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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "03ixktWsySVK"
+      },
+      "source": [
+        "# Evaluating RAG Pipelines with EvaluationHarness\n",
+        "\n",
+        "In this notebook, you'll learn how to use the [`EvaluationHarness`](https://docs.haystack.deepset.ai/v2.3-unstable/reference/evaluation-harness#evaluationharness) from the [haystack-experimental](https://github.com/deepset-ai/haystack-experimental) repository to assess the performance of Retrieval-Augmented Generation (RAG) pipelines over the [SQUAD dataset](https://huggingface.co/datasets/rajpurkar/squad_v2).\n",
+        "\n",
+        "The `EvaluationHarness` acts as an evaluation orchestrator, streamlining the assessment of pipeline performance and making the evaluation process simpler and more efficient."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "toc",
+        "id": "EfthWDSjyddb"
+      },
+      "source": [
+        ">[Evaluating RAG Pipelines with EvaluationHarness](#scrollTo=03ixktWsySVK)\n",
+        "\n",
+        ">>[Setup Development Environment](#scrollTo=836SIRGsyHNX)\n",
+        "\n",
+        ">>[Dataset preparation](#scrollTo=EQV5nZjFiH3A)\n",
+        "\n",
+        ">>[Indexing Pipeline](#scrollTo=tpw_fLaIiH3B)\n",
+        "\n",
+        ">>[RAG Pipeline](#scrollTo=BKcUXXI5iH3B)\n",
+        "\n",
+        ">>[EvaluationHarness](#scrollTo=6HQQDLYFiH3B)\n",
+        "\n",
+        ">>>[Alternative: EvaluationHarness for Keyword-based Retrieval](#scrollTo=Hq0jsE-qrskw)\n",
+        "\n",
+        ">>[Analyzing the Results](#scrollTo=Pq4wKakNqLaJ)\n",
+        "\n",
+        ">>[Evaluating and Comparing Different Pipelines](#scrollTo=KdIEKAf-urVL)\n",
+        "\n",
+        ">>[BONUS: EvaluationHarness for Custom RAG Pipelines](#scrollTo=IrMIAFgMiH3C)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "836SIRGsyHNX"
+      },
+      "source": [
+        "## Setup Development Environment\n",
+        "\n",
+        "To start, install `haystack-ai`, `haystack-experimental` and other dependencies:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OAfJA3OeiH2_",
+        "outputId": "a8ba0ad0-b72a-44cf-dc23-35da8547fd09"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install -U git+https://github.com/deepset-ai/haystack-experimental.git@main\n",
+        "pip install git+https://github.com/deepset-ai/haystack@main\n",
+        "pip install datasets\n",
+        "pip install sentence-transformers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HS7_Y1Si0y0B"
+      },
+      "source": [
+        "Provide an [OpenAI API key](https://platform.openai.com/api-keys) to ensure that LLM-based evaluators can query the OpenAI API:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "6wojeXKGiH2_"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "\n",
+        "if \"OPENAI_API_KEY\" not in os.environ:\n",
+        "  os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
+        "\n",
+        "# If you're running this notebook on Google Colab, you might need to the following instead:\n",
+        "#\n",
+        "# from google.colab import userdata\n",
+        "# if \"OPENAI_API_KEY\" not in os.environ:\n",
+        "#  os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wtLIYZHA1Izf"
+      },
+      "source": [
+        "Add the imports. All the imports that we'll need to create the following:\n",
+        "- An indexing pipeline that stores documents from our chosen dataset in a document store.\n",
+        "- A retrieval pipeline that uses a query to retrieve relevant documents from the document store."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "lM81EFFCiH3A"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "from typing import List, Dict\n",
+        "from collections import defaultdict\n",
+        "from pathlib import Path\n",
+        "import random\n",
+        "from datasets import load_dataset, Dataset\n",
+        "from tqdm import tqdm\n",
+        "\n",
+        "from haystack import Document, Pipeline\n",
+        "from haystack.components.builders import AnswerBuilder, PromptBuilder\n",
+        "from haystack.components.embedders import (\n",
+        "    SentenceTransformersDocumentEmbedder,\n",
+        "    SentenceTransformersTextEmbedder,\n",
+        ")\n",
+        "from haystack.components.generators import OpenAIGenerator\n",
+        "from haystack.components.retrievers import (\n",
+        "    InMemoryEmbeddingRetriever,\n",
+        "    InMemoryBM25Retriever,\n",
+        ")\n",
+        "from haystack.components.writers import DocumentWriter\n",
+        "\n",
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "from haystack.document_stores.types import DuplicatePolicy\n",
+        "from haystack_experimental.evaluation.harness.rag import (\n",
+        "    DefaultRAGArchitecture,\n",
+        "    RAGEvaluationHarness,\n",
+        "    RAGEvaluationMetric,\n",
+        "    RAGEvaluationInput,\n",
+        "    RAGEvaluationOverrides,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EQV5nZjFiH3A"
+      },
+      "source": [
+        "## Dataset preparation\n",
+        "\n",
+        "The following steps will load the SQUAD dataset, preprocess them for the indexing pipeline and store them to a local folder in the current working directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "lmPpGcPqiH3A"
+      },
+      "outputs": [],
+      "source": [
+        "# Helper functions to load the SQUAD dataset.\n",
+        "def aggregate_wiki_title(data: Dataset, agg_wiki_title: Dict[str, Dict[str, List[str]]]):\n",
+        "    for idx, x in enumerate(data.iter(batch_size=1)):\n",
+        "        if x[\"context\"] not in agg_wiki_title[x[\"title\"][0]][\"context\"]:\n",
+        "            agg_wiki_title[x[\"title\"][0]][\"context\"].append(x[\"context\"])\n",
+        "        agg_wiki_title[x[\"title\"][0]][\"question_answers\"].append(\n",
+        "            {\"question\": x[\"question\"], \"answers\": x[\"answers\"]}\n",
+        "        )\n",
+        "\n",
+        "def load_transformed_squad():\n",
+        "    with open(\"transformed_squad/questions.jsonl\", \"r\") as f:\n",
+        "        questions = [json.loads(x) for x in f.readlines()]\n",
+        "    for idx, question in enumerate(questions):\n",
+        "        question[\"query_id\"] = f\"query_{idx}\"\n",
+        "\n",
+        "    def create_document(text: str, name: str):\n",
+        "        return Document(content=text, meta={\"name\": name})\n",
+        "\n",
+        "    # walk through the files in the directory and transform each text file into a Document\n",
+        "    documents = []\n",
+        "    for root, dirs, files in os.walk(\"transformed_squad/articles/\"):\n",
+        "        for article in files:\n",
+        "            with open(f\"{root}/{article}\", \"r\") as f:\n",
+        "                raw_texts = f.read().split(\"\\n\")\n",
+        "                for text in raw_texts:\n",
+        "                    documents.append(\n",
+        "                        create_document(text, article.replace(\".txt\", \"\"))\n",
+        "                    )\n",
+        "\n",
+        "    return questions, documents"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 318,
+          "referenced_widgets": [
+            "d7ee8d7dbae7494fbfcdbfdbe9600b62",
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+            "eee55f66c7874b9098fc21177624dcaf",
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+            "fcf8d6386d7242c1860cf7debab18453",
+            "777c766e80514c45be5a15191b801231"
+          ]
+        },
+        "id": "WJLOQggLiH3A",
+        "outputId": "6c468c5f-38f1-4218-be4c-75207630008b"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d7ee8d7dbae7494fbfcdbfdbe9600b62",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/7.62k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d40572fc9223469eb9a382573d26cac8",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "88baffe59b4c49e791b0b4852a034983",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/1.82M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0188b7e6fd754008b3985049805a6085",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/87599 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f06df5bcff3b4781a7b0ba55669d9ec4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating validation split:   0%|          | 0/10570 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 490/490 [00:00<00:00, 14124.95it/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "data_train = load_dataset(\"squad\", split=\"train\")\n",
+        "data_validation = load_dataset(\"squad\", split=\"validation\")\n",
+        "agg_wiki_title = defaultdict(\n",
+        "    lambda: {\"context\": [], \"question_answers\": [], \"text\": \"\"}\n",
+        ")\n",
+        "aggregate_wiki_title(data_train, agg_wiki_title)\n",
+        "aggregate_wiki_title(data_validation, agg_wiki_title)\n",
+        "\n",
+        "# merge the context into a single document\n",
+        "for article in tqdm(agg_wiki_title.keys()):\n",
+        "    agg_wiki_title[article][\"text\"] = \"\\n\".join(\n",
+        "        [x[0] for x in agg_wiki_title[article][\"context\"]]\n",
+        "    )\n",
+        "\n",
+        "# create documents\n",
+        "for article in agg_wiki_title.keys():\n",
+        "    out_path = Path(\"transformed_squad/articles/\")\n",
+        "    out_path.mkdir(parents=True, exist_ok=True)\n",
+        "    with open(f\"{str(out_path)}/{article}.txt\", \"w\") as f:\n",
+        "        f.write(agg_wiki_title[article][\"text\"])\n",
+        "\n",
+        "# create question/answers\n",
+        "questions = Path(\"transformed_squad/\")\n",
+        "questions.mkdir(parents=True, exist_ok=True)\n",
+        "with open(f\"{str(questions)}/questions.jsonl\", \"w\") as f:\n",
+        "    for article in agg_wiki_title.keys():\n",
+        "        for entry in agg_wiki_title[article][\"question_answers\"]:\n",
+        "            f.write(\n",
+        "                json.dumps(\n",
+        "                    {\n",
+        "                        \"question\": entry[\"question\"][0],\n",
+        "                        \"document_name\": article,\n",
+        "                        \"answers\": entry[\"answers\"][0],\n",
+        "                    }\n",
+        "                )\n",
+        "                + \"\\n\"\n",
+        "            )\n",
+        "\n",
+        "questions, documents = load_transformed_squad()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K0y_Fr3cipE9"
+      },
+      "source": [
+        "After processing the SQUAD dataset, each datapoint in `questions` will include `question`, `document_name`, `answers` and `query_id` fields.\n",
+        "\n",
+        "Examples from `questions`:\n",
+        "```python\n",
+        "[{'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',\n",
+        "  'document_name': 'University_of_Notre_Dame',\n",
+        "  'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]},\n",
+        "  'query_id': 'query_0'},\n",
+        " {'question': 'What is in front of the Notre Dame Main Building?',\n",
+        "  'document_name': 'University_of_Notre_Dame',\n",
+        "  'answers': {'text': ['a copper statue of Christ'], 'answer_start': [188]},\n",
+        "  'query_id': 'query_1'}\n",
+        " ...  \n",
+        "]\n",
+        "```\n",
+        "Example from `documents`:\n",
+        "```python\n",
+        "Document(id=8c7ed44d52b4bdafc7990688a0ca63c3c8765a87362888349aedf67cb926f648,\n",
+        "  content: 'Hoover began using wiretapping in the 1920s during Prohibition to arrest bootleggers. In the 1927 case Olmstead v. United States, in which a bootlegger was caught through telephone tapping, the United States Supreme Court ruled that FBI wiretaps did not violate the Fourth Amendment as unlawful search and seizure, as long as the FBI did not break into a person's home to complete the tapping. After Prohibition's repeal, Congress passed the Communications Act of 1934, which outlawed non-consensual phone tapping, but allowed bugging. In the 1939 case Nardone v. United States, the court ruled that due to the 1934 law, evidence the FBI obtained by phone tapping was inadmissible in court. After the 1967 case Katz v. United States overturned the 1927 case that had allowed bugging, Congress passed the Omnibus Crime Control Act, allowing public authorities to tap telephones during investigations as long as they obtain a warrant beforehand.',\n",
+        "  meta: {'name': 'Federal_Bureau_of_Investigation'})\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tpw_fLaIiH3B"
+      },
+      "source": [
+        "## Indexing Pipeline\n",
+        "\n",
+        "Create a pipeline to ingest your data into [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore). Before you index them, you will create embeddings with [`sentence-transformers/all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). For detailed instructions on building pipelines, refer to the [Docs: Creating Pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mH5Xg-BbiH3B",
+        "outputId": "ee560989-8f54-42f3-a7bb-0dc601696468"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<haystack.core.pipeline.pipeline.Pipeline object at 0x7ebffc3aab00>\n",
+              "🚅 Components\n",
+              "  - doc_embedder: SentenceTransformersDocumentEmbedder\n",
+              "  - doc_writer: DocumentWriter\n",
+              "🛤️ Connections\n",
+              "  - doc_embedder.documents -> doc_writer.documents (List[Document])"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "document_store = InMemoryDocumentStore()\n",
+        "\n",
+        "doc_writer = DocumentWriter(\n",
+        "    document_store=document_store, policy=DuplicatePolicy.SKIP\n",
+        ")\n",
+        "doc_embedder = SentenceTransformersDocumentEmbedder(\n",
+        "    model=\"sentence-transformers/all-MiniLM-L6-v2\"\n",
+        ")\n",
+        "\n",
+        "indexing_pipe = Pipeline()\n",
+        "indexing_pipe.add_component(instance=doc_embedder, name=\"doc_embedder\")\n",
+        "indexing_pipe.add_component(instance=doc_writer, name=\"doc_writer\")\n",
+        "\n",
+        "indexing_pipe.connect(\"doc_embedder.documents\", \"doc_writer.documents\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "koTNlYsGl9Od"
+      },
+      "source": [
+        "Run the `indexing_pipe` with subset of documents to speed up the process. This step takes around 2-3 minutes on CPU."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 473,
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+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "61fb8c4a8d3c4e9aa0e797a92d383aba",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "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"
+          ]
+        },
+        {
+          "data": {
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+            ]
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+        },
+        {
+          "data": {
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+            ]
+          },
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+          "output_type": "display_data"
+        },
+        {
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+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "model_id": "9008e5438b434e1ebf30d878a66f1f33",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "model_id": "0d5a8a644f0a41f1ae0aecd30be18c27",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8de7f5af99214b3794a5237b16aa3fcf",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'doc_writer': {'documents_written': 1000}}"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "documents = random.sample(documents, 1000)\n",
+        "indexing_pipe.run({\"doc_embedder\": {\"documents\": documents}})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BKcUXXI5iH3B"
+      },
+      "source": [
+        "## RAG Pipeline\n",
+        "\n",
+        "Create a helper function to build an retrieval augmented generation pipeline with embedding-based retrieval. This function will take `document_store` and `top_k` as parameters.\n",
+        "\n",
+        "Learn the details of building a RAG pipeline in 📚 [Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline).\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "69WymZArsdEa"
+      },
+      "outputs": [],
+      "source": [
+        "def build_emb_rag_pipeline(document_store: InMemoryDocumentStore, top_k: int = 2) -> Pipeline:\n",
+        "    template = \"\"\"\n",
+        "        You have to answer the following question based on the given context information only.\n",
+        "\n",
+        "        Context:\n",
+        "        {% for document in documents %}\n",
+        "            {{ document.content }}\n",
+        "        {% endfor %}\n",
+        "\n",
+        "        Question: {{question}}\n",
+        "        Answer:\n",
+        "        \"\"\"\n",
+        "\n",
+        "    pipeline = Pipeline()\n",
+        "    pipeline.add_component(\n",
+        "        \"query_embedder\",\n",
+        "        SentenceTransformersTextEmbedder(\n",
+        "            model=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
+        "            progress_bar=False,\n",
+        "        ),\n",
+        "    )\n",
+        "    pipeline.add_component(\n",
+        "        \"retriever\", InMemoryEmbeddingRetriever(document_store, top_k=top_k)\n",
+        "    )\n",
+        "    pipeline.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+        "    pipeline.add_component(\n",
+        "        \"generator\", OpenAIGenerator(model=\"gpt-3.5-turbo\")\n",
+        "    )\n",
+        "    pipeline.add_component(\"answer_builder\", AnswerBuilder())\n",
+        "\n",
+        "    pipeline.connect(\"query_embedder\", \"retriever.query_embedding\")\n",
+        "    pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
+        "    pipeline.connect(\"prompt_builder\", \"generator\")\n",
+        "    pipeline.connect(\"generator.replies\", \"answer_builder.replies\")\n",
+        "    pipeline.connect(\"generator.meta\", \"answer_builder.meta\")\n",
+        "    pipeline.connect(\"retriever\", \"answer_builder.documents\")\n",
+        "\n",
+        "    return pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aVyHkxxIsy3y"
+      },
+      "source": [
+        "Create your RAG pipeline with the `document_store` you initialized above and the `top_k` value of 2."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "PRTN7QTdiH3B"
+      },
+      "outputs": [],
+      "source": [
+        "emb_rag_pipeline = build_emb_rag_pipeline(document_store, top_k=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6HQQDLYFiH3B"
+      },
+      "source": [
+        "## EvaluationHarness\n",
+        "\n",
+        "You will evaluate your RAG pipeline using the `EvaluationHarness`. The `EvaluationHarness` executes a pipeline with a given set of inputs and evaluates its outputs with an evaluation pipeline using Haystack's built-in [Evaluators](https://docs.haystack.deepset.ai/docs/evaluators). This means you don't need to create a separate evaluation pipeline.\n",
+        "\n",
+        "The [`RAGEvaluationHarness`](https://docs.haystack.deepset.ai/v2.3-unstable/reference/evaluation-harness#ragevaluationharness) class, derived from the Evaluation Harness, simplifies the evaluation process specifically for RAG pipelines. It comes with a predefined set of evaluation metrics, detailed in the [`RAGEvaluationMetric`](https://docs.haystack.deepset.ai/v2.3-unstable/reference/evaluation-harness#ragevaluationmetric) enum, and basic RAG architecture examples, listed in the [`DefaultRAGArchitecture`](https://docs.haystack.deepset.ai/v2.3-unstable/reference/evaluation-harness#defaultragarchitecture) enum.\n",
+        "\n",
+        "Now, create a harness to evaluate the embedding-based RAG pipeline. For evaluating the RAG pipeline mentioned above, use the `DefaultRAGArchitecture.GENERATION_WITH_EMBEDDING_RETRIEVAL` architecture. You will evaluate the pipeline using the [DocumentMAPEvaluator](https://docs.haystack.deepset.ai/docs/documentmapevaluator), [DocumentRecallEvaluator](https://docs.haystack.deepset.ai/docs/documentrecallevaluator), and [FaithfulnessEvaluator](https://docs.haystack.deepset.ai/docs/faithfulnessevaluator).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "vxjAjfHWiH3B"
+      },
+      "outputs": [],
+      "source": [
+        "emb_eval_harness = RAGEvaluationHarness(emb_rag_pipeline,\n",
+        "                                        rag_components=DefaultRAGArchitecture.GENERATION_WITH_EMBEDDING_RETRIEVAL,\n",
+        "                                        metrics={\n",
+        "                                            RAGEvaluationMetric.DOCUMENT_MAP,\n",
+        "                                            RAGEvaluationMetric.DOCUMENT_RECALL_SINGLE_HIT,\n",
+        "                                            RAGEvaluationMetric.FAITHFULNESS\n",
+        "                                        })"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "STMZx9BMnA2D"
+      },
+      "source": [
+        "Then, initialize the inputs to the `EvaluationHarness`. These inputs will be automatically passed to RAG pipeline and the evaluation pipeline that the harness internally uses."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "VMZSO3ODiH3B"
+      },
+      "outputs": [],
+      "source": [
+        "input_questions = random.sample(questions, 10)\n",
+        "\n",
+        "eval_questions = [q[\"question\"] for q in input_questions]\n",
+        "ground_truth_answers = [q[\"answers\"][\"text\"][0] for q in input_questions]\n",
+        "ground_truth_documents = [\n",
+        "        [\n",
+        "            doc\n",
+        "            for doc in document_store.storage.values()\n",
+        "            if doc.meta[\"name\"] == q[\"document_name\"]\n",
+        "        ]\n",
+        "        for q in input_questions\n",
+        "    ]\n",
+        "\n",
+        "eval_harness_input = RAGEvaluationInput(\n",
+        "    queries=eval_questions,\n",
+        "    ground_truth_answers=ground_truth_answers,\n",
+        "    ground_truth_documents=ground_truth_documents,\n",
+        "    rag_pipeline_inputs={\n",
+        "        \"prompt_builder\": {\"question\": eval_questions},\n",
+        "        \"answer_builder\": {\"query\": eval_questions},\n",
+        "    },\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ga6oZAfKpN_2"
+      },
+      "source": [
+        "Launch an evaluation run for `EvaluationHarness` with the inputs above."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "62pvFSbuiH3B",
+        "outputId": "611237b6-050a-4300-a3cf-15c61d106251"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 10/10 [00:07<00:00,  1.31it/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "emb_eval_run = emb_eval_harness.run(inputs=eval_harness_input, run_name=\"emb_eval_run\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Hq0jsE-qrskw"
+      },
+      "source": [
+        "### Alternative: EvaluationHarness for Keyword-based Retrieval\n",
+        "\n",
+        "Instead of an embedding-based retrieval, you can perform keyword-based retrieval in your RAG pipeline and evaluate it with `EvaluationHarness`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eVplXn2ViH3B",
+        "outputId": "f361718a-1939-4cfe-be9b-62e93260cbfa"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 10/10 [00:07<00:00,  1.29it/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Helper function to create an keyword-based RAG pipeline.\n",
+        "def build_keyword_rag_pipeline(document_store: InMemoryDocumentStore, top_k: int = 2) -> Pipeline:\n",
+        "    template = \"\"\"\n",
+        "        You have to answer the following question based on the given context information only.\n",
+        "\n",
+        "        Context:\n",
+        "        {% for document in documents %}\n",
+        "            {{ document.content }}\n",
+        "        {% endfor %}\n",
+        "\n",
+        "        Question: {{question}}\n",
+        "        Answer:\n",
+        "        \"\"\"\n",
+        "\n",
+        "    pipeline = Pipeline()\n",
+        "    pipeline.add_component(\n",
+        "        \"retriever\", InMemoryBM25Retriever(document_store, top_k=top_k)\n",
+        "    )\n",
+        "    pipeline.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+        "    pipeline.add_component(\n",
+        "        \"generator\", OpenAIGenerator(model=\"gpt-3.5-turbo\")\n",
+        "    )\n",
+        "    pipeline.add_component(\"answer_builder\", AnswerBuilder())\n",
+        "\n",
+        "    pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
+        "    pipeline.connect(\"prompt_builder\", \"generator\")\n",
+        "    pipeline.connect(\"generator.replies\", \"answer_builder.replies\")\n",
+        "    pipeline.connect(\"generator.meta\", \"answer_builder.meta\")\n",
+        "    pipeline.connect(\"retriever\", \"answer_builder.documents\")\n",
+        "\n",
+        "    return pipeline\n",
+        "\n",
+        "# Build your new RAG pipeline\n",
+        "keyword_rag_pipeline = build_keyword_rag_pipeline(document_store, top_k=2)\n",
+        "\n",
+        "# Create a new `RAGEvaluationHarness` with the new pipeline and `DefaultRAGArchitecture.GENERATION_WITH_KEYWORD_RETRIEVAL` architecture.\n",
+        "keyword_eval_harness = RAGEvaluationHarness(keyword_rag_pipeline,\n",
+        "                                            rag_components=DefaultRAGArchitecture.GENERATION_WITH_KEYWORD_RETRIEVAL,\n",
+        "                                            metrics={\n",
+        "                                                RAGEvaluationMetric.DOCUMENT_MAP,\n",
+        "                                                RAGEvaluationMetric.DOCUMENT_RECALL_SINGLE_HIT,\n",
+        "                                                RAGEvaluationMetric.FAITHFULNESS\n",
+        "                                            })\n",
+        "\n",
+        "# Define another set of `RAGEvaluationInput` for the keyword-based pipeline.\n",
+        "keyword_eval_harness_input = RAGEvaluationInput(\n",
+        "    queries=eval_questions,\n",
+        "    ground_truth_answers=ground_truth_answers,\n",
+        "    ground_truth_documents=ground_truth_documents,\n",
+        "    rag_pipeline_inputs={\n",
+        "        \"prompt_builder\": {\"question\": eval_questions},\n",
+        "        \"answer_builder\": {\"query\": eval_questions},\n",
+        "    },\n",
+        ")\n",
+        "\n",
+        "# Run EvaluationHarness with the new set of inputs\n",
+        "keyword_eval_run = keyword_eval_harness.run(inputs=keyword_eval_harness_input, run_name=\"keyword_eval_run\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pq4wKakNqLaJ"
+      },
+      "source": [
+        "## Analyzing the Results\n",
+        "\n",
+        "Now that the evaluation is completed, you can analyze the results:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 160
+        },
+        "id": "r6E7ISahiH3C",
+        "outputId": "8b8a26ed-fa71-4214-e372-4d17b90138fc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Evaluation score report:\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"emb_eval_run\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"metrics\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"metric_faithfulness\",\n          \"metric_doc_map\",\n          \"metric_doc_recall_single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07637626158259735,\n        \"min\": 0.35,\n        \"max\": 0.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.35,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-70ab18ca-2d4d-4da0-9c7e-b73dc5eb1661\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>metrics</th>\n",
+              "      <th>score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>metric_faithfulness</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>metric_doc_map</td>\n",
+              "      <td>0.35</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>metric_doc_recall_single</td>\n",
+              "      <td>0.40</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70ab18ca-2d4d-4da0-9c7e-b73dc5eb1661')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-70ab18ca-2d4d-4da0-9c7e-b73dc5eb1661 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-70ab18ca-2d4d-4da0-9c7e-b73dc5eb1661');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-875f4631-476a-44d5-8556-ccbfd38f340a\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-875f4631-476a-44d5-8556-ccbfd38f340a')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-875f4631-476a-44d5-8556-ccbfd38f340a button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                    metrics  score\n",
+              "0       metric_faithfulness   0.50\n",
+              "1            metric_doc_map   0.35\n",
+              "2  metric_doc_recall_single   0.40"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "print(\"Evaluation score report:\")\n",
+        "emb_eval_run.results.score_report()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zv_bC6_brMen"
+      },
+      "source": [
+        "You can display your evaluation results as a pandas dataframe and get a more detailed view"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 727
+        },
+        "id": "PHCZW12AiH3C",
+        "outputId": "514aaa51-7f95-4e8c-985c-8d03543472e2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Evaluation score dataframe:\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"emb_eval_run\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"questions\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"IANA, or zoneinfo, updates are installed as a part of what ordinary function when changes to DST policy are made?\",\n          \"Who designed the Palacio Taranco?\",\n          \"What is another name for Benaadir?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contexts\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"responses\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"IANA or zoneinfo updates are installed as a part of regular software updates when changes to DST policy are made.\",\n          \"The Palacio Taranco was designed by architects Bello and Reboratti between 1920 and 1940.\",\n          \"Mogadishu\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ground_truth_answers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"system maintenance\",\n          \"French architects Charles Louis Girault and Jules Chifflot Le\\u00f3n\",\n          \"Coastal Somali\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ground_truth_documents\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metric_faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5270462766947299,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metric_doc_map\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4743416490252569,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metric_doc_recall_single\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5163977794943223,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4deddce9-a499-480b-a46a-b8db51fc2396\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>questions</th>\n",
+              "      <th>contexts</th>\n",
+              "      <th>responses</th>\n",
+              "      <th>ground_truth_answers</th>\n",
+              "      <th>ground_truth_documents</th>\n",
+              "      <th>metric_faithfulness</th>\n",
+              "      <th>metric_doc_map</th>\n",
+              "      <th>metric_doc_recall_single</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>What could be remotely activated after a suces...</td>\n",
+              "      <td>[However, relatively few organisations maintai...</td>\n",
+              "      <td>After a successful breach, remotely activated ...</td>\n",
+              "      <td>sensors</td>\n",
+              "      <td>[Serious financial damage has been caused by s...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Who designed the Palacio Taranco?</td>\n",
+              "      <td>[The Pocitos district, near the beach of the s...</td>\n",
+              "      <td>The Palacio Taranco was designed by architects...</td>\n",
+              "      <td>French architects Charles Louis Girault and Ju...</td>\n",
+              "      <td>[The Pocitos district, near the beach of the s...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Who claimed she had been unfairly fired by Eto...</td>\n",
+              "      <td>[In the United States especially, several high...</td>\n",
+              "      <td>Debra LaFave</td>\n",
+              "      <td>Sarah Forsyth</td>\n",
+              "      <td>[As the School grew, more students were allowe...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Jack Kerouac authored what iconic book?</td>\n",
+              "      <td>[Lothar Wolfgang Nordheim described von Neuman...</td>\n",
+              "      <td>The iconic book authored by Jack Kerouac is \"O...</td>\n",
+              "      <td>On the Road</td>\n",
+              "      <td>[At the end of the first decade of the 21st ce...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>What did the Gulf War inadvertently do in the ...</td>\n",
+              "      <td>[By 1989 Germany was nearing reunification and...</td>\n",
+              "      <td>The Gulf War inadvertently led to a reduction ...</td>\n",
+              "      <td>worked to radicalize the Islamist movement</td>\n",
+              "      <td>[In its focus on the Caliphate, the party take...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>What is another name for Benaadir?</td>\n",
+              "      <td>[Carnaval de Solsona takes place in Solsona, L...</td>\n",
+              "      <td>Mogadishu</td>\n",
+              "      <td>Coastal Somali</td>\n",
+              "      <td>[The revolutionary army established large-scal...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>What weather factor produces a higher heat index?</td>\n",
+              "      <td>[The climate has become warmer in Montana and ...</td>\n",
+              "      <td>The warmer climate in Montana, which has led t...</td>\n",
+              "      <td>humidity</td>\n",
+              "      <td>[METRO began light rail service on January 1, ...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>If a lead wasn't soldered manually, what devic...</td>\n",
+              "      <td>[After World War II, two new competing formats...</td>\n",
+              "      <td>If a lead wasn't soldered manually, a vacuum s...</td>\n",
+              "      <td>wave soldering machine</td>\n",
+              "      <td>[Panelization is a procedure whereby a number ...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>IANA, or zoneinfo, updates are installed as a ...</td>\n",
+              "      <td>[DST clock shifts sometimes complicate timekee...</td>\n",
+              "      <td>IANA or zoneinfo updates are installed as a pa...</td>\n",
+              "      <td>system maintenance</td>\n",
+              "      <td>[DST clock shifts sometimes complicate timekee...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>What is another category of building that was ...</td>\n",
+              "      <td>[Building activity occurred in numerous noble ...</td>\n",
+              "      <td>Another category of building that was establis...</td>\n",
+              "      <td>the palace</td>\n",
+              "      <td>[On the exterior, the verticality is emphasise...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4deddce9-a499-480b-a46a-b8db51fc2396')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4deddce9-a499-480b-a46a-b8db51fc2396 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4deddce9-a499-480b-a46a-b8db51fc2396');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-65bc4694-0aeb-48be-b0b0-ace124356ee8\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-65bc4694-0aeb-48be-b0b0-ace124356ee8')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-65bc4694-0aeb-48be-b0b0-ace124356ee8 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                           questions  \\\n",
+              "0  What could be remotely activated after a suces...   \n",
+              "1                  Who designed the Palacio Taranco?   \n",
+              "2  Who claimed she had been unfairly fired by Eto...   \n",
+              "3            Jack Kerouac authored what iconic book?   \n",
+              "4  What did the Gulf War inadvertently do in the ...   \n",
+              "5                 What is another name for Benaadir?   \n",
+              "6  What weather factor produces a higher heat index?   \n",
+              "7  If a lead wasn't soldered manually, what devic...   \n",
+              "8  IANA, or zoneinfo, updates are installed as a ...   \n",
+              "9  What is another category of building that was ...   \n",
+              "\n",
+              "                                            contexts  \\\n",
+              "0  [However, relatively few organisations maintai...   \n",
+              "1  [The Pocitos district, near the beach of the s...   \n",
+              "2  [In the United States especially, several high...   \n",
+              "3  [Lothar Wolfgang Nordheim described von Neuman...   \n",
+              "4  [By 1989 Germany was nearing reunification and...   \n",
+              "5  [Carnaval de Solsona takes place in Solsona, L...   \n",
+              "6  [The climate has become warmer in Montana and ...   \n",
+              "7  [After World War II, two new competing formats...   \n",
+              "8  [DST clock shifts sometimes complicate timekee...   \n",
+              "9  [Building activity occurred in numerous noble ...   \n",
+              "\n",
+              "                                           responses  \\\n",
+              "0  After a successful breach, remotely activated ...   \n",
+              "1  The Palacio Taranco was designed by architects...   \n",
+              "2                                       Debra LaFave   \n",
+              "3  The iconic book authored by Jack Kerouac is \"O...   \n",
+              "4  The Gulf War inadvertently led to a reduction ...   \n",
+              "5                                          Mogadishu   \n",
+              "6  The warmer climate in Montana, which has led t...   \n",
+              "7  If a lead wasn't soldered manually, a vacuum s...   \n",
+              "8  IANA or zoneinfo updates are installed as a pa...   \n",
+              "9  Another category of building that was establis...   \n",
+              "\n",
+              "                                ground_truth_answers  \\\n",
+              "0                                            sensors   \n",
+              "1  French architects Charles Louis Girault and Ju...   \n",
+              "2                                      Sarah Forsyth   \n",
+              "3                                        On the Road   \n",
+              "4         worked to radicalize the Islamist movement   \n",
+              "5                                     Coastal Somali   \n",
+              "6                                           humidity   \n",
+              "7                             wave soldering machine   \n",
+              "8                                 system maintenance   \n",
+              "9                                         the palace   \n",
+              "\n",
+              "                              ground_truth_documents  metric_faithfulness  \\\n",
+              "0  [Serious financial damage has been caused by s...                  1.0   \n",
+              "1  [The Pocitos district, near the beach of the s...                  1.0   \n",
+              "2  [As the School grew, more students were allowe...                  0.0   \n",
+              "3  [At the end of the first decade of the 21st ce...                  0.0   \n",
+              "4  [In its focus on the Caliphate, the party take...                  1.0   \n",
+              "5  [The revolutionary army established large-scal...                  0.0   \n",
+              "6  [METRO began light rail service on January 1, ...                  1.0   \n",
+              "7  [Panelization is a procedure whereby a number ...                  0.0   \n",
+              "8  [DST clock shifts sometimes complicate timekee...                  1.0   \n",
+              "9  [On the exterior, the verticality is emphasise...                  0.0   \n",
+              "\n",
+              "   metric_doc_map  metric_doc_recall_single  \n",
+              "0             1.0                       1.0  \n",
+              "1             1.0                       1.0  \n",
+              "2             0.5                       1.0  \n",
+              "3             0.0                       0.0  \n",
+              "4             0.0                       0.0  \n",
+              "5             0.0                       0.0  \n",
+              "6             0.0                       0.0  \n",
+              "7             0.0                       0.0  \n",
+              "8             1.0                       1.0  \n",
+              "9             0.0                       0.0  "
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "print(\"Evaluation score dataframe:\")\n",
+        "emb_eval_run.results.to_pandas()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KdIEKAf-urVL"
+      },
+      "source": [
+        "## Evaluating and Comparing Different Pipelines\n",
+        "\n",
+        "To evaluate alternative approaches, you can initiate another evaluation run using the same inputs but with different overrides, leveraging [`RAGEvaluationOverrides`](https://docs.haystack.deepset.ai/v2.3-unstable/reference/evaluation-harness#ragevaluationoverrides).\n",
+        "\n",
+        "Now, update the model used with `OpenAIGenerator` in the RAG pipeline and execute the same EvaluationHarness instance:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tkCPsQrGiH3C",
+        "outputId": "bd32b7ad-5da0-4dac-f9ac-a34ca44785b4"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 10/10 [00:10<00:00,  1.06s/it]\n"
+          ]
+        }
+      ],
+      "source": [
+        "overrides = RAGEvaluationOverrides(rag_pipeline={\n",
+        "    \"generator\": {\"model\": \"gpt-4-turbo\"},\n",
+        "})\n",
+        "emb_eval_run_gpt4 = emb_eval_harness.run(inputs=eval_harness_input, run_name=\"emb_eval_run_gpt4\", overrides=overrides)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q1YPapD2wYyA"
+      },
+      "source": [
+        "Compare the results of the two evaluation runs with [`comparative_individual_scores_report()`](https://docs.haystack.deepset.ai/v2.3-unstable/reference/evaluation-api#baseevaluationrunresultcomparative_individual_scores_report). The results for the new pipeline will have the `emb_eval_run_gpt4_*` name."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "zUK-kZr9iH3C",
+        "outputId": "1152de55-7778-4fae-df77-32f55afa2edd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Comparison of the two evaluation runs:\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
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+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>questions</th>\n",
+              "      <th>contexts</th>\n",
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+              "      <th>ground_truth_documents</th>\n",
+              "      <th>emb_eval_run_metric_faithfulness</th>\n",
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+              "      <th>emb_eval_run_metric_doc_recall_single</th>\n",
+              "      <th>emb_eval_run_gpt4_metric_faithfulness</th>\n",
+              "      <th>emb_eval_run_gpt4_metric_doc_map</th>\n",
+              "      <th>emb_eval_run_gpt4_metric_doc_recall_single</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
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+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>What could be remotely activated after a suces...</td>\n",
+              "      <td>[However, relatively few organisations maintai...</td>\n",
+              "      <td>After a successful breach, remotely activated ...</td>\n",
+              "      <td>sensors</td>\n",
+              "      <td>[Serious financial damage has been caused by s...</td>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>1.0</td>\n",
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+              "      <td>Who designed the Palacio Taranco?</td>\n",
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+              "      <td>The Palacio Taranco was designed by architects...</td>\n",
+              "      <td>French architects Charles Louis Girault and Ju...</td>\n",
+              "      <td>[The Pocitos district, near the beach of the s...</td>\n",
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+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Who claimed she had been unfairly fired by Eto...</td>\n",
+              "      <td>[In the United States especially, several high...</td>\n",
+              "      <td>Debra LaFave</td>\n",
+              "      <td>Sarah Forsyth</td>\n",
+              "      <td>[As the School grew, more students were allowe...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.5</td>\n",
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+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Jack Kerouac authored what iconic book?</td>\n",
+              "      <td>[Lothar Wolfgang Nordheim described von Neuman...</td>\n",
+              "      <td>The iconic book authored by Jack Kerouac is \"O...</td>\n",
+              "      <td>On the Road</td>\n",
+              "      <td>[At the end of the first decade of the 21st ce...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>What did the Gulf War inadvertently do in the ...</td>\n",
+              "      <td>[By 1989 Germany was nearing reunification and...</td>\n",
+              "      <td>The Gulf War inadvertently led to a reduction ...</td>\n",
+              "      <td>worked to radicalize the Islamist movement</td>\n",
+              "      <td>[In its focus on the Caliphate, the party take...</td>\n",
+              "      <td>1.0</td>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>What is another name for Benaadir?</td>\n",
+              "      <td>[Carnaval de Solsona takes place in Solsona, L...</td>\n",
+              "      <td>Mogadishu</td>\n",
+              "      <td>Coastal Somali</td>\n",
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+              "      <td>0.0</td>\n",
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+              "      <td>0.0</td>\n",
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+              "      <td>0.0</td>\n",
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+              "      <th>6</th>\n",
+              "      <td>What weather factor produces a higher heat index?</td>\n",
+              "      <td>[The climate has become warmer in Montana and ...</td>\n",
+              "      <td>The warmer climate in Montana, which has led t...</td>\n",
+              "      <td>humidity</td>\n",
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+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>If a lead wasn't soldered manually, what devic...</td>\n",
+              "      <td>[After World War II, two new competing formats...</td>\n",
+              "      <td>If a lead wasn't soldered manually, a vacuum s...</td>\n",
+              "      <td>wave soldering machine</td>\n",
+              "      <td>[Panelization is a procedure whereby a number ...</td>\n",
+              "      <td>0.0</td>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>IANA, or zoneinfo, updates are installed as a ...</td>\n",
+              "      <td>[DST clock shifts sometimes complicate timekee...</td>\n",
+              "      <td>IANA or zoneinfo updates are installed as a pa...</td>\n",
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+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>What is another category of building that was ...</td>\n",
+              "      <td>[Building activity occurred in numerous noble ...</td>\n",
+              "      <td>Another category of building that was establis...</td>\n",
+              "      <td>the palace</td>\n",
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+              "        if (!dataTable) return;\n",
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+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-c208e93f-b1da-4434-b4f9-b3c2434b4be6 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                           questions  \\\n",
+              "0  What could be remotely activated after a suces...   \n",
+              "1                  Who designed the Palacio Taranco?   \n",
+              "2  Who claimed she had been unfairly fired by Eto...   \n",
+              "3            Jack Kerouac authored what iconic book?   \n",
+              "4  What did the Gulf War inadvertently do in the ...   \n",
+              "5                 What is another name for Benaadir?   \n",
+              "6  What weather factor produces a higher heat index?   \n",
+              "7  If a lead wasn't soldered manually, what devic...   \n",
+              "8  IANA, or zoneinfo, updates are installed as a ...   \n",
+              "9  What is another category of building that was ...   \n",
+              "\n",
+              "                                            contexts  \\\n",
+              "0  [However, relatively few organisations maintai...   \n",
+              "1  [The Pocitos district, near the beach of the s...   \n",
+              "2  [In the United States especially, several high...   \n",
+              "3  [Lothar Wolfgang Nordheim described von Neuman...   \n",
+              "4  [By 1989 Germany was nearing reunification and...   \n",
+              "5  [Carnaval de Solsona takes place in Solsona, L...   \n",
+              "6  [The climate has become warmer in Montana and ...   \n",
+              "7  [After World War II, two new competing formats...   \n",
+              "8  [DST clock shifts sometimes complicate timekee...   \n",
+              "9  [Building activity occurred in numerous noble ...   \n",
+              "\n",
+              "                                           responses  \\\n",
+              "0  After a successful breach, remotely activated ...   \n",
+              "1  The Palacio Taranco was designed by architects...   \n",
+              "2                                       Debra LaFave   \n",
+              "3  The iconic book authored by Jack Kerouac is \"O...   \n",
+              "4  The Gulf War inadvertently led to a reduction ...   \n",
+              "5                                          Mogadishu   \n",
+              "6  The warmer climate in Montana, which has led t...   \n",
+              "7  If a lead wasn't soldered manually, a vacuum s...   \n",
+              "8  IANA or zoneinfo updates are installed as a pa...   \n",
+              "9  Another category of building that was establis...   \n",
+              "\n",
+              "                                ground_truth_answers  \\\n",
+              "0                                            sensors   \n",
+              "1  French architects Charles Louis Girault and Ju...   \n",
+              "2                                      Sarah Forsyth   \n",
+              "3                                        On the Road   \n",
+              "4         worked to radicalize the Islamist movement   \n",
+              "5                                     Coastal Somali   \n",
+              "6                                           humidity   \n",
+              "7                             wave soldering machine   \n",
+              "8                                 system maintenance   \n",
+              "9                                         the palace   \n",
+              "\n",
+              "                              ground_truth_documents  \\\n",
+              "0  [Serious financial damage has been caused by s...   \n",
+              "1  [The Pocitos district, near the beach of the s...   \n",
+              "2  [As the School grew, more students were allowe...   \n",
+              "3  [At the end of the first decade of the 21st ce...   \n",
+              "4  [In its focus on the Caliphate, the party take...   \n",
+              "5  [The revolutionary army established large-scal...   \n",
+              "6  [METRO began light rail service on January 1, ...   \n",
+              "7  [Panelization is a procedure whereby a number ...   \n",
+              "8  [DST clock shifts sometimes complicate timekee...   \n",
+              "9  [On the exterior, the verticality is emphasise...   \n",
+              "\n",
+              "   emb_eval_run_metric_faithfulness  emb_eval_run_metric_doc_map  \\\n",
+              "0                               1.0                          1.0   \n",
+              "1                               1.0                          1.0   \n",
+              "2                               0.0                          0.5   \n",
+              "3                               0.0                          0.0   \n",
+              "4                               1.0                          0.0   \n",
+              "5                               0.0                          0.0   \n",
+              "6                               1.0                          0.0   \n",
+              "7                               0.0                          0.0   \n",
+              "8                               1.0                          1.0   \n",
+              "9                               0.0                          0.0   \n",
+              "\n",
+              "   emb_eval_run_metric_doc_recall_single  \\\n",
+              "0                                    1.0   \n",
+              "1                                    1.0   \n",
+              "2                                    1.0   \n",
+              "3                                    0.0   \n",
+              "4                                    0.0   \n",
+              "5                                    0.0   \n",
+              "6                                    0.0   \n",
+              "7                                    0.0   \n",
+              "8                                    1.0   \n",
+              "9                                    0.0   \n",
+              "\n",
+              "   emb_eval_run_gpt4_metric_faithfulness  emb_eval_run_gpt4_metric_doc_map  \\\n",
+              "0                                    1.0                               1.0   \n",
+              "1                                    1.0                               1.0   \n",
+              "2                                    0.0                               0.5   \n",
+              "3                                    0.0                               0.0   \n",
+              "4                                    1.0                               0.0   \n",
+              "5                                    0.0                               0.0   \n",
+              "6                                    1.0                               0.0   \n",
+              "7                                    0.0                               0.0   \n",
+              "8                                    1.0                               1.0   \n",
+              "9                                    1.0                               0.0   \n",
+              "\n",
+              "   emb_eval_run_gpt4_metric_doc_recall_single  \n",
+              "0                                         1.0  \n",
+              "1                                         1.0  \n",
+              "2                                         1.0  \n",
+              "3                                         0.0  \n",
+              "4                                         0.0  \n",
+              "5                                         0.0  \n",
+              "6                                         0.0  \n",
+              "7                                         0.0  \n",
+              "8                                         1.0  \n",
+              "9                                         0.0  "
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "print(\"Comparison of the two evaluation runs:\")\n",
+        "emb_eval_run.results.comparative_individual_scores_report(emb_eval_run_gpt4.results)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IrMIAFgMiH3C"
+      },
+      "source": [
+        "## BONUS: EvaluationHarness for Custom RAG Pipelines\n",
+        "\n",
+        "In the above code, we've primarily focused on using the `DefaultRAGArchitecture`s of the `RAGEvaluationHarness` class. They provide a straightforward way of getting started with the evaluation of simple RAG pipelines which use prototypical components. The `RAGEvaluationHarness` can also be used to evaluate arbitrarily complex RAG pipelines. This is done by providing the harness with some extra metadata about the pipeline to be evaluated.\n",
+        "\n",
+        "To use an arbitrary pipeline with the harness, the latter requires information about the following components (c.f `RAGExpectedComponent`):\n",
+        "- Query processor - Component that processes the input query.\n",
+        "    - Expects one input that contains the query string.\n",
+        "- Document retriever - Component that retrieves documents based on the input query.\n",
+        "    - Expects one output that contains the retrieved documents.\n",
+        "- Response generator - Component that generates responses based on the query and the retrieved documents.\n",
+        "    - Expects one output that contains the LLM's response(s).\n",
+        "\n",
+        "For each of the above, the user needs to provide the following metadata (c.f `RAGExpectedComponentMetadata`):\n",
+        "- The name of the component as seen in the pipeline.\n",
+        "- A mapping of the component's expected inputs to their corresponding input names.\n",
+        "- A mapping of the component's expected outputs to their corresponding output names.\n",
+        "\n",
+        "For example, let's consider `RAGExpectedComponent.QUERY_PROCESSOR`: Assume we have a RAG pipeline with an [`OpenAITextEmbedder`](https://github.com/deepset-ai/haystack/blob/0ceeb733baabe2b3658ee7065c4441a632ef465d/haystack/components/embedders/openai_text_embedder.py#L18) component called `\"txt_embedder\"`. Since the harness is responsible for passing the pipeline's input (the query) to the `OpenAITextEmbedder`, it needs to know the name of the component. Furthermore, it also needs to know the [name of `OpenAITextEmbedder`'s input](https://github.com/deepset-ai/haystack/blob/0ceeb733baabe2b3658ee7065c4441a632ef465d/haystack/components/embedders/openai_text_embedder.py#L135) through which the query should be supplied. The metadata for the above looks this:\n",
+        "```python\n",
+        "query_processor_metadata = RAGExpectedComponentMetadata(\n",
+        "    name=\"txt_embedder\",\n",
+        "    input_mapping={\n",
+        "        \"query\": \"text\"\n",
+        "    }\n",
+        ")\n",
+        "```\n",
+        "Similarly, for `RAGExpectedComponent.DOCUMENT_RETRIEVER`: Assume the RAG pipeline has an [`InMemoryEmbeddingRetriever`](https://github.com/deepset-ai/haystack/blob/0ceeb733baabe2b3658ee7065c4441a632ef465d/haystack/components/retrievers/in_memory/embedding_retriever.py#L12) component named `\"mem_retriever\"` and is connected to `\"txt_embedder\"`.\n",
+        "```python\n",
+        "document_retriever_metadata = RAGExpectedComponentMetadata(\n",
+        "    name=\"mem_retriever\",\n",
+        "    output_mapping={\n",
+        "        \"retrieved_documents\": \"documents\"\n",
+        "    }\n",
+        ")\n",
+        "```\n",
+        "Both `\"query\"` and `\"retrieved_documents\"` are \"meta\" identifiers used by the harness to specify expected inputs and outputs. They are specific to each `RAGExpectedComponent` enum variant and are documented in their docstrings."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "SAZ7pdIOiH3C"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a harness to evaluate a custom RAG pipeline.\n",
+        "# Commented out because the `custom_rag_pipeline` is not defined in this notebook.\n",
+        "\n",
+        "# custom_eval_harness = RAGEvaluationHarness(\n",
+        "#     rag_pipeline=custom_rag_pipeline,\n",
+        "#     rag_components={\n",
+        "#         RAGExpectedComponent.QUERY_PROCESSOR: RAGExpectedComponentMetadata(\n",
+        "#             \"query_embedder\", input_mapping={\"query\": \"text\"}\n",
+        "#         ),\n",
+        "#         RAGExpectedComponent.DOCUMENT_RETRIEVER: RAGExpectedComponentMetadata(\n",
+        "#             \"retriever\",\n",
+        "#             output_mapping={\"retrieved_documents\": \"documents\"},\n",
+        "#         ),\n",
+        "#         RAGExpectedComponent.RESPONSE_GENERATOR: RAGExpectedComponentMetadata(\n",
+        "#             \"generator\", output_mapping={\"replies\": \"replies\"}\n",
+        "#         ),\n",
+        "#     },\n",
+        "#     metrics={\n",
+        "#         RAGEvaluationMetric.DOCUMENT_MAP,\n",
+        "#         RAGEvaluationMetric.DOCUMENT_RECALL_SINGLE_HIT,\n",
+        "#         RAGEvaluationMetric.FAITHFULNESS\n",
+        "#     })"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N5KqumKtiH3C"
+      },
+      "source": [
+        "There is no strict requirement when it comes which components can act as a query processor, a document retriever or a response generator. For instance, it's perfecty fine if the query processor and the document retriever are the same component. In fact, this is the case when using a keyword-based retriever which directly accepts the query (as opposed to having a query embedder in front of it)."
+      ]
+    }
+  ],
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+    "colab": {
+      "provenance": []
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+      "display_name": "dev",
+      "language": "python",
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