{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWWPapD1mhqs"
      },
      "source": [
        "# Prompt Optimization with DSPy\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/stanfordnlp/dspy/main/docs/images/DSPy8.png\" width=\"400\" style=\"display:inline;\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n",
        "<img src=\"https://haystack.deepset.ai/images/haystack-ogimage.png\" width=\"430\" style=\"display:inline;\">\n",
        "\n",
        "When building applications with LLMs, writing effective prompts is a long process of trial and error.\n",
        "Often, if you switch models, you also have to change the prompt.\n",
        "What if you could automate this process?\n",
        "\n",
        "That's where **DSPy** comes in - a framework designed to algorithmically optimize prompts for Language Models.\n",
        "By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task.\n",
        "\n",
        "In this notebook, we will see **how to combine DSPy with the robustness of Haystack Pipelines**.\n",
        "- ▶️ Start from a Haystack RAG pipeline with a basic prompt\n",
        "- 🎯 Define a goal (in this case, get correct and concise answers)\n",
        "- 📊 Create a DSPy program, define data and metrics\n",
        "- ✨ Optimize and evaluate -> improved prompt\n",
        "- 🚀 Build a refined Haystack RAG pipeline using the optimized prompt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-j3AJ3lne-o"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5QfUIMRLhSO4",
        "outputId": "b432bef4-d564-42f5-d5d8-f594d8bc9da7"
      },
      "outputs": [],
      "source": [
        "! pip install haystack-ai datasets dspy-ai sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "aOqHCizEnlll"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from getpass import getpass\n",
        "from rich import print\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "45hUq3KCp-bG"
      },
      "source": [
        "## Load data\n",
        "\n",
        "We will use the first 1000 rows of a [labeled PubMed dataset](https://huggingface.co/datasets/vblagoje/PubMedQA_instruction/viewer/default/train?row=0) with questions, contexts and answers.\n",
        "\n",
        "Initially, we will use only the contexts as documents and write them to a Document Store.\n",
        "\n",
        "(Later, we will also use the questions and answers from a small subset of the dataset to create training and dev sets for optimization.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328,
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            "241b648b359241c4af0dc2529f3252a9",
            "786c8b7d40384d128d0d988271a10456",
            "2c49722f712b40c08514c039f42bba1b",
            "1a2536789c964f86b6e4edf39a518240",
            "e1e9623b842547f298921749f58476af",
            "846f16d9e1ec4336a4ad359806fbc9ea",
            "7e135e12c7c449369e57e2f1b55b6b47",
            "9cb1852db48145f59fc1488312cd4cc1",
            "fce744d9caaa49c6b9c17e29508045b9",
            "b605d29ec34f4d77bb9a00fdd50f5358",
            "13e1e34794354b49bfdca31ec5e9edd2",
            "4609e1c42f45480cbc7f0953c97bec0d",
            "f0af93cf651b4e3eb490a4b703793d17",
            "b3a7f42732de43bd81c1cf176d446916",
            "b0fafe574a9e4e1fa6a612cafc8c835b",
            "a5719ee3a1dd47d28ce7e064eb35cc91",
            "71a2913352a84e53bde4d1e9acd0ef10",
            "0b39b2d5be1449a88ae57ef838147765",
            "41d7efebb42a4bf984706bbbbf0ba3af",
            "ce47d2ca6dab4b2680ff48d55d9d4151",
            "75a9ae8113634affa0b593757b156b99",
            "d4870883c1e744f59f300501686b9230",
            "3a29e2876e034d9ca3cca863fb6d1b2f",
            "a624eb70eb134eec8695a8109de97b54",
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            "d050b0352e2c4fcf9548b00d4b526876",
            "f3f8c33238da4e15813109dcee5e466c",
            "dd3ebedd8c1c498f8ca9b71990a2cfcc",
            "bb7a4c0fc13f43e69c9f34108216627b",
            "a7188fbcf58543cba1f4903670d80a51",
            "65eced541bb44e3ea673901f914182b4",
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            "1607c24223fd48b98c324ee7be05549b",
            "fdeac1f591b144b8be07650a8f0a78f7",
            "b5fab88eaf0d4f57aee642673db833e9",
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            "7eb27ae9f97b4f1790e1f2fdc5f3a353",
            "6f5e4135e71f4d60ad6376832ddf5e8d",
            "c89f219a7beb41939c304ab7a10a370e",
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            "75bcfe3484794857afee6e317c8437c5",
            "88621a6ab29445dc99770d906ea3d786",
            "68e76c66d54f4a43ae2d07632e028fbd",
            "54cf6e08fded4f3084ae3206560880de"
          ]
        },
        "id": "JKluba-UhmQJ",
        "outputId": "aa39918c-56f1-429a-b134-d9129c4da570"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:81: UserWarning: \n",
            "Access to the secret `HF_TOKEN` has not been granted on this notebook.\n",
            "You will not be requested again.\n",
            "Please restart the session if you want to be prompted again.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0176e06faff343fb817c8cdc70fc86f1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/498 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "95c5d65c2e8b4166a96c798c66fff20e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "97526f3ce3f34e4b92d1c24b4f01e8a8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/274M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e135e12c7c449369e57e2f1b55b6b47",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/986k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0b39b2d5be1449a88ae57ef838147765",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d050b0352e2c4fcf9548b00d4b526876",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/272458 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "64909e03673f4837b3734e806324a153",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "from haystack import Document\n",
        "\n",
        "dataset = load_dataset(\"vblagoje/PubMedQA_instruction\", split=\"train\")\n",
        "dataset = dataset.select(range(1000))\n",
        "docs = [Document(content=doc[\"context\"]) for doc in dataset]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iGBGnCQhbuG",
        "outputId": "57a19480-ee61-47b1-d1c7-2b6744335ab7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
        "\n",
        "document_store = InMemoryDocumentStore()\n",
        "document_store.write_documents(docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELk2EJYwLD4L",
        "outputId": "3b2aacd3-453e-4c70-e9c5-8d4ff84a0cae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Document(id=f6fde0752a035f7a15860dfa6c45d3ee05380198f18abf43b7b4923ec44c9985, content: 'Chronic rhinosinusitis (CRS) is a heterogeneous disease with an uncertain pathogenesis. Group 2 inna...'),\n",
              " Document(id=8889ef27dbfe0b3cc5ba24652b393fc9e39cd49d21db1b7dbeb8a79363b4fb12, content: 'Phosphatidylethanolamine N-methyltransferase (PEMT), a liver enriched enzyme, is responsible for app...'),\n",
              " Document(id=699ac0bd51891960eb58709be9f2ffef41fcbc7ea51f247c7b922e3ad1e358c3, content: 'Psammaplin A (PsA) is a natural product isolated from marine sponges, which has been demonstrated to...'),\n",
              " Document(id=8c714387bf3999ef9b3e11997d8dc5f894ee2ffc281202db4c1a66faf65e5752, content: 'This study examined links between DNA methylation and birth weight centile (BWC), and explored the i...'),\n",
              " Document(id=7a59e616dab6b59e767513a13b3cd3551843c52d3349c1fd2a6c827b9885912c, content: 'Tumor microenvironment immunity is associated with breast cancer outcome. A high lymphocytic infiltr...')]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "document_store.filter_documents()[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTy01somr_xl"
      },
      "source": [
        "## Initial Haystack pipeline\n",
        "\n",
        "Let's create a simple RAG Pipeline in Haystack. For more information, see [the documentation](https://docs.haystack.deepset.ai/docs/get_started).\n",
        "\n",
        "Next, we will see how to improve the prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "epwj4HFRh86d",
        "outputId": "8a1f1bf0-474f-489d-82bb-6a9b735948ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<haystack.core.pipeline.pipeline.Pipeline object at 0x7d5a270a8ee0>\n",
              "🚅 Components\n",
              "  - retriever: InMemoryBM25Retriever\n",
              "  - prompt_builder: PromptBuilder\n",
              "  - llm: OpenAIGenerator\n",
              "🛤️ Connections\n",
              "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
              "  - prompt_builder.prompt -> llm.prompt (str)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
        "from haystack.components.generators import OpenAIGenerator\n",
        "from haystack.components.builders import PromptBuilder\n",
        "from haystack import Pipeline\n",
        "\n",
        "\n",
        "retriever = InMemoryBM25Retriever(document_store, top_k=3)\n",
        "generator = OpenAIGenerator(model=\"gpt-4o-mini\")\n",
        "\n",
        "template = \"\"\"\n",
        "Given the following information, answer the question.\n",
        "\n",
        "Context:\n",
        "{% for document in documents %}\n",
        "    {{ document.content }}\n",
        "{% endfor %}\n",
        "\n",
        "Question: {{question}}\n",
        "Answer:\n",
        "\"\"\"\n",
        "\n",
        "prompt_builder = PromptBuilder(template=template)\n",
        "\n",
        "\n",
        "rag_pipeline = Pipeline()\n",
        "rag_pipeline.add_component(\"retriever\", retriever)\n",
        "rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n",
        "rag_pipeline.add_component(\"llm\", generator)\n",
        "\n",
        "rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
        "rag_pipeline.connect(\"prompt_builder\", \"llm\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9phmVO2-uWIR"
      },
      "source": [
        "Let's ask some questions..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "189ee9ea4c7540c78fdcd2a9079ca27e",
            "661a96c5cd784bafb3fe3a6d31de0997",
            "92c229240b64454bb9fd188e232d6532",
            "7c667c06678c407f80d99de6c4e19aec",
            "f2732c6f3aa142de82322f3438f3ab4c",
            "e4acc331c71b404794b98dbf89107e23",
            "8eee7dc3d1974b4ea23cb45ad6dbcef2",
            "19da3f6831e44654b030f93fbcf79634",
            "c11bfeb9f31448ccbe2744171c52700a",
            "cff9e0a4664e4d79a53961b5eb252877",
            "52499dbc1de04ec2b3bc061e27986324"
          ]
        },
        "id": "IHrnIn0HjE8q",
        "outputId": "0c80267d-5bd6-4f24-d8d6-295649354f36"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "189ee9ea4c7540c78fdcd2a9079ca27e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Ketamine inhibits the proliferation of rat neural stem cells in a dose-dependent manner at concentrations of <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">200</span>, \n",
              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">500</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">800</span>, and 1000µM. Additionally, ketamine decreases intracellular <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Ca</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>+<span style=\"font-weight: bold\">)</span> concentration, suppresses protein \n",
              "kinase C-α <span style=\"font-weight: bold\">(</span>PKCα<span style=\"font-weight: bold\">)</span> activation, and phosphorylation of extracellular signal-regulated kinases <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> <span style=\"font-weight: bold\">(</span>ERK1/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span> in rat \n",
              "neural stem cells. These effects do not seem to be mediated through caspase-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-dependent apoptosis.\n",
              "</pre>\n"
            ],
            "text/plain": [
              "Ketamine inhibits the proliferation of rat neural stem cells in a dose-dependent manner at concentrations of \u001b[1;36m200\u001b[0m, \n",
              "\u001b[1;36m500\u001b[0m, \u001b[1;36m800\u001b[0m, and 1000µM. Additionally, ketamine decreases intracellular \u001b[1;35mCa\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m+\u001b[1m)\u001b[0m concentration, suppresses protein \n",
              "kinase C-α \u001b[1m(\u001b[0mPKCα\u001b[1m)\u001b[0m activation, and phosphorylation of extracellular signal-regulated kinases \u001b[1;36m1\u001b[0m/\u001b[1;36m2\u001b[0m \u001b[1m(\u001b[0mERK1/\u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m in rat \n",
              "neural stem cells. These effects do not seem to be mediated through caspase-\u001b[1;36m3\u001b[0m-dependent apoptosis.\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "question = \"What effects does ketamine have on rat neural stem cells?\"\n",
        "\n",
        "response = rag_pipeline.run({\"retriever\": {\"query\": question}, \"prompt_builder\": {\"question\": question}})\n",
        "\n",
        "print(response[\"llm\"][\"replies\"][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129,
          "referenced_widgets": [
            "a7f6736511a0468d9515842be69b238a",
            "ac2e4d65292b48719c75c7572da67653",
            "ea10506e5c0f4a1ba0bc8ace2b67874d",
            "1adcbd457ca6454f8a8e1e1d846c674a",
            "903fad0e2cbc45d68c027f73ea9b7ec7",
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        "id": "M8_NgGKQcwoM",
        "outputId": "3b0862e8-4e2f-41b5-873b-6f0bd85dfee3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a7f6736511a0468d9515842be69b238a",
              "version_major": 2,
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Yes, the anterior cingulate cortex <span style=\"font-weight: bold\">(</span>ACC<span style=\"font-weight: bold\">)</span> is linked to pain-induced depression. The first study mentioned in the \n",
              "context compared the role of the ACC and the posterior insular cortex in chronic pain. It was found that lesions in\n",
              "the ACC prevented the anxiodepressive consequences of chronic pain, indicating a connection between the ACC and \n",
              "pain-induced depression. Additionally, optogenetic stimulation of the ACC was able to induce anxiety and \n",
              "depressive-like behaviors in naïve animals.\n",
              "</pre>\n"
            ],
            "text/plain": [
              "Yes, the anterior cingulate cortex \u001b[1m(\u001b[0mACC\u001b[1m)\u001b[0m is linked to pain-induced depression. The first study mentioned in the \n",
              "context compared the role of the ACC and the posterior insular cortex in chronic pain. It was found that lesions in\n",
              "the ACC prevented the anxiodepressive consequences of chronic pain, indicating a connection between the ACC and \n",
              "pain-induced depression. Additionally, optogenetic stimulation of the ACC was able to induce anxiety and \n",
              "depressive-like behaviors in naïve animals.\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "question = \"Is the anterior cingulate cortex linked to pain-induced depression?\"\n",
        "\n",
        "response = rag_pipeline.run({\"retriever\": {\"query\": question}, \"prompt_builder\": {\"question\": question}})\n",
        "\n",
        "print(response[\"llm\"][\"replies\"][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aopLjLs3J_eO"
      },
      "source": [
        "The answers seems correct, but suppose that **our use case requires shorter answers**. How can we adjust the prompt to achieve this effect while maintaining correctness?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZQd-J9zzpCF"
      },
      "source": [
        "## DSPy\n",
        "\n",
        "We will use DSPy to automatically improve the prompt for our goal: getting correct and short answers.\n",
        "\n",
        "We will perform several steps:\n",
        "- define a DSPy module for RAG\n",
        "- create training and dev sets\n",
        "- define a metric\n",
        "- evaluate the unoptimized RAG module\n",
        "- optimize the module\n",
        "- evaluate the optimized RAG\n",
        "\n",
        "Broadly speaking, these steps follow those listed in the [DSPy guide](https://dspy-docs.vercel.app/docs/building-blocks/solving_your_task)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "-lnVFxBRmaVq"
      },
      "outputs": [],
      "source": [
        "import dspy\n",
        "from dspy.primitives.prediction import Prediction\n",
        "\n",
        "\n",
        "lm = dspy.OpenAI(model='gpt-4o-mini')\n",
        "dspy.settings.configure(lm=lm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QzUT-Nqc1GRp"
      },
      "source": [
        "### DSPy Signature\n",
        "\n",
        "The RAG module involves two main tasks (smaller modules): retrieval and generation.\n",
        "\n",
        "For generation, we need to define a signature: a declarative specification of input/output behavior of a DSPy module.\n",
        "In particular, the generation module receives the `context` and a `question` as input and returns an `answer`.\n",
        "\n",
        "In DSPy, the docstring and the field description are used to create the prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "apBnI8zymKIj"
      },
      "outputs": [],
      "source": [
        "class GenerateAnswer(dspy.Signature):\n",
        "    \"\"\"Answer questions with short factoid answers.\"\"\"\n",
        "\n",
        "    context = dspy.InputField(desc=\"may contain relevant facts\")\n",
        "    question = dspy.InputField()\n",
        "    answer = dspy.OutputField(desc=\"short and precise answer\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmee88jC29qp"
      },
      "source": [
        "### DSPy RAG module\n",
        "\n",
        "- the `__init__` method can be used to declare sub-modules.\n",
        "- the logic of the module is contained in the `forward` method.\n",
        "---\n",
        "- `ChainOfThought` module encourages Language Model reasoning with a specific prompt (\"Let's think step by step\") and examples. [Paper](https://arxiv.org/abs/2201.11903)\n",
        "- we want to reuse our Haystack retriever and the already indexed data, so we also define a `retrieve` method.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "oUMRCJ18mtkf"
      },
      "outputs": [],
      "source": [
        "class RAG(dspy.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n",
        "\n",
        "    # this makes it possible to use the Haystack retriever\n",
        "    def retrieve(self, question):\n",
        "        results = retriever.run(query=question)\n",
        "        passages = [res.content for res in results['documents']]\n",
        "        return Prediction(passages=passages)\n",
        "\n",
        "    def forward(self, question):\n",
        "        context = self.retrieve(question).passages\n",
        "        prediction = self.generate_answer(context=context, question=question)\n",
        "        return dspy.Prediction(context=context, answer=prediction.answer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yX1q7a_J5YgP"
      },
      "source": [
        "### Create training and dev sets\n",
        "\n",
        "In general, to use DSPy for prompt optimization, you have to prepare some examples for your task (or use a similar dataset).\n",
        "\n",
        "The training set is used for optimization, while the dev set is used for evaluation.\n",
        "\n",
        "We create them using respectively 20 and 50 examples (question and answer) from our original labeled PubMed dataset.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "eI7zQfZRLTCx"
      },
      "outputs": [],
      "source": [
        "trainset, devset=[],[]\n",
        "\n",
        "for i,ex in enumerate(dataset):\n",
        "  example = dspy.Example(question = ex[\"instruction\"], answer=ex[\"response\"]).with_inputs('question')\n",
        "\n",
        "  if i<20:\n",
        "    trainset.append(example)\n",
        "  elif i<70:\n",
        "    devset.append(example)\n",
        "  else:\n",
        "    break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRjG8dPU8dpp"
      },
      "source": [
        "### Define a metric\n",
        "\n",
        "Defining a metric is a crucial step for evaluating and optimizing our prompt.\n",
        "\n",
        "As we show in this example, metrics can be defined in a very customized way.\n",
        "\n",
        "In our case, we want to focus on two aspects: correctness and brevity of the answers.\n",
        "- for correctness, we use semantic similarity between the predicted answer and the ground truth answer ([Haystack SASEvaluator](https://docs.haystack.deepset.ai/docs/sasevaluator)). SAS score varies between 0 and 1.\n",
        "- to encourage short answers, we add a penalty for long answers based on a simple mathematical formulation. The penalty varies between 0 (for answers of 20 words or less) and 0.5 (for answers of 40 words or more)."
      ]
    },
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          "name": "stderr",
          "output_type": "stream",
          "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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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "from haystack.components.evaluators import SASEvaluator\n",
        "sas_evaluator = SASEvaluator()\n",
        "\n",
        "def mixed_metric(example, pred, trace=None):\n",
        "    semantic_similarity = sas_evaluator.run(ground_truth_answers=[example.answer], predicted_answers=[pred.answer])[\"score\"]\n",
        "\n",
        "    n_words=len(pred.answer.split())\n",
        "    long_answer_penalty=0\n",
        "    if 20<n_words<40:\n",
        "      long_answer_penalty = 0.025 * (n_words - 20)\n",
        "    elif n_words>=40:\n",
        "      long_answer_penalty = 0.5\n",
        "\n",
        "    return semantic_similarity - long_answer_penalty"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQX9ypSN-cEU"
      },
      "source": [
        "### Evaluate unoptimized RAG module\n",
        "\n",
        "Let's first check how the unoptimized RAG module performs on the dev set.\n",
        "Then we will optimize it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "wDs-_SlsoyuL"
      },
      "outputs": [],
      "source": [
        "uncompiled_rag = RAG()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/50 [00:00<?, ?it/s]"
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        },
        {
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
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          "output_type": "stream",
          "text": [
            "Average Metric: 0.7792506217956543 / 1  (77.9):   2%|▏         | 1/50 [00:04<04:03,  4.97s/it]"
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        {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
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          "output_type": "stream",
          "text": [
            "Average Metric: 0.8678842559456825 / 2  (43.4):   4%|▍         | 2/50 [00:06<02:16,  2.85s/it]"
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        },
        {
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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          "metadata": {},
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        {
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          "output_type": "stream",
          "text": [
            "Average Metric: 1.7318382635712624 / 3  (57.7):   6%|▌         | 3/50 [00:08<02:00,  2.57s/it]"
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        },
        {
          "data": {
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              "model_id": "353da76e8d8c4e768adfcd7b57710937",
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
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          "output_type": "stream",
          "text": [
            "Average Metric: 2.391386426985264 / 4  (59.8):   8%|▊         | 4/50 [00:10<01:50,  2.41s/it]"
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        {
          "data": {
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
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          "text": [
            "Average Metric: 3.1987738981842995 / 5  (64.0):  10%|█         | 5/50 [00:12<01:40,  2.23s/it]"
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        {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
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          "text": [
            "Average Metric: 3.8259158387780188 / 6  (63.8):  12%|█▏        | 6/50 [00:14<01:36,  2.20s/it]"
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        },
        {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
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          "output_type": "stream",
          "text": [
            "Average Metric: 4.748057033121586 / 7  (67.8):  14%|█▍        | 7/50 [00:16<01:27,  2.04s/it]"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 5.402349318563938 / 8  (67.5):  16%|█▌        | 8/50 [00:18<01:25,  2.03s/it]"
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        },
        {
          "data": {
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              "model_id": "a22473eb60194e7c94a2c156d1c2a41a",
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 5.476948383450508 / 9  (60.9):  18%|█▊        | 9/50 [00:19<01:13,  1.80s/it]"
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        },
        {
          "data": {
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              "model_id": "dc996a39957545129ceedd3b51f8790a",
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 5.55394357740879 / 10  (55.5):  20%|██        | 10/50 [00:22<01:21,  2.03s/it]"
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        },
        {
          "data": {
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          },
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        },
        {
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          "text": [
            "Average Metric: 6.18200332224369 / 11  (56.2):  22%|██▏       | 11/50 [00:24<01:16,  1.96s/it]"
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        },
        {
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 6.7211928993463514 / 12  (56.0):  24%|██▍       | 12/50 [00:26<01:14,  1.97s/it]"
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          },
          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 6.770032941550016 / 13  (52.1):  26%|██▌       | 13/50 [00:27<01:07,  1.82s/it]"
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        {
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 7.499305187910795 / 14  (53.6):  28%|██▊       | 14/50 [00:30<01:11,  1.99s/it]"
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        },
        {
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          },
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        },
        {
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          "text": [
            "Average Metric: 8.13342869207263 / 15  (54.2):  30%|███       | 15/50 [00:31<01:03,  1.82s/it]"
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        },
        {
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        },
        {
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          "text": [
            "Average Metric: 8.83576548025012 / 16  (55.2):  32%|███▏      | 16/50 [00:33<01:03,  1.88s/it]"
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        },
        {
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          },
          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 9.76700830385089 / 17  (57.5):  34%|███▍      | 17/50 [00:35<01:06,  2.01s/it]"
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        },
        {
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 10.354306184500457 / 18  (57.5):  36%|███▌      | 18/50 [00:38<01:07,  2.12s/it]"
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        },
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          },
          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 10.397395987808705 / 19  (54.7):  38%|███▊      | 19/50 [00:39<01:00,  1.94s/it]"
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        },
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          "metadata": {},
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        {
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          "text": [
            "Average Metric: 10.470511642098428 / 20  (52.4):  40%|████      | 20/50 [00:41<00:53,  1.77s/it]"
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        },
        {
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 11.366389837861062 / 21  (54.1):  42%|████▏     | 21/50 [00:43<00:55,  1.92s/it]"
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        },
        {
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          },
          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 11.44212095141411 / 22  (52.0):  44%|████▍     | 22/50 [00:45<00:59,  2.13s/it]"
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        },
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 11.885303872823716 / 23  (51.7):  46%|████▌     | 23/50 [00:48<01:00,  2.24s/it]"
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        },
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 12.638070541620255 / 24  (52.7):  48%|████▊     | 24/50 [00:50<00:55,  2.12s/it]"
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        },
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 13.284867602586747 / 25  (53.1):  50%|█████     | 25/50 [00:52<00:52,  2.11s/it]"
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        },
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 13.344285535812379 / 26  (51.3):  52%|█████▏    | 26/50 [00:53<00:46,  1.93s/it]"
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        },
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          },
          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 13.874915313720704 / 27  (51.4):  54%|█████▍    | 27/50 [00:56<00:51,  2.23s/it]"
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        },
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 14.445346760749818 / 28  (51.6):  56%|█████▌    | 28/50 [00:59<00:49,  2.27s/it]"
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 14.46531408391893 / 29  (49.9):  58%|█████▊    | 29/50 [01:00<00:42,  2.04s/it]"
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        },
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 14.587132939323784 / 30  (48.6):  60%|██████    | 30/50 [01:02<00:37,  1.89s/it]"
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        },
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          },
          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 14.979336270317436 / 31  (48.3):  62%|██████▏   | 31/50 [01:04<00:39,  2.06s/it]"
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 15.796318897232414 / 32  (49.4):  64%|██████▍   | 32/50 [01:06<00:38,  2.11s/it]"
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 16.553095170482994 / 33  (50.2):  66%|██████▌   | 33/50 [01:08<00:34,  2.05s/it]"
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        },
        {
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          "text": [
            "Average Metric: 16.621720285341144 / 34  (48.9):  68%|██████▊   | 34/50 [01:10<00:29,  1.87s/it]"
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 17.3888682436198 / 35  (49.7):  70%|███████   | 35/50 [01:12<00:31,  2.09s/it]"
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          "metadata": {},
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        },
        {
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          "text": [
            "Average Metric: 17.926523668691516 / 36  (49.8):  72%|███████▏  | 36/50 [01:15<00:31,  2.22s/it]"
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        {
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          "text": [
            "Average Metric: 18.750893218442798 / 37  (50.7):  74%|███████▍  | 37/50 [01:17<00:27,  2.09s/it]"
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        {
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            "Average Metric: 19.441286904737353 / 38  (51.2):  76%|███████▌  | 38/50 [01:19<00:25,  2.15s/it]"
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        {
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            "Average Metric: 19.763766403123736 / 39  (50.7):  78%|███████▊  | 39/50 [01:22<00:26,  2.41s/it]"
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        {
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          "text": [
            "Average Metric: 20.659051413461565 / 40  (51.6):  80%|████████  | 40/50 [01:24<00:24,  2.41s/it]"
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        {
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            "Average Metric: 20.696448824182152 / 41  (50.5):  82%|████████▏ | 41/50 [01:26<00:19,  2.17s/it]"
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        {
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            "Average Metric: 21.432477793470024 / 42  (51.0):  84%|████████▍ | 42/50 [01:28<00:16,  2.05s/it]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 21.788538274541498 / 43  (50.7):  86%|████████▌ | 43/50 [01:30<00:14,  2.05s/it]"
          ]
        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 21.783919956721366 / 44  (49.5):  88%|████████▊ | 44/50 [01:31<00:11,  1.90s/it]"
          ]
        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 22.562956538237632 / 45  (50.1):  90%|█████████ | 45/50 [01:33<00:09,  1.94s/it]"
          ]
        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 22.630986932851375 / 46  (49.2):  92%|█████████▏| 46/50 [01:35<00:06,  1.75s/it]"
          ]
        },
        {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 23.399105254746974 / 47  (49.8):  94%|█████████▍| 47/50 [01:37<00:05,  1.86s/it]"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1fa9133b1f654d509c2bf586630a662e",
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 23.46025380138308 / 48  (48.9):  96%|█████████▌| 48/50 [01:38<00:03,  1.75s/it]"
          ]
        },
        {
          "data": {
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              "model_id": "bf7de0a682a846559d786194da0a546a",
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 23.981951561011375 / 49  (48.9):  98%|█████████▊| 49/50 [01:40<00:01,  1.84s/it]"
          ]
        },
        {
          "data": {
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              "model_id": "962107dac174410283fefd395d8fe324",
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 24.406892337836325 / 50  (48.8): 100%|██████████| 50/50 [01:43<00:00,  2.07s/it]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_9725c th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_9725c td {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_9725c_row0_col0, #T_9725c_row0_col1, #T_9725c_row0_col2, #T_9725c_row0_col3, #T_9725c_row0_col4, #T_9725c_row1_col0, #T_9725c_row1_col1, #T_9725c_row1_col2, #T_9725c_row1_col3, #T_9725c_row1_col4, #T_9725c_row2_col0, #T_9725c_row2_col1, #T_9725c_row2_col2, #T_9725c_row2_col3, #T_9725c_row2_col4, #T_9725c_row3_col0, #T_9725c_row3_col1, #T_9725c_row3_col2, #T_9725c_row3_col3, #T_9725c_row3_col4, #T_9725c_row4_col0, #T_9725c_row4_col1, #T_9725c_row4_col2, #T_9725c_row4_col3, #T_9725c_row4_col4 {\n",
              "  text-align: left;\n",
              "  white-space: pre-wrap;\n",
              "  word-wrap: break-word;\n",
              "  max-width: 400px;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_9725c\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_9725c_level0_col0\" class=\"col_heading level0 col0\" >question</th>\n",
              "      <th id=\"T_9725c_level0_col1\" class=\"col_heading level0 col1\" >example_answer</th>\n",
              "      <th id=\"T_9725c_level0_col2\" class=\"col_heading level0 col2\" >context</th>\n",
              "      <th id=\"T_9725c_level0_col3\" class=\"col_heading level0 col3\" >pred_answer</th>\n",
              "      <th id=\"T_9725c_level0_col4\" class=\"col_heading level0 col4\" >mixed_metric</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_9725c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_9725c_row0_col0\" class=\"data row0 col0\" >Is increased time from neoadjuvant chemoradiation to surgery associated with higher pathologic complete response rates in esophageal cancer?</td>\n",
              "      <td id=\"T_9725c_row0_col1\" class=\"data row0 col1\" >A longer interval between completion of neoadjuvant chemoradiation and surgery was associated with higher pathologic complete response rates without an impact on surgical morbidity.</td>\n",
              "      <td id=\"T_9725c_row0_col2\" class=\"data row0 col2\" >['The interval between neoadjuvant chemoradiation treatment and surgery has been described as an important predictor of pathologic response to therapy in nonesophageal cancer sites. We...</td>\n",
              "      <td id=\"T_9725c_row0_col3\" class=\"data row0 col3\" >Yes, increased time from neoadjuvant chemoradiation to surgery is associated with higher pathologic complete response rates in esophageal cancer.</td>\n",
              "      <td id=\"T_9725c_row0_col4\" class=\"data row0 col4\" >✔️ [0.7792506217956543]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9725c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_9725c_row1_col0\" class=\"data row1 col0\" >Is epileptic focus localization based on resting state interictal MEG recordings feasible irrespective of the presence or absence of spikes?</td>\n",
              "      <td id=\"T_9725c_row1_col1\" class=\"data row1 col1\" >Our preliminary results suggest that accurate localization of the epileptogenic focus may be accomplished using noninvasive spontaneous \"resting-state\" recordings of relatively brief duration and without...</td>\n",
              "      <td id=\"T_9725c_row1_col2\" class=\"data row1 col2\" >['To investigate whether epileptogenic focus localization is possible based on resting state connectivity analysis of magnetoencephalographic (MEG) data. A multivariate autoregressive (MVAR) model was constructed...</td>\n",
              "      <td id=\"T_9725c_row1_col3\" class=\"data row1 col3\" >Yes.</td>\n",
              "      <td id=\"T_9725c_row1_col4\" class=\"data row1 col4\" >✔️ [0.08863363415002823]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9725c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_9725c_row2_col0\" class=\"data row2 col0\" >Does seminal Helicobacter pylori treatment improve sperm motility in infertile asthenozoospermic men?</td>\n",
              "      <td id=\"T_9725c_row2_col1\" class=\"data row2 col1\" >H pylori treatment significantly improves sperm motility in infertile asthenozoospermic men with elevated seminal H pylori IgA.</td>\n",
              "      <td id=\"T_9725c_row2_col2\" class=\"data row2 col2\" >['To assess the effect of treatment of seminal Helicobacter pylori in infertile asthenozoospermic men. In all, 223 infertile asthenozoospermic men were consecutively selected. They were...</td>\n",
              "      <td id=\"T_9725c_row2_col3\" class=\"data row2 col3\" >Yes, seminal Helicobacter pylori treatment improved sperm motility in infertile asthenozoospermic men.</td>\n",
              "      <td id=\"T_9725c_row2_col4\" class=\"data row2 col4\" >✔️ [0.8639540076255798]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9725c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_9725c_row3_col0\" class=\"data row3 col0\" >Does a migrating ciliary gate compartmentalize the site of axoneme assembly in Drosophila spermatids?</td>\n",
              "      <td id=\"T_9725c_row3_col1\" class=\"data row3 col1\" >Our findings demonstrate that the ciliary gate can migrate away from the base of the cilium, thereby functioning independently of the centriole and of a...</td>\n",
              "      <td id=\"T_9725c_row3_col2\" class=\"data row3 col2\" >['In most cells, the cilium is formed within a compartment separated from the cytoplasm. Entry into the ciliary compartment is regulated by a specialized gate...</td>\n",
              "      <td id=\"T_9725c_row3_col3\" class=\"data row3 col3\" >Yes, a migrating ciliary gate compartmentalizes the site of axoneme assembly in Drosophila spermatids.</td>\n",
              "      <td id=\"T_9725c_row3_col4\" class=\"data row3 col4\" >✔️ [0.6595481634140015]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9725c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_9725c_row4_col0\" class=\"data row4 col0\" >Is individual Public Transportation Accessibility Positively Associated with Self-Reported Active Commuting?</td>\n",
              "      <td id=\"T_9725c_row4_col1\" class=\"data row4 col1\" >This study extends the knowledge about the driving forces of using public transportation for commuting by examining the individual public transportation accessibility. Findings suggest that...</td>\n",
              "      <td id=\"T_9725c_row4_col2\" class=\"data row4 col2\" >['Active commuters have lower risk of chronic disease. Understanding which of the, to some extent, modifiable characteristics of public transportation that facilitate its use is...</td>\n",
              "      <td id=\"T_9725c_row4_col3\" class=\"data row4 col3\" >Yes, individual public transportation accessibility is positively associated with self-reported active commuting.</td>\n",
              "      <td id=\"T_9725c_row4_col4\" class=\"data row4 col4\" >✔️ [0.8073874711990356]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d5a1fb6de70>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "\n",
              "                <div style='\n",
              "                    text-align: center;\n",
              "                    font-size: 16px;\n",
              "                    font-weight: bold;\n",
              "                    color: #555;\n",
              "                    margin: 10px 0;'>\n",
              "                    ... 45 more rows not displayed ...\n",
              "                </div>\n",
              "                "
            ],
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              "<IPython.core.display.HTML object>"
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          "metadata": {},
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        {
          "data": {
            "text/plain": [
              "48.81"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from dspy.evaluate.evaluate import Evaluate\n",
        "\n",
        "evaluate = Evaluate(\n",
        "    metric=mixed_metric, devset=devset, num_threads=1, display_progress=True, display_table=5\n",
        ")\n",
        "evaluate(uncompiled_rag)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qOJRB6h6_SHE"
      },
      "source": [
        "### Optimization\n",
        "\n",
        "We can now compile/optimized the DSPy program we created.\n",
        "\n",
        "This can be done using a teleprompter/optimizer, based on our metric and training set.\n",
        "\n",
        "In particular, `BootstrapFewShot` tries to improve the metric in the training set by adding few shot examples to the prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
          "referenced_widgets": [
            "97e1125881c04b71834382ef8209dd75",
            "e493c98c96a544daaa3874b96771e8b3",
            "b53cca04315b486f9a1d2f7a15dba335",
            "d3cf7df24adf45759285eb3ee0bb22bd",
            "cf12b53c2354423097f772efd2181ddb",
            "c5db9011510d43e886f6fc1cdcd72046",
            "bdc64623270941c5b99228bb435b9a8d",
            "e331763ada694bafac5d88d600ea964f",
            "7338f0ad31174e83b75143021351d1bc",
            "188b705a9dcd40af8c854a1a3b7c144b",
            "325440e8d0134d808f00b451448c7326",
            "c8fd84f3caf241799859793cb6097693",
            "f80d866e63684472ad99522af1d16e1e",
            "59d7d71ad20d4c9683acba7d5479922f",
            "4e7eeb2f2a164454b23ce2a29884af91",
            "d2604691d3474d66bd73989852eaf466",
            "a62789019e044646b6734c6a7901d4f0",
            "6b2dc89e6fe84accb0f7161bc91e243f",
            "3ac08d9430384595ac76f56bf75cdcf9",
            "f484901a047e4b2bb589763c46609e6a",
            "acb85577910a47b6bcdabca83b9a8827",
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            "4592db37c1a04f99bb92d670beb6ee76",
            "0eb33d8edd3346a98374e7b5a51d15a7",
            "3e7640b104b24bdaadf5a6df101e79a8",
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            "0e5af8c86de04837ba08a5a8495e6efa",
            "b6ef55c27cde4f36b84738f8fa100492",
            "da3f10aa29d943a192cae32b660731e8",
            "7b1d00461fe04836b811ab9b018c5963",
            "27b1c53aca784b7790f1c9e062d4e23e"
          ]
        },
        "id": "vpdIhvOHvT_V",
        "outputId": "7783fad9-9014-4f1d-f8c5-f943065982d4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/20 [00:00<?, ?it/s]"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "97e1125881c04b71834382ef8209dd75",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  5%|▌         | 1/20 [00:04<01:17,  4.06s/it]"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8fd84f3caf241799859793cb6097693",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 10%|█         | 2/20 [00:09<01:26,  4.80s/it]"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4592db37c1a04f99bb92d670beb6ee76",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 15%|█▌        | 3/20 [00:12<01:10,  4.16s/it]"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "409b0cd181794a3d870f3a3a7ac8f9ff",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 20%|██        | 4/20 [00:14<00:59,  3.71s/it]\n"
          ]
        }
      ],
      "source": [
        "from dspy.teleprompt import BootstrapFewShot\n",
        "\n",
        "optimizer = BootstrapFewShot(metric=mixed_metric)\n",
        "\n",
        "compiled_rag = optimizer.compile(RAG(), trainset=trainset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aa2dU0BAngE"
      },
      "source": [
        "### Evaluate optimized RAG module\n",
        "\n",
        "Let's now see if the training has been successful, evaluating the compiled RAG module on the dev set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/50 [00:00<?, ?it/s]"
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        },
        {
          "data": {
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 0.7792506217956543 / 1  (77.9):   2%|▏         | 1/50 [00:02<02:01,  2.47s/it]"
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        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 1.6343245267868043 / 2  (81.7):   4%|▍         | 2/50 [00:05<02:07,  2.66s/it]"
          ]
        },
        {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
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          "output_type": "stream",
          "text": [
            "Average Metric: 2.5054827690124513 / 3  (83.5):   6%|▌         | 3/50 [00:07<02:04,  2.65s/it]"
          ]
        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 3.1650309324264527 / 4  (79.1):   8%|▊         | 4/50 [00:10<01:58,  2.57s/it]"
          ]
        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
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          "text": [
            "Average Metric: 3.9724184036254884 / 5  (79.4):  10%|█         | 5/50 [00:12<01:50,  2.45s/it]"
          ]
        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
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          "output_type": "stream",
          "text": [
            "Average Metric: 4.743226420879364 / 6  (79.1):  12%|█▏        | 6/50 [00:15<01:52,  2.55s/it]"
          ]
        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 5.667502892017365 / 7  (81.0):  14%|█▍        | 7/50 [00:17<01:44,  2.42s/it]"
          ]
        },
        {
          "data": {
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              "model_id": "3f407a4b71ca467f8a1540c410b90d0c",
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 6.321795177459717 / 8  (79.0):  16%|█▌        | 8/50 [00:20<01:48,  2.58s/it]"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 6.726730477809906 / 9  (74.7):  18%|█▊        | 9/50 [00:22<01:36,  2.35s/it]"
          ]
        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 7.618233811855316 / 10  (76.2):  20%|██        | 10/50 [00:25<01:48,  2.72s/it]"
          ]
        },
        {
          "data": {
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
          },
          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 8.246293556690215 / 11  (75.0):  22%|██▏       | 11/50 [00:27<01:39,  2.56s/it]"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
            ]
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 8.786842536926269 / 12  (73.2):  24%|██▍       | 12/50 [00:30<01:38,  2.58s/it]"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42ea3d0a708f47beb7a5d2d7d9d9202d",
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              "version_minor": 0
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            "text/plain": [
              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 9.534724307060241 / 13  (73.3):  26%|██▌       | 13/50 [00:33<01:41,  2.76s/it]"
          ]
        },
        {
          "data": {
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              "model_id": "a7230fa8a1934daabed2174d343dc737",
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Metric: 10.26323206424713 / 14  (73.3):  28%|██▊       | 14/50 [00:36<01:37,  2.70s/it]"
          ]
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        {
          "data": {
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        {
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          "text": [
            "Average Metric: 10.897355568408965 / 15  (72.6):  30%|███       | 15/50 [00:38<01:25,  2.45s/it]"
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        {
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          "text": [
            "Average Metric: 11.573097360134124 / 16  (72.3):  32%|███▏      | 16/50 [00:41<01:30,  2.67s/it]"
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              "model_id": "feac8a734eef418fa01894a97180400f",
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        {
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          "text": [
            "Average Metric: 12.40762699842453 / 17  (73.0):  34%|███▍      | 17/50 [00:45<01:41,  3.07s/it]"
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        },
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              "model_id": "4f85950b40c44f39b8c39ebd21ca4146",
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        },
        {
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          "text": [
            "Average Metric: 13.000101816654205 / 18  (72.2):  36%|███▌      | 18/50 [00:48<01:34,  2.96s/it]"
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        {
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          "text": [
            "Average Metric: 13.781478297710418 / 19  (72.5):  38%|███▊      | 19/50 [00:50<01:28,  2.85s/it]"
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          "text": [
            "Average Metric: 14.491218817234039 / 20  (72.5):  40%|████      | 20/50 [00:53<01:24,  2.83s/it]"
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          "text": [
            "Average Metric: 15.387097012996673 / 21  (73.3):  42%|████▏     | 21/50 [00:56<01:23,  2.87s/it]"
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          "metadata": {},
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          "text": [
            "Average Metric: 16.21189798116684 / 22  (73.7):  44%|████▍     | 22/50 [00:59<01:21,  2.92s/it]"
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          "text": [
            "Average Metric: 16.994540107250213 / 23  (73.9):  46%|████▌     | 23/50 [01:02<01:16,  2.85s/it]"
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          "text": [
            "Average Metric: 17.73131192922592 / 24  (73.9):  48%|████▊     | 24/50 [01:04<01:10,  2.70s/it]"
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            "Average Metric: 18.378108990192413 / 25  (73.5):  50%|█████     | 25/50 [01:07<01:07,  2.69s/it]"
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            "Average Metric: 18.83699902296066 / 26  (72.4):  52%|█████▏    | 26/50 [01:10<01:07,  2.79s/it]"
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        {
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          "text": [
            "Average Metric: 19.51262959241867 / 27  (72.3):  54%|█████▍    | 27/50 [01:12<01:03,  2.77s/it]"
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            "Average Metric: 20.026981341838834 / 28  (71.5):  56%|█████▌    | 28/50 [01:15<01:00,  2.75s/it]"
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        {
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          "text": [
            "Average Metric: 20.51759134531021 / 29  (70.8):  58%|█████▊    | 29/50 [01:18<00:55,  2.66s/it]"
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          "metadata": {},
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          "text": [
            "Average Metric: 21.18675976991653 / 30  (70.6):  60%|██████    | 30/50 [01:21<00:55,  2.76s/it]"
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        {
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          "text": [
            "Average Metric: 21.72026966810226 / 31  (70.1):  62%|██████▏   | 31/50 [01:25<00:59,  3.13s/it]"
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          "metadata": {},
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        {
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          "text": [
            "Average Metric: 22.53638147115707 / 32  (70.4):  64%|██████▍   | 32/50 [01:28<01:00,  3.34s/it]"
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        {
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            "Average Metric: 23.25495295524597 / 33  (70.5):  66%|██████▌   | 33/50 [01:31<00:54,  3.19s/it]"
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        {
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          "text": [
            "Average Metric: 23.510267066955564 / 34  (69.1):  68%|██████▊   | 34/50 [01:36<00:58,  3.65s/it]"
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        {
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          "text": [
            "Average Metric: 23.84202444553375 / 35  (68.1):  70%|███████   | 35/50 [01:39<00:53,  3.59s/it]"
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        {
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            "Average Metric: 24.518707108497615 / 36  (68.1):  72%|███████▏  | 36/50 [01:42<00:47,  3.37s/it]"
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        {
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            "Average Metric: 25.346190285682674 / 37  (68.5):  74%|███████▍  | 37/50 [01:45<00:40,  3.12s/it]"
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        {
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            "Average Metric: 26.039887082576747 / 38  (68.5):  76%|███████▌  | 38/50 [01:47<00:33,  2.82s/it]"
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        {
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            "Average Metric: 26.78020550012588 / 39  (68.7):  78%|███████▊  | 39/50 [01:49<00:29,  2.64s/it]"
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        {
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          "text": [
            "Average Metric: 27.52990843057632 / 40  (68.8):  80%|████████  | 40/50 [01:52<00:25,  2.59s/it]"
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        {
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            "Average Metric: 28.188934934139247 / 41  (68.8):  82%|████████▏ | 41/50 [01:54<00:22,  2.53s/it]"
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        {
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          "text": [
            "Average Metric: 28.92496390342712 / 42  (68.9):  84%|████████▍ | 42/50 [01:57<00:20,  2.57s/it]"
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          "text": [
            "Average Metric: 29.63569481372833 / 43  (68.9):  86%|████████▌ | 43/50 [01:59<00:17,  2.50s/it]"
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          "text": [
            "Average Metric: 30.31016762256622 / 44  (68.9):  88%|████████▊ | 44/50 [02:02<00:15,  2.61s/it]"
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        {
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          "text": [
            "Average Metric: 31.04774541854858 / 45  (69.0):  90%|█████████ | 45/50 [02:05<00:14,  2.90s/it]"
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        {
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            "Average Metric: 31.84949089288711 / 46  (69.2):  92%|█████████▏| 46/50 [02:08<00:11,  2.75s/it]"
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            "Average Metric: 32.622128200531 / 47  (69.4):  94%|█████████▍| 47/50 [02:11<00:08,  2.72s/it]"
          ]
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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            "Average Metric: 33.349305582046505 / 48  (69.5):  96%|█████████▌| 48/50 [02:13<00:05,  2.76s/it]"
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            "Average Metric: 33.87233598232269 / 49  (69.1):  98%|█████████▊| 49/50 [02:19<00:03,  3.77s/it]"
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            "Average Metric: 34.496535253524776 / 50  (69.0): 100%|██████████| 50/50 [02:22<00:00,  2.86s/it]\n"
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              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b7ca0_level0_col0\" class=\"col_heading level0 col0\" >question</th>\n",
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              "      <th id=\"T_b7ca0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_b7ca0_row0_col0\" class=\"data row0 col0\" >Is increased time from neoadjuvant chemoradiation to surgery associated with higher pathologic complete response rates in esophageal cancer?</td>\n",
              "      <td id=\"T_b7ca0_row0_col1\" class=\"data row0 col1\" >A longer interval between completion of neoadjuvant chemoradiation and surgery was associated with higher pathologic complete response rates without an impact on surgical morbidity.</td>\n",
              "      <td id=\"T_b7ca0_row0_col2\" class=\"data row0 col2\" >['The interval between neoadjuvant chemoradiation treatment and surgery has been described as an important predictor of pathologic response to therapy in nonesophageal cancer sites. We...</td>\n",
              "      <td id=\"T_b7ca0_row0_col3\" class=\"data row0 col3\" >Yes, increased time from neoadjuvant chemoradiation to surgery is associated with higher pathologic complete response rates in esophageal cancer.</td>\n",
              "      <td id=\"T_b7ca0_row0_col4\" class=\"data row0 col4\" >✔️ [0.7792506217956543]</td>\n",
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              "      <th id=\"T_b7ca0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_b7ca0_row1_col0\" class=\"data row1 col0\" >Is epileptic focus localization based on resting state interictal MEG recordings feasible irrespective of the presence or absence of spikes?</td>\n",
              "      <td id=\"T_b7ca0_row1_col1\" class=\"data row1 col1\" >Our preliminary results suggest that accurate localization of the epileptogenic focus may be accomplished using noninvasive spontaneous \"resting-state\" recordings of relatively brief duration and without...</td>\n",
              "      <td id=\"T_b7ca0_row1_col2\" class=\"data row1 col2\" >['To investigate whether epileptogenic focus localization is possible based on resting state connectivity analysis of magnetoencephalographic (MEG) data. A multivariate autoregressive (MVAR) model was constructed...</td>\n",
              "      <td id=\"T_b7ca0_row1_col3\" class=\"data row1 col3\" >Yes, epileptic focus localization based on resting state interictal MEG recordings is feasible irrespective of the presence or absence of spikes.</td>\n",
              "      <td id=\"T_b7ca0_row1_col4\" class=\"data row1 col4\" >✔️ [0.8550739049911499]</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_b7ca0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_b7ca0_row2_col0\" class=\"data row2 col0\" >Does seminal Helicobacter pylori treatment improve sperm motility in infertile asthenozoospermic men?</td>\n",
              "      <td id=\"T_b7ca0_row2_col1\" class=\"data row2 col1\" >H pylori treatment significantly improves sperm motility in infertile asthenozoospermic men with elevated seminal H pylori IgA.</td>\n",
              "      <td id=\"T_b7ca0_row2_col2\" class=\"data row2 col2\" >['To assess the effect of treatment of seminal Helicobacter pylori in infertile asthenozoospermic men. In all, 223 infertile asthenozoospermic men were consecutively selected. They were...</td>\n",
              "      <td id=\"T_b7ca0_row2_col3\" class=\"data row2 col3\" >Yes, seminal Helicobacter pylori treatment improves sperm motility in infertile asthenozoospermic men.</td>\n",
              "      <td id=\"T_b7ca0_row2_col4\" class=\"data row2 col4\" >✔️ [0.871158242225647]</td>\n",
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              "      <th id=\"T_b7ca0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_b7ca0_row3_col0\" class=\"data row3 col0\" >Does a migrating ciliary gate compartmentalize the site of axoneme assembly in Drosophila spermatids?</td>\n",
              "      <td id=\"T_b7ca0_row3_col1\" class=\"data row3 col1\" >Our findings demonstrate that the ciliary gate can migrate away from the base of the cilium, thereby functioning independently of the centriole and of a...</td>\n",
              "      <td id=\"T_b7ca0_row3_col2\" class=\"data row3 col2\" >['In most cells, the cilium is formed within a compartment separated from the cytoplasm. Entry into the ciliary compartment is regulated by a specialized gate...</td>\n",
              "      <td id=\"T_b7ca0_row3_col3\" class=\"data row3 col3\" >Yes, a migrating ciliary gate compartmentalizes the site of axoneme assembly in Drosophila spermatids.</td>\n",
              "      <td id=\"T_b7ca0_row3_col4\" class=\"data row3 col4\" >✔️ [0.6595481634140015]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7ca0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_b7ca0_row4_col0\" class=\"data row4 col0\" >Is individual Public Transportation Accessibility Positively Associated with Self-Reported Active Commuting?</td>\n",
              "      <td id=\"T_b7ca0_row4_col1\" class=\"data row4 col1\" >This study extends the knowledge about the driving forces of using public transportation for commuting by examining the individual public transportation accessibility. Findings suggest that...</td>\n",
              "      <td id=\"T_b7ca0_row4_col2\" class=\"data row4 col2\" >['Active commuters have lower risk of chronic disease. Understanding which of the, to some extent, modifiable characteristics of public transportation that facilitate its use is...</td>\n",
              "      <td id=\"T_b7ca0_row4_col3\" class=\"data row4 col3\" >Yes, individual public transportation accessibility is positively associated with self-reported active commuting.</td>\n",
              "      <td id=\"T_b7ca0_row4_col4\" class=\"data row4 col4\" >✔️ [0.8073874711990356]</td>\n",
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              "68.99"
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      ],
      "source": [
        "evaluate = Evaluate(\n",
        "    metric=mixed_metric, devset=devset, num_threads=1, display_progress=True, display_table=5\n",
        ")\n",
        "evaluate(compiled_rag)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cW2I3IPxBE4R"
      },
      "source": [
        "Based on our simple metric, we got a significant improvement!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgADv_j2BObc"
      },
      "source": [
        "### Inspect the optimized prompt\n",
        "\n",
        "Let's take a look at the few shot examples that made our results improve..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "9QNXRNLiTNH-",
        "outputId": "9ebefc53-5dae-4782-d273-4e1573ab84a2"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "Answer questions with short factoid answers.\n",
            "\n",
            "---\n",
            "\n",
            "Question: Do tumor-infiltrating immune cell profiles and their change after neoadjuvant chemotherapy predict response and prognosis of breast cancer?\n",
            "Answer: Breast cancer immune cell subpopulation profiles, determined by immunohistochemistry-based computerized analysis, identify groups of patients characterized by high response (in the pre-treatment setting) and poor prognosis (in the post-treatment setting). Further understanding of the mechanisms underlying the distribution of immune cells and their changes after chemotherapy may contribute to the development of new immune-targeted therapies for breast cancer.\n",
            "\n",
            "Question: Do large portion sizes increase bite size and eating rate in overweight women?\n",
            "Answer: Increasing portion size led to a larger bite size and faster eating rate, but a slower reduction in eating speed during the meal. These changes may underlie greater energy intakes with exposure to large portions. Interventions to reduce bite size and slow eating rate may provide individuals with strategies to reduce the risk of overconsumption.\n",
            "\n",
            "Question: Are secretory phospholipases A2 secreted from ciliated cells and increase mucin and eicosanoid secretion from goblet cells?\n",
            "Answer: sPLA2 are secreted from ciliated cells and appear to induce mucin and cysLT secretion from goblet cells, strongly suggesting that airway goblet cells are proinflammatory effector cells.\n",
            "\n",
            "Question: Are reasons why erupted third molars extracted in a public university in Mexico?\n",
            "Answer: Women and patients 18 to 34 years of age had erupted 3M extracted more frequently, primarily for prosthetic reasons. The age profile indicated a trend in demand for services that differ from those of overall tooth extractions, but not for the trend across gender.\n",
            "\n",
            "Question: Does increased Syk phosphorylation lead to overexpression of TRAF6 in peripheral B cells of patients with systemic lupus erythematosus?\n",
            "Answer: Our results suggest that the activated Syk-mediated TRAF6 pathway leads to aberrant activation of B cells in SLE, and also highlight Syk as a potential target for B-cell-mediated processes in SLE.\n",
            "\n",
            "Question: Are routine preoperative restaging CTs after neoadjuvant chemoradiation for locally advanced rectal cancer low yield : a retrospective case study?\n",
            "Answer: Because of the financial costs and established risks of intravenous contrast and cumulative radiation exposure, it may be advisable to take a more selective approach to preoperative imaging. Larger, prospective studies may enable identification of an at-risk cohort who would benefit most from restaging CT.\n",
            "\n",
            "Question: Does the Boston keratoprosthesis provide a wide depth of focus?\n",
            "Answer: The KPro's wide depth-of-focus makes the visual acuity less dependent on an exact refractive correction at distance and explains the 'pseudoaccomodation' experienced by these patients. This is primarily due to the small pupil diameter of the KPro. The current manufacturing steps in 0.50 dioptre increments appears to be sufficient.\n",
            "\n",
            "Question: Do obese patients with idiopathic pulmonary fibrosis have a higher 90-day mortality risk with bilateral lung transplantation?\n",
            "Answer: Our results suggest that obese patients who receive a BLT may be at higher risk of 90-day mortality compared with patients of normal weight. Further study is needed to obtain more detailed information about comorbidities and other risk factors for early death that are not included in the OPTN database.\n",
            "\n",
            "Question: Is admission hyperglycemia associated with failed reperfusion following fibrinolytic therapy in patients with STEMI : results of a retrospective study?\n",
            "Answer: In patients with STEMI who undergo FT, admission hyperglycemia is an independent predictor of the failure of fibrinolysis.\n",
            "\n",
            "Question: Is hidradenitis suppurativa a systemic disease with substantial comorbidity burden : a chart-verified case-control analysis?\n",
            "Answer: Control subjects were not validated for absence of HS and comorbidity validation was not performed for either group.\n",
            "\n",
            "Question: Do systematic Reviews Published in Emergency Medicine Journals Routinely Search Clinical Trials Registries : A Cross-Sectional Analysis?\n",
            "Answer: Systematic reviews published in emergency medicine journals do not routinely include searches of clinical trials registries. By helping authors identify unpublished trial data, the addition of registry searches may improve the validity of systematic reviews.\n",
            "\n",
            "Question: Does promoter variant rs2301228 on the neural cell adhesion molecule 1 gene confer risk of schizophrenia in Han Chinese?\n",
            "Answer: Our results provide direct evidence for NCAM1 as a susceptibility gene for schizophrenia, which offers support to a neurodevelopmental model and neuronal connectivity hypothesis in the onset of schizophrenia.\n",
            "\n",
            "---\n",
            "\n",
            "Follow the following format.\n",
            "\n",
            "Context: may contain relevant facts\n",
            "\n",
            "Question: ${question}\n",
            "\n",
            "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n",
            "\n",
            "Answer: short and precise answer\n",
            "\n",
            "---\n",
            "\n",
            "Context:\n",
            "[1] «Chronic rhinosinusitis (CRS) is a heterogeneous disease with an uncertain pathogenesis. Group 2 innate lymphoid cells (ILC2s) represent a recently discovered cell population which has been implicated in driving Th2 inflammation in CRS; however, their relationship with clinical disease characteristics has yet to be investigated. The aim of this study was to identify ILC2s in sinus mucosa in patients with CRS and controls and compare ILC2s across characteristics of disease. A cross-sectional study of patients with CRS undergoing endoscopic sinus surgery was conducted. Sinus mucosal biopsies were obtained during surgery and control tissue from patients undergoing pituitary tumour resection through transphenoidal approach. ILC2s were identified as CD45(+) Lin(-) CD127(+) CD4(-) CD8(-) CRTH2(CD294)(+) CD161(+) cells in single cell suspensions through flow cytometry. ILC2 frequencies, measured as a percentage of CD45(+) cells, were compared across CRS phenotype, endotype, inflammatory CRS subtype and other disease characteristics including blood eosinophils, serum IgE, asthma status and nasal symptom score. 35 patients (40% female, age 48 ± 17 years) including 13 with eosinophilic CRS (eCRS), 13 with non-eCRS and 9 controls were recruited. ILC2 frequencies were associated with the presence of nasal polyps (P = 0.002) as well as high tissue eosinophilia (P = 0.004) and eosinophil-dominant CRS (P = 0.001) (Mann-Whitney U). They were also associated with increased blood eosinophilia (P = 0.005). There were no significant associations found between ILC2s and serum total IgE and allergic disease. In the CRS with nasal polyps (CRSwNP) population, ILC2s were increased in patients with co-existing asthma (P = 0.03). ILC2s were also correlated with worsening nasal symptom score in CRS (P = 0.04).»\n",
            "[2] «Allergic airway inflammation is triggered by allergen exposure through several steps including release of IL-33, which promotes cytokine (IL-5, IL-13) production by type 2 innate lymphoid cells (ILC2s). MicroRNA (miR)-155 has recently been described to regulate adaptive responses in allergic inflammation. However, the role of miR-155 in the regulation of ILC2s remains unexplored. We sought to elucidate the contribution of miR-155 in ILC2 expansion using experimental murine models of allergic airway inflammation. To determine the role of miR-155 in the regulation of ILC2s in allergic airway inflammation, miR-155 deficient (miR-155 miR-155 was 10-fold upregulated in WT-derived ILC2s in response to IL-33. Furthermore, miR-155»\n",
            "[3] «The mechanisms and immune pathways associated with chronic rhinosinusitis (CRS) are not fully understood. Immunological changes during acute exacerbation of CRS may provide valuable clues to the pathogenesis and perpetuation of the disease. To characterize local and systemic immune responses associated with acute worsening of sinonasal symptoms during exacerbation in CRS with nasal polyps (CRSwNP) compared to controls. This was a non-interventional prospective study of individuals with CRSwNP and normal controls. Subjects underwent a baseline visit with collection of nasal secretions, nasal washes, and serum specimens. Within 3 days of acute worsening of sinonasal symptoms, subjects underwent a study visit, followed by a post-visit 2 weeks later. The sinonasal outcome test-22 (SNOT-22) scores and immunological parameters in the specimens were analysed using a novel, unsupervised learning method and by conventional univariate analysis. Both CRSwNP patients and control subjects showed a significant increase in SNOT-22 scores during acute exacerbation. Increased nasal levels of IL-6, IL-5, and eosinophil major basic protein were observed in CRSwNP patients. A network analysis of serum specimens revealed changes in a set of immunological parameters, which are distinctly associated with CRSwNP but not with controls. In particular, systemic increases in VEGF and GM-CSF levels were notable and were validated by a conventional analysis.»\n",
            "\n",
            "Question: Are group 2 innate lymphoid cells ( ILC2s ) increased in chronic rhinosinusitis with nasal polyps or eosinophilia?\n",
            "\n",
            "Reasoning: Let's think step by step in order to Answer: Yes, ILC2 frequencies are increased in chronic rhinosinusitis with nasal polyps, high tissue eosinophilia, and eosinophil-dominant CRS. They are also associated with increased blood eosinophilia and worsening nasal symptom scores.\n",
            "\n",
            "Answer: Yes, ILC2 frequencies are increased in chronic rhinosinusitis with nasal polyps, high tissue eosinophilia, and eosinophil-dominant CRS. They are also associated with increased blood eosinophilia and worsening nasal symptom scores.\n",
            "\n",
            "---\n",
            "\n",
            "Context:\n",
            "[1] «Phosphatidylethanolamine N-methyltransferase (PEMT), a liver enriched enzyme, is responsible for approximately one third of hepatic phosphatidylcholine biosynthesis. When fed a high-fat diet (HFD), Pemt(-/-) mice are protected from HF-induced obesity; however, they develop steatohepatitis. The vagus nerve relays signals between liver and brain that regulate peripheral adiposity and pancreas function. Here we explore a possible role of the hepatic branch of the vagus nerve in the development of diet induced obesity and steatohepatitis in Pemt(-/-) mice. 8-week old Pemt(-/-) and Pemt(+/+) mice were subjected to hepatic vagotomy (HV) or capsaicin treatment, which selectively disrupts afferent nerves, and were compared to sham-operated or vehicle-treatment, respectively. After surgery, mice were fed a HFD for 10 weeks. HV abolished the protection against the HFD-induced obesity and glucose intolerance in Pemt(-/-) mice. HV normalized phospholipid content and prevented steatohepatitis in Pemt(-/-) mice. Moreover, HV increased the hepatic anti-inflammatory cytokine interleukin-10, reduced chemokine monocyte chemotactic protein-1 and the ER stress marker C/EBP homologous protein. Furthermore, HV normalized the expression of mitochondrial electron transport chain proteins and of proteins involved in fatty acid synthesis, acetyl-CoA carboxylase and fatty acid synthase in Pemt(-/-) mice. However, disruption of the hepatic afferent vagus nerve by capsaicin failed to reverse either the protection against the HFD-induced obesity or the development of HF-induced steatohepatitis in Pemt(-/-) mice.»\n",
            "[2] «Nonalcoholic steatohepatitis (NASH) is associated with cirrhosis and hepatocellular carcinoma. Reactive oxygen species (ROS) and reactive nitrogen species (RNS) play key roles in the development of the disease. However, the therapeutic target of NASH has not been fully defined and new treatments are needed. We investigated the protective effects of the antioxidant indole-derived NecroX-7 in a NASH mouse model using leptin-deficient ob/ob and methionine- and choline-deficient (MCD) diet-fed ob/ob mice. Six-week-old male mice were divided into three groups: ob/+ mice, ob/ob mice treated with vehicle and ob/ob mice treated daily with NecroX-7 (20 mg/kg) for 4 weeks. To study the effects of NecroX-7 in a fibrosis model, NASH was induced by feeding ob/ob mice an MCD diet. The effects of NecroX-7 on NASH progression were evaluated using biochemical, histological and molecular markers. NecroX-7-treated ob/ob mice had a marked decrease in serum aspartate aminotransferase and alanine transaminase compared with vehicle-treated controls. Interestingly, hepatic steatosis and lipid peroxidation were significantly improved by NecroX-7 treatment. NecroX-7 inhibited tert-butylhydroperoxide- and H2 O2 -induced mitochondrial ROS/RNS in primary hepatocytes and attenuated mitochondrial dysfunction in vitro and in vivo. Furthermore, NecroX-7-treated mice exhibited fewer infiltrating macrophages and reduced hepatic tumour necrosis factor-alpha expression. Hepatic fibrosis in MCD-fed ob/ob mice was significantly decreased by NecroX-7 treatment.»\n",
            "[3] «The ability of a treatment method to interfere with tinnitus-related neural activity patterns, such as cortical gamma rhythms, has been suggested to indicate its potential in relieving tinnitus. Therapeutic modulation of gamma-band oscillations with vagus nerve stimulation has been recently reported in epileptic patients. The aim of this study was to investigate the effects of transcutaneous vagus nerve stimulation (tVNS) on neural oscillatory patterns. We calculated the power spectral density and synchrony of magnetoencephalography recordings during auditory stimulation in seven tinnitus patients and eight normal-hearing control subjects. Comparisons between subject groups were performed to reveal electrophysiological markers of tinnitus. tVNS-specific effects within each group were studied by comparing recording blocks with and without tVNS. We also investigated the correlation of each measure with individual ratings of tinnitus distress, as measured by the tinnitus handicap inventory questionnaire. Tinnitus patients differed from controls in the baseline condition (no tVNS applied), measured by both cortical oscillatory power and synchronization, particularly at beta and gamma frequencies. Importantly, we found tVNS-induced changes in synchrony, correlating strongly with tinnitus handicap inventory scores, at whole-head beta-band (r = -0.857, p = 0.007), whole-head gamma-band (r = -0.952, p = 0.0003), and frontal gamma-band (r = -0.952, p = 0.0003).»\n",
            "\n",
            "Question: Does vagus nerve contribute to the development of steatohepatitis and obesity in phosphatidylethanolamine N-methyltransferase deficient mice?\n",
            "\n",
            "Reasoning: Let's think step by step in order to Answer: Yes, the hepatic branch of the vagus nerve plays a role in the development of diet-induced obesity and steatohepatitis in Pemt(-/-) mice. Disruption of the hepatic vagus nerve abolished protection against high-fat diet-induced obesity and glucose intolerance in these mice, while also normalizing phospholipid content and preventing steatohepatitis.\n",
            "\n",
            "Answer: Yes, the hepatic branch of the vagus nerve plays a role in the development of diet-induced obesity and steatohepatitis in Pemt(-/-) mice.\n",
            "\n",
            "---\n",
            "\n",
            "Context:\n",
            "[1] «Psammaplin A (PsA) is a natural product isolated from marine sponges, which has been demonstrated to have anticancer activity against several human cancer cell lines via the induction of cell cycle arrest and apoptosis. New drugs that are less toxic and more effective against multidrug-resistant cancers are urgently needed. We tested cell proliferation, cell cycle progression and autophagic cell death pathway in doxorubicin-resistant MCF-7 (MCF-7/adr) human breast cancer cells. The potency of PsA was further determined using an in vivo xenograft model.»\n",
            "[2] «The purpose of this study was to investigate the relationship between huntingtin-associated protein1 (HAP1) gene and radiation therapy of breast cancer cells. HAP1 gene was transfected into breast cancer MCF-7 cells, which was confirmed by quantitative reverse transcription-polymerase chain reaction analysis (qRT-PCR) and Western blot in vitro. The changes of cell radiosensitivity were assessed by colony formation assay. Apoptosis were examined by flow cytometry. The expressions of two radiation-induced genes were evaluated by Western blot. Tumor growth was investigated in nude mice xenograft models in vivo. Our data showed that HAP1 gene expression was significantly increased in HAP1-transfected MCF-7 cells in comparison with the parental cells or negative control cells. The survival rate in MCF-7/HAP1 cells was significantly decreased after irradiation (0, 2, 4, 6, 8Gy), compared to cells in MCF-7 and MCF-7/Pb groups in vitro. HAP1 gene increased apoptosis in MCF-7 cells after irradiation. Additionally, the tumor volume and weight in MCF-7/HAP1+RT group were observably lower than in MCF-7/HAP1 group and MCF-7/Pb+RT group.»\n",
            "[3] «Due to the chemo resistant nature of cancer cells and adverse effects of current therapies, researchers are looking for the most efficient therapeutic approach which has the lowest side effects and the highest toxicity on cancer cells. The aim of the present study was to investigate the synergic effect of Urtica dioica extract in combination with paclitaxel on cell death and invasion of human breast cancer MDA-MB-468 cell line. To determine the cytotoxic effects of Urtica dioica extract with paclitaxel, MTT assay was performed. The scratch test was exploited to assess the effects of Urtica dioica, Paclitaxel alone and combination on migration of cancer cells. The expression levels of snail-1, ZEB1, ZEB2, twist, Cdc2, cyclin B1 and Wee1 genes were quantified using qRT-PCR and western blot performed for snail-1expression. The effects of plant extract, Paclitaxel alone and combination on different phases of cell cycle was analyzed using flow cytometry. Results of MTT assay showed that Urtica dioica significantly destroyed cancer cells. Interestingly, Concurrent use of Urtica dioica extract with paclitaxel resulted in decreased IC50 dose of paclitaxel. Moreover, findings of scratch assay exhibited the inhibitory effects of Urtica dioica, Paclitaxel alone and combination on migration of MDA-MB-468 cell line. Our findings also demonstrated that the extract substantially decreased the Snail-1 and related gene expression. Ultimately, Cell cycle arrest occurred at G2/M phase post-treatment by deregulating Cdc2 and wee1.»\n",
            "\n",
            "Question: Does psammaplin A induce Sirtuin 1-dependent autophagic cell death in doxorubicin-resistant MCF-7/adr human breast cancer cells and xenografts?\n",
            "\n",
            "Reasoning: Let's think step by step in order to produce the answer. We know that psammaplin A has been shown to induce cell cycle arrest and apoptosis in cancer cells. Additionally, the study tested cell proliferation, cell cycle progression, and autophagic cell death pathway in doxorubicin-resistant MCF-7/adr human breast cancer cells.\n",
            "\n",
            "Answer: Psammaplin A induces Sirtuin 1-dependent autophagic cell death in doxorubicin-resistant MCF-7/adr human breast cancer cells and xenografts.\n",
            "\n",
            "---\n",
            "\n",
            "Context:\n",
            "[1] «This study examined links between DNA methylation and birth weight centile (BWC), and explored the impact of genetic variation. Using HumanMethylation450 arrays, we examined candidate gene-associated CpGs in cord blood from newborns with low (<15th centile), medium (40-60th centile) and high (>85th centile) BWC (n = 12). Candidates were examined in an investigation cohort (n = 110) using pyrosequencing and genotyping for putative methylation-associated polymorphisms performed using standard PCR. Array analysis identified 314 candidate genes associated with BWC extremes, four of which showed ≥ 4 BWC-linked CpGs. Of these, PM20D1 and MI886 suggested genetically determined methylation levels. However, methylation at three CpGs in FGFR2 remained significantly associated with high BWC (p = 0.004-0.027).»\n",
            "[2] «Studies performed in Andean populations living in high altitude, indicate that the reduced availability of oxygen could be associated to both a fetal growth retardation and a lower birth weight. These variables are predictive of morbidity and mortality during the first year of life. To study perinatal variables of newborns of mothers living at contrasting altitudinal levels, harboring different degrees of Aymara ancestry. Review of medical records of 5,295 women whose deliveries occurred between February 2004 and August 2010. Information was obtained on place of residence, grouped into two categories: coast (150 to 3,000 m) and high plateau (3,000 to 4,300 m), ancestry was estimated using number of Aymara surnames that were homologated to percentages of Amerindian admixture, gestational age, birth weight, height, head circumference and obstetric variables. Gestational age showed a tendency to increase and birth weight, height and head circumference to decrease with altitude of residence. Only weight reached statistical significance. Women with Aymara ancestry gave birth to children with a significantly higher gestational age, weight and cranial circumference.»\n",
            "[3] «Preterm delivery and sub-optimal fetal growth are associated with each other and affect both mother and infant. Our aim was to determine (i) whether there are detectable differences in DNA methylation between early and late gestation and (ii) whether changes in DNA methylation from entry are associated with spontaneous preterm delivery with and without reduced fetal growth. We conducted a case-control study nested within a large prospective cohort. Gene specific methylation was measured by Methyl-Profiler PCR Array in a Human Breast Cancer Signature Panel of 24 genes from maternal peripheral leukocytes genomic DNA at entry and 3 There was significantly decrease in DNA methylation in 15 of 24 genes during the 3»\n",
            "\n",
            "Question: Is methylation of the FGFR2 gene associated with high birth weight centile in humans?\n",
            "\n",
            "Reasoning: Let's think step by step in order to produce the answer. We know that the study identified methylation at three CpGs in the FGFR2 gene that remained significantly associated with high birth weight centile.\n",
            "\n",
            "Answer: Yes, methylation of the FGFR2 gene is associated with high birth weight centile in humans.\n",
            "\n",
            "---\n",
            "\n",
            "Context:\n",
            "[1] «Minimally invasive retroperitoneoscopic surgery (MIS) for psoas abscess (PA) in patients with thoracolumbar tuberculosis is not well-illustrated and has not reached the status of being fully clinically assessed when we review the English literatures. The aim of this study is to introduce and investigate on efficacy and feasibility of MIS (retroperitoneoscopic technique) for PA in patients with thoracolumbar tuberculosis. From January 2008 to 2013, 39 consecutive patients of the diagnosis of PA with thoracolumbar tuberculosis received the debridement of abscesses and cavity walls of abscesses by the retroperitoneoscopic technique (MIS) in combination with anti-tuberculosis chemotherapy. Medical records and follow-up data were retrospectively studied. CRP and ESR of every patient preoperatively and postoperatively were analyzed Immediate relief in clinical symptoms and signs, and amelioration in imaging and laboratory examinations were obviously observed in all the patients. The follow-up had proceeded for 12-48 (mean 23) months. No complication was observed during the follow-up postoperatively.»\n",
            "[2] «Current diagnostic tests for gastroesophageal reflux disease (GERD) do not consistently measure chronicity of reflux. Mucosal impedance (MI) is a minimally invasive measurement to assess esophageal conductivity changes due to GERD. We aimed to investigate MI pattern in patients with symptoms of extraesophageal reflux (EER) in a prospective longitudinal cohort study. Patients with potential symptoms of EER undergoing esophagogastroduodenoscopy (EGD) with wireless pH monitoring were studied. Participants included those with erosive esophagitis (E+), normal EGD/abnormal pH (E-/pH+), and normal EGD/normal pH (E-/pH-). MI was measured from the site of injury in patients with E+, as well as at 2, 5, and 10 cm above the squamocolumnar junction (SCJ) in all participants. Forty-one patients with symptoms of EER were studied. MI measurements at 2 cm above the SCJ were significantly (P = 0.04) different among the three groups, with MI lowest for E+ and greatest for E-/pH- patients. Although not statistically significant, there is a graded increase in median (interquartile range) MI axially along the esophagus at 5 cm (P = 0.20) and at 10 cm (P = 0.27) above the SCJ, with those with reflux (E+ and E-/pH+) having a lower MI than those without.»\n",
            "[3] «We evaluated the current role of minimally invasive surgery (MIS) in children with small bowel obstruction (SBO) at our institution. A retrospective review of patients undergoing MIS for acute SBO was performed from 2008 to 2013. The study population was compared with a historical control including patients from 2001 to 2008. There were 71 patients who met inclusion criteria; 35 were male, and 36 were female. Sixty-two children underwent laparoscopy for their first episode of SBO, and 12 underwent laparoscopy for recurrent SBO, accounting for 74 episodes of SBO managed with MIS. The most common etiology of SBO was adhesions (n=40). Laparoscopy and laparoscopic-assisted procedures were associated with shorter nasogastric tube decompression (1.4±2 days [P<.001] and 1.5±2.7 days [P=.002], respectively) and time to regular diet (3.9±4 days [P=.002] and 4.6±2.8 days [P=.024], respectively) compared with those converted to laparotomy (5.1±4.9 days of nasogastric tube decompressions and 8±4.7 days to regular diet). There was no difference in postoperative morbidity comparing laparoscopy (11%), laparoscopic-assisted (5%), and laparoscopic converted to open procedures (18%) (P=.48).»\n",
            "\n",
            "Question: Do minimally invasive retroperitoneoscopic surgery for psoas abscess with thoracolumbar tuberculosis?\n",
            "\n",
            "Reasoning: Let's think step by step in order to Answer: Yes, minimally invasive retroperitoneoscopic surgery for psoas abscess in patients with thoracolumbar tuberculosis is effective and feasible. Immediate relief in clinical symptoms and signs, along with improvement in imaging and laboratory examinations, was observed in all patients who underwent this procedure.\n",
            "\n",
            "Answer:\u001b[32m Yes, minimally invasive retroperitoneoscopic surgery for psoas abscess in patients with thoracolumbar tuberculosis is effective and feasible.\u001b[0m\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\n\\n\\nAnswer questions with short factoid answers.\\n\\n---\\n\\nQuestion: Do tumor-infiltrating immune cell profiles and their change after neoadjuvant chemotherapy predict response and prognosis of breast cancer?\\nAnswer: Breast cancer immune cell subpopulation profiles, determined by immunohistochemistry-based computerized analysis, identify groups of patients characterized by high response (in the pre-treatment setting) and poor prognosis (in the post-treatment setting). Further understanding of the mechanisms underlying the distribution of immune cells and their changes after chemotherapy may contribute to the development of new immune-targeted therapies for breast cancer.\\n\\nQuestion: Do large portion sizes increase bite size and eating rate in overweight women?\\nAnswer: Increasing portion size led to a larger bite size and faster eating rate, but a slower reduction in eating speed during the meal. These changes may underlie greater energy intakes with exposure to large portions. Interventions to reduce bite size and slow eating rate may provide individuals with strategies to reduce the risk of overconsumption.\\n\\nQuestion: Are secretory phospholipases A2 secreted from ciliated cells and increase mucin and eicosanoid secretion from goblet cells?\\nAnswer: sPLA2 are secreted from ciliated cells and appear to induce mucin and cysLT secretion from goblet cells, strongly suggesting that airway goblet cells are proinflammatory effector cells.\\n\\nQuestion: Are reasons why erupted third molars extracted in a public university in Mexico?\\nAnswer: Women and patients 18 to 34 years of age had erupted 3M extracted more frequently, primarily for prosthetic reasons. The age profile indicated a trend in demand for services that differ from those of overall tooth extractions, but not for the trend across gender.\\n\\nQuestion: Does increased Syk phosphorylation lead to overexpression of TRAF6 in peripheral B cells of patients with systemic lupus erythematosus?\\nAnswer: Our results suggest that the activated Syk-mediated TRAF6 pathway leads to aberrant activation of B cells in SLE, and also highlight Syk as a potential target for B-cell-mediated processes in SLE.\\n\\nQuestion: Are routine preoperative restaging CTs after neoadjuvant chemoradiation for locally advanced rectal cancer low yield : a retrospective case study?\\nAnswer: Because of the financial costs and established risks of intravenous contrast and cumulative radiation exposure, it may be advisable to take a more selective approach to preoperative imaging. Larger, prospective studies may enable identification of an at-risk cohort who would benefit most from restaging CT.\\n\\nQuestion: Does the Boston keratoprosthesis provide a wide depth of focus?\\nAnswer: The KPro's wide depth-of-focus makes the visual acuity less dependent on an exact refractive correction at distance and explains the 'pseudoaccomodation' experienced by these patients. This is primarily due to the small pupil diameter of the KPro. The current manufacturing steps in 0.50 dioptre increments appears to be sufficient.\\n\\nQuestion: Do obese patients with idiopathic pulmonary fibrosis have a higher 90-day mortality risk with bilateral lung transplantation?\\nAnswer: Our results suggest that obese patients who receive a BLT may be at higher risk of 90-day mortality compared with patients of normal weight. Further study is needed to obtain more detailed information about comorbidities and other risk factors for early death that are not included in the OPTN database.\\n\\nQuestion: Is admission hyperglycemia associated with failed reperfusion following fibrinolytic therapy in patients with STEMI : results of a retrospective study?\\nAnswer: In patients with STEMI who undergo FT, admission hyperglycemia is an independent predictor of the failure of fibrinolysis.\\n\\nQuestion: Is hidradenitis suppurativa a systemic disease with substantial comorbidity burden : a chart-verified case-control analysis?\\nAnswer: Control subjects were not validated for absence of HS and comorbidity validation was not performed for either group.\\n\\nQuestion: Do systematic Reviews Published in Emergency Medicine Journals Routinely Search Clinical Trials Registries : A Cross-Sectional Analysis?\\nAnswer: Systematic reviews published in emergency medicine journals do not routinely include searches of clinical trials registries. By helping authors identify unpublished trial data, the addition of registry searches may improve the validity of systematic reviews.\\n\\nQuestion: Does promoter variant rs2301228 on the neural cell adhesion molecule 1 gene confer risk of schizophrenia in Han Chinese?\\nAnswer: Our results provide direct evidence for NCAM1 as a susceptibility gene for schizophrenia, which offers support to a neurodevelopmental model and neuronal connectivity hypothesis in the onset of schizophrenia.\\n\\n---\\n\\nFollow the following format.\\n\\nContext: may contain relevant facts\\n\\nQuestion: ${question}\\n\\nReasoning: Let's think step by step in order to ${produce the answer}. We ...\\n\\nAnswer: short and precise answer\\n\\n---\\n\\nContext:\\n[1] «Chronic rhinosinusitis (CRS) is a heterogeneous disease with an uncertain pathogenesis. Group 2 innate lymphoid cells (ILC2s) represent a recently discovered cell population which has been implicated in driving Th2 inflammation in CRS; however, their relationship with clinical disease characteristics has yet to be investigated. The aim of this study was to identify ILC2s in sinus mucosa in patients with CRS and controls and compare ILC2s across characteristics of disease. A cross-sectional study of patients with CRS undergoing endoscopic sinus surgery was conducted. Sinus mucosal biopsies were obtained during surgery and control tissue from patients undergoing pituitary tumour resection through transphenoidal approach. ILC2s were identified as CD45(+) Lin(-) CD127(+) CD4(-) CD8(-) CRTH2(CD294)(+) CD161(+) cells in single cell suspensions through flow cytometry. ILC2 frequencies, measured as a percentage of CD45(+) cells, were compared across CRS phenotype, endotype, inflammatory CRS subtype and other disease characteristics including blood eosinophils, serum IgE, asthma status and nasal symptom score. 35 patients (40% female, age 48 ± 17 years) including 13 with eosinophilic CRS (eCRS), 13 with non-eCRS and 9 controls were recruited. ILC2 frequencies were associated with the presence of nasal polyps (P = 0.002) as well as high tissue eosinophilia (P = 0.004) and eosinophil-dominant CRS (P = 0.001) (Mann-Whitney U). They were also associated with increased blood eosinophilia (P = 0.005). There were no significant associations found between ILC2s and serum total IgE and allergic disease. In the CRS with nasal polyps (CRSwNP) population, ILC2s were increased in patients with co-existing asthma (P = 0.03). ILC2s were also correlated with worsening nasal symptom score in CRS (P = 0.04).»\\n[2] «Allergic airway inflammation is triggered by allergen exposure through several steps including release of IL-33, which promotes cytokine (IL-5, IL-13) production by type 2 innate lymphoid cells (ILC2s). MicroRNA (miR)-155 has recently been described to regulate adaptive responses in allergic inflammation. However, the role of miR-155 in the regulation of ILC2s remains unexplored. We sought to elucidate the contribution of miR-155 in ILC2 expansion using experimental murine models of allergic airway inflammation. To determine the role of miR-155 in the regulation of ILC2s in allergic airway inflammation, miR-155 deficient (miR-155 miR-155 was 10-fold upregulated in WT-derived ILC2s in response to IL-33. Furthermore, miR-155»\\n[3] «The mechanisms and immune pathways associated with chronic rhinosinusitis (CRS) are not fully understood. Immunological changes during acute exacerbation of CRS may provide valuable clues to the pathogenesis and perpetuation of the disease. To characterize local and systemic immune responses associated with acute worsening of sinonasal symptoms during exacerbation in CRS with nasal polyps (CRSwNP) compared to controls. This was a non-interventional prospective study of individuals with CRSwNP and normal controls. Subjects underwent a baseline visit with collection of nasal secretions, nasal washes, and serum specimens. Within 3\\xa0days of acute worsening of sinonasal symptoms, subjects underwent a study visit, followed by a post-visit 2\\xa0weeks later. The sinonasal outcome test-22 (SNOT-22) scores and immunological parameters in the specimens were analysed using a novel, unsupervised learning method and by conventional univariate analysis. Both CRSwNP patients and control subjects showed a significant increase in SNOT-22 scores during acute exacerbation. Increased nasal levels of IL-6, IL-5, and eosinophil major basic protein were observed in CRSwNP patients. A network analysis of serum specimens revealed changes in a set of immunological parameters, which are distinctly associated with CRSwNP but not with controls. In particular, systemic increases in VEGF and GM-CSF levels were notable and were validated by a conventional analysis.»\\n\\nQuestion: Are group 2 innate lymphoid cells ( ILC2s ) increased in chronic rhinosinusitis with nasal polyps or eosinophilia?\\n\\nReasoning: Let's think step by step in order to Answer: Yes, ILC2 frequencies are increased in chronic rhinosinusitis with nasal polyps, high tissue eosinophilia, and eosinophil-dominant CRS. They are also associated with increased blood eosinophilia and worsening nasal symptom scores.\\n\\nAnswer: Yes, ILC2 frequencies are increased in chronic rhinosinusitis with nasal polyps, high tissue eosinophilia, and eosinophil-dominant CRS. They are also associated with increased blood eosinophilia and worsening nasal symptom scores.\\n\\n---\\n\\nContext:\\n[1] «Phosphatidylethanolamine N-methyltransferase (PEMT), a liver enriched enzyme, is responsible for approximately one third of hepatic phosphatidylcholine biosynthesis. When fed a high-fat diet (HFD), Pemt(-/-) mice are protected from HF-induced obesity; however, they develop steatohepatitis. The vagus nerve relays signals between liver and brain that regulate peripheral adiposity and pancreas function. Here we explore a possible role of the hepatic branch of the vagus nerve in the development of diet induced obesity and steatohepatitis in Pemt(-/-) mice. 8-week old Pemt(-/-) and Pemt(+/+) mice were subjected to hepatic vagotomy (HV) or capsaicin treatment, which selectively disrupts afferent nerves, and were compared to sham-operated or vehicle-treatment, respectively. After surgery, mice were fed a HFD for 10 weeks. HV abolished the protection against the HFD-induced obesity and glucose intolerance in Pemt(-/-) mice. HV normalized phospholipid content and prevented steatohepatitis in Pemt(-/-) mice. Moreover, HV increased the hepatic anti-inflammatory cytokine interleukin-10, reduced chemokine monocyte chemotactic protein-1 and the ER stress marker C/EBP homologous protein. Furthermore, HV normalized the expression of mitochondrial electron transport chain proteins and of proteins involved in fatty acid synthesis, acetyl-CoA carboxylase and fatty acid synthase in Pemt(-/-) mice. However, disruption of the hepatic afferent vagus nerve by capsaicin failed to reverse either the protection against the HFD-induced obesity or the development of HF-induced steatohepatitis in Pemt(-/-) mice.»\\n[2] «Nonalcoholic steatohepatitis (NASH) is associated with cirrhosis and hepatocellular carcinoma. Reactive oxygen species (ROS) and reactive nitrogen species (RNS) play key roles in the development of the disease. However, the therapeutic target of NASH has not been fully defined and new treatments are needed. We investigated the protective effects of the antioxidant indole-derived NecroX-7 in a NASH mouse model using leptin-deficient ob/ob and methionine- and choline-deficient (MCD) diet-fed ob/ob mice. Six-week-old male mice were divided into three groups: ob/+ mice, ob/ob mice treated with vehicle and ob/ob mice treated daily with NecroX-7 (20 mg/kg) for 4 weeks. To study the effects of NecroX-7 in a fibrosis model, NASH was induced by feeding ob/ob mice an MCD diet. The effects of NecroX-7 on NASH progression were evaluated using biochemical, histological and molecular markers. NecroX-7-treated ob/ob mice had a marked decrease in serum aspartate aminotransferase and alanine transaminase compared with vehicle-treated controls. Interestingly, hepatic steatosis and lipid peroxidation were significantly improved by NecroX-7 treatment. NecroX-7 inhibited tert-butylhydroperoxide- and H2 O2 -induced mitochondrial ROS/RNS in primary hepatocytes and attenuated mitochondrial dysfunction in vitro and in vivo. Furthermore, NecroX-7-treated mice exhibited fewer infiltrating macrophages and reduced hepatic tumour necrosis factor-alpha expression. Hepatic fibrosis in MCD-fed ob/ob mice was significantly decreased by NecroX-7 treatment.»\\n[3] «The ability of a treatment method to interfere with tinnitus-related neural activity patterns, such as cortical gamma rhythms, has been suggested to indicate its potential in relieving tinnitus. Therapeutic modulation of gamma-band oscillations with vagus nerve stimulation has been recently reported in epileptic patients. The aim of this study was to investigate the effects of transcutaneous vagus nerve stimulation (tVNS) on neural oscillatory patterns. We calculated the power spectral density and synchrony of magnetoencephalography recordings during auditory stimulation in seven tinnitus patients and eight normal-hearing control subjects. Comparisons between subject groups were performed to reveal electrophysiological markers of tinnitus. tVNS-specific effects within each group were studied by comparing recording blocks with and without tVNS. We also investigated the correlation of each measure with individual ratings of tinnitus distress, as measured by the tinnitus handicap inventory questionnaire. Tinnitus patients differed from controls in the baseline condition (no tVNS applied), measured by both cortical oscillatory power and synchronization, particularly at beta and gamma frequencies. Importantly, we found tVNS-induced changes in synchrony, correlating strongly with tinnitus handicap inventory scores, at whole-head beta-band (r = -0.857, p = 0.007), whole-head gamma-band (r = -0.952, p = 0.0003), and frontal gamma-band (r = -0.952, p = 0.0003).»\\n\\nQuestion: Does vagus nerve contribute to the development of steatohepatitis and obesity in phosphatidylethanolamine N-methyltransferase deficient mice?\\n\\nReasoning: Let's think step by step in order to Answer: Yes, the hepatic branch of the vagus nerve plays a role in the development of diet-induced obesity and steatohepatitis in Pemt(-/-) mice. Disruption of the hepatic vagus nerve abolished protection against high-fat diet-induced obesity and glucose intolerance in these mice, while also normalizing phospholipid content and preventing steatohepatitis.\\n\\nAnswer: Yes, the hepatic branch of the vagus nerve plays a role in the development of diet-induced obesity and steatohepatitis in Pemt(-/-) mice.\\n\\n---\\n\\nContext:\\n[1] «Psammaplin A (PsA) is a natural product isolated from marine sponges, which has been demonstrated to have anticancer activity against several human cancer cell lines via the induction of cell cycle arrest and apoptosis. New drugs that are less toxic and more effective against multidrug-resistant cancers are urgently needed. We tested cell proliferation, cell cycle progression and autophagic cell death pathway in doxorubicin-resistant MCF-7 (MCF-7/adr) human breast cancer cells. The potency of PsA was further determined using an in vivo xenograft model.»\\n[2] «The purpose of this study was to investigate the relationship between huntingtin-associated protein1 (HAP1) gene and radiation therapy of breast cancer cells. HAP1 gene was transfected into breast cancer MCF-7 cells, which was confirmed by quantitative reverse transcription-polymerase chain reaction analysis (qRT-PCR) and Western blot in vitro. The changes of cell radiosensitivity were assessed by colony formation assay. Apoptosis were examined by flow cytometry. The expressions of two radiation-induced genes were evaluated by Western blot. Tumor growth was investigated in nude mice xenograft models in vivo. Our data showed that HAP1 gene expression was significantly increased in HAP1-transfected MCF-7 cells in comparison with the parental cells or negative control cells. The survival rate in MCF-7/HAP1 cells was significantly decreased after irradiation (0, 2, 4, 6, 8Gy), compared to cells in MCF-7 and MCF-7/Pb groups in vitro. HAP1 gene increased apoptosis in MCF-7 cells after irradiation. Additionally, the tumor volume and weight in MCF-7/HAP1+RT group were observably lower than in MCF-7/HAP1 group and MCF-7/Pb+RT group.»\\n[3] «Due to the chemo resistant nature of cancer cells and adverse effects of current therapies, researchers are looking for the most efficient therapeutic approach which has the lowest side effects and the highest toxicity on cancer cells. The aim of the present study was to investigate the synergic effect of Urtica dioica extract in combination with paclitaxel on cell death and invasion of human breast cancer MDA-MB-468 cell line. To determine the cytotoxic effects of Urtica dioica extract with paclitaxel, MTT assay was performed. The scratch test was exploited to assess the effects of Urtica dioica, Paclitaxel alone and combination on migration of cancer cells. The expression levels of snail-1, ZEB1, ZEB2, twist, Cdc2, cyclin B1 and Wee1 genes were quantified using qRT-PCR and western blot performed for snail-1expression. The effects of plant extract, Paclitaxel alone and combination on different phases of cell cycle was analyzed using flow cytometry. Results of MTT assay showed that Urtica dioica significantly destroyed cancer cells. Interestingly, Concurrent use of Urtica dioica extract with paclitaxel resulted in decreased IC50 dose of paclitaxel. Moreover, findings of scratch assay exhibited the inhibitory effects of Urtica dioica, Paclitaxel alone and combination on migration of MDA-MB-468 cell line. Our findings also demonstrated that the extract substantially decreased the Snail-1 and related gene expression. Ultimately, Cell cycle arrest occurred at G2/M phase post-treatment by deregulating Cdc2 and wee1.»\\n\\nQuestion: Does psammaplin A induce Sirtuin 1-dependent autophagic cell death in doxorubicin-resistant MCF-7/adr human breast cancer cells and xenografts?\\n\\nReasoning: Let's think step by step in order to produce the answer. We know that psammaplin A has been shown to induce cell cycle arrest and apoptosis in cancer cells. Additionally, the study tested cell proliferation, cell cycle progression, and autophagic cell death pathway in doxorubicin-resistant MCF-7/adr human breast cancer cells.\\n\\nAnswer: Psammaplin A induces Sirtuin 1-dependent autophagic cell death in doxorubicin-resistant MCF-7/adr human breast cancer cells and xenografts.\\n\\n---\\n\\nContext:\\n[1] «This study examined links between DNA methylation and birth weight centile (BWC), and explored the impact of genetic variation. Using HumanMethylation450 arrays, we examined candidate gene-associated CpGs in cord blood from newborns with low (<15th centile), medium (40-60th centile) and high (>85th centile) BWC (n = 12). Candidates were examined in an investigation cohort (n = 110) using pyrosequencing and genotyping for putative methylation-associated polymorphisms performed using standard PCR. Array analysis identified 314 candidate genes associated with BWC extremes, four of which showed ≥ 4 BWC-linked CpGs. Of these, PM20D1 and MI886 suggested genetically determined methylation levels. However, methylation at three CpGs in FGFR2 remained significantly associated with high BWC (p = 0.004-0.027).»\\n[2] «Studies performed in Andean populations living in high altitude, indicate that the reduced availability of oxygen could be associated to both a fetal growth retardation and a lower birth weight. These variables are predictive of morbidity and mortality during the first year of life. To study perinatal variables of newborns of mothers living at contrasting altitudinal levels, harboring different degrees of Aymara ancestry. Review of medical records of 5,295 women whose deliveries occurred between February 2004 and August 2010. Information was obtained on place of residence, grouped into two categories: coast (150 to 3,000 m) and high plateau (3,000 to 4,300 m), ancestry was estimated using number of Aymara surnames that were homologated to percentages of Amerindian admixture, gestational age, birth weight, height, head circumference and obstetric variables. Gestational age showed a tendency to increase and birth weight, height and head circumference to decrease with altitude of residence. Only weight reached statistical significance. Women with Aymara ancestry gave birth to children with a significantly higher gestational age, weight and cranial circumference.»\\n[3] «Preterm delivery and sub-optimal fetal growth are associated with each other and affect both mother and infant. Our aim was to determine (i) whether there are detectable differences in DNA methylation between early and late gestation and (ii) whether changes in DNA methylation from entry are associated with spontaneous preterm delivery with and without reduced fetal growth. We conducted a case-control study nested within a large prospective cohort. Gene specific methylation was measured by Methyl-Profiler PCR Array in a Human Breast Cancer Signature Panel of 24 genes from maternal peripheral leukocytes genomic DNA at entry and 3 There was significantly decrease in DNA methylation in 15 of 24 genes during the 3»\\n\\nQuestion: Is methylation of the FGFR2 gene associated with high birth weight centile in humans?\\n\\nReasoning: Let's think step by step in order to produce the answer. We know that the study identified methylation at three CpGs in the FGFR2 gene that remained significantly associated with high birth weight centile.\\n\\nAnswer: Yes, methylation of the FGFR2 gene is associated with high birth weight centile in humans.\\n\\n---\\n\\nContext:\\n[1] «Minimally invasive retroperitoneoscopic surgery (MIS) for psoas abscess (PA) in patients with thoracolumbar tuberculosis is not well-illustrated and has not reached the status of being fully clinically assessed when we review the English literatures. The aim of this study is to introduce and investigate on efficacy and feasibility of MIS (retroperitoneoscopic technique) for PA in patients with thoracolumbar tuberculosis. From January 2008 to 2013, 39 consecutive patients of the diagnosis of PA with thoracolumbar tuberculosis received the debridement of abscesses and cavity walls of abscesses by the retroperitoneoscopic technique (MIS) in combination with anti-tuberculosis chemotherapy. Medical records and follow-up data were retrospectively studied. CRP and ESR of every patient preoperatively and postoperatively were analyzed Immediate relief in clinical symptoms and signs, and amelioration in imaging and laboratory examinations were obviously observed in all the patients. The follow-up had proceeded for 12-48 (mean 23) months. No complication was observed during the follow-up postoperatively.»\\n[2] «Current diagnostic tests for gastroesophageal reflux disease (GERD) do not consistently measure chronicity of reflux. Mucosal impedance (MI) is a minimally invasive measurement to assess esophageal conductivity changes due to GERD. We aimed to investigate MI pattern in patients with symptoms of extraesophageal reflux (EER) in a prospective longitudinal cohort study. Patients with potential symptoms of EER undergoing esophagogastroduodenoscopy (EGD) with wireless pH monitoring were studied. Participants included those with erosive esophagitis (E+), normal EGD/abnormal pH (E-/pH+), and normal EGD/normal pH (E-/pH-). MI was measured from the site of injury in patients with E+, as well as at 2, 5, and 10 cm above the squamocolumnar junction (SCJ) in all participants. Forty-one patients with symptoms of EER were studied. MI measurements at 2\\u2009cm above the SCJ were significantly (P\\u2009=\\u20090.04) different among the three groups, with MI lowest for E+ and greatest for E-/pH- patients. Although not statistically significant, there is a graded increase in median (interquartile range) MI axially along the esophagus at 5\\u2009cm (P\\u2009=\\u20090.20) and at 10\\u2009cm (P\\u2009=\\u20090.27) above the SCJ, with those with reflux (E+ and E-/pH+) having a lower MI than those without.»\\n[3] «We evaluated the current role of minimally invasive surgery (MIS) in children with small bowel obstruction (SBO) at our institution. A retrospective review of patients undergoing MIS for acute SBO was performed from 2008 to 2013. The study population was compared with a historical control including patients from 2001 to 2008. There were 71 patients who met inclusion criteria; 35 were male, and 36 were female. Sixty-two children underwent laparoscopy for their first episode of SBO, and 12 underwent laparoscopy for recurrent SBO, accounting for 74 episodes of SBO managed with MIS. The most common etiology of SBO was adhesions (n=40). Laparoscopy and laparoscopic-assisted procedures were associated with shorter nasogastric tube decompression (1.4±2 days [P<.001] and 1.5±2.7 days [P=.002], respectively) and time to regular diet (3.9±4 days [P=.002] and 4.6±2.8 days [P=.024], respectively) compared with those converted to laparotomy (5.1±4.9 days of nasogastric tube decompressions and 8±4.7 days to regular diet). There was no difference in postoperative morbidity comparing laparoscopy (11%), laparoscopic-assisted (5%), and laparoscopic converted to open procedures (18%) (P=.48).»\\n\\nQuestion: Do minimally invasive retroperitoneoscopic surgery for psoas abscess with thoracolumbar tuberculosis?\\n\\nReasoning: Let's think step by step in order to Answer: Yes, minimally invasive retroperitoneoscopic surgery for psoas abscess in patients with thoracolumbar tuberculosis is effective and feasible. Immediate relief in clinical symptoms and signs, along with improvement in imaging and laboratory examinations, was observed in all patients who underwent this procedure.\\n\\nAnswer:\\x1b[32m Yes, minimally invasive retroperitoneoscopic surgery for psoas abscess in patients with thoracolumbar tuberculosis is effective and feasible.\\x1b[0m\\n\\n\\n\""
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lm.inspect_history(n=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSn00x5FBisv"
      },
      "source": [
        "## Optimized Haystack Pipeline\n",
        "\n",
        "We can now use the static part of the optimized prompt (including examples) and create a better Haystack RAG Pipeline.\n",
        "\n",
        "We include an `AnswerBuilder`, to capture only the relevant part of the generation (all text after `Answer: `)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "YTjyH6JXgScl"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "\n",
        "static_prompt = lm.inspect_history(n=1).rpartition(\"---\\n\")[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EqSHHH9Z-bF",
        "outputId": "89e4fe67-a608-427e-f103-158010e1890d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<haystack.core.pipeline.pipeline.Pipeline object at 0x7d59ed5f3880>\n",
              "🚅 Components\n",
              "  - retriever: InMemoryBM25Retriever\n",
              "  - prompt_builder: PromptBuilder\n",
              "  - llm: OpenAIGenerator\n",
              "  - answer_builder: AnswerBuilder\n",
              "🛤️ Connections\n",
              "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
              "  - prompt_builder.prompt -> llm.prompt (str)\n",
              "  - llm.replies -> answer_builder.replies (List[str])"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
        "from haystack.components.generators import OpenAIGenerator\n",
        "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
        "from haystack import Pipeline\n",
        "\n",
        "\n",
        "template = static_prompt+\"\"\"\n",
        "---\n",
        "\n",
        "Context:\n",
        "{% for document in documents %}\n",
        "    «{{ document.content }}»\n",
        "{% endfor %}\n",
        "\n",
        "Question: {{question}}\n",
        "Reasoning: Let's think step by step in order to\n",
        "\"\"\"\n",
        "\n",
        "new_prompt_builder = PromptBuilder(template=template)\n",
        "\n",
        "new_retriever = InMemoryBM25Retriever(document_store, top_k=3)\n",
        "new_generator = OpenAIGenerator(model=\"gpt-4o-mini\")\n",
        "\n",
        "answer_builder = AnswerBuilder(pattern=\"Answer: (.*)\")\n",
        "\n",
        "\n",
        "optimized_rag_pipeline = Pipeline()\n",
        "optimized_rag_pipeline.add_component(\"retriever\", new_retriever)\n",
        "optimized_rag_pipeline.add_component(\"prompt_builder\", new_prompt_builder)\n",
        "optimized_rag_pipeline.add_component(\"llm\", new_generator)\n",
        "optimized_rag_pipeline.add_component(\"answer_builder\", answer_builder)\n",
        "\n",
        "optimized_rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
        "optimized_rag_pipeline.connect(\"prompt_builder\", \"llm\")\n",
        "optimized_rag_pipeline.connect(\"llm.replies\", \"answer_builder.replies\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fe1pHA7dCe3f"
      },
      "source": [
        "Let's ask the same questions as before..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "yi-iWJwzaF1Y",
        "outputId": "b71eca16-41f2-4f49-8163-b4daa702dc00"
      },
      "outputs": [
        {
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              "version_major": 2,
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              "Ranking by BM25...:   0%|          | 0/1000 [00:00<?, ? docs/s]"
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Ketamine at higher concentrations inhibits the proliferation of rat neural stem cells, while not affecting \n",
              "apoptosis. Additionally, it decreases intracellular calcium concentration and suppresses PKCα activation and ERK1/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>\n",
              "phosphorylation in these cells.\n",
              "</pre>\n"
            ],
            "text/plain": [
              "Ketamine at higher concentrations inhibits the proliferation of rat neural stem cells, while not affecting \n",
              "apoptosis. Additionally, it decreases intracellular calcium concentration and suppresses PKCα activation and ERK1/\u001b[1;36m2\u001b[0m\n",
              "phosphorylation in these cells.\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "question = \"What effects does ketamine have on rat neural stem cells?\"\n",
        "\n",
        "response = optimized_rag_pipeline.run({\"retriever\": {\"query\": question}, \"prompt_builder\": {\"question\": question}, \"answer_builder\": {\"query\": question}})\n",
        "\n",
        "print(response[\"answer_builder\"][\"answers\"][0].data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
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        "colab": {
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Yes, the study found that lesions in the anterior cingulate cortex <span style=\"font-weight: bold\">(</span>ACC<span style=\"font-weight: bold\">)</span> prevented the anxiodepressive consequences\n",
              "of chronic pain, indicating a link between the ACC and pain-induced depression.\n",
              "</pre>\n"
            ],
            "text/plain": [
              "Yes, the study found that lesions in the anterior cingulate cortex \u001b[1m(\u001b[0mACC\u001b[1m)\u001b[0m prevented the anxiodepressive consequences\n",
              "of chronic pain, indicating a link between the ACC and pain-induced depression.\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "question = \"Is the anterior cingulate cortex linked to pain-induced depression?\"\n",
        "response = optimized_rag_pipeline.run({\"retriever\": {\"query\": question}, \"prompt_builder\": {\"question\": question}, \"answer_builder\": {\"query\": question}})\n",
        "\n",
        "print(response[\"answer_builder\"][\"answers\"][0].data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJa3ySwTCkgj"
      },
      "source": [
        "The answer are correct and shorter than before!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "*(Notebook by [Stefano Fiorucci](https://github.com/anakin87))*"
      ]
    }
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