result}\")  # Debug print\n",
        "        gc.collect()\n",
        "        torch.cuda.empty_cache()\n",
        "        return result\n",
        "\n",
        "    def clear_memory(self):\n",
        "        all_docs = self.document_store.filter_documents()\n",
        "        if all_docs:\n",
        "            doc_ids = [doc.id for doc in all_docs]\n",
        "            self.document_store.delete_documents(doc_ids)\n",
        "        gc.collect()\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "# Usage example\n",
        "if __name__ == \"__main__\":\n",
        "    rag_system = AdvancedRAGSystem()\n",
        "\n",
        "    # Warm up the generator\n",
        "    rag_system.query_pipeline.get_component(\"generator\").warm_up()\n",
        "\n",
        "    # Add documents\n",
        "    raw_docs = [Document(content=\"The capital of France is Paris.\")]\n",
        "    rag_system.add_documents(raw_docs)\n",
        "\n",
        "    # Query the system\n",
        "    result = rag_system.query(\"What is the capital of France?\")\n",
        "    if \"generator\" in result and result[\"generator\"] and \"replies\" in result[\"generator\"]:\n",
        "        print(result[\"generator\"][\"replies\"][0])\n",
        "    else:\n",
        "        print(\"No response generated.\")\n",
        "\n",
        "    # Clear memory after usage\n",
        "    rag_system.clear_memory()"
      ],
      "metadata": {
        "id": "WMZAIOGI-ASd",
        "outputId": "d905ab28-4b81-4e5e-df0f-afcf2a23b01a",
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
          ]
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        },
        {
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        {
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            ],
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            ],
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              "model_id": "0c7b7401a8e34f73aec6967a9e568938"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/SentenceTransformer.py:174: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v3 of SentenceTransformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
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        {
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "47aecb93b94841d09f1736baf8250b0c"
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          },
          "metadata": {}
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        {
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26109426599245479fb303adc3175697"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Indexing result: {'writer': {'documents_written': 1}}\n",
            "Document store contents: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 1024)]\n",
            "Documents in store before query: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 1024)]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71801dd5de81460e8fe6f3e3bf134fea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Formatted documents: - The capital of France is Paris.\n",
            "Template variables: {'documents': '- The capital of France is Paris.', 'query': 'What is the capital of France?'}\n",
            "Query result: {'generator': {'replies': [' {answer}.\\n    \"\"\"\\n\\n    # Initialize the answer as an empty string\\n    answer = \"\"\\n\\n    # Check if the query is in the documents\\n    if query in documents:\\n        # If the query is found, set the answer to the query itself\\n        answer = query\\n\\n    # Check if the query is in the answer\\n    elif query in answer:\\n        # If the query is found, set the answer to the query itself\\n        answer = query\\n\\n    # If the query is not found, return a message indicating that the query is not found\\n    else:\\n        answer = \"The query \\'{query}\\' is not found in the given context.\".format(query=query)\\n\\n    # Return the answer\\n    return answer\\n\\n\\n# Test the function\\ndocuments = [\"This is a sample document.\", \"Another sample document.\"]\\nquery = \"sample\"\\nprint(find_answer(documents, query))  # Output: The query\\'sample\\' is not found in the given context.\\nquery = \"document\"\\nprint(find_answer(documents, query))  # Output: The query \\'document\\' is not found in the given context.\\nquery = \"This\"\\nprint(find_answer(documents, query))  # Output: The query \\'This\\' is not found in the given context.\\nquery = \"sample document\"\\nprint(find_answer(documents, query))  # Output: sample document\\nquery = \"sample\"\\nprint(find_answer(documents, query))  # Output: sample\\nquery = \"another\"\\nprint(find_answer(documents, query))  # Output: The query \\'another\\' is not found in the given context.\\nquery = \"This is a sample\"\\nprint(find_answer(documents, query))  # Output: The query \\'This is a sample\\' is not found in the given context.\\nquery = \"a\"\\nprint(find_answer(documents, query))  # Output: The query \\'a\\' is not found in the given context.\\nquery = \"sample document\"\\nprint(find_answer(documents, query))  # Output: sample document\\nquery = \"sample document\"\\nprint(find_answer(documents, query))  # Output: sample document\\nquery = \"sample document\"\\nprint(find_answer(documents, query))  # Output: sample document\\nquery = \"sample document\"\\nprint(find_answer(documents, query))  # Output: sample document\\nquery = \"sample document\"\\nprint(find_answer(documents, query))  # Output: sample document\\nquery']}}\n",
            " {answer}.\n",
            "    \"\"\"\n",
            "\n",
            "    # Initialize the answer as an empty string\n",
            "    answer = \"\"\n",
            "\n",
            "    # Check if the query is in the documents\n",
            "    if query in documents:\n",
            "        # If the query is found, set the answer to the query itself\n",
            "        answer = query\n",
            "\n",
            "    # Check if the query is in the answer\n",
            "    elif query in answer:\n",
            "        # If the query is found, set the answer to the query itself\n",
            "        answer = query\n",
            "\n",
            "    # If the query is not found, return a message indicating that the query is not found\n",
            "    else:\n",
            "        answer = \"The query '{query}' is not found in the given context.\".format(query=query)\n",
            "\n",
            "    # Return the answer\n",
            "    return answer\n",
            "\n",
            "\n",
            "# Test the function\n",
            "documents = [\"This is a sample document.\", \"Another sample document.\"]\n",
            "query = \"sample\"\n",
            "print(find_answer(documents, query))  # Output: The query'sample' is not found in the given context.\n",
            "query = \"document\"\n",
            "print(find_answer(documents, query))  # Output: The query 'document' is not found in the given context.\n",
            "query = \"This\"\n",
            "print(find_answer(documents, query))  # Output: The query 'This' is not found in the given context.\n",
            "query = \"sample document\"\n",
            "print(find_answer(documents, query))  # Output: sample document\n",
            "query = \"sample\"\n",
            "print(find_answer(documents, query))  # Output: sample\n",
            "query = \"another\"\n",
            "print(find_answer(documents, query))  # Output: The query 'another' is not found in the given context.\n",
            "query = \"This is a sample\"\n",
            "print(find_answer(documents, query))  # Output: The query 'This is a sample' is not found in the given context.\n",
            "query = \"a\"\n",
            "print(find_answer(documents, query))  # Output: The query 'a' is not found in the given context.\n",
            "query = \"sample document\"\n",
            "print(find_answer(documents, query))  # Output: sample document\n",
            "query = \"sample document\"\n",
            "print(find_answer(documents, query))  # Output: sample document\n",
            "query = \"sample document\"\n",
            "print(find_answer(documents, query))  # Output: sample document\n",
            "query = \"sample document\"\n",
            "print(find_answer(documents, query))  # Output: sample document\n",
            "query = \"sample document\"\n",
            "print(find_answer(documents, query))  # Output: sample document\n",
            "query\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    rag_system = AdvancedRAGSystem()\n",
        "\n",
        "\n",
        "\n",
        "    # Add documents\n",
        "    raw_docs = [Document(content=\"The capital of France is Paris.\")]\n",
        "    rag_system.add_documents(raw_docs)\n",
        "\n",
        "    # Query the system\n",
        "    result = rag_system.query(\"What is the capital of France?\")\n",
        "    if \"generator\" in result and result[\"generator\"] and \"replies\" in result[\"generator\"]:\n",
        "        print(result[\"generator\"][\"replies\"][0])\n",
        "    else:\n",
        "        print(\"No response generated.\")\n",
        "\n",
        "    # Clear memory after usage\n",
        "    rag_system.clear_memory()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "20f6e06921e640409b0c562628aa5bf8",
            "5b5ceeb6bfda476fbfaf416b620c0a57",
            "382bcc6e0b6f474a8acb2ec1089d386f",
            "f4fbd890b490461383d8917769c545c3",
            "2d688ffc27dc41b39878fc9433c20308",
            "473c2e74515b40e19528ad57a71ac8aa",
            "5aa87160a8b34848938d11a283847c79",
            "d7a44c7fdfc64f8d8dd787f1bd3d95df",
            "0d82f319b93c41d4a7f74edc0cbe17d4",
            "6bdb95fbe66243f8a67ae677cff97142",
            "27eb39a9cd8e42289676ca6cc6553027",
            "87a83574782b4908915512ae195d4c22",
            "fb34c89b6ac4482b9e5dec545906a09f",
            "8814e63fc8c44ffa90bee3d8946e7d45",
            "f49d28e4e30a498e94c3fc89accde57d",
            "d56600e9568a4b65a520b5795be8dc08",
            "0d2d466383d04ef8b4ad863a699c15e2",
            "cc43c6c9373c4baeb2dd6e59be0acd56",
            "e613cb73210946488f88d4a4f3560db1",
            "e1119f45148a472984997138346bd205",
            "f5d2977a00934752a90a4be0ab7b5080",
            "370f68d8bc7e4ceba63e0b13ead6abb7",
            "76bff910e86542b7b9d64f9d128b4aa8",
            "fa77a61d52b7434ab524aeb627be5e62",
            "ff4b1c48087b4a8581a7d136afae151d",
            "7529ac4a666648959c461ee1091045f8",
            "39b7ea5b7df046478789b788cbaaff09",
            "d5c7dd70cc82458fb307b8612029440f",
            "57b2cf8dc8a54ef3ba9a364040ca2807",
            "dcc44a4c50834359944e1a7dfd76c580",
            "2ad333ffdf9e44d18d4d7de3930c2571",
            "47820393a8584ec7924b3b8e398466ca",
            "cce67d330dd84accbf836281dc622b8a"
          ]
        },
        "id": "Jf4O0lQ8KnG-",
        "outputId": "6772a41e-cf8f-472b-cd94-65ced459e90f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20f6e06921e640409b0c562628aa5bf8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Indexing result: {'writer': {'documents_written': 1}}\n",
            "Document store contents: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 1024)]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Documents in store before query: [Document(id=48f2efc93c0a1ccc335639efc5a416440dce3ee857d851757d9d9dfd9a6f7faf, content: 'The capital of France is Paris.', meta: {'source_id': '4552697f972d0d5f3d116fcf78d85ce15fd43314f5312dd064b0d85317d4635b', 'page_number': 1}, embedding: vector of size 1024)]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "87a83574782b4908915512ae195d4c22"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76bff910e86542b7b9d64f9d128b4aa8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Formatted documents: - The capital of France is Paris.\n",
            "Template variables: {'documents': '- The capital of France is Paris.', 'query': 'What is the capital of France?'}\n",
            "Query result: {'generator': {'replies': [' \"\"\"\\n\\n        # Create a dictionary to store the answers\\n        answers = {}\\n\\n        # Loop through the documents\\n        for document in documents:\\n            # Extract the title and text from the document\\n            title = document[\"title\"]\\n            text = document[\"text\"]\\n\\n            # Split the text into sentences\\n            sentences = text.split(\". \")\\n\\n            # Loop through the sentences\\n            for sentence in sentences:\\n                # Check if the query is in the sentence\\n                if query in sentence:\\n                    # If the query is in the sentence, extract the answer\\n                    answer = sentence.split(\":\")[1].strip()\\n                    # Store the answer in the dictionary\\n                    answers[title] = answer\\n\\n        # Return the answer based on the query\\n        return answers.get(query, \"Not found\")\\n    except Exception as e:\\n        # Handle any exceptions that occur\\n        print(f\"An error occurred: {str(e)}\")\\n        return \"Error occurred\"\\n```\\n\\nThis code defines a function `answer_question` that takes a list of documents and a query as input. The function loops through the documents, extracts the title and text, splits the text into sentences, and checks if the query is in each sentence. If the query is found, the function extracts the answer and stores it in a dictionary. Finally, the function returns the answer based on the query.\\n\\nThe function uses a dictionary to store the answers, with the document title as the key and the answer as the value. If the query is not found in any of the documents, the function returns \"Not found\".\\n\\nThe function also includes error handling to catch any exceptions that may occur during execution.\\n\\nYou can use this function to answer questions based on the given context. For example:\\n```python\\ndocuments = [\\n    {\"title\": \"Document 1\", \"text\": \"This is document 1. The answer is 42.\"},\\n    {\"title\": \"Document 2\", \"text\": \"This is document 2. The answer is 21.\"},\\n    {\"title\": \"Document 3\", \"text\": \"This is document 3. The answer is 99.\"}\\n]\\n\\nquery = \"The answer is\"\\nprint(answer_question(documents, query))  # Output: 42\\n```\\nNote that this is a simple example, and you may want to add more features to your question-answering system, such as handling multiple queries, handling out-of-vocabulary words, or using more advanced']}}\n",
            " \"\"\"\n",
            "\n",
            "        # Create a dictionary to store the answers\n",
            "        answers = {}\n",
            "\n",
            "        # Loop through the documents\n",
            "        for document in documents:\n",
            "            # Extract the title and text from the document\n",
            "            title = document[\"title\"]\n",
            "            text = document[\"text\"]\n",
            "\n",
            "            # Split the text into sentences\n",
            "            sentences = text.split(\". \")\n",
            "\n",
            "            # Loop through the sentences\n",
            "            for sentence in sentences:\n",
            "                # Check if the query is in the sentence\n",
            "                if query in sentence:\n",
            "                    # If the query is in the sentence, extract the answer\n",
            "                    answer = sentence.split(\":\")[1].strip()\n",
            "                    # Store the answer in the dictionary\n",
            "                    answers[title] = answer\n",
            "\n",
            "        # Return the answer based on the query\n",
            "        return answers.get(query, \"Not found\")\n",
            "    except Exception as e:\n",
            "        # Handle any exceptions that occur\n",
            "        print(f\"An error occurred: {str(e)}\")\n",
            "        return \"Error occurred\"\n",
            "```\n",
            "\n",
            "This code defines a function `answer_question` that takes a list of documents and a query as input. The function loops through the documents, extracts the title and text, splits the text into sentences, and checks if the query is in each sentence. If the query is found, the function extracts the answer and stores it in a dictionary. Finally, the function returns the answer based on the query.\n",
            "\n",
            "The function uses a dictionary to store the answers, with the document title as the key and the answer as the value. If the query is not found in any of the documents, the function returns \"Not found\".\n",
            "\n",
            "The function also includes error handling to catch any exceptions that may occur during execution.\n",
            "\n",
            "You can use this function to answer questions based on the given context. For example:\n",
            "```python\n",
            "documents = [\n",
            "    {\"title\": \"Document 1\", \"text\": \"This is document 1. The answer is 42.\"},\n",
            "    {\"title\": \"Document 2\", \"text\": \"This is document 2. The answer is 21.\"},\n",
            "    {\"title\": \"Document 3\", \"text\": \"This is document 3. The answer is 99.\"}\n",
            "]\n",
            "\n",
            "query = \"The answer is\"\n",
            "print(answer_question(documents, query))  # Output: 42\n",
            "```\n",
            "Note that this is a simple example, and you may want to add more features to your question-answering system, such as handling multiple queries, handling out-of-vocabulary words, or using more advanced\n"
          ]
        }
      ]
    }
  ]
}