examples/Evaluating_RAG_with_RAGAs/Evaluating_RAG_with_RAGAs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AwhxwHTf4VZp"
   },
   "source": [
    "## Evaluating RAG with RAGAs using GPT-4o\n",
    "\n",
    "Ragas is a **framework for evaluating Retrieval Augmented Generation (RAG) pipelines**.\n",
    "\n",
    "Ragas provides you with the tools/metrics based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. Ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.\n",
    "\n",
    "GPT4-o is used as an LLM to generate responses out of semantically close context chunks.\n",
    "\n",
    "![flow.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1mr4fXemsYci",
    "outputId": "d45c8226-3f4a-4bd6-cb4b-62e7e83d4023"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install langchain openai lancedb ragas -q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z8hT0Jn74ZmT"
   },
   "source": [
    "### Setup `OPENAI_API_KEY` as an environment variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YHgQd_1rI04R"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-....\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mM0tf_vo6GbI"
   },
   "source": [
    "### Load .txt file and convert them into chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IkLbg-_1I3Rt",
    "outputId": "38f3beb7-b4bd-423e-b77f-f2c79f16990e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:langchain_text_splitters.base:Created a chunk of size 215, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 232, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 242, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 219, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 304, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 205, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 332, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 215, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 203, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 281, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 201, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 250, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 325, which is longer than the specified 200\n",
      "WARNING:langchain_text_splitters.base:Created a chunk of size 242, which is longer than the specified 200\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/hwchase17/chroma-langchain/master/state_of_the_union.txt\"\n",
    "res = requests.get(url)\n",
    "with open(\"state_of_the_union.txt\", \"w\") as f:\n",
    "    f.write(res.text)\n",
    "\n",
    "# Load the data\n",
    "loader = TextLoader(\"./state_of_the_union.txt\")\n",
    "documents = loader.load()\n",
    "\n",
    "# Chunk the data\n",
    "text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=10)\n",
    "chunks = text_splitter.split_documents(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pgetSLZXEJ2Q"
   },
   "source": [
    "### Setup Retriever\n",
    "\n",
    "Retriever utilizes **LanceDB** for scalable vector search and advanced retrieval in RAG, delivering blazing fast performance for searching large sets of embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "2PYhU_vvJC0P"
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "from langchain.vectorstores import LanceDB\n",
    "import lancedb\n",
    "\n",
    "openai_embed = OpenAIEmbeddings()\n",
    "\n",
    "# Setup lancedb\n",
    "db = lancedb.connect(\"/tmp/lancedb\")\n",
    "table = db.create_table(\n",
    "    \"raga_eval\",\n",
    "    data=[{\"vector\": openai_embed.embed_query(\"Hello World\"), \"text\": \"Hello World\"}],\n",
    "    mode=\"overwrite\",\n",
    ")\n",
    "\n",
    "# Populate vector database\n",
    "vectorstore = LanceDB.from_documents(\n",
    "    connection=db, documents=chunks, embedding=openai_embed\n",
    ")\n",
    "\n",
    "# Define vectorstore as retriever to enable semantic search\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9CFnNEfuExj7"
   },
   "source": [
    "### Setup RAG Pipeline with Prompt template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-TiQhbNyLSKv",
    "outputId": "7ab35c53-74f5-471d-e51b-bc1068873d12"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-fe6c5e5e17b4>:7: LangChainDeprecationWarning: The class `ChatOpenAI` was deprecated in LangChain 0.0.10 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-openai package and should be used instead. To use it run `pip install -U :class:`~langchain-openai` and import as `from :class:`~langchain_openai import ChatOpenAI``.\n",
      "  llm = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n"
     ]
    }
   ],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.schema.runnable import RunnablePassthrough\n",
    "from langchain.schema.output_parser import StrOutputParser\n",
    "\n",
    "# Define LLM\n",
    "llm = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n",
    "\n",
    "# Define Prompt template\n",
    "template = \"\"\"You are an assistant for question-answering tasks.\n",
    "Use the following pieces of retrieved context to answer the question.\n",
    "If you don't know the answer, just say that you don't know.\n",
    "Use two sentences maximum and keep the answer concise.\n",
    "Question: {question}\n",
    "Context: {context}\n",
    "Answer:\n",
    "\"\"\"\n",
    "\n",
    "prompt = ChatPromptTemplate.from_template(template)\n",
    "\n",
    "# Setup RAG pipeline\n",
    "rag_chain = (\n",
    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ge8JtkNXFXhI"
   },
   "source": [
    "### Sample Questions with their Expected Answers\n",
    "\n",
    "Define a set of questions with their answers for creating dataset including ground truth, generated answers with their context using which they are generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PGiU57QJMP0J",
    "outputId": "02907160-556f-4c52-8246-ce8983ad0d7d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-d89a36ccef76>:20: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
      "  [docs.page_content for docs in retriever.get_relevant_documents(query)]\n"
     ]
    }
   ],
   "source": [
    "from datasets import Dataset\n",
    "\n",
    "questions = [\n",
    "    \"What did the president say about Justice Breyer?\",\n",
    "    \"What did the president say about Intel's CEO?\",\n",
    "    \"What did the president say about gun violence?\",\n",
    "]\n",
    "ground_truth = [\n",
    "    \"The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.\",\n",
    "    \"The president said that Pat Gelsinger is ready to increase Intel's investment to $100 billion.\",\n",
    "    \"The president asked Congress to pass proven measures to reduce gun violence.\",\n",
    "]\n",
    "answers = []\n",
    "contexts = []\n",
    "\n",
    "# Inference\n",
    "for query in questions:\n",
    "    answers.append(rag_chain.invoke(query))\n",
    "    contexts.append(\n",
    "        [docs.page_content for docs in retriever.get_relevant_documents(query)]\n",
    "    )\n",
    "\n",
    "# To dict\n",
    "data = {\n",
    "    \"question\": questions,\n",
    "    \"answer\": answers,\n",
    "    \"contexts\": contexts,\n",
    "    \"ground_truth\": ground_truth,\n",
    "}\n",
    "\n",
    "# Convert dict to dataset\n",
    "dataset = Dataset.from_dict(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "szBZ1nwkFruF"
   },
   "source": [
    "### RAGA Evaluation Pipeline\n",
    "\n",
    "Simple pipeline of RAGA for evaluation with the listed metrics to understand and evaluate the RAG system.\n",
    "\n",
    "**Metrics** on which we will evaulate are answer_correctness,\n",
    "faithfulness,\n",
    "answer_similarity,\n",
    "context_precision,\n",
    "context_utilization,\n",
    "context_recall,\n",
    "context_relevancy,\n",
    "answer_relevancy, and\n",
    "context_entity_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "c47a266b53544dc082e9c5279d8cfd9f",
      "cf05971b750c4504acd2267f2ef8bd49",
      "cf8c29dd27b34058bc7a408fac30f4f4",
      "9c03315f38924551a5e367dad53ea1ca",
      "58fdb24797da4e8487b52d26dd84e836",
      "02560cc7f0104df496b1e2f4ce917c6f",
      "5106c77bdaa946cfa9585f88b26cef6c",
      "3ef897bc6f8a4f32b4b81438f9796106",
      "7597383882584bd29c08e3371c238b3c",
      "9ae18f1d919949c8935a9b8f270e93c3",
      "6f5710bdbc0c4bfbbc5e684745402e47"
     ]
    },
    "id": "Samkm2TnMUQA",
    "outputId": "795b3f93-4aac-4fbb-de64-2b06480cc57a"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c47a266b53544dc082e9c5279d8cfd9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating:   0%|          | 0/21 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ragas import evaluate\n",
    "from ragas.metrics import (\n",
    "    answer_correctness,\n",
    "    faithfulness,\n",
    "    answer_similarity,\n",
    "    context_precision,\n",
    "    context_recall,\n",
    "    answer_relevancy,\n",
    "    context_entity_recall,\n",
    ")\n",
    "\n",
    "\n",
    "# evaluating dataest on listed metrics\n",
    "result = evaluate(\n",
    "    dataset=dataset,\n",
    "    metrics=[\n",
    "        answer_correctness,\n",
    "        faithfulness,\n",
    "        answer_similarity,\n",
    "        context_precision,\n",
    "        context_recall,\n",
    "        answer_relevancy,\n",
    "        context_entity_recall,\n",
    "    ],\n",
    ")\n",
    "\n",
    "\n",
    "df = result.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "77QVRXtVMbCG",
    "outputId": "3fd675f0-6511-486c-f919-08155c08d06c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"user_input\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"What did the president say about Justice Breyer?\",\n          \"What did the president say about Intel's CEO?\",\n          \"What did the president say about gun violence?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"retrieved_contexts\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"response\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"The president honored Justice Stephen Breyer for his dedication to serving the country, acknowledging him as an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.\",\n          \"The president said that Intel\\u2019s CEO, Pat Gelsinger, told him they are ready to increase their investment from $20 billion to $100 billion.\",\n          \"The president called for Congress to pass proven measures to reduce gun violence, including universal background checks, banning assault weapons and high-capacity magazines, and repealing the liability shield for gun manufacturers.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reference\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.\",\n          \"The president said that Pat Gelsinger is ready to increase Intel's investment to $100 billion.\",\n          \"The president asked Congress to pass proven measures to reduce gun violence.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_correctness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23687834777938732,\n        \"min\": 0.5359520548823975,\n        \"max\": 0.9950187529685631,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6641079904090759,\n          0.9950187529685631,\n          0.5359520548823975\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28867513459481287,\n        \"min\": 0.5,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.5,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"semantic_similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02143452649654375,\n        \"min\": 0.9421462473505888,\n        \"max\": 0.9800750118742526,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9421462473505888,\n          0.9800750118742526\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28867513456594535,\n        \"min\": 0.49999999995,\n        \"max\": 0.9999999999,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9999999999,\n          0.49999999995\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01817965674116965,\n        \"min\": 0.9284069541601007,\n        \"max\": 0.9644181895157772,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9644181895157772\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_entity_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2204792754233461,\n        \"min\": 0.3333333322222222,\n        \"max\": 0.7499999981250001,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.3333333322222222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "      --hover-bg-color: #E2EBFA;\n",
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       "      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",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
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       "      .colab-df-generate:hover {\n",
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       "      [theme=dark] .colab-df-generate {\n",
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       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
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       "        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",
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       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "\n",
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       "\n",
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      ],
      "text/plain": [
       "                                         user_input  \\\n",
       "0  What did the president say about Justice Breyer?   \n",
       "1     What did the president say about Intel's CEO?   \n",
       "2    What did the president say about gun violence?   \n",
       "\n",
       "                                  retrieved_contexts  \\\n",
       "0  [And I did that 4 days ago, when I nominated C...   \n",
       "1  [Intel’s CEO, Pat Gelsinger, who is here tonig...   \n",
       "2  [And I ask Congress to pass proven measures to...   \n",
       "\n",
       "                                            response  \\\n",
       "0  The president honored Justice Stephen Breyer f...   \n",
       "1  The president said that Intel’s CEO, Pat Gelsi...   \n",
       "2  The president called for Congress to pass prov...   \n",
       "\n",
       "                                           reference  answer_correctness  \\\n",
       "0  The president said that Justice Breyer has ded...            0.664108   \n",
       "1  The president said that Pat Gelsinger is ready...            0.995019   \n",
       "2  The president asked Congress to pass proven me...            0.535952   \n",
       "\n",
       "   faithfulness  semantic_similarity  context_precision  context_recall  \\\n",
       "0           1.0             0.942146                0.5             1.0   \n",
       "1           0.5             0.980075                1.0             1.0   \n",
       "2           1.0             0.943808                1.0             1.0   \n",
       "\n",
       "   answer_relevancy  context_entity_recall  \n",
       "0          0.964418               0.333333  \n",
       "1          0.950759               0.750000  \n",
       "2          0.928407               0.666667  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluation metrics\n",
    "df"
   ]
  }
 ],
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   "provenance": []
  },
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   "display_name": "Python 3",
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   "name": "python"
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